{"id":146,"date":"2026-02-10T04:03:17","date_gmt":"2026-02-10T04:03:17","guid":{"rendered":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/?post_type=chapter&#038;p=146"},"modified":"2026-02-18T21:04:16","modified_gmt":"2026-02-18T21:04:16","slug":"the-brains-reward-system","status":"publish","type":"chapter","link":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/chapter\/the-brains-reward-system\/","title":{"raw":"The Brain's Reward System","rendered":"The Brain&#8217;s Reward System"},"content":{"raw":"<h2>Reading Objectives<\/h2>\r\nBy the end of this chapter, you should be able to:\r\n<ul>\r\n \t<li>Identify the key components of the mesolimbic dopamine pathway and describe their roles in mediating reward and motivation.<\/li>\r\n \t<li>Explain the concepts of reward prediction error, \u201cwanting\u201d versus \u201cliking,\u201d and incentive sensitization as they relate to the dopamine system and addictive behaviors.<\/li>\r\n \t<li>Analyze how the opponent-process theory illustrates the balance between pleasure and pain, and how repeated substance use shifts the hedonic set-point toward dysphoria and craving.<\/li>\r\n \t<li>Evaluate the impact of chronic drug use on neural circuitry, with attention to neuroadaptations such as receptor downregulation and impaired prefrontal control.<\/li>\r\n \t<li>Assess how neuroimaging techniques such as fMRI and PET reveal functional and neurochemical changes in the mesolimbic dopamine pathway associated with addiction.<\/li>\r\n<\/ul>\r\n<h2>Key Terms<\/h2>\r\n<ul>\r\n \t<li><strong>Mesolimbic Dopamine Pathway<\/strong>: A neural circuit that originates in the ventral tegmental area (VTA) and projects to limbic regions such as the nucleus accumbens. It plays a central role in reward processing and motivation. Here, \u201creward\u201d is a latent construct. It is an internal motivational state inferred from behavior and neural signals rather than directly observed.<\/li>\r\n \t<li><strong>Dopamine<\/strong>: A neurotransmitter involved in reinforcement learning and motivation. Dopamine signals the importance of outcomes and cues, shaping future behavior by influencing pursuit and learning rather than pleasure alone.<\/li>\r\n \t<li><strong>Ventral Tegmental Area (VTA)<\/strong>: A midbrain region that produces dopamine and serves as a primary source of dopaminergic input to the mesolimbic pathway.<\/li>\r\n \t<li><strong>Nucleus Accumbens (NAc)<\/strong>: A structure in the ventral striatum that receives dopamine from the VTA and integrates information about reward, motivation, and goal-directed action.<\/li>\r\n \t<li><strong>Reward Prediction Error (RPE)<\/strong>: The difference between what is expected and what actually occurs. Dopamine increases or decreases signal whether an outcome is better or worse than anticipated, updating learning.<\/li>\r\n \t<li><strong>Incentive Sensitization<\/strong>: A process in which repeated drug exposure heightens the motivational pull or \u201cwanting\u201d of substance-related cues, even when the pleasurable effects or \u201cliking\u201d diminish.<\/li>\r\n \t<li><strong>Opponent-Process Theory<\/strong>: A model describing how initial pleasurable responses to a stimulus are followed by an opposing process that produces discomfort or stress, shifting the balance between reward and pain over time.<\/li>\r\n \t<li><strong>\u201cWanting\u201d vs. \u201cLiking\u201d<\/strong>: The distinction between dopamine-driven motivation to seek a reward (\u201cwanting\u201d) and the hedonic pleasure experienced during consumption (\u201cliking\u201d).<\/li>\r\n \t<li><strong>Functional Magnetic Resonance Imaging (fMRI)<\/strong>: An imaging technique that measures brain activity through changes in blood flow, allowing researchers to study functional neural circuits during tasks or at rest.<\/li>\r\n \t<li><strong>Positron Emission Tomography (PET)<\/strong>: An imaging method that uses radioactive tracers to visualize and quantify neurochemical processes such as dopamine receptor availability and neurotransmitter release.<\/li>\r\n<\/ul>\r\n<h2>Introduction<\/h2>\r\nThe brain constantly balances pleasure and pain, much like a seesaw. Dopamine is often described as the brain\u2019s \u201cmolecule of more\u201d because it drives motivation and pursuit rather than satisfaction itself. When something rewarding occurs, dopamine helps reinforce the behaviors and cues that led to it.\r\n\r\nAfter pleasure, however, the brain activates an opposing response that produces stress or dysphoria. This dynamic is described by opponent-process theory. Early on, the pleasurable effects outweigh the discomfort. With repeated pursuit of rewards without sufficient balance, whether from substances, screens, or highly palatable foods, the opposing process grows stronger. Over time, the \u201cpain side\u201d begins to dominate.\r\n\r\nAs this shift unfolds, people may find that rewards no longer produce the same enjoyment. Instead, they are pursued to relieve discomfort or to feel normal. This gradual change in the hedonic set-point, moving from pleasure toward dysphoria and craving, is a defining feature of addiction and a central focus of this chapter.\r\n\r\n[caption id=\"attachment_149\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-149 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-1024x683.png\" alt=\"Four-panel infographic explaining the pleasure\u2013pain balance in addiction. Panel 1 shows a dopamine \u201cspike\u201d tipping a seesaw toward pleasure. Panel 2 shows \u201canti-reward gremlins\u201d pushing back on the pain side to restore balance. Panel 3 shows repeated hits shifting the set-point so the pain side grows and pleasure shrinks. Panel 4 shows a craving loop where use brings brief relief but leads to more pain over time.\" width=\"1024\" height=\"683\" \/> Pleasure\u2013pain balance and opponent-process theory across repeated substance \u201chits,\u201d illustrating how reward effects shrink and anti-reward responses grow over time, contributing to craving and compulsive use. Concept inspired by Anna Lembke\u2019s pleasure\u2013pain balance framing in Dopamine Nation (Lembke, 2021).[\/caption]\r\n\r\nIn the brain\u2019s intricate machinery, pleasure and pain sit on opposite ends of a delicate seesaw, a metaphor popularized by <strong>Anna Lembke<\/strong> (Lembke, 2021). When we experience pleasure, dopamine surges tip the balance toward reward. That surge then triggers a compensatory \u201canti-reward\u201d response, often illustrated as small stress-inducing forces piling onto the pain side of the seesaw. With repeated exposure, these anti-reward forces accumulate, shifting the balance toward pain and driving persistent craving.\r\n\r\nIn this module, we explore how the limbic\u2013basal ganglia pathway underlies this transformation. You will learn the crucial distinction between <strong>\u201cwanting,\u201d<\/strong> the dopamine-driven urge to seek rewards, and <strong>\u201cliking,\u201d<\/strong> the actual pleasure derived from them. We examine how chronic substance use reshapes neural circuits, locking in compulsive habits and intense cravings long after the initial euphoria fades. We also show how modern brain-imaging methods reveal these deep-seated changes, reinforcing the view that addiction reflects fundamental neurobiological processes rather than a simple failure of willpower.\r\n<h3>A Quick Overview of the Human Brain<\/h3>\r\nBefore describing the reward pathway, it helps to establish some basic vocabulary. The brain coordinates chemical and electrical signals that regulate everything from life-sustaining functions like breathing and digestion to perception, emotion, thought, and social interaction. The human brain contains roughly <strong>86 billion nerve cells<\/strong>, called neurons, along with a variety of supporting cells (SAMHSA, 2016).\r\n\r\nEach neuron has three main components. The <strong>cell body<\/strong> houses the nucleus and directs the neuron\u2019s activities. The <strong>axon<\/strong> is a long projection that sends signals to other cells. The <strong>dendrites<\/strong> are branching structures that receive incoming signals from neighboring neurons. Together, these components form vast networks that allow information to flow through the brain, setting the stage for understanding how reward, motivation, and addiction emerge from neural circuitry.\r\n\r\n[caption id=\"attachment_151\" align=\"aligncenter\" width=\"624\"]<img class=\"wp-image-151 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Diagram-of-a-Neuron.jpg\" alt=\"Diagram showing dopamine signaling in a neuron: dopamine is made in the cell body, transported down the axon, released at the terminal, and binds receptors across the synapse (with an inset showing vesicles, dopamine molecules, and receptors).\" width=\"624\" height=\"553\" \/> Figure 2. Dopamine neurotransmission overview: synthesis in the cell body, axonal transport, release from the terminal, and receptor binding at the synapse. Source: Substance Abuse and Mental Health Services Administration (SAMHSA), 2016 (public domain U.S. Government material; reuse permitted with attribution).[\/caption]\r\n\r\nNeurons communicate using chemical messengers called <strong>neurotransmitters<\/strong>. These chemicals cross a tiny gap between cells, known as a <strong>synapse<\/strong>, and bind to <strong>receptors<\/strong> on neighboring neurons. Some neurotransmitters inhibit or dampen activity in the receiving neuron, making it less likely to fire. Others are excitatory, increasing the likelihood that the neuron will pass the signal along. Behavior and cognition emerge from the balance of these excitatory and inhibitory influences across large networks of neurons.\r\n\r\nNeurons are not wired randomly. They tend to cluster into specialized <strong>circuits<\/strong> that carry out particular functions. Some circuits support higher-order processes such as thinking, learning, emotion, and memory. Others are more directly tied to action, linking the brain to muscles to produce movement, or to sensory systems that process information from the eyes, ears, and skin. Addiction-related processes arise not from a single \u201caddiction center,\u201d but from interactions among multiple circuits that normally support motivation, learning, and self-control.\r\n<h3>Core Neuroanatomy of the Mesolimbic Dopamine Pathway<\/h3>\r\nThe mesolimbic dopamine pathway has two central hubs. The <strong>ventral tegmental area (VTA)<\/strong> is a cluster of dopamine-producing neurons located in the midbrain. The <strong>nucleus accumbens (NAc)<\/strong> sits in the ventral striatum and acts as a key integration site for reward, motivation, and learning signals.\r\n\r\nWhen outcomes are <strong>better than expected<\/strong>, dopamine neurons in the VTA fire in brief bursts, releasing dopamine into the nucleus accumbens. When outcomes are <strong>worse than expected<\/strong>, these neurons reduce or pause their firing. This rise or fall in dopamine is not just a pleasure signal. It functions as a <strong>teaching signal<\/strong>, updating the brain about whether predictions were accurate and guiding future behavior. In this sense, dopamine is the core \u201ccurrency\u201d of learning in the mesolimbic pathway.\r\n\r\nAlthough the VTA and NAc are the central actors, they do not work in isolation. Regions such as the <strong>prefrontal cortex<\/strong>, <strong>amygdala<\/strong>, <strong>hippocampus<\/strong>, and <strong>extended amygdala<\/strong> interact with this pathway to shape decision-making, emotional responses, memory for reward-related cues, and the formation of habits. These interactions become especially important for understanding cue-driven craving and compulsive behavior, which we will unpack in more detail in Module 9.\r\n\r\n[caption id=\"attachment_152\" align=\"aligncenter\" width=\"624\"]<img class=\"wp-image-152 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Dopamine-Pathways.png\" alt=\"Simplified side-view brain diagram highlighting major dopamine pathways with arrows projecting from midbrain to limbic and cortical targets.\" width=\"624\" height=\"437\" \/> Figure 3. Simplified schematic of major dopamine pathways relevant to reward and addiction-related circuitry. Image credit: National Institute on Drug Abuse (NIDA), National Institutes of Health (2012). U.S. government work (public domain), unless otherwise noted on the original source page.[\/caption]\r\n<h2>Function of the Mesolimbic Dopamine Pathway<\/h2>\r\n<h3>Reward Prediction Error<\/h3>\r\nRecall that dopamine neurons in the ventral tegmental area (VTA) send signals to the nucleus accumbens (NAc) and other limbic and cortical structures. When rewards exceed expectations, these VTA neurons increase dopamine release, reinforcing the behaviors that led to the positive outcome. This raises a deeper question. Why does dopamine fire, and what information is it conveying?\r\n\r\nDopamine neurons signal a <strong>reward prediction error (RPE)<\/strong>, which reflects how actual outcomes compare with expectations. If a reward is better than expected, such as studying for a B and receiving an A+, dopamine neurons show a brief surge in activity. That surge reinforces the actions that led to the unexpectedly good result (Schultz, 1998). In contrast, when outcomes are worse than expected, dopamine neuron firing is reduced, guiding the brain to revise future behavior.\r\n\r\nThis RPE mechanism functions as a teaching signal. A dopamine spike communicates, \u201cThis was better than expected. Strengthen whatever led here.\u201d A dopamine dip communicates, \u201cThis outcome fell short. Adjust your strategy.\u201d Across repeated experiences, these signals help the brain learn which cues, contexts, and actions reliably lead to rewarding outcomes.\r\n\r\nImportantly, reward prediction errors shape not only learning but also motivation. As cues become associated with dopamine surges, they can acquire powerful motivational pull. This helps explain why people may pursue rewards intensely, even when the actual experience delivers less pleasure than anticipated.\r\n\r\n[caption id=\"attachment_159\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-159 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-1024x683.png\" alt=\"Four-panel infographic titled \u201cFigure 4: Reward Prediction Error.\u201d Each panel uses a social media \u201clikes\u201d example to show how dopamine-like teaching signals reflect the mismatch between expected and received outcomes. Panel 1 shows a cue (\u201cNew post\u201d) and an expectation of about 10 likes with a flat baseline trace. Panel 2 shows a positive RPE: the post receives 50 likes and the trace shows a sharp upward spike. Panel 3 shows a negative RPE: fewer likes than expected and the trace dips below baseline. Panel 4 shows learning: expectations update upward (about 40 likes), the received outcome matches, and the RPE signal becomes small while the cue becomes motivating.\" width=\"1024\" height=\"683\" \/> Figure 4. Reward prediction error (RPE) illustrated with social feedback. Positive RPE occurs when outcomes exceed expectations (spike), negative RPE occurs when outcomes fall short (dip), and learning updates expectations so prediction errors shrink over time. Created by the author with generative AI.