The aim of this review is to explore how metabolic changes induced by diets high in saturated fat (HFD) affect nucleus accumbens (NAc) dopamine neurotransmission and food intake, and to explore how stress and inflammation influence this process. Recent evidence linked diet-induced obesity and HFD with reduced dopamine release and reuptake. Altered dopamine neurotransmission could disrupt satiety circuits between NAc dopamine terminals and projections to the hypothalamus. The NAc directs learning and motivated behaviours based on homeostatic needs and psychological states. Therefore, impaired dopaminergic responses to palatable food could contribute to weight gain by disrupting responses to food cues or stress, which impacts type and quantity of food consumed. Specifically, saturated fat promotes neuronal resistance to anorectic hormones and activation of immune cells that release proinflammatory cytokines. Insulin has been shown to regulate dopamine neurotransmission by enhancing satiety, but less is known about effects of diet-induced stress. Therefore, changes to dopamine signalling due to HFD warrant further examination to characterise crosstalk of cytokines with endocrine and neurotransmitter signals. A HFD promotes a proinflammatory environment that may disrupt neuronal endocrine function and dopamine signalling that could be exacerbated by the hypothalamic–pituitary–adrenal and κ-opioid receptor stress systems. Together, these adaptive changes may dysregulate eating by changing NAc dopamine during hedonic versus homeostatic food intake. This could drive palatable food cravings during energy restriction and hinder weight loss. Understanding links between HFD and dopamine neurotransmission will inform treatment strategies for diet-induced obesity and identify molecular candidates for targeted therapeutics. Food intake is controlled by many factors, including an obesogenic food environment with ubiquitous access to cheap, calorie-rich, palatable foods. Herein, ‘palatable foods’ are defined as those with high energy density primarily from fat or sugar. Over-consumption of palatable food is proposed to shift brain dopamine signalling within the nucleus accumbens (NAc)3. The NAc is a limbic–motor interface which integrates salient stimuli with memory and context, reward availability and value, sensory information, physiological state and homeostatic needs4. It sends efferent projections to cortical and motor regions, processing environmental and biological stimuli to drive motivated behaviour. Disrupting homeostasis in the NAc by stimulating excessive dopamine release may contribute to obesity with extended access to highly palatable foods that acutely cause phasic dopamine release in the NAc6. Perturbations to NAc dopamine by consuming a diet high in saturated fat (HFD) may disrupt natural NAc food reward learning and reduce NAc dopamine tone over time11. Changes in dopamine tone or phasic dopamine release in the NAc may significantly impact food seeking13, reward12 and satiety14. However, the mechanisms by which diet and obesity alter dopamine neurotransmission and behaviour are not fully characterised. Therefore, the purpose of this review is to highlight the literature and identify research gaps related to mechanisms by which diet-induced obesity interfere with NAc dopamine, including interactions between inflammation, physiological stress and κ-opioid receptor function which together with endocrine hormones modulate NAc dopamine to influence food intake behaviours.Overweight and obesity prevalence has steadily increased with 42·4 % of US adults currently classified as obese15. These dopamine neurons form synaptic terminals with γ-aminobutyric (GABA)-releasing medium spiny neurons (MSNs), comprising ~90–95 % of neurons within the NAc16, which express dopamine D1 receptor (D1R) and dopamine D2 receptor (D2R) subtypes that propagate dopamine signalling to control cortical and motor processing17. An excellent review of homeostatic crosstalk with the dopamine reward system by Ferrario and colleagues highlighted how glucose and endocrine indicators of energy status  inhibit or excite dopaminergic reward activity directly in the VTA, NAc and striatum2. Hormones also indirectly modulate dopaminergic activity by targeting key homeostatic regions in the hypothalamus that initiate food seeking behaviours via GABAergic and glutamatergic inputs to VTA and NAc18. There are multiple subtypes of these neurons within the lateral hypothalamus (LH) and arcuate nucleus (ARC) that respond to energy status by releasing appetitive neuropeptides. These include orexin/hypocretin , melanin-concentrating hormone , neurotensin , neuropeptide Y (NPY)/agouti-related peptide (AGRP)  and pro-opiomelanocortin (POMC)/cocaine- and amphetamine-regulated transcript (CART) 2. These appetitive systems underly many mechanisms by which HFD and stress perturb dopamine control of food intake and will be discussed in detail throughout this review. In addition to homeostatic engagement of hypothalamus afferents to the VTA and NAc, feeding is also controlled by a NAc to hypothalamus satiety circuit. This was demonstrated by direct inhibition or stimulation of D1R-expressing MSNs that project from the NAc shell to the LH, where D1R inhibition increased licking for fat and sugar but stimulation decreased ingestive responses19. Overall, NAc dopamine neurotransmission and subsequent GABA output controls motivated behaviour, and homeostatic signals from the hypothalamus comprise important inputs that regulate feeding. Therefore, diet-induced disruption to these circuits may be particularly consequential for individuals restricting food intake  to NAc controlling Pavlovian reward learningd intake .20. The NAc assimilates these signals to determine hedonic value and sends GABA via the ‘direct’ route with direct control of dopamine release with afferents to VTA, internal globus pallidus or substantia nigra (MSNs with D1Rs), or the ‘indirect’ route via globus pallidus externa and ventral pallidum (MSNs with D2Rs). GABA afferents to the thalamus then modulate excitatory output to the cortex that controls behavioural selection and motor activity17. D1Rs have lower affinity for dopamine than do D2Rs22 and respond to phasic dopamine release due to unexpected rewards and cue learning that promotes cyclic adenosine monophosphate (cAMP) signalling with downstream phosphorylation of dopamine-regulating proteins15 and increased MSN firing probability23. Conversely, D2Rs are activated at lower dopamine concentrations by spontaneous pacemaking activity of dopamine neurons24 than D1Rs, and activate opposing intracellular signalling, to decrease MSN firing probability23. Consequently, D2Rs communicate dopamine tone so that phasic release events (unexpected rewards) or lack thereof (absence of expected reward) alter concentration that is detected by D1Rs to promote response. This dopamine signalling pattern is important for NAc learning that relies on phasic dopamine release initiated by a rewarding, unconditioned stimulus that becomes tied to a conditioned cue over repeated exposures. In the theory of reward prediction error25, hedonic value is determined based on magnitude of dopamine release, which is up- or down-regulated upon further cue exposures. This reward-learning model is central to motivation and survival and may become ‘hijacked’ by palatable foods.The ventral striatum is central to reward processing, integrating glutamatergic and GABAergic inputs from the hypothalamus, cortex, amygdala and hippocampus with dopaminergic projections from the VTA or substantia nigra26. Mesolimbic dopamine increased during lever pressing to obtain food27, and NAc core dopamine depletion reduced response in fixed ratio tasks with more pronounced decreases in higher ratio schedules28. Moreover, food deprivation augments dopaminergic responses to food, demonstrated by increased NAc dopamine in response to maize oil feeding after food restriction6. Interestingly, phasic dopamine release was enhanced in food-restricted rats following cues that predict sucrose versus cues for saccharin5. This suggested energy-providing foods have greater salience than energy-null foods when homeostatic energy needs influence physiological state, but dopamine release induced by food cues that initiate food intake could be enhanced in individuals with obesity. Indeed, those who were obese showed enhanced striatal and NAc dopamine release in response to palatable food images after consumption of an energy-dense meal29. This was in contrast to healthy, lean individuals whose meal pleasantness ratings correlated with striatal dopamine release30 and who experienced striatal dopaminergic activation immediately during milkshake consumption and 20 min post-ingestion when gut signals reached the brain31. Additionally, when food access is not interrupted but availability of palatable foods is limited, dopamine release is increased during access to the preferred food under limited compared with ad libitum access in rodents. For instance, constant access to sucrose for 21 d failed to evoke the same magnitude of dopamine release in the NAc shell as daily intermittent access to sucrose14. Therefore, dopamine release in response to food can be influenced by physiological state and food availability, and the NAc controls initial hedonic responses to palatable food intake and promotes cue-associated learning and motivation to obtain food that is interfaced with homeostatic need.Dopamine pathways control motivated and habitual behaviour, including that related to food. The VTA–NAc mesolimbic dopaminergic pathway influences motivated behaviour by enhancing willingness to work for rewards37. Furthermore, HFD intake activates inflammatory processes that may contribute to neuronal insulin resistance40. Central insulin and leptin resistance attenuate satiation and reward valuation of palatable foods by altering NAc synaptic dopamine and disrupt orexigenic and anorectic communication between the LH, ARC and VTA43. Finally, chronic HFD intake shifts opioid control of NAc dopamine neurotransmission44, which could amplify stress-induced feeding45 and have consequences for obese individuals on energy restricted diets. Overall, studies presented below demonstrate HFD consumption acutely increases NAc dopamine47, but prolonged intake reduces capacity for dopamine neurotransmission through repeated stimulation of dopamine receptors48, resistance to