Reward processing deficits are prominent in major depressive disorder (MDD) and contribute to appetitive phenotypes: hyperphagic (HyperMDD) and hypophagic (HypoMDD). However, few studies have examined neurobiological processes underlying these phenotypes, and whether they stem from aberrant responsivity specific to food or rewards more broadly. This study probed group differences in functional connectivity during anticipation and outcome phases of food vs. monetary rewards across appetitive MDD phenotypes. 14 unmedicated HyperMDDs, 20 unmedicated HypoMDDs, and 36 healthy controls completed food and monetary incentive delay tasks while undergoing fMRI. Psychophysiological interaction analyses examined functional connectivity changes during relevant task conditions/reward processing components: anticipation, successful reward outcome, and failed reward outcomes. Findings revealed dissociable patterns of fronto-striatal and salience network connectivity during anticipatory reward processing among groups, reflecting differential top-down control of food versus monetary cues. HypoMDD was associated with heightened connectivity to food cues and negative connectivity to monetary cues, while HyperMDD exhibited the opposite patterns. MDD groups also differed in connectivity during reward outcome: HypoMDD demonstrated increased functional connectivity for successful food outcome and reduced functional connectivity for successful money outcome whereas HyperMDD demonstrated reduced connectivity during successful food outcome and increased connectivity during successful money reward outcome. For failed reward outcomes, only HyperMDD demonstrated reduced connectivity for failed food outcomes, and increased connectivity for failed money outcomes. These findings suggest that appetitive MDD phenotypes are distinguished by opposing circuitry profiles that bias responsivity toward food versus monetary rewards, patterns that may help guide the development of phenotype- and reward-specific interventions.

Neuroimaging of memory reveals stronger striatal responses to Hits (encoded stimuli called old) vs. Correct Rejections (CRs; lures called new), possibly because remembering old items is rewarding. If so, then Hits should elicit higher valence ratings than CRs regardless of whether the retrieved memories are emotional or neutral. Alternatively, memory may interact with emotion such that while positive and neutral Hits drive valence up, negative Hits drive valence down (relative to negative CRs). We investigated this issue in 47 healthy participants who encoded negative, neutral, and positive pictures, completed a memory test, and then rated the emotions elicited by each picture. Three main analyses were conducted. First, because emotional experience may reflect beliefs more than objective accuracy, we examined valence in relation to perceived oldness, comparing Hits and False Alarms (FAs; lures called old) to Misses (encoded stimuli called new) and CRs. Second, reflecting prior work, we examined valence for Hits, FAs, Misses, and CRs separately. Third, we investigated whether the impact of Hits and CRs on valence varied with confidence. Identical analyses examined arousal ratings. For neutral and positive pictures, valence was higher for Hits vs. CRs. By contrast, for negative pictures, valence was lower for Hits vs. CRs. Arousal was consistently higher for Hits vs. CRs. Positive FAs also consistently elicited increased valence, and effects were stronger for high-confidence memories. The impact of memory on subjective experience thus depends on the emotional nature of the memoranda: retrieving neutral and positive memories feels good, but retrieving negative memories feels bad.

OBJECTIVE:Memory function underlies mental and behavioral health. While the role of the central nervous system (CNS) during episodic memory encoding and retrieval is well researched, the interplay between the CNS and the autonomic nervous system (ANS) during these processes is not. We addressed this gap by analyzing fluctuations in CNS-ANS network coupling during an episodic memory task (EMT). METHODS:Sixty-seven healthy adults completed an EMT consisting of three stages: Encoding, Retrieval 1, and Retrieval 2. From electroencephalogram (EEG) recordings, we estimated time-varying CNS indices via alpha and theta power. Simultaneously, from electrocardiogram (ECG) and seismocardiogram (SCG) recordings, we estimated time-varying parasympathetic and sympathetic indices via cardiac vagal index (CVI) and pre-ejection period, respectively (PEP). Using time delay stability and surrogate data analysis, for each protocol condition, we assessed coverage, the percentage of significant coupling links, over the CVI-Neural and PEP-Neural networks. RESULTS:Median coverage of CVI-Neural and PEP-Neural increased from Baseline to Encoding (+13.2%, $p$<0.001 and +7.9%, $p$<0.001), decreased from Encoding to Retrieval 1 (-5.3%, $p$<0.05 and -5.3%, $p$<0.001), and increased from Retrieval 1 to Retrieval 2 (+5.3%, $p$<0.001 and +5.3%, $p$<0.05). CONCLUSION:This is the first work to integrate EEG, ECG, and SCG time series to elucidate CNS-ANS interactions during an EMT. We demonstrated that healthy adults experienced parallel changes in CNS-parasympathetic and CNS-sympathetic network coverage during the task. SIGNIFICANCE:In the future, measuring neural and cardiomechanical signals to estimate CNS-ANS coupling while probing memory function in clinical populations may help derive new mental health screening biomarkers.