[\/caption]\r\n<p data-start=\"0\" data-end=\"526\">This reward-prediction mechanism is the brain\u2019s natural version of what computer scientists call <strong data-start=\"97\" data-end=\"123\">reinforcement learning<\/strong>, a process by which agents learn to maximize future rewards through feedback. Dopamine\u2019s prediction-error signals continuously update the brain\u2019s internal \u201cvalue map,\u201d strengthening behaviors that produce better-than-expected outcomes and weakening those that disappoint. This learning logic brings us to a critical distinction in the dopamine system: the difference between <strong data-start=\"499\" data-end=\"510\">wanting<\/strong> and <strong data-start=\"515\" data-end=\"525\">liking<\/strong>.<\/p>\r\n\r\n<h3 data-start=\"528\" data-end=\"550\">Wanting vs. Liking<\/h3>\r\n<p data-start=\"566\" data-end=\"1201\">Dopamine primarily drives \u201cwanting\u201d\u2014the motivational force that energizes pursuit of rewards\u2014more than \u201cliking,\u201d the pleasure experienced when a reward is consumed (Berridge &amp; Robinson, 2016). A useful mechanistic label for cue-triggered wanting is <strong data-start=\"815\" data-end=\"837\">incentive salience<\/strong>: the process by which reward-related cues become <strong data-start=\"887\" data-end=\"911\">motivational magnets<\/strong> that grab attention and energize approach behavior. These processes are dissociable in the brain. Dopamine-related signaling is strongly tied to incentive motivation\/craving and approach behavior, while \u201cliking\u201d depends more heavily on opioid-based hedonic hotspots that generate pleasure.<\/p>\r\n<p data-start=\"1203\" data-end=\"1708\">Both wanting and liking are <strong data-start=\"1231\" data-end=\"1252\">latent constructs<\/strong>: we do not observe them directly. Instead, we infer them from patterns of behavior (e.g., approach, effort, reaction time), self-report (e.g., craving ratings), and neural measures that serve as proxies for circuit engagement. In cue-driven situations, increased wanting is often inferred when cues draw attention and trigger approach\/effort\u2014consistent with increased incentive salience\u2014even if the pleasurable experience of consumption does not increase.<\/p>\r\n<p data-start=\"1710\" data-end=\"2305\">A simple illustration makes the distinction concrete. Imagine you have a strong preference for chocolate cake. Seeing the cake in a display case can trigger intense wanting: your attention locks in, your mouth waters, and you feel a pull toward eating it. In incentive-salience terms, the cue (the sight of cake) has been tagged with motivational power. Now imagine taking a bite. The rich, chocolate flavor produces enjoyment. That pleasure is liking. Importantly, wanting can grow stronger even as liking stays the same or declines\u2014a divergence that becomes central to understanding addiction.<\/p>\r\n\r\n\r\n[caption id=\"attachment_157\" align=\"aligncenter\" width=\"627\"]<img class=\"wp-image-157 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/wanting-vs-liking-roi.jpg\" alt=\"Two-panel brain-imaging figure comparing neural maps for wanting (Panel A) and liking (Panel B). Warm-colored clusters (yellow to red) are overlaid on grayscale brain slices, with a color scale from 0 to 8 above each panel.\" width=\"627\" height=\"697\" \/> Figure 5. Neural activation maps for wanting (A) and liking (B), shown as thresholded warm-color overlays on grayscale brain images (color bar 0\u20138). Source: Soutschek et al. (2021), bioRxiv, https:\/\/doi.org\/10.1101\/2021.06.20.449203.[\/caption]\r\n\r\nThis wanting\u2013liking split is formalized in the incentive-sensitization theory of addiction.\r\n<div class=\"textbox textbox--examples\"><header class=\"textbox__header\">\r\n<p class=\"textbox__title\">Incentive-Salience Hypothesis<\/p>\r\n\r\n<\/header>\r\n<div class=\"textbox__content\">\r\n\r\nThe <strong data-start=\"2684\" data-end=\"2717\">incentive-salience hypothesis<\/strong> proposes that the brain can tag reward-related cues (or mental representations of rewards) with motivational power, so those cues come to trigger strong wanting. In this view, cues become motivational magnets that grab attention and energize approach behavior even when the actual pleasure of consumption (liking) is unchanged or reduced (Berridge &amp; Robinson, 2016). <strong data-start=\"3085\" data-end=\"3112\">Incentive sensitization<\/strong> refers to the long-term increase in the brain\u2019s propensity to assign incentive salience to drug cues after repeated drug use\u2014helping explain intense cue-evoked craving and relapse risk even when the drug is no longer very pleasurable.\r\n\r\n<\/div>\r\n<\/div>\r\n<p data-start=\"3006\" data-end=\"3333\">In substance use disorders, dopamine-linked wanting can grow increasingly intense even as liking declines. This pattern reflects incentive sensitization (cues trigger exaggerated incentive salience) alongside tolerance (reduced pleasure from the substance itself). The result is powerful craving without proportional enjoyment.<\/p>\r\n<p data-start=\"3335\" data-end=\"3797\">Neuroanatomy helps explain this divergence. Dopamine-related signaling in the nucleus accumbens (NAc), especially the core, is strongly implicated in cue-triggered wanting and approach behavior. In contrast, hedonic liking is mediated in part by opioid-based \u201chedonic hotspots,\u201d including regions in and around the NAc shell. Because these systems are partially separable, craving can intensify even as pleasure fades\u2014one of the clearest signatures of addiction.<\/p>\r\n\r\n\r\n[caption id=\"attachment_163\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-163 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM.png\" alt=\"Four-panel (2\u00d72) infographic explaining \u201cwanting\u201d versus \u201cliking\u201d using chocolate cake. Top-left: a cue and cake trigger WANTING (craving) with a brain callout labeled \u201cDopamine \u2192 NAc core.\u201d Top-right: a person runs, showing WANTING drives action, with \u201cDopamine signal\u201d and a rising bar chart. Bottom-left: LIKING is shown as pleasure while eating cake, with a brain callout labeled \u201cOpioid hotspots \u2192 NAc shell,\u201d plus hearts. Bottom-right: WANTING and LIKING diverge, with WANTING high and increasing (incentive sensitization) while LIKING is low and decreasing (tolerance), and a note that craving can stay high even when pleasure fades.\" width=\"1024\" height=\"1024\" \/> Figure 6. Wanting versus liking. Dopamine-related signals primarily support wanting (motivation and pursuit), while opioid-related \u201chedonic hotspots\u201d support liking (experienced pleasure). With incentive sensitization and tolerance, wanting can increase even as liking decreases. Created by the author with generative AI.[\/caption]\r\n<h3>The Pleasure\u2013Pain Balance<\/h3>\r\nWhile reward prediction errors fine-tune behavior by comparing outcomes to expectations, the brain also works to maintain emotional stability through <strong>homeostatic regulation<\/strong>. Homeostasis refers to the self-regulating processes that keep biological systems within functional ranges while adapting to changing conditions. After pleasurable experiences, dopamine surges are typically followed by brief dips in mood or motivation. This counter-response restores equilibrium, much like a seesaw tipping back after a high.\r\n\r\nThis pleasure\u2013pain balance keeps motivation calibrated. It allows us to enjoy rewards and learn from them without becoming trapped in constant highs or immobilized by lows. Under typical conditions, the system resets efficiently, preserving sensitivity to everyday rewards.\r\n\r\nProblems arise when dopamine spikes are repeated and intense, as with addictive drugs or highly stimulating behaviors. Over time, the brain adapts by reducing dopamine sensitivity and increasing <strong>anti-reward<\/strong> or stress-related signals. Natural rewards begin to feel less satisfying, while baseline stress and irritability increase. The individual is no longer chasing pleasure alone but is increasingly motivated to escape discomfort.\r\n\r\nIn this way, a system designed to support learning and balance can be pushed into dysregulation. Repeated overstimulation drives cycles of craving, tolerance, and dysphoria. Although the specific neuroadaptations vary by substance, they share a common core: a shifted hedonic balance in which the brain\u2019s reward system is recalibrated toward pain rather than pleasure.\r\n<h2>Substance-Specific Effects on the Mesolimbic System<\/h2>\r\nInitially, drugs trigger exaggerated dopamine spikes, producing large positive reward prediction errors. These surges create intense feelings of pleasure and strongly reinforce substance use. With repeated exposure, however, the brain adapts. Neuroadaptations such as receptor downregulation, reduced dopamine sensitivity, and increased anti-reward signaling, including systems involving dynorphin and corticotropin-releasing factor (CRF), begin to dominate. The result is a shift in the brain\u2019s pleasure\u2013pain balance toward pain.\r\n\r\nAs this balance shifts, motivation changes. Behavior is no longer driven primarily by the pursuit of pleasure but by the need to avoid withdrawal, stress, and dysphoria (Lembke, 2021). What once felt rewarding becomes necessary simply to feel normal.\r\n\r\nIn essence, addiction reflects a corrupted version of the brain\u2019s natural reward system. Pathological craving, or excessive \u201cwanting,\u201d persists even as genuine enjoyment, or \u201cliking,\u201d diminishes. Although this core process is shared across substances, the precise neurobiological mechanisms and downstream consequences vary depending on the drug involved.\r\n\r\n[caption id=\"attachment_165\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-165 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-1024x683.png\" alt=\"Four-panel infographic showing a progression of addiction neuroadaptation. Panel 1: \u201cFirst exposure: dopamine super-spike\u201d with a cue and reward and a large dopamine spike labeled large positive prediction error. Panel 2: \u201cTolerance: downregulation\u201d with repeated exposure over time and reduced dopamine response and D2 down icons. Panel 3: \u201cAnti-reward and stress take over\u201d with a seesaw tipped toward CRF and dynorphin and away from pleasure. Panel 4: \u201cCraving outlives pleasure (wanting &gt; liking)\u201d showing a cue triggering high wanting and low liking, with drug icons and the note that craving persists as enjoyment fades. Footer note says it is a conceptual schematic and details differ by drug.\" width=\"1024\" height=\"683\" \/> Figure 7. Neuroadaptations in addiction over time. Early exposure produces a large dopamine \u201csuper-spike\u201d (positive prediction error). With repeated use, dopamine responses diminish (tolerance) alongside downregulation (illustrated with reduced D2). Anti-reward stress systems such as CRF and dynorphin increase, shifting affect toward dysphoria. Over time, cue-triggered wanting (craving) can remain high even as liking (pleasure) declines. Created by the author with generative AI.[\/caption]\r\n<table class=\"shaded\"><caption>Table 1. Substance Effects on the Mesolimbic Dopamine System<\/caption>\r\n<thead>\r\n<tr>\r\n<th>Substance<\/th>\r\n<th>Mechanism of Dopamine Activation<\/th>\r\n<th>Dopamine Impact<\/th>\r\n<th>Withdrawal Profile<\/th>\r\n<th>Key Neuroadaptations<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>Alcohol<\/td>\r\n<td>Disinhibits VTA dopamine neurons via GABA modulation; increases endogenous opioids<\/td>\r\n<td>Moderate dopamine increase; enhanced initial euphoria<\/td>\r\n<td>Moderate to severe: anxiety, anhedonia, irritability<\/td>\r\n<td>\u2193 D\u2082 receptors, \u2191 CRF (stress), impaired frontal cortex function<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Cannabis<\/td>\r\n<td>THC activates CB\u2081 receptors \u2192 disinhibits dopamine in VTA<\/td>\r\n<td>Mild to moderate dopamine increase<\/td>\r\n<td>Mild: irritability, sleep disruption, low motivation<\/td>\r\n<td>\u2193 dopamine release (long-term), blunted response to rewards<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Opioids<\/td>\r\n<td>Inhibit GABA interneurons \u2192 disinhibit VTA dopamine neurons; direct activation of opioid receptors<\/td>\r\n<td>Strong dopamine surge plus direct hedonic effects<\/td>\r\n<td>Severe: dysphoria, physical pain, intense craving<\/td>\r\n<td>\u2193 D\u2082 receptors, \u2193 endogenous opioid tone, cue-induced dopamine sensitization<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Cocaine<\/td>\r\n<td>Blocks dopamine reuptake \u2192 large synaptic dopamine accumulation<\/td>\r\n<td>Very strong, rapid dopamine spike<\/td>\r\n<td>High craving, mood crashes, anhedonia<\/td>\r\n<td>\u2193 D\u2082 receptors, \u2193 dopamine release capacity, strong cue reactivity<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<p data-start=\"186\" data-end=\"459\">Below is an overview of how common substances\u2014alcohol, cannabis, opioids, and cocaine\u2014affect the mesolimbic dopamine system. Although their pharmacological mechanisms differ, all ultimately distort dopamine signaling in ways that promote craving, tolerance, and dependence.<\/p>\r\n\r\n<h3 data-start=\"461\" data-end=\"472\">Alcohol<\/h3>\r\n<p data-start=\"474\" data-end=\"768\">Alcohol initially boosts dopamine release by <strong data-start=\"519\" data-end=\"589\">disinhibiting dopamine neurons in the ventral tegmental area (VTA)<\/strong>. This occurs primarily through suppression of inhibitory GABA neurons and increased release of endogenous opioids, producing mild euphoria and relaxation (Ludlow et al., 2009).<\/p>\r\n<p data-start=\"770\" data-end=\"1306\">With chronic use, the brain adapts by reducing dopamine receptor availability, particularly <strong data-start=\"862\" data-end=\"878\">D\u2082 receptors<\/strong>, which blunts pleasure from both alcohol and natural rewards. During withdrawal, stress systems become overactive, producing anxiety, dysphoria, and irritability. These aversive states strongly motivate continued drinking as a form of negative reinforcement. Long-term alcohol misuse also impairs <strong data-start=\"1176\" data-end=\"1197\">prefrontal cortex<\/strong> functioning, contributing to impulsivity, poor decision-making, and heightened craving (Hienz et al., 2004).<\/p>\r\n\r\n<h3 data-start=\"1308\" data-end=\"1320\">Cannabis<\/h3>\r\n<p data-start=\"1322\" data-end=\"1666\">\u03949-tetrahydrocannabinol (THC), the primary psychoactive compound in cannabis, increases dopamine release indirectly by activating <strong data-start=\"1452\" data-end=\"1469\">CB\u2081 receptors<\/strong> on GABA interneurons in the VTA, thereby disinhibiting dopamine neurons (Bloomfield et al., 2016). The resulting dopamine increase is typically smaller than that produced by stimulants or opioids.<\/p>\r\n<p data-start=\"1668\" data-end=\"2229\">With heavy or chronic use, cannabis is associated with reduced dopamine responsiveness, leading to a <strong data-start=\"1769\" data-end=\"1795\">hypodopaminergic state<\/strong> that may underlie reduced motivation and reward sensitivity, sometimes described as an amotivational profile. PET imaging studies show diminished dopamine release capacity in frequent cannabis users, even though physical withdrawal symptoms are generally mild (Bloomfield et al., 2016). Notably, the cannabinoid system remains a promising therapeutic target, particularly through non-intoxicating compounds such as cannabidiol (CBD).