hormonal and homeostatic signals43, and up-regulated inflammatory signalling39.Prolonged consumption of highly palatable diets may disrupt dopamine reward signalling. Chronic HFD intake and diet-induced obesity impact VTA dopamine neuron activity and interfere with mechanisms regulating dopamine at synaptic terminals within the NAc. Changes include lowered D2R binding potential as well as reduced dopamine transporter (DAT) function and membrane localisation49. Further, intake of a HFD or intraperitoneal injection of lipid solution acutely increased NAc dopamine46, and sucrose intake dose-dependently increased NAc dopamine47. Conversely, chronic HFD feeding reduced maximal dopamine reuptake rate (Vmax)34, while extended access to a Western diet (WD), a HFD with added sugar, decreased striatal and NAc core dopamine release and reuptake43. Further, while obesogenic diet and food restriction both reduced striatal DAT surface expression and reuptake50, obesogenic diet decreased but food restriction increased D2R protein expression. These findings suggest food restriction primes the dopamine system to respond to food exposure. HFD-induced changes develop over time, as 6-week but not 2-week exposure to HFD decreased NAc dopamine Vmax and membrane-associated DAT expression33. Effects of HFD also depend on fat type, as rats chronically fed 50 % saturated HFD experienced reduced DAT and increased D1R protein expression versus 50 % monounsaturated olive oil or control diet32. Similarly, consumption of a HFD versus control diet reduced dopamine reuptake Vmax and attenuated phasic dopamine release, which did not occur in mice fed a diet high in polyunsaturated flax-seed oil51. Further, consumption of a WD versus low-fat control diet for 12 weeks reduced NAc D1R protein expression but increased total D2R and p-dopamine- and cAMP-regulated phosphoprotein-32 (DARPP-32) protein expression9, the latter which activates D1R-mediated signalling downstream52. HFD feeding from lactation through adulthood similarly increased NAc DARPP-32 but decreased D1R and D2R gene expression that was exacerbated after HFD was removed for 4 weeks48. Conversely, HFD-induced reduction in D1Rs and D2Rs was restored by HFD removal. These results suggest adaptive changes in DAT, D1R and D2R expression due to repeated HFD-mediated activation of D1R signal transduction leading to down-regulated D1R and D2R availability. Interestingly, D1R-expressing MSNs were activated in proportion to palatable food consumption while intra-uterine protein-restriction-induced reduction of NAc shell D1Rs increased palatable milk consumption53, leading Durst et al. (2019) to suggest D1R stimulation during consumption builds to a ‘satiety threshold’ sent to LH GABA neurons. Endocannabinoid-mediated synaptic plasticity of this circuit was later demonstrated to promote overeating after food restriction or exposure to HFD54. Therefore, the NAc–LH satiety circuit may be inhibited due to HFD-induced disruption of D1R signalling.Chronic HFD intake alters dopamine neurotransmission to promote food seeking with obesity susceptibility dependent on the activation of NAc dopamine and alteration to dopamine receptors and the DAT. For example, genetic differences in the DAT gene significantly increased likelihood of obesity55. Furthermore, calorie restriction was used to maintain similar intake between groups, and chronic HFD in absence of obesity decreased NAc D2R protein expression and increased impulsivity in a task to obtain food56. Conversely, obesity in absence of diet manipulations also affects D2Rs, as chow-fed, obese mice with genetic leptin receptor inactivation increased NAc and striatal D2R availability in vivo using [11C]raclopride but decreased D2R availability ex vivo using [3H]spiperone57. Interestingly, these differences were eliminated between calorie-restricted lean and obese mice57. Based on the radio-ligands used, results suggested obesity reduced striatal dopamine concentration and postsynaptic D2R availability. Conversely, 3–4 weeks of limited daily access to a cafeteria diet reduced ethanol intake but increased D2R autoreceptor function58. One group used [3H]raclopride37 to assess diet-induced alterations to D2Rs over time and found that 20 d on a 40 % HFD versus chow increased D2R binding density in the NAc and striatum that was maintained after HFD removal35. Conversely, diet-induced obesity that developed over 20 weeks of HFD feeding decreased striatal D2R binding36 but increased NAc core and striatal D2R mRNA expression37. Further, obesity-resistant mice had increased NAc DAT binding36, which may promote dopamine clearance and protect against obesity. These studies suggest palatable foods up-regulate dopamine neurotransmission which is shifted by chronic HFD intake to decreased D2R binding and capacity for dopamine release. However, clinical research showed negative correlation between age and D2R binding throughout midbrain regions with body mass index (BMI) correlating positively with D2R availability only for those over 30 years old59. This suggests that adolescent striatal development and decline of D2R expression patterns with age could partially explain associations between D2R availability and BMI. Overall, HFD-induced obesity alters function and expression of NAc proteins regulating dopamine to reduce capacity for NAc dopaminergic reward that promotes overeating and weight gain.Much attention has also been paid by neuroimaging studies to striatal D2R availability. Decreased NAc core but increased NAc shell D2R binding potential were linked to impulsive behaviour17. The latter stimulate dopamine release via acetylcholine that activates acetylcholine receptors on dopamine axon terminals60. Activation of MSNs relies on glutamate targeting ionotropic N-methyl-d-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPAR) on CINs to release acetylcholine61. Glutamate also negatively regulates dopamine directly by metabotropic glutamate receptors62 and indirectly via MSN retrograde H2O2 release63. Glutamatergic inputs to NAc communicate physical and nutritive qualities of food, memory, physiological need and environmental cues20, which prompts initiation or cessation of feeding. Indeed, either AMPA and NMDA receptor agonism64 or antagonism65 in the NAc shell can induce voracious feeding. However, consumption of a WD66 or a HFD68 increased AMPA/NMDA receptor ratio67, prolonged excitatory postsynaptic currents onto MSNs66, and increased NAc phosphorylated GluR1 AMPA subunit32 and NAc shell NMDAR and metabotropic glutamate receptor68 expression, but inhibited ability to induce long-term depression onto MSNs67, effects which together increased motivation to obtain palatable food68. Further, the NAc receives GABA from VTA69, globus pallidus externa70, cortex71, bed nucleus of the stria terminalis72, and local MSNs and interneurons24. GABA signals reduce dopamine concentration73 to stop cue-associated reward behaviour70 or food intake and induce avoidance behaviour71. These effects occur directly via dopamine axon terminal74 GABAB-receptor-mediated reduction of dopamine73 and indirectly via GABAA-receptor-mediated reduction of acetylcholine73. Conversely, NAc shell GABAA and GABAB antagonism decreased food intake while fasted75, but agonism increased fat and sucrose76 as well as regular food intake while sated77, suggesting GABA inhibits NAc-LH MSNs to disinhibit feeding. However, GABAB agonism also inhibited bingeing on HFD during an intermittent access paradigm78. Overall, glutamatergic and cholinergic signalling within NAc promotes food intake, while the system of GABAergic disinhibition throughout the NAc, VTA and hypothalamus may decrease dopamine release and stop intake or disinhibit feeding via NAc shell MSNs, but prolonged HFD intake reduces sensitivity of these systems to promote overeating and highlights the complexity of dopaminergic regulation of ingestive behaviours.Control of VTA–NAc dopamine signals that initiate motivated actions relies on a complex network of acetylcholine, glutamate and GABA, which are all affected by HFD intake. About 5 % of NAc neurons are GABAergic or cholinergic interneurons (CIN)80. The VTA receives glutamatergic and GABAergic signals from local interneurons and limbic and cortical regions80 as well as local somatodendritic dopamine release that negatively regulates dopamine neuron excitability24. Glutamatergic and cholinergic inputs activate receptors on VTA dopamine neurons to promote NAc dopamine release81. The VTA receives orexin from LH, NPY from orexigenic glucose-sensing ARC neurons82, and anorectic POMC/CART signals from ARC2. The VTA integrates this information related to homeostasis and environmental cues with dopamine neurons projecting to the NAc. However, HFD intake reduced VTA tyrosine hydroxylase (TH) mRNA10 and protein83 expression, which occurred regardless of obesity and was restored after switching to low-fat diet10. This showed HFD intake reduced capacity for dopamine synthesis, because TH is the rate-limiting enzyme in synthesising dopamine84. Further, 6-week ad libitum HFD feeding attenuated D2R agonist quinpirole-induced inhibition of VTA dopamine neuron firing, suggesting D2R desensitisation85, whereas intermittent access to cafeteria diet for 3 weeks increased inhibitory effects of quinpirole and reduced ethanol and sucrose intake58, showing sensitivity of VTA autoreceptor function to type and length of diet. HFD intake similarly reduced excitability of mouse VTA GABA neurons86 that reduce NAc dopamine concentration73 and stop behaviour (e.g. food intake)71. Overall, HFD and obesity affect VTA dopamine and GABA neuron protein expression and function, and a further diet-induced effect includes altered sensitivity to direct responses of VTA neurons to hormonal indicators of energy status.VTA dopaminergic output underlies motivated behaviours and is affected by consumption of a HFD87 with leptin-receptor-expressing dopamine and GABA neurons projecting to the NAc88, and activation of these receptors decreases food intake. Insulin in the VTA is important in reducing dopamine neuron activity to control behaviour, demonstrated by reduced locomotion and NAc dopamine after VTA application of insulin89. In the VTA, insulin reduced somatodendritic dopamine release and hedonic feeding, which was abolished by blocking the DAT90. Furthermore, insulin induced AMPAR- and endocannabinoid-mediated long-term depression in VTA dopamine neurons