Background: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improved patient outcomes. Sleep/wake behavior has emerged as a potential biomarker for TBI classification, yet the optimal time window in which to identify sleep/wake changes after TBI remains unclear. Methods: We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP $ > 268 \frac{pg}{ml}$). We utilized a deep learning model to identify the impact of time from trauma and duration of data collection on the model's ability to distinguish between TBI-positive (TBI+) and TBI-negative (TBI-) cases. Results: Our analysis showed that sleep/wake data from the first 7 days after TBI most accurately identified TBI. Sleep-wake data from the first 7, 14, and 21 days after trauma achieved sensitivity/specificity of 81%/25%, 40%/66%, and 45%/58%, respectively. F1 scores of deep learning models developed from the first 7, 14, and 21 days were 22%, 21%, and 20%, respectively. Conclusions: The results suggest that early sleep/wake data has promise for assisting with TBI identification. Significance: In the future, the incorporation of sleep/wake derived biomarkers into TBI identification tools could assist in the identification of individuals with potential TBI for further screening and intervention.

Depression is a prevalent psychiatric condition that commonly emerges in adolescence and young adulthood and is associated with reward processing abnormalities. The Probabilistic Reward Task (PRT) is widely used to investigate the impact of depression on reward processing, but prior studies have not comprehensively addressed the reinforcement learning and decision-making mechanisms involved in the task. In 726 adolescents and young adults with varying levels of depression, we collected PRT data and applied a novel computational model with response-outcome learning and evidence accumulation processes to provide new insights into the cognitive processes implicated in depression. Compared to participants with no history of psychopathology, those with depressive disorders showed reduced impact of learned response values on decision bias toward the more frequently rewarded action. In addition, higher levels of anhedonia were associated with slower evidence accumulation during decision-making. Together, these findings improved our understanding of the reinforcement learning and decision-making mechanisms assessed by the PRT and their associations with depression.

Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed – in heterogeneous samples of trauma-exposed and trauma naive individuals – to determine if such a neural signature is a robust – and potentially a pretrauma – marker of vulnerability. We analyzed data from the ENIGMA-PTSD dataset (n = 2,814) and the Human Connectome Project – Young Adult (HCP-YA) dataset (n = 890) to investigate whether structural covariance of early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress. Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms and no association was observed between loadings and perceived stress measures in the HCP-YA dataset. Structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.

BACKGROUND:Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests that the structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed in heterogeneous samples of trauma-exposed and trauma-naïve individuals to determine whether such a neural signature is a robust marker of vulnerability. METHODS:We analyzed data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-PTSD dataset (N = 2814) and the HCP-YA (Human Connectome Project-Young Adult) dataset (N = 890) to investigate whether the structural covariance of the early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress. RESULTS:Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms, and no association was observed between loadings and perceived stress measures in the HCP-YA dataset. CONCLUSIONS:The structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.

Cognitive flexibility broadly describes behavioral alterations made in response to environmental changes and is fundamental for survival. While human and non-human animal assessments of cognitive flexibility are available, a systematic cross-species comparison of behavioral, neurophysiological, and computational markers of cognitive flexibility has not been reported. Using versions of a probabilistic reversal learning task aligned between humans and rats, electroencephalogram recordings reveal a frontal reward positivity (RewP) associated with unexpected reward outcomes. Reinforcement Q-learning models of both species' task behavior reveal that prediction error (PE) magnitude was significantly related to RewP amplitude. The stimulant drug modafinil alters PEs in rats without affecting the RewP in either species. These findings reveal analogous neurophysiological markers associated with PEs in humans and rats using equivalent tasks and identical computational analyses. This translational approach may improve the predictive validity of tests for novel pharmacotherapies and accelerate neuropsychiatric treatment by assessing neural mechanisms conserved across species.

Major Depressive Disorder (MDD) is associated with emotional memory deficits with substantial downstream consequences, but treatment is limited by a poor understanding of the upstream mechanisms that drive such behavior. Our previous work linked depression to a negative retrieval bias rooted in abnormal evidence accumulation (Cataldo et al., 2023). Computational modeling with the Drift Diffusion Model can account for such a bias in two ways: increased familiarity, in which depression strengthens evidence for all negative memories—even false ones; or motivated retrieval, in which depression increases the propensity to judge all evidence as “old”—even if it is weak. Thus, it is unclear whether depression affects the quality of negative memories or the way they are acted upon, limiting both basic and applied depression research. The current work distinguishes the familiarity vs. motivated retrieval accounts via the Parceling Recognition Into Strength and Motivation (PRISM) task, which isolates memory strength from decision processes by extending single-item recognition behavior to forced choices between targets and lures (Starns et al., 2018). In a sample of 53 community adults ranging in depressive severity, we found that the negative retrieval bias extended across both single-item and forced-choice recognition, thus supporting the false familiarity account. A qualitative analysis of participants’ self-reported strategies further indicated that increased schema use may be an important mechanism. In sum, the current results provide critical evidence that the negative retrieval bias results from disrupted memory representations, and point to schemas as a promising context for studying such representations.