<\/p>\r\n\r\n<h3 data-start=\"2231\" data-end=\"2287\">Opioids (Heroin, Morphine, Prescription Painkillers)<\/h3>\r\n<p data-start=\"2289\" data-end=\"2623\">Opioids are highly addictive because they act on the reward system through <strong data-start=\"2364\" data-end=\"2393\">two converging mechanisms<\/strong>. First, they directly activate \u03bc-opioid receptors, producing strong hedonic effects. Second, they indirectly increase dopamine release by inhibiting GABA interneurons in the VTA, disinhibiting dopamine neurons (Hou et al., 2012).<\/p>\r\n<p data-start=\"2625\" data-end=\"3265\">Chronic opioid exposure leads to pronounced reductions in dopamine receptor availability and overall dopamine release capacity. At the same time, the dopamine system becomes <strong data-start=\"2799\" data-end=\"2834\">sensitized to drug-related cues<\/strong>, producing intense craving that can persist long after abstinence. Withdrawal is marked by severe dysphoria, physical pain, and stress, illustrating the extreme pleasure-to-pain shift characteristic of opioid addiction. Pharmacological treatments such as <strong data-start=\"3090\" data-end=\"3103\">methadone<\/strong> and <strong data-start=\"3108\" data-end=\"3125\">buprenorphine<\/strong> stabilize opioid signaling and reduce withdrawal and craving, supporting recovery by dampening reward-system volatility (Hou et al., 2012).<\/p>\r\n\r\n<h3 data-start=\"3267\" data-end=\"3299\">Cocaine and Other Stimulants<\/h3>\r\n<p data-start=\"3301\" data-end=\"3585\">Stimulants such as cocaine produce rapid and intense dopamine spikes by <strong data-start=\"3373\" data-end=\"3403\">blocking dopamine reuptake<\/strong>, causing dopamine to accumulate in synapses (Volkow &amp; Morales, 2015). The magnitude and speed of this dopamine elevation are uniquely high, making stimulants especially reinforcing.<\/p>\r\n<p data-start=\"3587\" data-end=\"4225\">Repeated stimulant use triggers rapid <strong data-start=\"3625\" data-end=\"3661\">dopamine receptor downregulation<\/strong>, particularly of D\u2082 receptors, and a marked decline in baseline dopamine responsiveness. Chronic cocaine users often experience profound anhedonia, low motivation, and emotional flattening when not using the drug. Cue-induced craving becomes dominant, overpowering natural reward-seeking behaviors. Recovery of dopamine function can take months or years, contributing to high relapse risk. Behavioral treatments, including <strong data-start=\"4085\" data-end=\"4111\">contingency management<\/strong>, remain central to care and aim to support gradual restoration of reward-system balance (Volkow &amp; Morales, 2015).<\/p>\r\n\r\n\r\n[caption id=\"attachment_168\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-168 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-1024x683.png\" alt=\"Slide titled \u201cDopamine D\u2082 Receptors\u201d with three bullet points explaining that D\u2082 receptors regulate dopamine signaling, chronic substance use downregulates D\u2082 (fewer receptors, lower sensitivity), and lower D\u2082 function can reduce reward responsiveness and promote compulsive seeking. A diagram shows dopamine released from a presynaptic terminal into the synapse and binding to D\u2082 receptors on a postsynaptic membrane. An inset labeled \u201cDownregulation\u201d compares many receptors in a \u201cTypical\u201d state versus fewer in a \u201cDownregulated\u201d state. Footer note: \u201cConceptual schematic: receptor numbers shown for illustration.\u201d\" width=\"1024\" height=\"683\" \/> Figure 8. Conceptual schematic of dopamine D\u2082 receptor signaling and downregulation. Dopamine released from a presynaptic neuron binds D\u2082 receptors on the postsynaptic membrane. Chronic substance exposure is often associated with reduced D\u2082 receptor availability or sensitivity, which can blunt reward responsiveness and contribute to compulsive reward-seeking behavior. Created by the author with generative AI.[\/caption]\r\n\r\nWhile each substance affects the mesolimbic dopamine pathway in distinct ways, they converge on a common outcome: distorted dopamine signaling that transforms healthy reward seeking into pathological craving and dependence. Across drugs, exaggerated dopamine responses, compensatory neuroadaptations, and heightened anti-reward signaling reshape motivation so that behavior becomes organized around relief from discomfort rather than the pursuit of pleasure.\r\n\r\nUnderstanding these mechanisms has practical and ethical importance. It informs more effective prevention and treatment strategies, and it supports a compassionate, evidence-based view of addiction as a neurobiological disorder rather than a moral or personal failing.\r\n\r\nHaving examined how substances alter dopamine pathways, the next logical step is to explore how scientists <strong>visualize<\/strong> these changes in the living human brain. This brings us to the field of neuroimaging.\r\n<div class=\"textbox textbox--examples\"><header class=\"textbox__header\">\r\n<p class=\"textbox__title\">Digital Dopamine: Social Media and the Mesolimbic Pathway<\/p>\r\n\r\n<\/header>\r\n<div class=\"textbox__content\">\r\n<p data-start=\"1567\" data-end=\"2161\"><strong data-start=\"1567\" data-end=\"1613\">Variable social rewards keep us scrolling.<\/strong> Social media interactions, such as Instagram \u201cLikes,\u201d follows, and TikTok views, function as potent yet unpredictable social rewards. Brain imaging studies show that when adolescents view images with many \u201cLikes,\u201d there is increased activation in the nucleus accumbens (NAc), the same dopamine-rich region engaged by food, drugs, and money (Sherman et al., 2016). Stronger accumbens responses to social reward predict more frequent checking of social media feeds, reinforcing a habit loop that parallels substance addiction (Sherman et al., 2018).<\/p>\r\n<p data-start=\"2166\" data-end=\"2657\"><strong data-start=\"2166\" data-end=\"2219\">Algorithms amplify dopamine-driven reward cycles.<\/strong> Platforms like TikTok intensify this process through algorithmic curation. When participants view personalized content on TikTok\u2019s \u201cFor You\u201d page, fMRI studies show greater activation of the ventral tegmental area (VTA), the midbrain source of dopamine signaling, compared to generic feeds (Lin et al., 2021). This variable-ratio schedule of unpredictable but personalized rewards promotes persistent engagement and compulsive scrolling.<\/p>\r\n<p data-start=\"2662\" data-end=\"3044\"><strong data-start=\"2662\" data-end=\"2709\">Chasing the digital hit reshapes the brain.<\/strong> Chronic social media engagement does not merely activate reward circuits in the moment. Smartphone usage studies show that frequent Instagram and Facebook checking is associated with reduced gray matter volume in the nucleus accumbens, a pattern similar to structural changes observed in long-term substance use (Montag et al., 2017).<\/p>\r\n<p data-start=\"3049\" data-end=\"3470\"><strong data-start=\"3049\" data-end=\"3104\">The pleasure\u2013pain seesaw applies without chemicals.<\/strong> Heavy social media users often report withdrawal-like symptoms, including irritability, anxiety, and low mood, during periods of digital abstinence. These effects mirror the opponent-process imbalance described earlier in this module, demonstrating that powerful dopamine-driven reward loops can emerge even in the absence of psychoactive substances (Lembke, 2021).<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n\r\n[caption id=\"attachment_169\" align=\"aligncenter\" width=\"1024\"]<img class=\"wp-image-169 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-1024x683.png\" alt=\"Four-panel infographic titled \u201cDigital Dopamine.\u201d Panel 1 shows a person checking a phone with a \u201c1,200 likes\u201d icon; a brain callout highlights the nucleus accumbens (NAc) and labels \u201cdopamine,\u201d with the caption that high engagement signals activate the NAc. Panel 2 shows reward icons near the phone and a brain outline labeled \u201cPFC\u201d with a downward arrow, stating strong rewards can temporarily reduce top-down control from the prefrontal cortex (PFC). Panel 3 shows a \u201cFor You\u201d feed, a roulette wheel and dice labeled \u201cvariable ratio,\u201d and two brains labeled \u201cdopamine bursts\u201d and \u201cVTA,\u201d describing unpredictable personalized rewards amplifying dopamine bursts from the ventral tegmental area (VTA). Panel 4 shows a simplified chart labeled \u201cNAc gray matter\u201d decreasing with \u201cchronic checking,\u201d plus a sad person holding a phone with negative mood icons, stating heavy use is linked to NAc changes and withdrawal-like symptoms. Footer note says the schematic is conceptual and brain regions are simplified.\" width=\"1024\" height=\"683\" \/> Figure 9. Digital reward loops as a conceptual dopamine schematic. Social feedback and algorithmically variable rewards can drive dopamine-related learning signals in reward circuitry (for example NAc and VTA), while intense rewards may transiently weaken prefrontal top-down control (PFC). With chronic checking, some users may experience adaptation and negative affect when not engaging. Created by the author with generative AI.[\/caption]\r\n<h2 data-start=\"123\" data-end=\"183\">Imaging the Dopamine Pathway: fMRI, PET, and the MID Task<\/h2>\r\n<p data-start=\"185\" data-end=\"480\">How do we know so much about the dopamine system in humans without directly measuring neuron activity? Two advanced neuroimaging techniques\u2014functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET)\u2014allow scientists to observe how dopamine pathways operate in real time.<\/p>\r\n<p data-start=\"482\" data-end=\"978\">Functional Magnetic Resonance Imaging (fMRI) is a specialized type of MRI that detects brain activity indirectly by measuring changes in blood flow. MRI scanners are large, cylindrical devices that use powerful magnets and radio waves to create detailed images of the brain. During an fMRI scan, participants lie on a sliding table that moves into the scanner\u2019s tunnel-like center, where changes in blood oxygenation are captured and reconstructed into three-dimensional images of brain activity.<\/p>\r\nhttps:\/\/www.youtube.com\/watch?v=2M6frboBwh8&amp;source_ve_path=MTc4NDI0\r\n\r\nFigure 10: The ABCD study MRI training video for ABCD participants.\r\n<h2 data-start=\"157\" data-end=\"220\">Resting-State fMRI: Mapping the Brain\u2019s Default Connectivity<\/h2>\r\n<p data-start=\"222\" data-end=\"781\"><strong data-start=\"222\" data-end=\"253\">What is resting-state fMRI?<\/strong><br data-start=\"253\" data-end=\"256\" \/>Resting-state fMRI measures brain activity while a person lies quietly in the scanner without performing a specific task. Rather than capturing responses to stimuli, it records spontaneous, synchronized fluctuations in the blood-oxygen-level\u2013dependent (BOLD) signal across brain regions. These coordinated fluctuations reflect <em data-start=\"583\" data-end=\"618\">intrinsic functional connectivity<\/em>, meaning how strongly different regions communicate with one another at rest. The ABCD Study includes resting-state fMRI data for participants across development.<\/p>\r\n<p data-start=\"783\" data-end=\"1115\"><strong data-start=\"783\" data-end=\"829\">Why is this useful for addiction research?<\/strong><br data-start=\"829\" data-end=\"832\" \/>Resting-state fMRI is particularly valuable for identifying baseline differences in brain network organization between individuals with substance use disorders and healthy controls. Across studies, addiction is associated with altered connectivity within reward and control circuits.<\/p>\r\n<p data-start=\"1117\" data-end=\"1569\"><strong data-start=\"1117\" data-end=\"1163\">Key finding: reward\u2013control disconnection.<\/strong><br data-start=\"1163\" data-end=\"1166\" \/>A consistent result is disrupted functional connectivity between the nucleus accumbens (NAc), a core reward-processing region, and the prefrontal cortex (PFC), which supports executive control and decision-making. For example, stimulant users often show reduced NAc\u2013PFC connectivity, indicating weakened top-down regulation of reward-driven impulses by frontal control systems (Sutherland et al., 2012).<\/p>\r\n<p data-start=\"1571\" data-end=\"1927\"><strong data-start=\"1571\" data-end=\"1590\">Interpretation.<\/strong><br data-start=\"1590\" data-end=\"1593\" \/>These patterns suggest that addiction involves not only heightened reward sensitivity, but also impaired communication between motivational systems and self-regulatory control networks. Resting-state findings therefore provide neural evidence for compulsive drug-seeking as a circuit-level disorder, not simply a failure of willpower.<\/p>\r\n\r\n\r\n[caption id=\"attachment_171\" align=\"aligncenter\" width=\"468\"]<img class=\"wp-image-171 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11.png\" alt=\"Four-panel fMRI-style brain image with statistical activation overlays. The top row (labeled \u201cA\u201d) shows two views of the brain (sagittal and coronal) with multiple orange clusters. The bottom row (labeled \u201cB\u201d) shows two corresponding views with fewer, smaller orange clusters. Blue crosshairs mark a coordinate in each panel. A vertical color bar at the right indicates intensity of the statistical map.\" width=\"468\" height=\"468\" \/> Figure 11. Brain activation maps reproduced from Stewart et al. (2022) showing two contrast maps (A vs. B) with warmer colors indicating stronger statistical effects. Reproduced under CC BY 4.0 from Stewart et al., 2022, Nature Schizophrenia.[\/caption]\r\n<p data-start=\"110\" data-end=\"480\"><strong data-start=\"110\" data-end=\"122\">Figure 11<\/strong> is a real resting-state fRMI data visualization; it is not an image of a single person\u2019s brain, but a summary based on brain scans from multiple studies and participants (Tolomeo &amp; Yu 2022). It visualizes group differences in resting-state brain activity between individuals with substance use and behavioral addictions (SUD + BA) and healthy controls (HC).<\/p>\r\n<p data-start=\"482\" data-end=\"762\">\u2022 <strong data-start=\"484\" data-end=\"506\">Panel A (top row):<\/strong> Brain regions with increased connectivity in the SUD + BA group compared to controls. These include areas like the amygdala, thalamus, midbrain, caudate, and parahippocampal gyrus\u2014all part of circuits involved in emotion, motivation, and habit learning.<\/p>\r\n<p data-start=\"764\" data-end=\"972\">\u2022 <strong data-start=\"766\" data-end=\"791\">Panel B (bottom row):<\/strong> Brain regions with decreased connectivity in the SUD + BA group, such as parts of the posterior lobe and parahippocampal gyrus, which are linked to memory and sensory processing.<\/p>\r\n<p data-start=\"974\" data-end=\"1268\" data-is-last-node=\"\" data-is-only-node=\"\">The color scale (yellow to red) reflects the strength of these group differences in connectivity. These patterns suggest that addiction is associated with hyperconnectivity in reward and habit-related brain systems and disrupted connectivity in regions important for memory and self-regulation.