which was attenuated by genetic or diet-induced elevation of insulin92, showing a role of insulin in the VTA to signal satiety that is inhibited by HFD. VTA signalling is also affected by leptin resistance, as HFD consumption reduced ability of VTA leptin administration to limit food intake and weight gain in obesity-prone rats93 and induced leptin resistance specifically in the VTA and ARC41. Diet-induced obesity also spurred leptin resistance in ARC NPY/AGRP and POMC/CART neurons42 and LH neurotensin–galanin–GABA neurons95 necessary for reducing food intake by inhibiting orexin and activating VTA neurons95. Therefore, lack of hypothalamic and VTA insulin and leptin signalling may reduce capacity for NAc dopamine release to promote compensatory over-seeking of palatable food.VTA neurons express receptors for insulin and leptin96 and local production in the brain97, and activation of tyrosine kinase receptors by insulin promotes phosphatidylinositol-3 kinase (PI3K) and protein kinase B (Akt) or the mitogen-activated protein kinase (MAPK)/extracellular-signal-regulated kinase (ERK) pathways, the latter which is also known as the Ras-Raf-MAPK/ERK kinase (MEK)-ERK cascade96. Insulin valuates food reward by fine-tuning dopamine neurotransmission at NAc dopamine terminals. For example, NAc core and shell insulin administration increased dopamine release and reuptake in control animals but not in animals consuming HFD40. Furthermore, HFD-induced impairments were reversed by promoting insulin receptor substrates, while effects of insulin were abolished by inhibiting insulin receptor or PI3K34, implicating HFD-induced insulin resistance in impaired dopamine control. Insulin receptors expressed on NAc dopamine neuron terminals40 activate Akt and ERK to shuttle DAT to the plasma membrane to promote dopamine reuptake98. Dopamine release is also promoted by insulin as NAc CINs expressed insulin receptors at high density40 and released acetylcholine in response to insulin43, supporting necessity of insulin in encoding sucrose preference40. While an obesogenic diet blunted insulin-induced NAc dopamine release and reuptake43, food restriction alternately enhanced insulin receptor expression and stimulated dopamine reuptake50. Similar effects have been shown with impaired leptin signalling, as leptin-deficient mice had reduced electrically evoked NAc shell dopamine release and reduced TH and DAT expression88, whereas leptin increased activity of NAc DAT and TH and increased amphetamine-evoked dopamine release99. Leptin activates intracellular signalling cascades similarly to insulin in addition to the Janus-activated kinases (JAK)–signal transducers and activators of transcription (STAT)–suppressors of cytokine signalling (SOCS) pathway that reduce NPY/AGRP but increase POMC/CART and LH neurotensin neuronal activity100. Further, leptin is also transported into the brain101 and is expressed in plasma and cerebrospinal fluid in proportion to adipocyte size102 and adiposity103, suggesting that leptin conveys energy sufficiency. However, individuals with obesity have elevated plasma leptin100, supporting leptin resistance as a comorbidity of obesity. Collectively, these studies showed NAc insulin promotes reward seeking by encoding reward salience via increased dopamine release and maintenance of dopamine reuptake, and, whereas food restriction may prime dopamine responses through insulin, HFD-induced insulin and leptin resistance may reduce NAc dopamine neurotransmission to alternately promote food seeking. A putative contributor to leptin and insulin resistance associated with diet-induced obesity is chronic inflammation triggered by saturated fats and rapid adipose tissue expansion105.Insulin gains access to the brain via transport across the blood–brain barrier104. Indeed, increased expression of inflammatory genes triggered by hypoxia-inducible factor 1α were found in the adipose tissue of insulin-resistant individuals with obesity106. Further, obesity and HFD intake both stimulated cytokine release from peripheral and central immune cells108. Saturated fatty acids promote inflammation directly by promoting lipopolysaccharide (LPS) absorption109 and activating macrophages, microglia and astrocytes similarly to LPS by binding to toll-like receptor-4 (TLR4) and binding partners cluster of differentiation 14 and myeloid differentiation factor-2 (MD-2) to prompt receptor internalisation110. Toll-like immune receptors recognise pathogens, trigger nuclear factor-kappa B (NF-κB) signalling, and promote cytokine release111. Macrophages exposed to saturated fatty acids showed direct binding to MD-2 and TLR4, increased NF-κB and MAPK signalling, and interleukin-6 (IL-6) and tumour necrosis factor-α (TNFα) release39. Additionally, TNFα dampened the insulin signal via serine phosphorylation of insulin receptor substrate 1 in adipocytes112 whereas loss-of-function mutations in TNFα and TNFα receptors prevented HFD-induced insulin resistance113. Similarly, TLR4 gene mutation protected against HFD-induced obesity and promoted insulin signalling105. Insulin resistance due to inflammation is one key mediator of HFD-induced alterations to dopamine.Adipose tissue expansion in obesity reduces blood flow to adipocytes to induce hypoxia and release of cytokines causing local and systemic inflammation114. Additionally, inflammatory cytokines and reactive oxygen species reduced availability of the cofactor tetrahydrobiopterin required by TH for dopamine synthesis115. Further, systemic administration of proinflammatory cytokines IL-6 and IL-2 decreased NAc extracellular dopamine, though IL-1β had no effects116. Inflammatory processes may lower synaptic dopamine and alter feeding behaviours. Indeed, TLR4 knockout (KO) mice exhibited reduced preference for fat and sugar and attenuated WD-induced food intake, weight gain and palatable food preference117. Additionally, saturated HFD but not monounsaturated fat increased anxiety and depressive behaviour in conjunction with increased plasma cytokines and NAc cytokine and NF-κB transcriptional activity associated with heightened expression of microglial and astrocytic markers118. Further, intake of a free-choice cafeteria diet altered morphology of NAc MSNs and increased proinflammatory cytokine expression related to microglial activation, whereas microglial inhibition restored these effects and prevented diet-induced intake and weight gain119. Together, saturated fat and obesity may promote inflammation and insulin resistance that decrease dopamine synthesis, vesicular packaging, and capacity for dopamine release and reuptake. However, we posit that HFD-induced neuroinflammation uniquely promotes insulin resistance within the NAc as a primary driver of reduced reward value that promotes overconsumption of palatable foods, but a major gap involves lack of pharmacological investigation assessing interactions between insulin, LPS, inflammatory cytokines and microglial activation on behaviour and presynaptic dopamine neurotransmission in the NAc after chronic HFD intake during obesity.Inflammatory cytokines decrease dopamine packaging and signal transduction via reduced function and expression of vesicular monoamine transporter 2 and D2R but increase function or expression of DAT, which alters dopamine reuptake120 and diet-induced121 anxiety have been linked to disruptions in dopamine homeostasis. This next section highlights how acute or chronic stress responses alter food intake, discussing the impacts of dietary fat and induction of inflammation on these processes.Stress encompasses a variety of homeostatic disruptions which may be acute or chronic and physiological or psychological in nature. Food intake and body weight can change in response to stress depending on the type, intensity and duration of the stressor and activation of specific stress circuits. As discussed above, the physiological stress of diet-induced inflammation alters dopamine signalling in the NAc. Likewise, psychological stress related to substance use disorders123. Various stressors induce the hypothalamic paraventricular nucleus (PVN) to release corticotrophin-releasing factor (CRF), stimulating the pituitary gland to release adrenocorticotropin (ACTH) that targets adrenal glands to release glucocorticoids (corticosterone in rodents) and promote stress responses124. The hypothalamus also activates sympathetic neurons in response to stress that induce adrenal release of epinephrine and NPY to mobilise nutrients via glycogenolysis and gluconeogenesis124. In this context, CRF acutely produces anorexia and weight loss that may last several days124. Indeed, either a single exposure to LPS or acute immobilisation stress reduced food intake and body weight that persisted for over a week125. Human studies support this construct, as the most stressful event of each day increased the likelihood of eating less126, which was mediated by delayed digestive processes induced by acute stress127. Acute stress in healthy adults lacking comorbidities and stressors related to obesity appear to slow digestion and reduce food intake. However, induction of inflammation during saturated HFD consumption could interact with classical stress systems to promote effects of stress on reward pathways. For example, intraperitoneal LPS injection increased plasma ACTH and corticosterone and hypothalamic/pituitary proinflammatory cytokines in CRF KO and wild type mice128. Reciprocally, CRF promoted cortical microglial proliferation and dose-dependently increased TNFα release mediated by phosphorylation of MAPK intracellular signalling proteins shared by TLR4 activation129. Therefore, HPA axis and proinflammatory molecules engage in intracellular crosstalk and can independently promote stress. Acutely, stress mobilises nutrients and dampens food intake, but these behavioural effects might be altered during up-regulated inflammatory signalling. Overall, effects of HFD intake on the interactions between inflammatory and stress systems in the NAc have not been tested to determine effects on dopamine or behaviour during obesity or diet-related stressors  stress axis in a coordinated effort with metabolic, immune, autonomic nervous, and digestive systems to increase breathing and heart rate but slow digestion in preparation for ‘fight or flight’ responsetressors .131. This shows NAc dopamine neurotransmission is involved in learning behaviours to avoid negative stimuli. Stress may directly