BACKGROUND: The orbitofrontal cortex (OFC) is essential for decision making, and functional disruptions within the OFC are evident in schizophrenia. Postnatal phencyclidine (PCP) administration in rats is a neurodevelopmental manipulation that induces schizophrenia-relevant cognitive impairments. We aimed to determine whether manipulating OFC glutamate cell activity could ameliorate postnatal PCP-induced deficits in decision making. METHODS: Male and female Wistar rats (n = 110) were administered saline or PCP on postnatal days 7, 9, and 11. In adulthood, we expressed YFP (yellow fluorescent protein) (control), ChR2 (channelrhodopsin-2) (activation), or eNpHR 3.0 (enhanced halorhodopsin) (inhibition) in glutamate neurons within the ventromedial OFC (vmOFC). Rats were tested on the probabilistic reversal learning task once daily for 20 days while we manipulated the activity of vmOFC glutamate cells. Behavioral performance was analyzed using a Q-learning computational model of reinforcement learning. RESULTS: Compared with saline-treated rats expressing YFP, PCP-treated rats expressing YFP completed fewer reversals, made fewer win-stay responses, and had lower learning rates. We induced similar performance impairments in saline-treated rats by activating vmOFC glutamate cells (ChR2). Strikingly, PCP-induced performance deficits were ameliorated when the activity of vmOFC glutamate cells was inhibited (halorhodopsin). CONCLUSIONS: Postnatal PCP-induced deficits in decision making are associated with hyperactivity of vmOFC glutamate cells. Thus, normalizing vmOFC activity may represent a potential therapeutic target for decisionmaking deficits in patients with schizophrenia.

The Probabilistic Reward Task (PRT) probes the impact of Major Depressive Disorder (MDD) on reinforcement learning (RL), but MDD also affects decision-making. We acquired PRT data from depressed adults twice, fit the data with decision-making and RL models, and studied model psychometrics.

OBJECTIVE:Heightened reactivity to stress is associated with poor treatment outcome in people with substance use disorders (SUDs). Behavioral strategies can reduce stress reactivity; however, these strategies are understudied in people with SUDs. The objective of this study was to test the effect of two behavioral strategies (cognitive reappraisal and affect labeling) on stress reactivity in people with SUDs. METHOD:Treatment-seeking adults with SUDs (N = 119) were randomized to receive brief training in cognitive reappraisal, affect labeling, or a psychoeducational control, followed by a standardized stress induction. Markers of stress reactivity were collected before and following stress induction and included self-reported negative affect and substance craving, as well as salivary cortisol, and skin conductance response. RESULTS:Analyses of covariance did not indicate a significant effect of treatment condition on negative affect, cortisol, or skin conductance response. Participants in the affect labeling condition had greater increase in craving than those in the cognitive reappraisal condition; neither condition differed from control. CONCLUSIONS:Results indicated that, although participants were able to implement behavioral skills following a brief training, training condition did not modify stress reactivity, on average, relative to control. Future directions include consideration of individual differences in response to training and determination of whether higher "dosing" of skills via multiple sessions or extended practice is needed to influence stress reactivity in people with SUDs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The neurobiological mechanisms underlying the placebo phenomenon in patients with major depressive disorder (MDD) remain largely unknown. The progressive rise in rates of placebo responses within clinical trials over the past two decades may impede the detection of a true signal and thus present a major obstacle in new treatment development. Understanding the mechanisms would have several important implications, including (1) identifying biomarkers of placebo responders (thereby identifying those individuals who could benefit therapeutically from such interventions), (2) opening new avenues for manipulating such mechanisms to maximize symptom reduction, and (3) refining treatments with approaches that decrease (in clinical trials) or increase (in clinical practice) the placebo response. Here we investigated the research question: is the dopaminergic system one of the neurobiological underpinnings of the placebo response within MDD? Inspired by preclinical and clinical findings that have implicated dopamine in the occurrence, prediction, and expectation of reward, we hypothesized that dopaminergic activity in the mesolimbic system is a critical mediator of placebo response in MDD. To test this hypothesis, we designed a double-blind, placebo-controlled, sequential parallel comparison design clinical trial aimed at maximizing placebo antidepressant response. We integrated behavioral, imaging, and hemodynamic probes of mesocorticolimbic dopaminergic pathways within the context of manipulations of psychological constructs previously linked to placebo responses (e.g., expectation of improvement). The aim of this manuscript is to present the rationale of the study design and to demonstrate how a cross-modal methodology may be utilized to investigate the role of reward circuitry in placebo response in MDD.

BACKGROUND: Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach. METHODS: Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks. RESULTS: Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R =-0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R =-0.40, p = .005). CONCLUSIONS: We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.