<\/p>\r\n\r\n<h2 data-start=\"0\" data-end=\"60\">Task-Based fMRI \u2013 The Monetary Incentive Delay (MID) Task<\/h2>\r\n<p data-start=\"62\" data-end=\"392\">Task-based fMRI involves participants performing specific activities designed to engage particular neural circuits. A widely used paradigm in addiction research is the Monetary Incentive Delay (MID) task. Participants see cues indicating potential monetary gains or losses, prompting anticipation and engagement of reward systems.<\/p>\r\n<p data-start=\"394\" data-end=\"1045\" data-is-last-node=\"\" data-is-only-node=\"\">The MID task differentiates between anticipation (expecting a reward) and feedback (receiving the reward). Crucially, dopamine neurons primarily fire during reward anticipation, reflecting \"wanting,\" rather than during reward receipt, reflecting \"liking.\" Consistent with this, fMRI studies using the MID task reveal increased activation in the NAc during anticipation phases (Knutson et al., 2000). In addiction, MID task findings commonly demonstrate abnormal activation patterns: addicted individuals typically exhibit heightened NAc responses to drug-related cues and diminished activation to natural rewards, indicating altered reward processing.<\/p>\r\n\r\n\r\n[caption id=\"attachment_172\" align=\"aligncenter\" width=\"735\"]<img class=\"wp-image-172 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig12.jpg\" alt=\"Two-panel figure summarizing MID task results in the ABCD study: Panel A shows boxplots of hit rate and reaction time for loss, reward, and neutral conditions (all trials and by run). Panel B shows brain maps illustrating reward-related activation patterns, including a \u201creward success vs fail\u201d contrast highlighting ventral striatum regions.\" width=\"735\" height=\"430\" \/> Figure 12. Reproduced from Casey, B. J., Cannonier, T., Conley, M. I., et al. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43\u201354. Elsevier. Licensed under CC BY-NC-ND 4.0. DOI: 10.1016\/j.dcn.2018.03.001[\/caption]\r\n\r\nFigure 12 shows the preliminary results from the ABCD study\u2019s baseline data collection of MID fMRI data.\r\n\r\n<strong>Panel A (left side): Behavioral performance during the MID task<\/strong>\r\n<ul>\r\n \t<li><strong>Top graph (Hit Rate):<\/strong> Shows how often participants successfully responded (\"hit\") to targets when they could lose money (\"Loss\"), win money (\"Reward\"), or had no monetary consequence (\"Neutral\"). Higher values indicate better performance.<\/li>\r\n \t<li><strong>Bottom graph (Reaction Time):<\/strong> Shows how quickly (in milliseconds) participants responded to these same targets. Lower values mean faster reactions.<\/li>\r\n<\/ul>\r\n<strong>Panel B (right side): Brain activation during the MID task<\/strong>\r\n<ul>\r\n \t<li><strong>Top images (Surface brain maps):<\/strong> Illustrate overall activation patterns across the brain surface during the task, highlighting regions engaged by reward anticipation and processing.<\/li>\r\n \t<li><strong>Bottom image (Contrast image):<\/strong> Specifically highlights brain areas more activated when successfully winning money (\"reward success\") compared to not receiving money (\"reward fail\"). Warm colors (yellow\/red) indicate stronger activation in the ventral striatum and nucleus accumbens, areas central to dopamine-related reward processing.<\/li>\r\n<\/ul>\r\nThese images show that performance and brain activation during reward-related tasks like the MID are measurable using behavioral data and fMRI, allowing researchers to investigate the brain's reward circuitry in detail.\r\n<h3>PET Imaging: Neurochemical Insights into Dopamine<\/h3>\r\nWhile fMRI measures brain activity indirectly through blood flow changes, PET imaging directly assesses neurochemistry by using radioactive tracers that bind to specific molecules. A widely used PET tracer, [11C]-raclopride, binds to dopamine D\u2082 receptors. Raclopride's binding decreases when dopamine is released (since dopamine displaces raclopride), providing an indirect measure of dopamine release and receptor availability. PET imaging is not included in ABCD data collection.\r\n\r\nStudies employing PET consistently find reduced D\u2082 receptor availability in the striatum of individuals with substance use disorders, including cocaine, alcohol, and opioid dependence (Volkow et al., 2009). Lower D\u2082 receptor levels are associated with increased craving, impaired self-control, and higher relapse rates, reflecting a blunted dopamine reward system.\r\n\r\nCombining PET with fMRI provides complementary insights. For instance, individuals showing reduced NAc activation during reward anticipation in fMRI typically also demonstrate decreased D\u2082 receptor availability in PET, reinforcing the neurochemical underpinning of reward dysfunction in addiction.\r\n\r\n[caption id=\"attachment_173\" align=\"aligncenter\" width=\"624\"]<img class=\"wp-image-173 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig13.gif\" alt=\"Two side-by-side color brain scans labeled \u201ccontrol\u201d and \u201con cocaine.\u201d Warm colors indicate stronger imaging signal; the \u201con cocaine\u201d scan shows a visibly different signal pattern compared with the control scan.\" width=\"624\" height=\"416\" \/> Figure 13. Figure X. Functional brain imaging comparison between a control condition and acute cocaine exposure. The image illustrates changes in brain metabolic activity or blood flow following cocaine administration, adapted from materials provided by the National Institute on Drug Abuse (NIDA), NIH.[\/caption]\r\n\r\nFigure 13 is a PET image that uses radiolabeled glucose to visualize metabolic activity across the brain. Red areas represent high glucose uptake, meaning active brain function, while blue and black indicate low activity. The scan on the right shows that in a person on cocaine, much of the brain exhibits reduced glucose metabolism\u2014particularly in frontal regions\u2014indicating that cocaine impairs normal brain function and energy use, which can contribute to disrupted thinking, poor impulse control, and long-term cognitive deficits.\r\n<h2>Conclusion: Beyond Reward\u2014From Motivation to Control<\/h2>\r\nThe mesolimbic dopamine pathway, our core reward circuit, does not function in isolation\u2014it interacts dynamically with multiple neural networks that govern self-control, emotional regulation, habit formation, and decision-making. As we have explored in this module, dopamine fuels the \"go\" system that drives our pursuit of rewarding experiences. However, balanced behavior relies equally on the brain\u2019s \"stop\" system\u2014primarily involving frontal regions like the prefrontal cortex (PFC) and anterior cingulate cortex (ACC)\u2014that evaluates consequences and modulates impulsive actions. In Module 9, we will dive deeper into these interconnected circuits.\r\n\r\n<hr \/>\r\n\r\n<h2>References<\/h2>\r\nBerridge, K. C., &amp; Robinson, T. E. (2016). Liking, wanting, and the incentive-sensitization theory of addiction. <em>American Psychologist, 71<\/em>(8), 670\u2013679. <a href=\"https:\/\/doi.org\/10.1037\/amp0000059\">https:\/\/doi.org\/10.1037\/amp0000059<\/a>\r\n\r\nBloomfield, M. A. P., Ashok, A. H., Volkow, N. D., &amp; Howes, O. D. (2016). The effects of \u0394\u2079-tetrahydrocannabinol on the dopamine system. <em>Nature, 539<\/em>(7629), 369\u2013377. <a href=\"https:\/\/doi.org\/10.1038\/nature20153\">https:\/\/doi.org\/10.1038\/nature20153<\/a>\r\n\r\nCasey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., \u2026 &amp; Dale, A. M. (2018). The adolescent brain cognitive development (ABCD) study: Imaging acquisition across 21 sites. <em>Developmental Cognitive Neuroscience, 32<\/em>, 43\u201354. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.03.001\">https:\/\/doi.org\/10.1016\/j.dcn.2018.03.001<\/a>\r\n\r\nHeinz, A., Siessmeier, T., Wrase, J., Hermann, D., Klein, S., Gr\u00fcsser, S., \u2026 &amp; Bartenstein, P. (2004). Correlation between dopamine D(2) receptors in the ventral striatum and central processing of alcohol cues and craving. <em>American Journal of Psychiatry, 161<\/em>(10), 1783\u20131789. <a href=\"https:\/\/doi.org\/10.1176\/ajp.161.10.1783\">https:\/\/doi.org\/10.1176\/ajp.161.10.1783<\/a>\r\n\r\nHou, H., Tian, M., &amp; Zhang, H. (2012). Positron emission tomography molecular imaging of dopaminergic system in drug addiction. <em>The Anatomical Record, 295<\/em>(5), 722\u2013733. <a href=\"https:\/\/doi.org\/10.1002\/ar.22430\">https:\/\/doi.org\/10.1002\/ar.22430<\/a>\r\n\r\nKnutson, B., Westdorp, A., Kaiser, E., &amp; Hommer, D. (2000). FMRI visualization of brain activity during a monetary incentive delay task. <em>NeuroImage, 12<\/em>(1), 20\u201327. <a href=\"https:\/\/doi.org\/10.1006\/nimg.2000.0593\">https:\/\/doi.org\/10.1006\/nimg.2000.0593<\/a>\r\n\r\nLembke, A. (2021). <em>Dopamine Nation: Finding Balance in the Age of Indulgence.<\/em> New York, NY: Dutton.\r\n\r\nLudlow, K., Bradley, K., Allison, D., Taylor, S., Yorgason, J., Hansen, D., \u2026 &amp; Steffensen, S. (2009). Acute and chronic ethanol modulate dopamine D2-subtype receptor responses in ventral tegmental area GABA neurons. <em>Alcoholism: Clinical and Experimental Research, 33<\/em>(5), 804\u2013811. <a href=\"https:\/\/doi.org\/10.1111\/j.1530-0277.2009.00899.x\">https:\/\/doi.org\/10.1111\/j.1530-0277.2009.00899.x<\/a>\r\n\r\nMontag, C., Markowetz, A., B\u0142aszkiewicz, K., Andone, I., Lachmann, B., Sariyska, R., Trendafilov, B., Eibes, M., Kolb, J., Reuter, M., &amp; Weber, B. (2017). Facebook usage on smartphones and gray matter volume of the nucleus accumbens. <em>Behavioural Brain Research, 329<\/em>, 221\u2013228. <a href=\"https:\/\/doi.org\/10.1016\/j.bbr.2017.04.035\">https:\/\/doi.org\/10.1016\/j.bbr.2017.04.035<\/a>\r\n\r\nNational Institute on Drug Abuse. (2012). <em>Dopamine pathways<\/em> [Diagram]. Wikimedia Commons. <a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Dopamine_pathways.jpg\">https:\/\/commons.wikimedia.org\/wiki\/File:Dopamine_pathways.jpg<\/a>\r\n\r\nSAMHSA. (2016). <em>Facing Addiction in America: The Surgeon General\u2019s Report on Alcohol, Drugs, and Health.<\/em> U.S. Department of Health and Human Services. <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK424849\/\">https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK424849\/<\/a>\r\n\r\nSchultz, W. (1998). Predictive reward signal of dopamine neurons. <em>Journal of Neurophysiology, 80<\/em>(1), 1\u201327. <a href=\"https:\/\/doi.org\/10.1152\/jn.1998.80.1.1\">https:\/\/doi.org\/10.1152\/jn.1998.80.1.1<\/a>\r\n\r\nSherman, L. E., Greenfield, P. M., Hernandez, L. M., &amp; Dapretto, M. (2018). Peer influence via Instagram: Effects on brain and behavior in adolescence and young adulthood. <em>Child Development, 89<\/em>(1), 37\u201347. <a href=\"https:\/\/doi.org\/10.1111\/cdev.12838\">https:\/\/doi.org\/10.1111\/cdev.12838<\/a>\r\n\r\nSherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M., &amp; Dapretto, M. (2016). The power of the like in adolescence: Effects of peer influence on neural and behavioral responses to social media. <em>Psychological Science, 27<\/em>(7), 1027\u20131035. <a href=\"https:\/\/doi.org\/10.1177\/0956797616645673\">https:\/\/doi.org\/10.1177\/0956797616645673<\/a>\r\n\r\nSoutschek, A., Tobler, P. N., Kahnt, T., Quednow, B. B., &amp; Weber, S. C. (2021). Opioid antagonism reduces wanting by strengthening frontostriatal connectivity. <em>bioRxiv.<\/em> <a href=\"https:\/\/doi.org\/10.1101\/2021.06.20.449203\">https:\/\/doi.org\/10.1101\/2021.06.20.449203<\/a>\r\n\r\nSu, C., Zhou, H., Gong, L., Teng, B., Geng, F., &amp; Hu, Y. (2021). Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area. <em>NeuroImage, 237<\/em>, 118136. <a href=\"https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118136\">https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118136<\/a>\r\n\r\nTolomeo, S., &amp; Yu, R. (2022). Brain network dysfunctions in addiction: A meta-analysis of resting-state functional connectivity. <em>Translational Psychiatry, 12<\/em>(1), 41. <a href=\"https:\/\/doi.org\/10.1038\/s41398-022-01792-6\">https:\/\/doi.org\/10.1038\/s41398-022-01792-6<\/a>\r\n\r\nVolkow, N. D., &amp; Morales, M. (2015). The brain on drugs: From reward to addiction. <em>Cell, 162<\/em>(4), 712\u2013725. <a href=\"https:\/\/doi.org\/10.1016\/j.cell.2015.07.046\">https:\/\/doi.org\/10.1016\/j.cell.2015.07.046<\/a>\r\n\r\nVolkow, N. D., Fowler, J. S., Wang, G. J., Baler, R., &amp; Telang, F. (2009). Imaging dopamine\u2019s role in drug abuse and addiction. <em>Neuropharmacology, 56<\/em>(Suppl 1), 3\u20138. <a href=\"https:\/\/doi.org\/10.1016\/j.neuropharm.2008.05.022\">https:\/\/doi.org\/10.1016\/j.neuropharm.2008.05.022<\/a>","rendered":"<h2>Reading Objectives<\/h2>\n<p>By the end of this chapter, you should be able to:<\/p>\n<ul>\n<li>Identify the key components of the mesolimbic dopamine pathway and describe their roles in mediating reward and motivation.<\/li>\n<li>Explain the concepts of reward prediction error, \u201cwanting\u201d versus \u201cliking,\u201d and incentive sensitization as they relate to the dopamine system and addictive behaviors.<\/li>\n<li>Analyze how the opponent-process theory illustrates the balance between pleasure and pain, and how repeated substance use shifts the hedonic set-point toward dysphoria and craving.<\/li>\n<li>Evaluate the impact of chronic drug use on neural circuitry, with attention to neuroadaptations such as receptor downregulation and impaired prefrontal control.<\/li>\n<li>Assess how neuroimaging techniques such as fMRI and PET reveal functional and neurochemical changes in the mesolimbic dopamine pathway associated with addiction.<\/li>\n<\/ul>\n<h2>Key Terms<\/h2>\n<ul>\n<li><strong>Mesolimbic Dopamine Pathway<\/strong>: A neural circuit that originates in the ventral tegmental area (VTA) and projects to limbic regions such as the nucleus accumbens. It plays a central role in reward processing and motivation. Here, \u201creward\u201d is a latent construct. It is an internal motivational state inferred from behavior and neural signals rather than directly observed.<\/li>\n<li><strong>Dopamine<\/strong>: A neurotransmitter involved in reinforcement learning and motivation. Dopamine signals the importance of outcomes and cues, shaping future behavior by influencing pursuit and learning rather than pleasure alone.<\/li>\n<li><strong>Ventral Tegmental Area (VTA)<\/strong>: A midbrain region that produces dopamine and serves as a primary source of dopaminergic input to the mesolimbic pathway.<\/li>\n<li><strong>Nucleus Accumbens (NAc)<\/strong>: A structure in the ventral striatum that receives dopamine from the VTA and integrates information about reward, motivation, and goal-directed action.<\/li>\n<li><strong>Reward Prediction Error (RPE)<\/strong>: The difference between what is expected and what actually occurs. Dopamine increases or decreases signal whether an outcome is better or worse than anticipated, updating learning.<\/li>\n<li><strong>Incentive Sensitization<\/strong>: A process in which repeated drug exposure heightens the motivational pull or \u201cwanting\u201d of substance-related cues, even when the pleasurable effects or \u201cliking\u201d diminish.<\/li>\n<li><strong>Opponent-Process Theory<\/strong>: A model describing how initial pleasurable responses to a stimulus are followed by an opposing process that produces discomfort or stress, shifting the balance between reward and pain over time.