induce dopamine activity, as CRF dose-dependently increased VTA dopamine neuron firing which was abolished by antagonising CRF receptor 1132, and CRF may activate both VTA GABA and dopamine neurons133. However, pharmacological activation of the HPA axis increased progressive ratio response in HFD but not chow-fed groups, which was reversed by antagonising CRF receptor 1134. Importantly, acute stress does not occur in isolation, and up-regulation of inflammatory signalling by HFD consumption could alter effects of acute stress. For example, LPS application 24 h after acute, inescapable tail shock promoted hippocampal NF-κB, TNFα, IL-6 and IL-1β gene expression, corticosterone, and microglial NF-κB and IL-1β responsiveness that was fully attenuated when TLR2, TLR4135 and glucocorticoid receptors136 were blocked during stress, and this stress-induced priming of neuroinflammation was mediated by transition of microglia, but not astrocytes, from a quiescent to an active state137. There are also sex effects within stress-induced priming of inflammatory microglial activation. The same stress paradigm similarly primed central proinflammatory cytokines, reduced anti-inflammatory pathways, and acutely reduced sucrose intake in males and females138. However, LPS 24 h after tail shock or glucocorticoid injection induced peripheral proinflammatory cytokines and reduced central glucocorticoid receptors in females but induced microglial IL-6 and IL-1β mRNA expression specifically in males138. Overall, specific effects of acute stress on food intake depend on prior exposure to a stressor and complex peripheral and central inflammatory signalling, suggesting those with chronic, diet-induced elevated proinflammatory states might be primed to be more reactive and have alternate behavioural responses to chronic stress, though further study is required explore this interaction in dopaminergic centres that influence food intake, like the NAc.The immediate effects of acute stress are contextual and influenced by environmental factors. Interestingly, NAc dopamine release was increased during cues predicting foot shock, decreased during foot shock, then stimulated after lever pressing to stop the shock139. Furthermore, associating a place or flavour with NPY/AGRP neuron activation reduced preference for that place or food140, and AGRP neuron activity was reduced due to food cues140 and initiation of feeding141. Therefore, negative valence associated with firing of AGRP neurons may provide motivation to perform behaviours that induce positive valence like eating2 which could provide a stress alleviating effect. This is important given obesity-prone rats fed HFD for 6 weeks had greater HPA axis activity with greater central CRF concentrations and plasma corticosterone than chow counterparts142. Therefore, the transition in stress response that occurs over repeated stress exposures could be exacerbated by diet-induced obesity and greater stress reactivity. Acute restraint decreased ARC AGRP expression143 but increased hypothalamic POMC expression in conjunction with reduced food intake144. Conversely, chronic restraint increased LH AGRP and decreased ARC melanocortin 4 receptor143 that responds to POMC. This showed unique activation of hypothalamic anorectic versus orexigenic signalling, respectively, in response to acute versus chronic stress. Diet-induced inflammation may mediate these effects, as acute lipid infusion or palmitate exposure in AGRP/NPY neurons increased TNFα, proinflammatory cytokines and NPY expression145. Conversely, 8-week HFD consumption reduced TNFα and AGRP expression while 20-week consumption promoted TNFα in NPY and AGRP neurons145. This suggests palatable foods acutely induce hypothalamic inflammation in regions that innervate the NAc followed by up-regulation of protective mechanisms which are eventually overcome with extended HFD intake, and dietary responses to chronic stress likely depend on inflammatory status related to food composition and availability associated with responsivity of NPY/AGRP and POMC/CART neurons and NAc dopamine cue learning.Chronic, repeated stressors may affect food intake and body weight differently than acute stress, particularly in an obesogenic state which may promote socio-behavioural and physiological stressors. Effects of stress on hypothalamic nuclei may override homeostatic feeding. For example, inescapable foot shock engaged the HPA axis in addition to increased NPY but decreased AGRP expression, and α-melanocyte stimulating hormone released by ARC POMC/CART neurons increased stress-induced HPA activation146 and adipose tissue lipolysis147 during stress. However, these effects over an extended period could lead to hyperinsulinemia148, hyperleptinemia149, and insulin150 and leptin resistance. Indeed, 7 d of artificial glucocorticoid administration in healthy women increased food intake, plasma insulin and plasma leptin151, while exercise decreased cortisol response to stress (i.e. cortisol reactivity) accompanied by reduced disordered eating152. Maniam and Morris153 proposed glucocorticoid-induced stimulation of the dopamine system provides motivation to resolve stressful situations which may be ‘hijacked’ into motivation to obtain palatable foods. For example, in samples of women, presence of obesity increased cortisol reactivity154 which was associated with increased calorie consumption155. Downstream, glucocorticoids promote feeding by negatively regulating CRF and activating orexigenic NPY neurons153 that are inhibited by insulin and leptin and interconnected with the LH, NAc and VTA2. Therefore, during diet-induced obesity, chronic stress promotes glucocorticoid-induced activation of feeding centres and removes the stop signal from hypothalamus to VTA to promote dopamine-motivated feeding. It is important to note that glucocorticoids are involved in normal feeding responses, as restoring corticosterone to adrenalectomised rats increased saccharin156 and sucrose157 intake to control levels. On the other hand, access to lard blunted corticosterone response during restraint stress158. Therefore, diet interacts with glucocorticoids to modulate stress-induced feeding. Indeed, mice exposed to chronic social stress after 12 weeks on a HFD showed improved weight loss, reduced plasma insulin and leptin, and dampened anxio-depressive behaviours versus stressed controls or non-stressed HFD groups159, suggesting a potentially stress-alleviating effect of palatable food intake. Overall, diet-induced obesity produces neurochemical shifts and maladaptive stress responses, but an opportunity for future investigation lies in whether HPA axis and κ-opioid receptor stress responses drive food intake for obese individuals during the chronic diet-related stress of restricting calories for weight loss  to gate NAc dopamine signalling. Indeed, expression of CRF, ACTH and glucocorticoids systemically and centrally are co-regulated with dynorphin, a ligand for KORs, and its precursor prodynorphin with ligands in either system inducing expression and release reciprocally to promote stress responses177. Further, chronic intake of saturated HFD versus low fat178 or monounsaturated fat118 increased anxio-depressive behaviours, plasma corticosterone, and hypothalamic and NAc inflammatory cytokine and TLR4 expression. Conversely, prodynorphin overexpression promoted anti-inflammatory M2 versus M1 phenotype of hippocampal microglia and inhibited LPS-induced TLR4 activation of NF-κB to promote IL-4 and IL-10 but inhibit IL-1β and IL-6 release179. Reciprocally, intraplantar injection of IL-1β increased KOR mRNA expression in basal root ganglia neurons180. However, dynorphin, enkephalin and KOR expression in the NAc were not affected by WD intake or 18-d replacement with chow181 or 18 d of ad libitum or intermittent access to a HFD182. Therefore, while HFD intake itself does not appear to alter dynorphin/KOR expression, it appears that KORs dampen proinflammatory responses induced by saturated fat and could engage in intracellular crosstalk with inflammatory molecules similarly to the HPA axis. Human neuroimaging studies linked a dopaminergic response to the consumption of palatable food183, palatable food cues29 or smelling food odours184 with increased reports of pleasure184 and wanting29 of food. Therefore, changing a lifetime of food preferences by restricting energy intake to lose weight may be particularly stressful and activate stress pathways that dampen dopamine signalling.Neuroinflammation interacts with stress and reward systems. Indeed, exposing women to stress induced plasma IL-6 concentrations that correlated with reduced NAc activation during an fMRI reward learning task181. Therefore, it is possible that differences in KOR expression become neutralised over 18 d of diet replacement or by diet-induced weight gain, or that specifically KOR function could be up-regulated. HFD intake alters food preferences, as replacement of a WD45 or HFD8 with chow persistently reduced food intake and acutely increased plasma corticosterone and anxiety-like behaviour8. Further, whereas HFD intake promoted sucrose anhedonia185, palatable food removal increased preference for sucrose185 and motivation to obtain sucrose45 and fat8 that occurred as soon 3 d8 after diet removal but persisted for several weeks185. This indicates prolonged enhancement of salience for palatable food when a preferred diet is not available, and it is possible that KORs, which contribute to the rewarding properties of food, promote this effect. Further, HFD intake started at parturition reduced VTA TH and NAc D1R and D2R expression in male and female mice, but 4 weeks of HFD replacement exacerbated this reduction in the VTA of females and NAc of males but oppositely restored these effects in the VTA of males and NAc of females48. However, when HFD is introduced in adolescence, HFD replacement does not recover D2R expression in females with strong promotion of palatable food intake, though effects were attenuated during adult-onset HFD185. This suggests prolonged effects of a HFD on dopamine synthesis and signalling in the VTA alter the capacity for neurotransmission in the NAc. Changes in NAc neurotransmission occur via alterations to D1Rs and D2Rs resulting in increased salience of palatable foods when they are no longer available. These effects were significantly mediated by sex and age of palatable food introduction with high vulnerability during adolescence. Overall, individuals with clinical obesity or that persistently consume a HFD may be poorly adapted to control palatable food cravings during energy restriction owing to diet-induced or psychological stress that engages the HPA or KOR systems.In support of KOR system involvement during palatable food restriction, pair feeding of WD to chow intake levels in controls reduced NAc KOR expression in absence of obesity, but 18 d of WD replacement with chow did not alter KOR expressionNAc dopamine neurotransmission is affected by physiological state and access to highly palatable foods that promote obesity, inflammation and hormonal resistance. Chronic engagement of the HPA axis and KOR stress systems by repeated exposures to daily life stressors interact with the inflammatory and hormonal systems disrupted during obesity. Ultimately, palatable food intake acutely increases dopamine release and reuptake, but extended HFD intake reduces the capacity for dopamine neurotransmission. These trends are related to the physiological consequences of obesity that together promote the vulnerability to overeat in order to promote synaptic dopamine to combat obesity-related dysphoria and reduction of dopamine tone. The effect of dietary fat on dopamine’s influence overfeeding could be exacerbated by stress due to dietary restriction or removal of preferred foods. However, pharmacologically targeting receptors that mediate stress in the brain, like KORs, or controlling diet-induced inflammation that engages in crosstalk with KOR system-mediated stress may improve the success of weight loss interventions by attenuating the impact of stress on the dopamine system. Moreover, the dampening of dopamine neurotransmission by long-term consumption of a HFD, specifically high in saturated fat, could be potentiated by inflammation acting on dopamine neurons, which could heighten stress responses that further attenuate dopamine signalling. The result could be an increase in palatable food seeking and consumption to curb potential negative affect with an acute but transient increase in dopaminergic signalling.


Glioblastoma is one of the central nervous system most aggressive and lethal cancers with poor overall survival rate. Systemic treatment of glioblastoma remains the most challenging aspect due to the low permeability of the blood-brain barrier (BBB) and blood-tumor barrier (BTB), limiting therapeutics extravasation mainly in the core tumor as well as in its surrounding invading areas. It is now possible to overcome these barriers by using low-intensity focused ultrasound (LIFU) together with intravenously administered oscillating microbubbles (MBs). LIFU is a non-invasive technique using converging ultrasound waves which can alter the permeability of BBB/BTB to drug delivery in a specific brain/tumor region. This emerging technique has proven to be both safe and repeatable without causing injury to the brain parenchyma including neurons and other structures. Furthermore, LIFU is also approved by the FDA to treat essential tremors and Parkinson’s disease. It is currently under clinical trial in patients suffering from glioblastoma as a drug delivery strategy and liquid biopsy for glioblastoma biomarkers. The use of LIFU+MBs is a step-up in the world of drug delivery, where onco-therapeutics of different molecular sizes and weights can be delivered directly into the brain/tumor parenchyma. Initially, several potent drugs targeting glioblastoma were limited to cross the BBB/BTB; however, using LIFU+MBs, diverse therapeutics showed significantly higher uptake, improved tumor control, and overall survival among different species. Here, we highlight the therapeutic approach of LIFU+MBs mediated drug-delivery in the treatment of glioblastoma. Glioblastoma, the most common and primary brain tumor, accounts for more than half of total brain gliomas. It is a lethal and the most aggressive cancer of the central nervous system (CNS) with an overall poor prognosis , 2. AccoThe BBB, which was described over 100 years ago (in 1913 by Goldmann), is known to impede the delivery of macromolecular (>500 Da) up to 100% and micro-molecular drugs up to 98% from the peripheral circulation to the brain, including chemotherapeutics, thereby leaving the most novel and promising therapeutics unexploited –15. DespTo maintain stable and ideal function of the healthy CNS, several demands, such as proper oxygen/nutrients supply and precise regulation of cerebral blood flow, are maintained by three known barriers  , 20. TheThe NVU has a unique way of controlling the movement of hormones, ions, molecules, or cells either inside or out of the CNS which are very specifically regulated and referred to as the ‘gatekeeper’ for the CNS , 22. ECsOn the abluminal side, pericytes play a significant role in the regulation of vascular function  as well as expression of transporters, where its deficiency (in mice) displayed increased BBB permeability to both low and high molecular weight (MW) substances , 20, 22.On the other hand, the barrier between the newly synthesized microvasculature in a tumor microenvironment and its adjacent expanding tumor, which is formed due to the increased nutritional/oxygen demands, is termed the blood-tumor barrier (BTB) . While tBox 1Efflux transportersHeterogeneity of BTBEdemaIncreased interstitial pressureDense extracellular matrixDespite being characterized as leaky, the BTB has the equivalent capability of restricting drugs to variable extents, ranging from the core of the tumor to its periphery, where the BTB has the highest permeability compared to the latter , 15. Thivia mechanical effects) by their additive power at the focus point , is one of the basic methods extensively utilized for diagnostic imaging and therapeutic purposes in our daily lives . Besidesus point , 18. In us point . During us point . Unlike us point , 18. Appus point , 19. Theus point Box 2 ovus point .Box 2ImmediateNon-invasive (incisionless)PreciseRepeatableControllableReversibleSafeMinimal temperature increaseBBB/BTB disruption mechanism after applying LIFU+MBs is still debatable, where a combination of mechanical and functional changes Box 3 apBox 3Stress on ECsStretching of cerebral blood vesselsElevation of EC temperatureOpening of TJsAltered protein expressions in ECsIncrease in trans-endothelial fenestrationsIncreased formation of caveolaeRegulation of TJ integrated adhesion molecules Release of α2-macroglobulin by NVUDecrease of P-glycoprotein (drug efflux transporter)Ca  after LIFU+MBs application in gliomas, which plays an essential role in transcellular permeability in BBB/BTB (The constantly changing morphology (shape and size) and the oscillation of the MBs in the cerebral microvasculature during LIFU application causes fluid streaming around them, which in turn results in shear and circumferential stresses being exerted on blood vessel walls  , 34, 35. BBB/BTB . The adv BBB/BTB , 42. Per BBB/BTB . Figurevia ultrasound Duty cycleMicrobubble type, size, dosage and contentCranium impedanceType of transducerMechanical indexCavitation indexPresence of hairMechanical IndexCavitation IndexPresence of hairIn addition, acoustic pressure (measured in MPa), the ultrasound frequency used (measured in Hz), pulse duration (measured in ms), burst pulse repetition frequency (measured in Hz), duty cycle (percentage time the ultrasound is on), exposure duration (measured in minutes), MB type, size, dosage, and content are all parameters that play equivalent roles that can influence the permeability of BBB/BTB , 27, 34.99mTc-DTPA (in vivo imaging or other different ways of imaging techniques under the microscope (18F]2-fluoro-2-deoxy-sorbitol (18FDS) has also been proposed for PET imaging , the permeability from the extravascular-extracellular space (EES) toward capillaries can also be monitored, where the imbalance in the Ktrans/Kep ratio reveals the possibility of enhanced drug retention in the EES for drug delivery ]. These observations were similar for glioblastoma in the cerebrum as well as in brainstem gliomas  as shown in There are several observations that have been noted following LIFU+MBs mediated drug-delivery. By far, the most obvious one is a significant increase in the amount of therapeutics being delivered to the targeted brain/tumor parenchyma as compared to its non-sonicated controls [measured commonly by Many authors have investigated the safety of LIFU+MBs mediated BBB/BTB disruption protocol in preclinical studies with no long-term complication reported to date , 85. WitThe number of sessions that LIFU+MBs is applied has no effect on survival times. The latter is similar for both a single or double course  without via elevation of calcium-binding adaptor molecule 1 (Iba1) while astrocytic activation via increased glial fibrillary acidic protein (GFAP)  , 61. Altn (GFAP)  and evenn (GFAP) , it is un (GFAP)  and reson (GFAP) , 99, 102n (GFAP) , which in (GFAP) , 103, inn (GFAP) , 91. A pn (GFAP) . In addin (GFAP) . These rThe brain, which was considered to lack immunity since the early 20th century, has now contradictory evidence to prove the trigger of the innate and cellular immune response following sonication . As discThe BBB/BTB hinders the influx of substances into the CNS while simultaneously hindering the egress of tumor biomarkers into the peripheral circulation . This poIn the early days, delivering FUS to the brain required the removal of the skull which limited this procedure to be performed solely in the operating room . The mulvia a stereotactic frame and the space in between is eliminated using degassed water to minimize ultrasound attenuation , NaviFUS , and SonoCloud-9  which utilizes either implantable US devices or extracranially applied FUS devices to open the BBB/BTB with millimeter precisions , 91. Theenuation . Pre-treenuation . Furtherenuation , 108. Afenuation , 34, 56.The use of LIFU mediated BBB/BTB disruption has been exploited in the clinic, which was well-tolerated even after the delivery of Car, TMZ, Dox, and fluorescein , 110–113in vitro are not often reflected in vivo. Therefore, this novel technique opens the door to all those therapeutics that were previously considered not feasible or safe for further in-depth investigations. The different established existing methods such as paracellular, transcellular, transport proteins, efflux proteins, receptor-mediated transcytosis, adsorptive transcytosis, and cell mediated transcytosis pathways Liquid biopsies (where it can help to pre-establish molecular profiling of specific glioblastomas for tailor-made treatment strategies)Box 6MRgFUSMR thermometryNeuro-navigationReal time MBs activity monitoring (passive cavitation detection)MBs evolution In vivo imagingAblation of MBs to produce reactive oxygen speciesOf particular note, concerns relating to safety are aspects that should be thoroughly investigated. With that said, when opening the BBB/BTB, special attention should be given to the dosage of therapeutics, as there is a possibility of considerable amount of drug accumulation giving rise to adverse effects Box 7. TBox 7Microhemorrhage or macrohemorrhage (RBC extravasation)Inflammatory responseImmune cells infiltrationDamaged neuronsToxicity (due to excess drug delivery)RM wrote the original draft. RZ and YT supervised the project. BW, JZ, and XC contributed to major revisions to the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by National S&T Major Project (2018ZX10301201) and NSFC (82027803).