<\/li>\n<li><strong>\u201cWanting\u201d vs. \u201cLiking\u201d<\/strong>: The distinction between dopamine-driven motivation to seek a reward (\u201cwanting\u201d) and the hedonic pleasure experienced during consumption (\u201cliking\u201d).<\/li>\n<li><strong>Functional Magnetic Resonance Imaging (fMRI)<\/strong>: An imaging technique that measures brain activity through changes in blood flow, allowing researchers to study functional neural circuits during tasks or at rest.<\/li>\n<li><strong>Positron Emission Tomography (PET)<\/strong>: An imaging method that uses radioactive tracers to visualize and quantify neurochemical processes such as dopamine receptor availability and neurotransmitter release.<\/li>\n<\/ul>\n<h2>Introduction<\/h2>\n<p>The brain constantly balances pleasure and pain, much like a seesaw. Dopamine is often described as the brain\u2019s \u201cmolecule of more\u201d because it drives motivation and pursuit rather than satisfaction itself. When something rewarding occurs, dopamine helps reinforce the behaviors and cues that led to it.<\/p>\n<p>After pleasure, however, the brain activates an opposing response that produces stress or dysphoria. This dynamic is described by opponent-process theory. Early on, the pleasurable effects outweigh the discomfort. With repeated pursuit of rewards without sufficient balance, whether from substances, screens, or highly palatable foods, the opposing process grows stronger. Over time, the \u201cpain side\u201d begins to dominate.<\/p>\n<p>As this shift unfolds, people may find that rewards no longer produce the same enjoyment. Instead, they are pursued to relieve discomfort or to feel normal. This gradual change in the hedonic set-point, moving from pleasure toward dysphoria and craving, is a defining feature of addiction and a central focus of this chapter.<\/p>\n<figure id=\"attachment_149\" aria-describedby=\"caption-attachment-149\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-149 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-1024x683.png\" alt=\"Four-panel infographic explaining the pleasure\u2013pain balance in addiction. Panel 1 shows a dopamine \u201cspike\u201d tipping a seesaw toward pleasure. Panel 2 shows \u201canti-reward gremlins\u201d pushing back on the pain side to restore balance. Panel 3 shows repeated hits shifting the set-point so the pain side grows and pleasure shrinks. Panel 4 shows a craving loop where use brings brief relief but leads to more pain over time.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Fig1.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-149\" class=\"wp-caption-text\">Pleasure\u2013pain balance and opponent-process theory across repeated substance \u201chits,\u201d illustrating how reward effects shrink and anti-reward responses grow over time, contributing to craving and compulsive use. Concept inspired by Anna Lembke\u2019s pleasure\u2013pain balance framing in Dopamine Nation (Lembke, 2021).<\/figcaption><\/figure>\n<p>In the brain\u2019s intricate machinery, pleasure and pain sit on opposite ends of a delicate seesaw, a metaphor popularized by <strong>Anna Lembke<\/strong> (Lembke, 2021). When we experience pleasure, dopamine surges tip the balance toward reward. That surge then triggers a compensatory \u201canti-reward\u201d response, often illustrated as small stress-inducing forces piling onto the pain side of the seesaw. With repeated exposure, these anti-reward forces accumulate, shifting the balance toward pain and driving persistent craving.<\/p>\n<p>In this module, we explore how the limbic\u2013basal ganglia pathway underlies this transformation. You will learn the crucial distinction between <strong>\u201cwanting,\u201d<\/strong> the dopamine-driven urge to seek rewards, and <strong>\u201cliking,\u201d<\/strong> the actual pleasure derived from them. We examine how chronic substance use reshapes neural circuits, locking in compulsive habits and intense cravings long after the initial euphoria fades. We also show how modern brain-imaging methods reveal these deep-seated changes, reinforcing the view that addiction reflects fundamental neurobiological processes rather than a simple failure of willpower.<\/p>\n<h3>A Quick Overview of the Human Brain<\/h3>\n<p>Before describing the reward pathway, it helps to establish some basic vocabulary. The brain coordinates chemical and electrical signals that regulate everything from life-sustaining functions like breathing and digestion to perception, emotion, thought, and social interaction. The human brain contains roughly <strong>86 billion nerve cells<\/strong>, called neurons, along with a variety of supporting cells (SAMHSA, 2016).<\/p>\n<p>Each neuron has three main components. The <strong>cell body<\/strong> houses the nucleus and directs the neuron\u2019s activities. The <strong>axon<\/strong> is a long projection that sends signals to other cells. The <strong>dendrites<\/strong> are branching structures that receive incoming signals from neighboring neurons. Together, these components form vast networks that allow information to flow through the brain, setting the stage for understanding how reward, motivation, and addiction emerge from neural circuitry.<\/p>\n<figure id=\"attachment_151\" aria-describedby=\"caption-attachment-151\" style=\"width: 624px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-151 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Diagram-of-a-Neuron.jpg\" alt=\"Diagram showing dopamine signaling in a neuron: dopamine is made in the cell body, transported down the axon, released at the terminal, and binds receptors across the synapse (with an inset showing vesicles, dopamine molecules, and receptors).\" width=\"624\" height=\"553\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Diagram-of-a-Neuron.jpg 624w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Diagram-of-a-Neuron-300x266.jpg 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Diagram-of-a-Neuron-65x58.jpg 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Diagram-of-a-Neuron-225x199.jpg 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Diagram-of-a-Neuron-350x310.jpg 350w\" sizes=\"auto, (max-width: 624px) 100vw, 624px\" \/><figcaption id=\"caption-attachment-151\" class=\"wp-caption-text\">Figure 2. Dopamine neurotransmission overview: synthesis in the cell body, axonal transport, release from the terminal, and receptor binding at the synapse. Source: Substance Abuse and Mental Health Services Administration (SAMHSA), 2016 (public domain U.S. Government material; reuse permitted with attribution).<\/figcaption><\/figure>\n<p>Neurons communicate using chemical messengers called <strong>neurotransmitters<\/strong>. These chemicals cross a tiny gap between cells, known as a <strong>synapse<\/strong>, and bind to <strong>receptors<\/strong> on neighboring neurons. Some neurotransmitters inhibit or dampen activity in the receiving neuron, making it less likely to fire. Others are excitatory, increasing the likelihood that the neuron will pass the signal along. Behavior and cognition emerge from the balance of these excitatory and inhibitory influences across large networks of neurons.<\/p>\n<p>Neurons are not wired randomly. They tend to cluster into specialized <strong>circuits<\/strong> that carry out particular functions. Some circuits support higher-order processes such as thinking, learning, emotion, and memory. Others are more directly tied to action, linking the brain to muscles to produce movement, or to sensory systems that process information from the eyes, ears, and skin. Addiction-related processes arise not from a single \u201caddiction center,\u201d but from interactions among multiple circuits that normally support motivation, learning, and self-control.<\/p>\n<h3>Core Neuroanatomy of the Mesolimbic Dopamine Pathway<\/h3>\n<p>The mesolimbic dopamine pathway has two central hubs. The <strong>ventral tegmental area (VTA)<\/strong> is a cluster of dopamine-producing neurons located in the midbrain. The <strong>nucleus accumbens (NAc)<\/strong> sits in the ventral striatum and acts as a key integration site for reward, motivation, and learning signals.<\/p>\n<p>When outcomes are <strong>better than expected<\/strong>, dopamine neurons in the VTA fire in brief bursts, releasing dopamine into the nucleus accumbens. When outcomes are <strong>worse than expected<\/strong>, these neurons reduce or pause their firing. This rise or fall in dopamine is not just a pleasure signal. It functions as a <strong>teaching signal<\/strong>, updating the brain about whether predictions were accurate and guiding future behavior. In this sense, dopamine is the core \u201ccurrency\u201d of learning in the mesolimbic pathway.<\/p>\n<p>Although the VTA and NAc are the central actors, they do not work in isolation. Regions such as the <strong>prefrontal cortex<\/strong>, <strong>amygdala<\/strong>, <strong>hippocampus<\/strong>, and <strong>extended amygdala<\/strong> interact with this pathway to shape decision-making, emotional responses, memory for reward-related cues, and the formation of habits. These interactions become especially important for understanding cue-driven craving and compulsive behavior, which we will unpack in more detail in Module 9.<\/p>\n<figure id=\"attachment_152\" aria-describedby=\"caption-attachment-152\" style=\"width: 624px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-152 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Dopamine-Pathways.png\" alt=\"Simplified side-view brain diagram highlighting major dopamine pathways with arrows projecting from midbrain to limbic and cortical targets.\" width=\"624\" height=\"437\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Dopamine-Pathways.png 624w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Dopamine-Pathways-300x210.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Dopamine-Pathways-65x46.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Dopamine-Pathways-225x158.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/Dopamine-Pathways-350x245.png 350w\" sizes=\"auto, (max-width: 624px) 100vw, 624px\" \/><figcaption id=\"caption-attachment-152\" class=\"wp-caption-text\">Figure 3. Simplified schematic of major dopamine pathways relevant to reward and addiction-related circuitry. Image credit: National Institute on Drug Abuse (NIDA), National Institutes of Health (2012). U.S. government work (public domain), unless otherwise noted on the original source page.<\/figcaption><\/figure>\n<h2>Function of the Mesolimbic Dopamine Pathway<\/h2>\n<h3>Reward Prediction Error<\/h3>\n<p>Recall that dopamine neurons in the ventral tegmental area (VTA) send signals to the nucleus accumbens (NAc) and other limbic and cortical structures. When rewards exceed expectations, these VTA neurons increase dopamine release, reinforcing the behaviors that led to the positive outcome. This raises a deeper question. Why does dopamine fire, and what information is it conveying?<\/p>\n<p>Dopamine neurons signal a <strong>reward prediction error (RPE)<\/strong>, which reflects how actual outcomes compare with expectations. If a reward is better than expected, such as studying for a B and receiving an A+, dopamine neurons show a brief surge in activity. That surge reinforces the actions that led to the unexpectedly good result (Schultz, 1998). In contrast, when outcomes are worse than expected, dopamine neuron firing is reduced, guiding the brain to revise future behavior.<\/p>\n<p>This RPE mechanism functions as a teaching signal. A dopamine spike communicates, \u201cThis was better than expected. Strengthen whatever led here.\u201d A dopamine dip communicates, \u201cThis outcome fell short. Adjust your strategy.\u201d Across repeated experiences, these signals help the brain learn which cues, contexts, and actions reliably lead to rewarding outcomes.<\/p>\n<p>Importantly, reward prediction errors shape not only learning but also motivation. As cues become associated with dopamine surges, they can acquire powerful motivational pull. This helps explain why people may pursue rewards intensely, even when the actual experience delivers less pleasure than anticipated.<\/p>\n<figure id=\"attachment_159\" aria-describedby=\"caption-attachment-159\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-159 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-1024x683.png\" alt=\"Four-panel infographic titled \u201cFigure 4: Reward Prediction Error.\u201d Each panel uses a social media \u201clikes\u201d example to show how dopamine-like teaching signals reflect the mismatch between expected and received outcomes. Panel 1 shows a cue (\u201cNew post\u201d) and an expectation of about 10 likes with a flat baseline trace. Panel 2 shows a positive RPE: the post receives 50 likes and the trace shows a sharp upward spike. Panel 3 shows a negative RPE: fewer likes than expected and the trace dips below baseline. Panel 4 shows learning: expectations update upward (about 40 likes), the received outcome matches, and the RPE signal becomes small while the cue becomes motivating.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-02_49_25-PM.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-159\" class=\"wp-caption-text\">Figure 4. Reward prediction error (RPE) illustrated with social feedback. Positive RPE occurs when outcomes exceed expectations (spike), negative RPE occurs when outcomes fall short (dip), and learning updates expectations so prediction errors shrink over time. Created by the author with generative AI.<\/figcaption><\/figure>\n<p data-start=\"0\" data-end=\"526\">This reward-prediction mechanism is the brain\u2019s natural version of what computer scientists call <strong data-start=\"97\" data-end=\"123\">reinforcement learning<\/strong>, a process by which agents learn to maximize future rewards through feedback. Dopamine\u2019s prediction-error signals continuously update the brain\u2019s internal \u201cvalue map,\u201d strengthening behaviors that produce better-than-expected outcomes and weakening those that disappoint. This learning logic brings us to a critical distinction in the dopamine system: the difference between <strong data-start=\"499\" data-end=\"510\">wanting<\/strong> and <strong data-start=\"515\" data-end=\"525\">liking<\/strong>.<\/p>\n<h3 data-start=\"528\" data-end=\"550\">Wanting vs. Liking<\/h3>\n<p data-start=\"566\" data-end=\"1201\">Dopamine primarily drives \u201cwanting\u201d\u2014the motivational force that energizes pursuit of rewards\u2014more than \u201cliking,\u201d the pleasure experienced when a reward is consumed (Berridge &amp; Robinson, 2016). A useful mechanistic label for cue-triggered wanting is <strong data-start=\"815\" data-end=\"837\">incentive salience<\/strong>: the process by which reward-related cues become <strong data-start=\"887\" data-end=\"911\">motivational magnets<\/strong> that grab attention and energize approach behavior. These processes are dissociable in the brain. Dopamine-related signaling is strongly tied to incentive motivation\/craving and approach behavior, while \u201cliking\u201d depends more heavily on opioid-based hedonic hotspots that generate pleasure.