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children. Although the involvement of dopamine in this disorder seems to be established, the nature of dopaminergic dysfunction remains controversial. The purpose of this study was to test whether the key response characteristics of ADHD could be simulated by a mechanistic model that combines a decrease in tonic dopaminergic activity with an increase in phasic responses in cortical-striatal loops during learning reinforcement. To this end, we combined a dynamic model of dopamine with a neurocomputational model of the basal ganglia with multiple action channels. We also included a dynamic model of tonic and phasic dopamine release and control, and a learning procedure driven by tonic and phasic dopamine levels. In the model, the dopamine imbalance is the result of impaired presynaptic regulation of dopamine at the terminal level. Using this model, virtual individuals from a dopamine imbalance group and a control group were trained to associate four stimuli with four actions with fully informative reinforcement feedback. In a second phase, they were tested without feedback. Subjects in the dopamine imbalance group showed poorer performance with more variable reaction times due to the presence of fast and very slow responses, difficulty in choosing between stimuli even when they were of high intensity, and greater sensitivity to noise. Learning history was also significantly more variable in the dopamine imbalance group, explaining 75% of the variability in reaction time using quadratic regression. The response profile of the virtual subjects varied as a function of the learning history variability index to produce increasingly severe impairment, beginning with an increase in response variability alone, then accumulating a decrease in performance and finally a learning deficit. Although ADHD is certainly a heterogeneous disorder, these results suggest that typical features of ADHD can be explained by a phasic/tonic imbalance in dopaminergic activity alone. Attention Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder characterized by pervasive inattention, impulsivity, and restlessness that is inconsistent with the patient's age  and regulated by prefrontal cortical afferents, or transient (phasic), caused by bursts of firing of dopaminergic neurons Grace, . The dynAs an alternative we here tested the plausibility of a model that combines a decrease in tonic dopamine activity with an increase in phasic responses Grace, . In GracIn the present study, we used a mechanistic model of the basal ganglia dopaminergic system that we previously developed to help rationally improve pharmacological interventions in Parkinson's disease  and (2), where CDA(t) is the dopamine concentration (μM/L) in the synaptic cleft and AR(t) the autoreceptors occupancy.The remaining dopamine molecules can bind to dopaminergic autoreceptors located on the presynaptic neurons or to receptors on the postsynaptic neurons. In the present work, only dopaminergic receptors As indicated in Equation (1), the release of dopamine is divided into two terms to account for both tonic and phasic release. The recapture by DATs is a saturable process described by a Michaelis-Menten equation. All other mechanisms contributing to dopamine removal are assumed to be linear , the extracellular volume fraction (αvf) and the Avogadros constant (NA) parameters are not modified by autoreceptors occupancy. Since vesicular release probability (phasic) are decreased by autoreceptors . This issue will be discussed in more details in Section 2.3.The terminal density (ρ), the number of molecules released per vesicles fusion . According to Equations (1) and (3), this can be simulated in the model assuming υCDA = 0, which is close to the duration of dopamine neuron activity suppression after the absence of an expected reward  and (8). Setting the dopamine concentration at zero instantaneously when a punishment occurs is a simplification of the physiologic mechanisms and the pause in the firing rate was defined as in Dreyer et al. . It could be argued that, due to diffusion, only a fraction of phasic dopamine reaches autoreceptors and thus alters the release. Simulations were performed to integrate this concentration gradient on phasic dopamine reaching autoreceptors, but the results were not significantly different (not shown here), therefore the version presented here was chosen for simplicity.D1 and D2 receptors in the current work. The occupancy of receptors of type i ∈ {1, 2} in time is given by the following equation:Finally, dopamine molecules can bind to dopaminergic receptors, corresponding to i receptors, respectively. Receptors occupancy will be used in the neurocomputational model of basal ganglia as the postsynaptic effect of dopamine on the neurons in the different neurotransmission pathways . From a mathematical standpoint, the parameter km could also have been decreased to obtain similar results.Using the developed model, two groups of virtual individuals were created: control and dopamine imbalance individuals. The difference between the two groups lies in the modification of the Tonic and phasic dopamine are coding prediction error signals in the basal ganglia Schultz, . ADHD isS. The neuronal activities are normalized to obtain a value between 0 and 1. The connection between each region follows three neurotransmission pathways: direct, indirect and hyperdirect. The direct pathway promotes movement, the indirect inhibits it, and the hyperdirect pathway suppresses erroneous movements. D1 and D2 receptors occupancy have an excitatory effect in the direct pathway and an inhibitory effect in the indirect pathway, respectively. Both pathways are potentiated by the effect of cholinergic interneurons, also included in the model.The neurocomputational model presented here is an adaptation from the model developed in Baston et al. . It invoA representation of the neurocomputational model of basal ganglia is given in wij, where i and j are the postsynaptic and presynaptic regions, respectively. The values of these weights can be modified by the learning process. For simplicity, only matrices related to striatum, wGS, wNS, wGC, wNC, were considered to be plastic; these connections are represented by dashed lines in wGC and wNC are diagonal while wGS and wNS are full matrices. At the beginning of the learning process, these weight matrices are in a naive state, with no differentiation between the actions channels. Here are the initial value of the matrices:Impairments in reinforcement learning are thought to be involved in ADHD  at the same position of the rewarded action, and a smaller value at the positions of the punished actions, just to simplify the final analysis of the synapses. An input vector with different dimensions and with different values could be used as well, resulting in a more complex pattern of synapses. The idea here is to simply associate an input vector to a “winner takes all” output vector, considered as the selected response. The possible considered vectors for S are We here give the details of a typical trial of the learning process. A stimulus representation S, a reward prediction error of 1 is attributed. If however the second highest value (0.2) is chosen, a smaller reward prediction error of 0.1 is attributed. A punishment prediction error is given when the lowest value (0.1) is chosen. Rewards prediction errors are signaled by phasic dopamine peaks governed by Equation (6). When a punishment prediction error occurs, dopamine concentration drops to zero. This is equivalent to providing the virtual subjects with rewards and punishments, but we delivered directly the reward/punishment prediction error dopamine signals. These prediction errors are the differences between received and predicted rewards  or N (NoGo) in the same figure, and ΔwAB is the synaptic change computed at that step. Each row in these matrices represent the synapses entering the postsynaptic neuron, and each column those emerging from the presynaptic one. Hence, all matrices have 4 × 4 dimensions in the work presented here. This modification of the synaptic weights happens once every epoch between a latency period of 0.1s and for a duration of 0.05s once an action is chosen. The latency and duration are the same as the ones for the reward/punishment error prediction signal. The individual elements at position ij in the array ΔwAB are computed through the following equation (Hebb rule):where j of the region B, i of the region A and ϑpresynaptic, ϑpostsynaptic the pre- and postsynaptic thresholds. The positive part function (+) ensures that learning occurs only if the presynaptic neurons are excited and their activity is above the threshold. Dopamine is thought to have the ability to modulate synaptic plasticity, although the exact relationship does not seem to be established , only the elements with i = j are trained, compared to non-diagonal matrices wGS and wNS where all elements are trained. The gain parameter ϕ is proportional to the reward prediction error since, for example, a large reward prediction error will lead to a larger variation in the synaptic value than a small reward prediction error. The gain parameter is also proportional to the ratio of phasic peak and tonic dopamine. This ratio is calculated beforehand and considered as a constant. The equation is the following:where The dopamine ratio is higher in the dopamine imbalance group  compared to the control one , so the gain parameter ϕ is higher.Vmax. A higher rate of dopamine recapture is expected to lower the dopamine tonic concentration which in turn is expected to increase the phasic dopamine concentration, and thus in the tonic phasic dopamine ratio, through a lower occupancy of autoreceptor. The steps of the learning procedure of a subject are summarized below.The control and dopamine imbalance groups, with 10 virtual subjects each, were created with the model. The only difference between the two groups is in the value of wGS, wNS, wGC, wNC start in a naive state.The synaptic weight matrices S in sent to the cortex for 800 ms. The process will be repeated for the other 3 stimuli in a random order. Noise was added in the cortex, derived from a uniform distribution and ranging from 0 to 0.2. The seed of the noise differentiates between individuals within a group but not between groups, while the Vmax value differentiates between the two groups. For example, the control individual #1 has the same noise's seed as dopamine imbalance individual #1, but a different value of Vmax. At the end of the 800 ms, the subject receives either a large or a small reward prediction error signal according to his choice of the action that corresponds to the highest or the second strongest stimulus, respectively. Otherwise, the patient receives a punishment prediction error signal. Transient peaks of phasic dopamine are given accordingly and the Hebb rule is applied to modify the value of synapses. This process is repeated with the three other choices of S.Out of the four choices .During the test phase, there is always a response after a stimulus, being a success or a failure. The number of correct answers or successes represents the performance of the virtual individuals. Each individual is presented 100 stimuli. The mean and standard deviation of the percentage of successes and of the reaction times are computed in each simulated group. Stimulus of different amplitudes were also sent in the first action channel and the responses were recorded to study the differentiation between weak and strong signals. In order to compare the ability of differentiating between weak and strong signals, we repeated the task and computed the cortex activity for different values of noise added to the input signal  in the control group, but 20% higher in the dopamine imbalance group, with an average of 85.5 (SD = 67.8), excluding those who never reached the criterion.During the training phase, we computed the number of trials to obtain 5 successful responses over 10 successive trials. All participants in the normal group reached the learning criterion, but 2 participants in the dopamine imbalance group failed to do so even after 1,000 trials. The number of trials to reach criterion was on average 65.1  while the μ parameter was 0.82 times smaller (118 vs. 144).S. A series of 100 stimuli was again presented with noise directly added to the stimulus representation in the cortex S, with a mean of 1 and a standard deviation ranging from 0 to 1. As the standard deviation of the noise increases, the probability of having high intensity noise increases which further complicates decision making for the virtual patients and therefore affects the percentage of successes. We assessed the performance of the individuals in each group described in the above section by increasing the standard deviation of the noise added to the input signal During the test phase, we also computed the output activity in the cortex related to the response as a function of the input value of the stimulus. A stimulus of different amplitudes, ranging from 0.1 to 1, is sent in the first action channel while all three other channels receive noise of small amplitude. The mean, the 5th and the 95th output curves of the cortex neuronal activity in the first action channel as a function of the input signal value for each group are shown in By comparing neural activity at basal ganglia input and output, it is clear that in control subjects, the basal ganglia have a high neural gain. Response-related activity is suppressed until stimulus-related cortical activity reaches 0.5 in the control group. Output activity then increases rapidly for an input between 0.5 and 0.7 at which point it remains maximal. In contrast, in the dopamine imbalance group, activity is suppressed up to an input of 0.4, after which the gain increases rapidly but only for stimulus-related activity between 0.4 and 0.5. For stimulus-related activity values between 0.5 and 1, the gain is strongly attenuated as response-related activity increases from 4.5 to 7. However, the most striking aspect of the gain is the extreme variability of the output in the dopamine imbalance group, which ranges from 0 to 1 in response to stimulus-related activity values between 0.7 and 1. In this group, some individuals respond correctly and others have wrong responses which will lead to an output activity close to zero due to the winner-takes-all dynamic, thus inducing high variability. In contrast, in the controls, the variability is almost zero, except for the amplification phase, especially around the inflection point.wGS, wNS (stimulus-related synaptic weights) and wGC, wNC (response-related synaptic weights). These matrices start in a naive configuration, with no differentiation between the four action channels. They are modified during the training by using the Hebb Rule, with a gain parameter that is proportional to the phasic vs. tonic dopamine ratio.Four synaptic weights matrices were modified during training: Over the course of the 1,000 trials in the training phase, the matrix weights changed differently between the two groups, and between individual subjects within each group. Indeed, the trends of synaptic weight evolution were the same for the control and dopamine imbalance groups, but inter-individual differences in synaptic weights and their evolution during learning were much larger in the dopamine imbalance group. Hence, inter-individual differences were much larger at the end of the learning phase in the dopamine imbalance than in the control group. More details on the evolution of the synaptic weight matrices are given in the In the present section, a metric is developed to differentiate the performance in the test phase of the dopamine imbalance group from the control one based on their history during the training phase. During the training process, the history of rewards and punishments is stored in a vector with value 1 for a large reward, 0.1 for a small reward, −1 for a punishment and 0 for no response. It is therefore possible to study the history of each individual and to relate it to his performance in the test phase.There seems to be an initial phase in which there is an excess of errors. The virtual individuals start in a naive state, meaning no differentiation between the action channels. Hence, the initial responses have a random success rate of 25% and can lead to an excess of errors. In the second phase (> 500 epoch), rewards prediction errors dominate over punishments for all actions.weighted std) for the cumulative sum of history was computed for each individual, and expressed by the following equations:Individuals from the control group seem to learn each action in a proportional way for all action channels. The individuals in the dopamine imbalance group had a higher number of rewards for some action channels at the expense of the others. In order to quantify the inter-individual differences in learning, a weighted standard deviation  the cumulative sum of history vector for action j at epoch i and mean(i) is the mean of cumulative history at epoch i for all action channels. The standard deviation of the history (stdhistory) is weighted by a ratio to take into account the fact that the cumulative sum of history is either positive or negative. The ratio is the sum of negative cumulative sum of history divided by the sum of positive cumulative sum of history, leading to a larger ratio when the negative cumulative sum exceeds the positive one. Division by 1,000 is for scaling. The weighted stdhistory was larger in the dopamine imbalance group than the control one. In order to assess the relationship between the training and test phase, a plot of the standard deviation of the reaction times as a function of the weighted stdhistory value is depicted in weighted stdhistory and the standard deviation of reaction times were applied to the control group and the imbalance group, respectively. The individuals in the dopamine imbalance group could be divided into three subgroups  along the quadratic regression as seen in weighted stdhistory increases for individuals in group c, the performance further decreased with fewer correct responses, the μ parameters increased, and the σ and τ had intermediate values and were quite similar.where M/L , Vmax or Km could have been modified to obtain similar results. Beyond its density, the functional dynamics of DAT (characterized by its Km) may be altered by other changes  that may themselves be related to genetic mutations. For example, a variable number tandem repeat (VNTR) in the 3' regulatory region of the DAT gene results in two main forms (long 10R and short 9R). The10R form has been found to be associated with ADHD, at least in children and youth . Catechol-O-methyltransferase (COMT) regulates dopamine level by degrading it, mainly in the prefrontal cortex (PFC). COMT haplotypes showed different level of activity , Canada through the PGS-D program. Support was also provided by NSERC-Industrial Chair in Pharmacometrics funded by Novartis, Pfizer and Syneos, as well as FRQNT Projet d'équipe (FN).