<\/p>\n<p data-start=\"1203\" data-end=\"1708\">Both wanting and liking are <strong data-start=\"1231\" data-end=\"1252\">latent constructs<\/strong>: we do not observe them directly. Instead, we infer them from patterns of behavior (e.g., approach, effort, reaction time), self-report (e.g., craving ratings), and neural measures that serve as proxies for circuit engagement. In cue-driven situations, increased wanting is often inferred when cues draw attention and trigger approach\/effort\u2014consistent with increased incentive salience\u2014even if the pleasurable experience of consumption does not increase.<\/p>\n<p data-start=\"1710\" data-end=\"2305\">A simple illustration makes the distinction concrete. Imagine you have a strong preference for chocolate cake. Seeing the cake in a display case can trigger intense wanting: your attention locks in, your mouth waters, and you feel a pull toward eating it. In incentive-salience terms, the cue (the sight of cake) has been tagged with motivational power. Now imagine taking a bite. The rich, chocolate flavor produces enjoyment. That pleasure is liking. Importantly, wanting can grow stronger even as liking stays the same or declines\u2014a divergence that becomes central to understanding addiction.<\/p>\n<figure id=\"attachment_157\" aria-describedby=\"caption-attachment-157\" style=\"width: 627px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-157 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/wanting-vs-liking-roi.jpg\" alt=\"Two-panel brain-imaging figure comparing neural maps for wanting (Panel A) and liking (Panel B). Warm-colored clusters (yellow to red) are overlaid on grayscale brain slices, with a color scale from 0 to 8 above each panel.\" width=\"627\" height=\"697\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/wanting-vs-liking-roi.jpg 627w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/wanting-vs-liking-roi-270x300.jpg 270w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/wanting-vs-liking-roi-65x72.jpg 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/wanting-vs-liking-roi-225x250.jpg 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/wanting-vs-liking-roi-350x389.jpg 350w\" sizes=\"auto, (max-width: 627px) 100vw, 627px\" \/><figcaption id=\"caption-attachment-157\" class=\"wp-caption-text\">Figure 5. Neural activation maps for wanting (A) and liking (B), shown as thresholded warm-color overlays on grayscale brain images (color bar 0\u20138). Source: Soutschek et al. (2021), bioRxiv, https:\/\/doi.org\/10.1101\/2021.06.20.449203.<\/figcaption><\/figure>\n<p>This wanting\u2013liking split is formalized in the incentive-sensitization theory of addiction.<\/p>\n<div class=\"textbox textbox--examples\">\n<header class=\"textbox__header\">\n<p class=\"textbox__title\">Incentive-Salience Hypothesis<\/p>\n<\/header>\n<div class=\"textbox__content\">\n<p>The <strong data-start=\"2684\" data-end=\"2717\">incentive-salience hypothesis<\/strong> proposes that the brain can tag reward-related cues (or mental representations of rewards) with motivational power, so those cues come to trigger strong wanting. In this view, cues become motivational magnets that grab attention and energize approach behavior even when the actual pleasure of consumption (liking) is unchanged or reduced (Berridge &amp; Robinson, 2016). <strong data-start=\"3085\" data-end=\"3112\">Incentive sensitization<\/strong> refers to the long-term increase in the brain\u2019s propensity to assign incentive salience to drug cues after repeated drug use\u2014helping explain intense cue-evoked craving and relapse risk even when the drug is no longer very pleasurable.<\/p>\n<\/div>\n<\/div>\n<p data-start=\"3006\" data-end=\"3333\">In substance use disorders, dopamine-linked wanting can grow increasingly intense even as liking declines. This pattern reflects incentive sensitization (cues trigger exaggerated incentive salience) alongside tolerance (reduced pleasure from the substance itself). The result is powerful craving without proportional enjoyment.<\/p>\n<p data-start=\"3335\" data-end=\"3797\">Neuroanatomy helps explain this divergence. Dopamine-related signaling in the nucleus accumbens (NAc), especially the core, is strongly implicated in cue-triggered wanting and approach behavior. In contrast, hedonic liking is mediated in part by opioid-based \u201chedonic hotspots,\u201d including regions in and around the NAc shell. Because these systems are partially separable, craving can intensify even as pleasure fades\u2014one of the clearest signatures of addiction.<\/p>\n<figure id=\"attachment_163\" aria-describedby=\"caption-attachment-163\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-163 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM.png\" alt=\"Four-panel (2\u00d72) infographic explaining \u201cwanting\u201d versus \u201cliking\u201d using chocolate cake. Top-left: a cue and cake trigger WANTING (craving) with a brain callout labeled \u201cDopamine \u2192 NAc core.\u201d Top-right: a person runs, showing WANTING drives action, with \u201cDopamine signal\u201d and a rising bar chart. Bottom-left: LIKING is shown as pleasure while eating cake, with a brain callout labeled \u201cOpioid hotspots \u2192 NAc shell,\u201d plus hearts. Bottom-right: WANTING and LIKING diverge, with WANTING high and increasing (incentive sensitization) while LIKING is low and decreasing (tolerance), and a note that craving can stay high even when pleasure fades.\" width=\"1024\" height=\"1024\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM-300x300.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM-150x150.png 150w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM-768x768.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM-65x65.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM-225x225.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_09_44-PM-350x350.png 350w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-163\" class=\"wp-caption-text\">Figure 6. Wanting versus liking. Dopamine-related signals primarily support wanting (motivation and pursuit), while opioid-related \u201chedonic hotspots\u201d support liking (experienced pleasure). With incentive sensitization and tolerance, wanting can increase even as liking decreases. Created by the author with generative AI.<\/figcaption><\/figure>\n<h3>The Pleasure\u2013Pain Balance<\/h3>\n<p>While reward prediction errors fine-tune behavior by comparing outcomes to expectations, the brain also works to maintain emotional stability through <strong>homeostatic regulation<\/strong>. Homeostasis refers to the self-regulating processes that keep biological systems within functional ranges while adapting to changing conditions. After pleasurable experiences, dopamine surges are typically followed by brief dips in mood or motivation. This counter-response restores equilibrium, much like a seesaw tipping back after a high.<\/p>\n<p>This pleasure\u2013pain balance keeps motivation calibrated. It allows us to enjoy rewards and learn from them without becoming trapped in constant highs or immobilized by lows. Under typical conditions, the system resets efficiently, preserving sensitivity to everyday rewards.<\/p>\n<p>Problems arise when dopamine spikes are repeated and intense, as with addictive drugs or highly stimulating behaviors. Over time, the brain adapts by reducing dopamine sensitivity and increasing <strong>anti-reward<\/strong> or stress-related signals. Natural rewards begin to feel less satisfying, while baseline stress and irritability increase. The individual is no longer chasing pleasure alone but is increasingly motivated to escape discomfort.<\/p>\n<p>In this way, a system designed to support learning and balance can be pushed into dysregulation. Repeated overstimulation drives cycles of craving, tolerance, and dysphoria. Although the specific neuroadaptations vary by substance, they share a common core: a shifted hedonic balance in which the brain\u2019s reward system is recalibrated toward pain rather than pleasure.<\/p>\n<h2>Substance-Specific Effects on the Mesolimbic System<\/h2>\n<p>Initially, drugs trigger exaggerated dopamine spikes, producing large positive reward prediction errors. These surges create intense feelings of pleasure and strongly reinforce substance use. With repeated exposure, however, the brain adapts. Neuroadaptations such as receptor downregulation, reduced dopamine sensitivity, and increased anti-reward signaling, including systems involving dynorphin and corticotropin-releasing factor (CRF), begin to dominate. The result is a shift in the brain\u2019s pleasure\u2013pain balance toward pain.<\/p>\n<p>As this balance shifts, motivation changes. Behavior is no longer driven primarily by the pursuit of pleasure but by the need to avoid withdrawal, stress, and dysphoria (Lembke, 2021). What once felt rewarding becomes necessary simply to feel normal.<\/p>\n<p>In essence, addiction reflects a corrupted version of the brain\u2019s natural reward system. Pathological craving, or excessive \u201cwanting,\u201d persists even as genuine enjoyment, or \u201cliking,\u201d diminishes. Although this core process is shared across substances, the precise neurobiological mechanisms and downstream consequences vary depending on the drug involved.<\/p>\n<figure id=\"attachment_165\" aria-describedby=\"caption-attachment-165\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-165 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-1024x683.png\" alt=\"Four-panel infographic showing a progression of addiction neuroadaptation. Panel 1: \u201cFirst exposure: dopamine super-spike\u201d with a cue and reward and a large dopamine spike labeled large positive prediction error. Panel 2: \u201cTolerance: downregulation\u201d with repeated exposure over time and reduced dopamine response and D2 down icons. Panel 3: \u201cAnti-reward and stress take over\u201d with a seesaw tipped toward CRF and dynorphin and away from pleasure. Panel 4: \u201cCraving outlives pleasure (wanting &gt; liking)\u201d showing a cue triggering high wanting and low liking, with drug icons and the note that craving persists as enjoyment fades. Footer note says it is a conceptual schematic and details differ by drug.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/ChatGPT-Image-Feb-10-2026-07_25_27-PM.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-165\" class=\"wp-caption-text\">Figure 7. Neuroadaptations in addiction over time. Early exposure produces a large dopamine \u201csuper-spike\u201d (positive prediction error). With repeated use, dopamine responses diminish (tolerance) alongside downregulation (illustrated with reduced D2). Anti-reward stress systems such as CRF and dynorphin increase, shifting affect toward dysphoria. Over time, cue-triggered wanting (craving) can remain high even as liking (pleasure) declines. Created by the author with generative AI.<\/figcaption><\/figure>\n<table class=\"shaded\">\n<caption>Table 1. Substance Effects on the Mesolimbic Dopamine System<\/caption>\n<thead>\n<tr>\n<th>Substance<\/th>\n<th>Mechanism of Dopamine Activation<\/th>\n<th>Dopamine Impact<\/th>\n<th>Withdrawal Profile<\/th>\n<th>Key Neuroadaptations<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Alcohol<\/td>\n<td>Disinhibits VTA dopamine neurons via GABA modulation; increases endogenous opioids<\/td>\n<td>Moderate dopamine increase; enhanced initial euphoria<\/td>\n<td>Moderate to severe: anxiety, anhedonia, irritability<\/td>\n<td>\u2193 D\u2082 receptors, \u2191 CRF (stress), impaired frontal cortex function<\/td>\n<\/tr>\n<tr>\n<td>Cannabis<\/td>\n<td>THC activates CB\u2081 receptors \u2192 disinhibits dopamine in VTA<\/td>\n<td>Mild to moderate dopamine increase<\/td>\n<td>Mild: irritability, sleep disruption, low motivation<\/td>\n<td>\u2193 dopamine release (long-term), blunted response to rewards<\/td>\n<\/tr>\n<tr>\n<td>Opioids<\/td>\n<td>Inhibit GABA interneurons \u2192 disinhibit VTA dopamine neurons; direct activation of opioid receptors<\/td>\n<td>Strong dopamine surge plus direct hedonic effects<\/td>\n<td>Severe: dysphoria, physical pain, intense craving<\/td>\n<td>\u2193 D\u2082 receptors, \u2193 endogenous opioid tone, cue-induced dopamine sensitization<\/td>\n<\/tr>\n<tr>\n<td>Cocaine<\/td>\n<td>Blocks dopamine reuptake \u2192 large synaptic dopamine accumulation<\/td>\n<td>Very strong, rapid dopamine spike<\/td>\n<td>High craving, mood crashes, anhedonia<\/td>\n<td>\u2193 D\u2082 receptors, \u2193 dopamine release capacity, strong cue reactivity<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p data-start=\"186\" data-end=\"459\">Below is an overview of how common substances\u2014alcohol, cannabis, opioids, and cocaine\u2014affect the mesolimbic dopamine system. Although their pharmacological mechanisms differ, all ultimately distort dopamine signaling in ways that promote craving, tolerance, and dependence.<\/p>\n<h3 data-start=\"461\" data-end=\"472\">Alcohol<\/h3>\n<p data-start=\"474\" data-end=\"768\">Alcohol initially boosts dopamine release by <strong data-start=\"519\" data-end=\"589\">disinhibiting dopamine neurons in the ventral tegmental area (VTA)<\/strong>. This occurs primarily through suppression of inhibitory GABA neurons and increased release of endogenous opioids, producing mild euphoria and relaxation (Ludlow et al., 2009).<\/p>\n<p data-start=\"770\" data-end=\"1306\">With chronic use, the brain adapts by reducing dopamine receptor availability, particularly <strong data-start=\"862\" data-end=\"878\">D\u2082 receptors<\/strong>, which blunts pleasure from both alcohol and natural rewards. During withdrawal, stress systems become overactive, producing anxiety, dysphoria, and irritability. These aversive states strongly motivate continued drinking as a form of negative reinforcement. Long-term alcohol misuse also impairs <strong data-start=\"1176\" data-end=\"1197\">prefrontal cortex<\/strong> functioning, contributing to impulsivity, poor decision-making, and heightened craving (Hienz et al., 2004).<\/p>\n<h3 data-start=\"1308\" data-end=\"1320\">Cannabis<\/h3>\n<p data-start=\"1322\" data-end=\"1666\">\u03949-tetrahydrocannabinol (THC), the primary psychoactive compound in cannabis, increases dopamine release indirectly by activating <strong data-start=\"1452\" data-end=\"1469\">CB\u2081 receptors<\/strong> on GABA interneurons in the VTA, thereby disinhibiting dopamine neurons (Bloomfield et al., 2016). The resulting dopamine increase is typically smaller than that produced by stimulants or opioids.<\/p>\n<p data-start=\"1668\" data-end=\"2229\">With heavy or chronic use, cannabis is associated with reduced dopamine responsiveness, leading to a <strong data-start=\"1769\" data-end=\"1795\">hypodopaminergic state<\/strong> that may underlie reduced motivation and reward sensitivity, sometimes described as an amotivational profile. PET imaging studies show diminished dopamine release capacity in frequent cannabis users, even though physical withdrawal symptoms are generally mild (Bloomfield et al., 2016). Notably, the cannabinoid system remains a promising therapeutic target, particularly through non-intoxicating compounds such as cannabidiol (CBD).<\/p>\n<h3 data-start=\"2231\" data-end=\"2287\">Opioids (Heroin, Morphine, Prescription Painkillers)<\/h3>\n<p data-start=\"2289\" data-end=\"2623\">Opioids are highly addictive because they act on the reward system through <strong data-start=\"2364\" data-end=\"2393\">two converging mechanisms<\/strong>. First, they directly activate \u03bc-opioid receptors, producing strong hedonic effects. Second, they indirectly increase dopamine release by inhibiting GABA interneurons in the VTA, disinhibiting dopamine neurons (Hou et al., 2012).