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Although numerous studies have explored the relationship between selenium intake and thyroid diseases, few epidemiological studies have investigated the association between selenium intake and thyroid hormones. Therefore, we conducted this analysis to investigate the association between dietary selenium intake and thyroid hormones. Our sample included 5,575 adults (age ≥ 20) years from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. Thyroid hormones, including total triiodothyronine (T3), total thyroxine (T4), free T3 (FT3), free T4 (FT4), and thyroid-stimulating hormone (TSH), were detected. Multivariable linear regression models showed that log10-transformed selenium intake (LogSe) was negatively correlated with TT4  and TT4/TT3  in U.S. adults. Besides, additional stratified analyses by sex demonstrated that LogSe was negatively associated with TT4  and TT4/TT3  and positively associated with FT4/TT4  in male adults. Meanwhile, subgroup analysis by iodine status showed that LogSe was negatively associated with TT4 , FT4/FT3  and TT4/TT3  in iodine sufficiency but not in iodine deficiency adults. Our results demonstrated that the increased dietary selenium intake was negatively correlated with TT4 and TT4/TT3 in U.S. adults. Furthermore, the association between dietary selenium intake and thyroid hormones was more pronounced in males and iodine sufficiency adults. T4 and T3 exist in free and binding forms in peripheral blood, and the former enters target cells to exert their biological functions. In addition, T4 can be converted to T3 under the action of type 1 and 2 iodothyronine deiodinases   and thyroxine T4), are present in numerous tissues and play important roles in maintaining the normal development of the brain, heart, and other organs by controlling energy expenditure and thermogenesis, modulating lipid profiles, and maintaining the normal reproductive function, etc . The bio and thyr, are pre1, DIO2) .2O2 by thyroid follicular epithelial cells might damage the normal function of thyroid function .The National Health and Nutrition Examination Surveys (NHANES) was a nationwide and ongoing cross-sectional survey conducted among the non-institutionalized US population. To assemble a sample of participants who were representative of the civilian non-institutionalized U.S. population, a repeated 2-year cycle survey with a complex multistage probability sampling design was used. Detailed information about the survey design and methods has been described elsewhere . The NHAn = 1,758), at the age of 19 years or below , with the missing value of covariates . Also, pregnant women (n = 49) and participants with thyroid disease or thyroid cancer (n = 503) were excluded. Finally, a total of 5,575 participants were included in the present study. These participants represented a weighted population of 73.5 million non-institutionalized US adults. The flowchart of sample selection was presented in Two thousand seven to two thousand eight, 2009–2010, and 2011–2012 NHANES cycles were selected, and a total of 10,548 participants with complete thyroid function data constituted the study sample. We excluded participants under the minimum criteria on dietary recall status , total T3 (TT3), and free T3 (FT3) were determined using a competitive binding immunoenzymatic assay. Serum free T4 (FT4) was determined using a two-step enzyme immunoassay. Serum thyroid-stimulating hormone (TSH) was determined using a 3rd generation, two-site immunoenzymatic (“sandwich”) assay. Meanwhile, the ratios of FT4/FT3 and TT4/TT3 were calculated to reflect the metabolic level of peripheral T4, while the ratios of FT4/TT4 and FT3/TT3 were acquired to reveal the binding level of thyroid hormones with thyroid hormone-binding proteins .In NHANES, dietary intake information was assessed by two reliable 24-h dietary recall interviews. The first dietary recall interview was conducted in the mobile examination center (MEC) and the second interview was conducted by telephone 3–10 days later. The intakes of dietary selenium during the 24-h period prior to the interview were calculated based on the University of Texas Food Intake Analysis System and U.S. Department of Agriculture (USDA) Survey Nutrients Database. Then, the mean value of selenium intake from the two 24-h dietary recall interviews was adopted as the final dietary selenium intake.2)  , sex ma, educati2) , serum c2) , 29, dri2) , urine i2) , and fas2) .2. Serum cotinine was measured using isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry (ID HPLC-APCI MS/MS). Drinking status was ascertained via questionnaires. Urine iodine concentration was detected using inductively coupled plasma dynamic reaction cell mass spectroscopy (ICP-DRC-MS). In addition, fasting time was acquired by questionnaires before blood collection.Demographic data such as age, sex, race, education level, household income, and marital status were collected in an in-home interview. The body weight (kg) and height (m) were measured during mobile physical examination, and the BMI was calculated as BMI = weight/heightIn order to generate nationally representative estimates, SDMVPSU, and SDMVSTRA procedures were used to interpret NHANES's complex survey design, and WTMEC2YR was used to provide weight for all data.The continuous variables were shown as means ± standard deviations, and the categorical variables were presented as counts (percentages). Initially, the Scott–Rao chi-square test was used to compare dietary selenium intake levels of different groups. Then, the one-way analysis of variance (ANOVA) was used to assess FT4, TT4, FT3, TT3, TSH, and ratios of thyroid hormones  differences among different dietary selenium intake groups. Whereafter, two multivariable linear regression models were used to explore the association of dietary selenium intake with levels of FT4, TT4, FT3, TT3, TSH, FT4/FT3, TT4/TT3, FT4/TT4, and FT3/TT3. Due to right skewness, we log10-transformed dietary selenium intake to approximate normality assumptions. Model 1 (unadjusted) did not include any covariates. Model 2 was adjusted for sex, age, race, education level, household income, marital status, BMI, serum cotinine level, drinking, urine iodine concentration, and fasting time. Considering the effects of sex and urine iodine condition on thyroid function indices, we performed subgroup analyses to explore whether the association between dietary selenium intake and thyroid hormones was modified by sex and urine iodine condition. In addition, we carried out several restricted cubic spline (RCS) analyses to explore the non-linear dose-response relationship between dietary selenium intake and thyroid hormones in the whole and subgroup adults, and five knots were placed at the 5th, 25th, 50th, 75th, and 95th percentiles.P-values and confidence intervals (CI) were reported two-sided without adjustment for multiple testing. The p < 0.05 was the significance criterion in the Scott–Rao chi-square test, ANOVA, and RCS analyses. Confidence intervals that do not contain 0 were considered to indicate statistical significance in multivariable linear regression models.All statistical analyses were performed using STATA software (version 16.0), and R software . As shown in In the unadjusted model , dietarySubgroup analyses by sex  showed tMoreover, subgroup analysis by iodine status  showed tP for non-linearity = 0.708) and TT4/TT3 (P for non-linearity = 0.670) of whole adults, TT4 (P for non-linearity = 0.203), TT4/TT3 (P for non-linearity = 0.796), and FT4/TT4 (P for non-linearity = 0.072) of male adults, and TT4 (P for non-linearity = 0.715), FT4/FT3 (P for non-linearity = 0.095), and TT4/TT3 (P for non-linearity =0.663) of iodine-sufficient adults.In addition, we further used the restricted cubic splines to estimate the dose-response relationship between LogSe and thyroid hormones . OverallBased on a nationally representative survey of non-institutionalized US adults, we found inverse correlations between LogSe and TT4 and TT4/TT3 in U.S. adults. LogSe was also negatively associated with FT4/FT3 and positively associated with FT3, FT4/TT4, and FT3/TT3 in U.S. adults. However, these correlations were no longer significant when the model was further adjusted for potential confounders. When the subgroup analysis was performed according to sex, we found that LogSe was negatively correlated with TT4 and TT4/TT3 while positively correlated with FT4/TT4 within male adults. When subgroup analysis was carried out according to urine iodine concentration, we found that LogSe was negatively associated with TT4, FT4/FT3, and TT4/TT3 within iodine sufficiency adults.in vivo and in vitro are needed in future studies.Previous research showed that low selenium status was associated with an increased risk of thyroid disease , 33. HowFurthermore, the present study also found sex differences in the relationship between dietary selenium intake and thyroid hormone levels. For male adults, LogSe was negatively associated with TT4 and TT4/TT3 while positively correlated with FT4/TT4. Previous studies have demonstrated that estrogens could increase iodine uptake, thyroperoxidase activity, thyroglobulin expression, and modulate TSH levels –38. MoreIodine is also an essential micronutrient for the thyroid gland to synthesize thyroid hormones . PreviouSelenium is an essential nutrient element, which is rich in organ meats and seafood, followed by grain, cereals, and dairy products . HoweverThe strength of the present study was that this was the first large population-based study to date to reveal the relationship between dietary selenium intake and thyroid hormones in U.S. adults to the best of our knowledge, and our findings might be a complement to the literature regarding the association between selenium and thyroid health. However, the study also had some limitations. First, as with any cross-sectional study, we cannot ascertain causality between dietary selenium and thyroid hormone. Second, dietary data were collected using two days of 24-h dietary recall survey, which might cause an underestimation or overestimation of diet selenium consumption. Third, the data on dietary selenium supplementation was not assessed due to substantial missing data. Lastly, we excluded 4,973 participants due to missing values for covariates, complete information about dietary selenium intake, or were under the minimum criteria on dietary recall status, which might decrease the generalizability of our results.In conclusion, this study demonstrated that the increased dietary selenium intake was negatively correlated with TT4 and TT4/TT3 in U.S. adults. Furthermore, the association between dietary selenium intake and thyroid hormones was more pronounced in males and iodine sufficiency adults. However, further large-scale prospective studies are needed to confirm these findings in different populations.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.FL and JN contributed to the conception and design of this study. FL, KW, and M-GD performed the statistical analysis. FL, QF, XL, and YY wrote the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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