<\/p>\n<p data-start=\"2625\" data-end=\"3265\">Chronic opioid exposure leads to pronounced reductions in dopamine receptor availability and overall dopamine release capacity. At the same time, the dopamine system becomes <strong data-start=\"2799\" data-end=\"2834\">sensitized to drug-related cues<\/strong>, producing intense craving that can persist long after abstinence. Withdrawal is marked by severe dysphoria, physical pain, and stress, illustrating the extreme pleasure-to-pain shift characteristic of opioid addiction. Pharmacological treatments such as <strong data-start=\"3090\" data-end=\"3103\">methadone<\/strong> and <strong data-start=\"3108\" data-end=\"3125\">buprenorphine<\/strong> stabilize opioid signaling and reduce withdrawal and craving, supporting recovery by dampening reward-system volatility (Hou et al., 2012).<\/p>\n<h3 data-start=\"3267\" data-end=\"3299\">Cocaine and Other Stimulants<\/h3>\n<p data-start=\"3301\" data-end=\"3585\">Stimulants such as cocaine produce rapid and intense dopamine spikes by <strong data-start=\"3373\" data-end=\"3403\">blocking dopamine reuptake<\/strong>, causing dopamine to accumulate in synapses (Volkow &amp; Morales, 2015). The magnitude and speed of this dopamine elevation are uniquely high, making stimulants especially reinforcing.<\/p>\n<p data-start=\"3587\" data-end=\"4225\">Repeated stimulant use triggers rapid <strong data-start=\"3625\" data-end=\"3661\">dopamine receptor downregulation<\/strong>, particularly of D\u2082 receptors, and a marked decline in baseline dopamine responsiveness. Chronic cocaine users often experience profound anhedonia, low motivation, and emotional flattening when not using the drug. Cue-induced craving becomes dominant, overpowering natural reward-seeking behaviors. Recovery of dopamine function can take months or years, contributing to high relapse risk. Behavioral treatments, including <strong data-start=\"4085\" data-end=\"4111\">contingency management<\/strong>, remain central to care and aim to support gradual restoration of reward-system balance (Volkow &amp; Morales, 2015).<\/p>\n<figure id=\"attachment_168\" aria-describedby=\"caption-attachment-168\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-168 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-1024x683.png\" alt=\"Slide titled \u201cDopamine D\u2082 Receptors\u201d with three bullet points explaining that D\u2082 receptors regulate dopamine signaling, chronic substance use downregulates D\u2082 (fewer receptors, lower sensitivity), and lower D\u2082 function can reduce reward responsiveness and promote compulsive seeking. A diagram shows dopamine released from a presynaptic terminal into the synapse and binding to D\u2082 receptors on a postsynaptic membrane. An inset labeled \u201cDownregulation\u201d compares many receptors in a \u201cTypical\u201d state versus fewer in a \u201cDownregulated\u201d state. Footer note: \u201cConceptual schematic: receptor numbers shown for illustration.\u201d\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig8.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-168\" class=\"wp-caption-text\">Figure 8. Conceptual schematic of dopamine D\u2082 receptor signaling and downregulation. Dopamine released from a presynaptic neuron binds D\u2082 receptors on the postsynaptic membrane. Chronic substance exposure is often associated with reduced D\u2082 receptor availability or sensitivity, which can blunt reward responsiveness and contribute to compulsive reward-seeking behavior. Created by the author with generative AI.<\/figcaption><\/figure>\n<p>While each substance affects the mesolimbic dopamine pathway in distinct ways, they converge on a common outcome: distorted dopamine signaling that transforms healthy reward seeking into pathological craving and dependence. Across drugs, exaggerated dopamine responses, compensatory neuroadaptations, and heightened anti-reward signaling reshape motivation so that behavior becomes organized around relief from discomfort rather than the pursuit of pleasure.<\/p>\n<p>Understanding these mechanisms has practical and ethical importance. It informs more effective prevention and treatment strategies, and it supports a compassionate, evidence-based view of addiction as a neurobiological disorder rather than a moral or personal failing.<\/p>\n<p>Having examined how substances alter dopamine pathways, the next logical step is to explore how scientists <strong>visualize<\/strong> these changes in the living human brain. This brings us to the field of neuroimaging.<\/p>\n<div class=\"textbox textbox--examples\">\n<header class=\"textbox__header\">\n<p class=\"textbox__title\">Digital Dopamine: Social Media and the Mesolimbic Pathway<\/p>\n<\/header>\n<div class=\"textbox__content\">\n<p data-start=\"1567\" data-end=\"2161\"><strong data-start=\"1567\" data-end=\"1613\">Variable social rewards keep us scrolling.<\/strong> Social media interactions, such as Instagram \u201cLikes,\u201d follows, and TikTok views, function as potent yet unpredictable social rewards. Brain imaging studies show that when adolescents view images with many \u201cLikes,\u201d there is increased activation in the nucleus accumbens (NAc), the same dopamine-rich region engaged by food, drugs, and money (Sherman et al., 2016). Stronger accumbens responses to social reward predict more frequent checking of social media feeds, reinforcing a habit loop that parallels substance addiction (Sherman et al., 2018).<\/p>\n<p data-start=\"2166\" data-end=\"2657\"><strong data-start=\"2166\" data-end=\"2219\">Algorithms amplify dopamine-driven reward cycles.<\/strong> Platforms like TikTok intensify this process through algorithmic curation. When participants view personalized content on TikTok\u2019s \u201cFor You\u201d page, fMRI studies show greater activation of the ventral tegmental area (VTA), the midbrain source of dopamine signaling, compared to generic feeds (Lin et al., 2021). This variable-ratio schedule of unpredictable but personalized rewards promotes persistent engagement and compulsive scrolling.<\/p>\n<p data-start=\"2662\" data-end=\"3044\"><strong data-start=\"2662\" data-end=\"2709\">Chasing the digital hit reshapes the brain.<\/strong> Chronic social media engagement does not merely activate reward circuits in the moment. Smartphone usage studies show that frequent Instagram and Facebook checking is associated with reduced gray matter volume in the nucleus accumbens, a pattern similar to structural changes observed in long-term substance use (Montag et al., 2017).<\/p>\n<p data-start=\"3049\" data-end=\"3470\"><strong data-start=\"3049\" data-end=\"3104\">The pleasure\u2013pain seesaw applies without chemicals.<\/strong> Heavy social media users often report withdrawal-like symptoms, including irritability, anxiety, and low mood, during periods of digital abstinence. These effects mirror the opponent-process imbalance described earlier in this module, demonstrating that powerful dopamine-driven reward loops can emerge even in the absence of psychoactive substances (Lembke, 2021).<\/p>\n<\/div>\n<\/div>\n<figure id=\"attachment_169\" aria-describedby=\"caption-attachment-169\" style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-169 size-large\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-1024x683.png\" alt=\"Four-panel infographic titled \u201cDigital Dopamine.\u201d Panel 1 shows a person checking a phone with a \u201c1,200 likes\u201d icon; a brain callout highlights the nucleus accumbens (NAc) and labels \u201cdopamine,\u201d with the caption that high engagement signals activate the NAc. Panel 2 shows reward icons near the phone and a brain outline labeled \u201cPFC\u201d with a downward arrow, stating strong rewards can temporarily reduce top-down control from the prefrontal cortex (PFC). Panel 3 shows a \u201cFor You\u201d feed, a roulette wheel and dice labeled \u201cvariable ratio,\u201d and two brains labeled \u201cdopamine bursts\u201d and \u201cVTA,\u201d describing unpredictable personalized rewards amplifying dopamine bursts from the ventral tegmental area (VTA). Panel 4 shows a simplified chart labeled \u201cNAc gray matter\u201d decreasing with \u201cchronic checking,\u201d plus a sad person holding a phone with negative mood icons, stating heavy use is linked to NAc changes and withdrawal-like symptoms. Footer note says the schematic is conceptual and brain regions are simplified.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-1024x683.png 1024w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-300x200.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-768x512.png 768w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-65x43.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-225x150.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9-350x233.png 350w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig9.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption id=\"caption-attachment-169\" class=\"wp-caption-text\">Figure 9. Digital reward loops as a conceptual dopamine schematic. Social feedback and algorithmically variable rewards can drive dopamine-related learning signals in reward circuitry (for example NAc and VTA), while intense rewards may transiently weaken prefrontal top-down control (PFC). With chronic checking, some users may experience adaptation and negative affect when not engaging. Created by the author with generative AI.<\/figcaption><\/figure>\n<h2 data-start=\"123\" data-end=\"183\">Imaging the Dopamine Pathway: fMRI, PET, and the MID Task<\/h2>\n<p data-start=\"185\" data-end=\"480\">How do we know so much about the dopamine system in humans without directly measuring neuron activity? Two advanced neuroimaging techniques\u2014functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET)\u2014allow scientists to observe how dopamine pathways operate in real time.<\/p>\n<p data-start=\"482\" data-end=\"978\">Functional Magnetic Resonance Imaging (fMRI) is a specialized type of MRI that detects brain activity indirectly by measuring changes in blood flow. MRI scanners are large, cylindrical devices that use powerful magnets and radio waves to create detailed images of the brain. During an fMRI scan, participants lie on a sliding table that moves into the scanner\u2019s tunnel-like center, where changes in blood oxygenation are captured and reconstructed into three-dimensional images of brain activity.<\/p>\n<p><iframe loading=\"lazy\" id=\"oembed-1\" title=\"MRI Simulator Training Video\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/2M6frboBwh8?feature=oembed&#38;rel=0&#38;enablejsapi=1&#38;origin=https:\/\/openpub.libraries.rutgers.edu\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p>Figure 10: The ABCD study MRI training video for ABCD participants.<\/p>\n<h2 data-start=\"157\" data-end=\"220\">Resting-State fMRI: Mapping the Brain\u2019s Default Connectivity<\/h2>\n<p data-start=\"222\" data-end=\"781\"><strong data-start=\"222\" data-end=\"253\">What is resting-state fMRI?<\/strong><br data-start=\"253\" data-end=\"256\" \/>Resting-state fMRI measures brain activity while a person lies quietly in the scanner without performing a specific task. Rather than capturing responses to stimuli, it records spontaneous, synchronized fluctuations in the blood-oxygen-level\u2013dependent (BOLD) signal across brain regions. These coordinated fluctuations reflect <em data-start=\"583\" data-end=\"618\">intrinsic functional connectivity<\/em>, meaning how strongly different regions communicate with one another at rest. The ABCD Study includes resting-state fMRI data for participants across development.<\/p>\n<p data-start=\"783\" data-end=\"1115\"><strong data-start=\"783\" data-end=\"829\">Why is this useful for addiction research?<\/strong><br data-start=\"829\" data-end=\"832\" \/>Resting-state fMRI is particularly valuable for identifying baseline differences in brain network organization between individuals with substance use disorders and healthy controls. Across studies, addiction is associated with altered connectivity within reward and control circuits.<\/p>\n<p data-start=\"1117\" data-end=\"1569\"><strong data-start=\"1117\" data-end=\"1163\">Key finding: reward\u2013control disconnection.<\/strong><br data-start=\"1163\" data-end=\"1166\" \/>A consistent result is disrupted functional connectivity between the nucleus accumbens (NAc), a core reward-processing region, and the prefrontal cortex (PFC), which supports executive control and decision-making. For example, stimulant users often show reduced NAc\u2013PFC connectivity, indicating weakened top-down regulation of reward-driven impulses by frontal control systems (Sutherland et al., 2012).<\/p>\n<p data-start=\"1571\" data-end=\"1927\"><strong data-start=\"1571\" data-end=\"1590\">Interpretation.<\/strong><br data-start=\"1590\" data-end=\"1593\" \/>These patterns suggest that addiction involves not only heightened reward sensitivity, but also impaired communication between motivational systems and self-regulatory control networks. Resting-state findings therefore provide neural evidence for compulsive drug-seeking as a circuit-level disorder, not simply a failure of willpower.<\/p>\n<figure id=\"attachment_171\" aria-describedby=\"caption-attachment-171\" style=\"width: 468px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-171 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11.png\" alt=\"Four-panel fMRI-style brain image with statistical activation overlays. The top row (labeled \u201cA\u201d) shows two views of the brain (sagittal and coronal) with multiple orange clusters. The bottom row (labeled \u201cB\u201d) shows two corresponding views with fewer, smaller orange clusters. Blue crosshairs mark a coordinate in each panel. A vertical color bar at the right indicates intensity of the statistical map.\" width=\"468\" height=\"468\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11.png 468w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11-300x300.png 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11-150x150.png 150w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11-65x65.png 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11-225x225.png 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig11-350x350.png 350w\" sizes=\"auto, (max-width: 468px) 100vw, 468px\" \/><figcaption id=\"caption-attachment-171\" class=\"wp-caption-text\">Figure 11. Brain activation maps reproduced from Stewart et al. (2022) showing two contrast maps (A vs. B) with warmer colors indicating stronger statistical effects. Reproduced under CC BY 4.0 from Stewart et al., 2022, Nature Schizophrenia.<\/figcaption><\/figure>\n<p data-start=\"110\" data-end=\"480\"><strong data-start=\"110\" data-end=\"122\">Figure 11<\/strong> is a real resting-state fRMI data visualization; it is not an image of a single person\u2019s brain, but a summary based on brain scans from multiple studies and participants (Tolomeo &amp; Yu 2022). It visualizes group differences in resting-state brain activity between individuals with substance use and behavioral addictions (SUD + BA) and healthy controls (HC).<\/p>\n<p data-start=\"482\" data-end=\"762\">\u2022 <strong data-start=\"484\" data-end=\"506\">Panel A (top row):<\/strong> Brain regions with increased connectivity in the SUD + BA group compared to controls. These include areas like the amygdala, thalamus, midbrain, caudate, and parahippocampal gyrus\u2014all part of circuits involved in emotion, motivation, and habit learning.<\/p>\n<p data-start=\"764\" data-end=\"972\">\u2022 <strong data-start=\"766\" data-end=\"791\">Panel B (bottom row):<\/strong> Brain regions with decreased connectivity in the SUD + BA group, such as parts of the posterior lobe and parahippocampal gyrus, which are linked to memory and sensory processing.<\/p>\n<p data-start=\"974\" data-end=\"1268\" data-is-last-node=\"\" data-is-only-node=\"\">The color scale (yellow to red) reflects the strength of these group differences in connectivity. These patterns suggest that addiction is associated with hyperconnectivity in reward and habit-related brain systems and disrupted connectivity in regions important for memory and self-regulation.<\/p>\n<h2 data-start=\"0\" data-end=\"60\">Task-Based fMRI \u2013 The Monetary Incentive Delay (MID) Task<\/h2>\n<p data-start=\"62\" data-end=\"392\">Task-based fMRI involves participants performing specific activities designed to engage particular neural circuits. A widely used paradigm in addiction research is the Monetary Incentive Delay (MID) task. Participants see cues indicating potential monetary gains or losses, prompting anticipation and engagement of reward systems.<\/p>\n<p data-start=\"394\" data-end=\"1045\" data-is-last-node=\"\" data-is-only-node=\"\">The MID task differentiates between anticipation (expecting a reward) and feedback (receiving the reward). Crucially, dopamine neurons primarily fire during reward anticipation, reflecting &#8220;wanting,&#8221; rather than during reward receipt, reflecting &#8220;liking.&#8221; Consistent with this, fMRI studies using the MID task reveal increased activation in the NAc during anticipation phases (Knutson et al., 2000). In addiction, MID task findings commonly demonstrate abnormal activation patterns: addicted individuals typically exhibit heightened NAc responses to drug-related cues and diminished activation to natural rewards, indicating altered reward processing.<\/p>\n<figure id=\"attachment_172\" aria-describedby=\"caption-attachment-172\" style=\"width: 735px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-172 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig12.jpg\" alt=\"Two-panel figure summarizing MID task results in the ABCD study: Panel A shows boxplots of hit rate and reaction time for loss, reward, and neutral conditions (all trials and by run). Panel B shows brain maps illustrating reward-related activation patterns, including a \u201creward success vs fail\u201d contrast highlighting ventral striatum regions.\" width=\"735\" height=\"430\" srcset=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig12.jpg 735w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig12-300x176.jpg 300w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig12-65x38.jpg 65w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig12-225x132.jpg 225w, https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig12-350x205.jpg 350w\" sizes=\"auto, (max-width: 735px) 100vw, 735px\" \/><figcaption id=\"caption-attachment-172\" class=\"wp-caption-text\">Figure 12. Reproduced from Casey, B. J., Cannonier, T., Conley, M. I., et al. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43\u201354. Elsevier. Licensed under CC BY-NC-ND 4.0. DOI: 10.1016\/j.dcn.2018.03.001<\/figcaption><\/figure>\n<p>Figure 12 shows the preliminary results from the ABCD study\u2019s baseline data collection of MID fMRI data.<\/p>\n<p><strong>Panel A (left side): Behavioral performance during the MID task<\/strong><\/p>\n<ul>\n<li><strong>Top graph (Hit Rate):<\/strong> Shows how often participants successfully responded (&#8220;hit&#8221;) to targets when they could lose money (&#8220;Loss&#8221;), win money (&#8220;Reward&#8221;), or had no monetary consequence (&#8220;Neutral&#8221;). Higher values indicate better performance.<\/li>\n<li><strong>Bottom graph (Reaction Time):<\/strong> Shows how quickly (in milliseconds) participants responded to these same targets. Lower values mean faster reactions.<\/li>\n<\/ul>\n<p><strong>Panel B (right side): Brain activation during the MID task<\/strong><\/p>\n<ul>\n<li><strong>Top images (Surface brain maps):<\/strong> Illustrate overall activation patterns across the brain surface during the task, highlighting regions engaged by reward anticipation and processing.<\/li>\n<li><strong>Bottom image (Contrast image):<\/strong> Specifically highlights brain areas more activated when successfully winning money (&#8220;reward success&#8221;) compared to not receiving money (&#8220;reward fail&#8221;). Warm colors (yellow\/red) indicate stronger activation in the ventral striatum and nucleus accumbens, areas central to dopamine-related reward processing.<\/li>\n<\/ul>\n<p>These images show that performance and brain activation during reward-related tasks like the MID are measurable using behavioral data and fMRI, allowing researchers to investigate the brain&#8217;s reward circuitry in detail.<\/p>\n<h3>PET Imaging: Neurochemical Insights into Dopamine<\/h3>\n<p>While fMRI measures brain activity indirectly through blood flow changes, PET imaging directly assesses neurochemistry by using radioactive tracers that bind to specific molecules. A widely used PET tracer, [11C]-raclopride, binds to dopamine D\u2082 receptors. Raclopride&#8217;s binding decreases when dopamine is released (since dopamine displaces raclopride), providing an indirect measure of dopamine release and receptor availability. PET imaging is not included in ABCD data collection.<\/p>\n<p>Studies employing PET consistently find reduced D\u2082 receptor availability in the striatum of individuals with substance use disorders, including cocaine, alcohol, and opioid dependence (Volkow et al., 2009). Lower D\u2082 receptor levels are associated with increased craving, impaired self-control, and higher relapse rates, reflecting a blunted dopamine reward system.<\/p>\n<p>Combining PET with fMRI provides complementary insights. For instance, individuals showing reduced NAc activation during reward anticipation in fMRI typically also demonstrate decreased D\u2082 receptor availability in PET, reinforcing the neurochemical underpinning of reward dysfunction in addiction.<\/p>\n<figure id=\"attachment_173\" aria-describedby=\"caption-attachment-173\" style=\"width: 624px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-173 size-full\" src=\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/02\/fig13.gif\" alt=\"Two side-by-side color brain scans labeled \u201ccontrol\u201d and \u201con cocaine.\u201d Warm colors indicate stronger imaging signal; the \u201con cocaine\u201d scan shows a visibly different signal pattern compared with the control scan.\" width=\"624\" height=\"416\" \/><figcaption id=\"caption-attachment-173\" class=\"wp-caption-text\">Figure 13. Figure X. Functional brain imaging comparison between a control condition and acute cocaine exposure. The image illustrates changes in brain metabolic activity or blood flow following cocaine administration, adapted from materials provided by the National Institute on Drug Abuse (NIDA), NIH.<\/figcaption><\/figure>\n<p>Figure 13 is a PET image that uses radiolabeled glucose to visualize metabolic activity across the brain. Red areas represent high glucose uptake, meaning active brain function, while blue and black indicate low activity. The scan on the right shows that in a person on cocaine, much of the brain exhibits reduced glucose metabolism\u2014particularly in frontal regions\u2014indicating that cocaine impairs normal brain function and energy use, which can contribute to disrupted thinking, poor impulse control, and long-term cognitive deficits.<\/p>\n<h2>Conclusion: Beyond Reward\u2014From Motivation to Control<\/h2>\n<p>The mesolimbic dopamine pathway, our core reward circuit, does not function in isolation\u2014it interacts dynamically with multiple neural networks that govern self-control, emotional regulation, habit formation, and decision-making. As we have explored in this module, dopamine fuels the &#8220;go&#8221; system that drives our pursuit of rewarding experiences. However, balanced behavior relies equally on the brain\u2019s &#8220;stop&#8221; system\u2014primarily involving frontal regions like the prefrontal cortex (PFC) and anterior cingulate cortex (ACC)\u2014that evaluates consequences and modulates impulsive actions. In Module 9, we will dive deeper into these interconnected circuits.<\/p>\n<hr \/>\n<h2>References<\/h2>\n<p>Berridge, K. C., &amp; Robinson, T. E. (2016). Liking, wanting, and the incentive-sensitization theory of addiction. <em>American Psychologist, 71<\/em>(8), 670\u2013679. <a href=\"https:\/\/doi.org\/10.1037\/amp0000059\">https:\/\/doi.org\/10.1037\/amp0000059<\/a><\/p>\n<p>Bloomfield, M. A. P., Ashok, A. H., Volkow, N. D., &amp; Howes, O. D. (2016). The effects of \u0394\u2079-tetrahydrocannabinol on the dopamine system. <em>Nature, 539<\/em>(7629), 369\u2013377. <a href=\"https:\/\/doi.org\/10.1038\/nature20153\">https:\/\/doi.org\/10.1038\/nature20153<\/a><\/p>\n<p>Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., \u2026 &amp; Dale, A. M. (2018). The adolescent brain cognitive development (ABCD) study: Imaging acquisition across 21 sites. <em>Developmental Cognitive Neuroscience, 32<\/em>, 43\u201354. <a href=\"https:\/\/doi.org\/10.1016\/j.dcn.2018.03.001\">https:\/\/doi.org\/10.1016\/j.dcn.2018.03.001<\/a><\/p>\n<p>Heinz, A., Siessmeier, T., Wrase, J., Hermann, D., Klein, S., Gr\u00fcsser, S., \u2026 &amp; Bartenstein, P. (2004). Correlation between dopamine D(2) receptors in the ventral striatum and central processing of alcohol cues and craving. <em>American Journal of Psychiatry, 161<\/em>(10), 1783\u20131789. <a href=\"https:\/\/doi.org\/10.1176\/ajp.161.10.1783\">https:\/\/doi.org\/10.1176\/ajp.161.10.1783<\/a><\/p>\n<p>Hou, H., Tian, M., &amp; Zhang, H. (2012). Positron emission tomography molecular imaging of dopaminergic system in drug addiction. <em>The Anatomical Record, 295<\/em>(5), 722\u2013733. <a href=\"https:\/\/doi.org\/10.1002\/ar.22430\">https:\/\/doi.org\/10.1002\/ar.22430<\/a><\/p>\n<p>Knutson, B., Westdorp, A., Kaiser, E., &amp; Hommer, D. (2000). FMRI visualization of brain activity during a monetary incentive delay task. <em>NeuroImage, 12<\/em>(1), 20\u201327. <a href=\"https:\/\/doi.org\/10.1006\/nimg.2000.0593\">https:\/\/doi.org\/10.1006\/nimg.2000.0593<\/a><\/p>\n<p>Lembke, A. (2021). <em>Dopamine Nation: Finding Balance in the Age of Indulgence.<\/em> New York, NY: Dutton.<\/p>\n<p>Ludlow, K., Bradley, K., Allison, D., Taylor, S., Yorgason, J., Hansen, D., \u2026 &amp; Steffensen, S. (2009). Acute and chronic ethanol modulate dopamine D2-subtype receptor responses in ventral tegmental area GABA neurons. <em>Alcoholism: Clinical and Experimental Research, 33<\/em>(5), 804\u2013811. <a href=\"https:\/\/doi.org\/10.1111\/j.1530-0277.2009.00899.x\">https:\/\/doi.org\/10.1111\/j.1530-0277.2009.00899.x<\/a><\/p>\n<p>Montag, C., Markowetz, A., B\u0142aszkiewicz, K., Andone, I., Lachmann, B., Sariyska, R., Trendafilov, B., Eibes, M., Kolb, J., Reuter, M., &amp; Weber, B. (2017). Facebook usage on smartphones and gray matter volume of the nucleus accumbens. <em>Behavioural Brain Research, 329<\/em>, 221\u2013228. <a href=\"https:\/\/doi.org\/10.1016\/j.bbr.2017.04.035\">https:\/\/doi.org\/10.1016\/j.bbr.2017.04.035<\/a><\/p>\n<p>National Institute on Drug Abuse. (2012). <em>Dopamine pathways<\/em> [Diagram]. Wikimedia Commons. <a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Dopamine_pathways.jpg\">https:\/\/commons.wikimedia.org\/wiki\/File:Dopamine_pathways.jpg<\/a><\/p>\n<p>SAMHSA. (2016). <em>Facing Addiction in America: The Surgeon General\u2019s Report on Alcohol, Drugs, and Health.<\/em> U.S. Department of Health and Human Services. <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK424849\/\">https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK424849\/<\/a><\/p>\n<p>Schultz, W. (1998). Predictive reward signal of dopamine neurons. <em>Journal of Neurophysiology, 80<\/em>(1), 1\u201327. <a href=\"https:\/\/doi.org\/10.1152\/jn.1998.80.1.1\">https:\/\/doi.org\/10.1152\/jn.1998.80.1.1<\/a><\/p>\n<p>Sherman, L. E., Greenfield, P. M., Hernandez, L. M., &amp; Dapretto, M. (2018). Peer influence via Instagram: Effects on brain and behavior in adolescence and young adulthood. <em>Child Development, 89<\/em>(1), 37\u201347. <a href=\"https:\/\/doi.org\/10.1111\/cdev.12838\">https:\/\/doi.org\/10.1111\/cdev.12838<\/a><\/p>\n<p>Sherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M., &amp; Dapretto, M. (2016). The power of the like in adolescence: Effects of peer influence on neural and behavioral responses to social media. <em>Psychological Science, 27<\/em>(7), 1027\u20131035. <a href=\"https:\/\/doi.org\/10.1177\/0956797616645673\">https:\/\/doi.org\/10.1177\/0956797616645673<\/a><\/p>\n<p>Soutschek, A., Tobler, P. N., Kahnt, T., Quednow, B. B., &amp; Weber, S. C. (2021). Opioid antagonism reduces wanting by strengthening frontostriatal connectivity. <em>bioRxiv.<\/em> <a href=\"https:\/\/doi.org\/10.1101\/2021.06.20.449203\">https:\/\/doi.org\/10.1101\/2021.06.20.449203<\/a><\/p>\n<p>Su, C., Zhou, H., Gong, L., Teng, B., Geng, F., &amp; Hu, Y. (2021). Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area. <em>NeuroImage, 237<\/em>, 118136. <a href=\"https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118136\">https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118136<\/a><\/p>\n<p>Tolomeo, S., &amp; Yu, R. (2022). Brain network dysfunctions in addiction: A meta-analysis of resting-state functional connectivity. <em>Translational Psychiatry, 12<\/em>(1), 41. <a href=\"https:\/\/doi.org\/10.1038\/s41398-022-01792-6\">https:\/\/doi.org\/10.1038\/s41398-022-01792-6<\/a><\/p>\n<p>Volkow, N. D., &amp; Morales, M. (2015). The brain on drugs: From reward to addiction. <em>Cell, 162<\/em>(4), 712\u2013725. <a href=\"https:\/\/doi.org\/10.1016\/j.cell.2015.07.046\">https:\/\/doi.org\/10.1016\/j.cell.2015.07.046<\/a><\/p>\n<p>Volkow, N. D., Fowler, J. S., Wang, G. J., Baler, R., &amp; Telang, F. (2009). Imaging dopamine\u2019s role in drug abuse and addiction. <em>Neuropharmacology, 56<\/em>(Suppl 1), 3\u20138. <a href=\"https:\/\/doi.org\/10.1016\/j.neuropharm.2008.05.022\">https:\/\/doi.org\/10.1016\/j.neuropharm.2008.05.022<\/a><\/p>\n","protected":false},"author":30,"menu_order":1,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-146","chapter","type-chapter","status-publish","hentry"],"part":35,"_links":{"self":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/146","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/users\/30"}],"version-history":[{"count":18,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/146\/revisions"}],"predecessor-version":[{"id":148,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/146\/revisions\/148"}],"part":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/parts\/35"}],"metadata":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapters\/146\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/media?parent=146"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/pressbooks\/v2\/chapter-type?post=146"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/contributor?post=146"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/wp-json\/wp\/v2\/license?post=146"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}