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Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case‐control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data‐driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case‐control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Visual alterations under classic psychedelics can include rich phenomenological accounts of eyes-closed imagery. Preclinical evidence suggests agonism of the 5-HT2A receptor may reduce synaptic gain to produce psychedelic-induced imagery. However, this has not been investigated in humans. To infer the directed connectivity changes to visual connectivity underlying psychedelic visual imagery in healthy adults, a double-blind, randomised, placebo-controlled, cross-over study was performed, and dynamic causal modelling was applied to the resting state eyes-closed functional MRI scans of 24 subjects after administration of 0.2 mg/kg of the serotonergic psychedelic drug, psilocybin (magic mushrooms), or placebo. The effective connectivity model included the early visual area, fusiform gyrus, intraparietal sulcus, and inferior frontal gyrus. We observed a pattern of increased self-inhibition of both early visual and higher visual-association regions under psilocybin that was consistent with preclinical findings. We also observed a pattern of reduced inhibition from visual-association regions to earlier visual areas that indicated top-down connectivity is enhanced during visual imagery. The results were analysed with behavioural measures taken immediately after the scans, suggesting psilocybin-induced decreased sensitivity to neural inputs is associated with the perception of eyes-closed visual imagery. The findings inform our basic and clinical understanding of visual perception. They reveal neural mechanisms that, by affecting balance, may increase the impact of top-down feedback connectivity on perception, which could contribute to the visual imagery seen with eyes-closed during psychedelic experiences.

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of white matter abnormalities in a large, longitudinal cohort of CHR individuals comprising the North American Prodrome Longitudinal Study (NAPLS-3). Multi-shell diffusion magnetic resonance imaging data were collected across multiple timepoints (1–5 over 1 year) in 286 subjects (aged 12–32 years): 25 CHR individuals who transitioned to psychosis (CHR-P; 61 scans), 205 CHR subjects with unknown transition outcome after the 1-year follow-up period (CHR-U; 596 scans), and 56 healthy controls (195 scans). Linear mixed effects models were fitted to infer the impact of age and illness-onset on variation in the fractional anisotropy of cellular tissue (FAT) and the volume fraction of extracellular free water (FW). Baseline measures of white matter microstructure did not differentiate between HC, CHR-U and CHR-P individuals. However, age trajectories differed between the three groups in line with a developmental effect: CHR-P and CHR-U groups displayed higher FATin adolescence, and 4% lower FATby 30 years of age compared to controls. Furthermore, older CHR-P subjects (20+ years) displayed 4% higher FW in the forceps major (p< 0.05). Prospective analysis in CHR-P did not reveal a significant impact of illness onset on regional FATor FW, suggesting that transition to psychosis is not marked by dramatic change in white matter microstructure. Instead, clinical high risk for psychosis—regardless of transition outcome—is characterized by subtle age-related white matter changes that occur in tandem with development.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Reduced hippocampal volume is an established brain morphological feature of psychiatric conditions. Hippocampal volume is associated with brain connectivity in humans and nonhuman animals, and altered connectivity is associated with risk for psychiatric illness. Associations between hippocampal volume and connectivity are poorly characterized in humans, especially in phases of psychiatric illness that precede disease onset. This study examined associations between hippocampal volume and hippocampal functional connectivity during rest in 141 healthy control participants and 248 individuals at risk for psychosis. Significant inverse associations between hippocampal volume and hippocampal functional connectivity with the inferior parietal lobe (IPL) and thalamus were observed. Select associations between hippocampal functional connectivity and hippocampal volume were moderated by diagnostic group. Significant moderation results shifted from implicating the IPL to the temporal pole after we excluded participants on antipsychotic medication. Considered together, these findings imply that hippocampal functional connectivity with the temporoparietal junction, within a specialized subsystem of the default mode network, is sensitive to hippocampal volume.

The phenotype of schizophrenia, regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness stage, including temporal variation in neurobiology, can be targeted to develop phase-specific clinical assessment, biomarkers, and interventions. We review an earlier model whereby an initial glutamate signaling deficit progresses through different phases of allostatic adaptation, moving from potentially reversible functional abnormalities associated with early psychosis and working memory dysfunction, and ending with difficult-to-reverse structural changes after chronic illness. We integrate this model with evidence of dopaminergic abnormalities, including cortical D1dysfunction, which develop during adolescence. We discuss how this model and a focus on a potential critical window of intervention in the early stages of schizophrenia impact the approach to research design and clinical care. This impact includes stage-specific considerations for symptom assessment as well as genetic, cognitive, and neurophysiological biomarkers. We examine how phase-specific biomarkers of illness phase and brain development can be incorporated into current strategies for large-scale research and clinical programs implementing coordinated specialty care. We highlight working memory and D1dysfunction as early treatment targets that can substantially affect functional outcome.

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

Progressive grey matter loss has been demonstrated among clinical high-risk (CHR) individuals who convert to psychosis, but it is unknown whether these changes occur prior to psychosis onset. Identifying illness-related neurobiological mechanisms that occur prior to conversion is essential for targeted early intervention. Among participants in the third wave of the North American Prodrome Longitudinal Study (NAPLS3), this report investigated if steeper cortical thinning was observable prior to psychosis onset among CHR individuals who ultimately converted (CHR-C) and assessed the shortest possible time interval in which rates of cortical thinning differ between CHR-C, CHR non-converters (CHR-NC), and health controls (HC). 338 CHR-NC, 42 CHR-C, and 62 HC participants (age 19.3±4.2, 44.8% female, 52.5% racial/ethnic minority) completed up to 5 MRI scans across 8 months. Accelerated thinning among CHR-C compared to CHR-NC and HC was observed in multiple prefrontal, temporal, and parietal cortical regions. CHR-NC also exhibited accelerated cortical thinning compared to HC in several of these areas. Greater percent decrease in cortical thickness was observed among CHR-C compared to other groups across 2.9±1.8 months, on average, in several cortical areas. ROC analyses discriminating CHR-C from CHR-NC by percent thickness change in a left hemisphere region of interest, scanner, age, age2, and sex had an AUC of 0.74, with model predictive power driven primarily by percent thickness change. Findings indicate that accelerated cortical thinning precedes psychosis onset and differentiates CHR-C from CHR-NC and HC across short time intervals. Mechanisms underlying cortical thinning may provide novel treatment targets prior to psychosis onset.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen’s d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen’s d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Computing plays a critical role in the biological sciences but faces increasing challenges of scale and complexity. Quantum computing, a computational paradigm exploiting the unique properties of quantum mechanical analogs of classical bits, seeks to address many of these challenges. We discuss the potential for quantum computing to aid in the merging of insights across different areas of biological sciences.

Recent studies have examined the factor structure and associated correlates of three neurofunctional domains, executive function, incentive salience, and negative emotionality in the development and maintenance of alcohol use disorders in clinical samples. The current study sought to replicate and extend prior work by testing this 3‐factor model, utilizing both exact and similar phenotypic measures, as well as novel measures, in a non–treatment‐seeking sample. Self‐report measures of alcohol addiction, impulsivity, behavior, and exposure to early‐life stress were collected as part of baseline assessments for alcohol imaging and pharmacotherapy studies in 335 individuals. Confirmatory factor analysis (CFA) was used to examine model structure and fit. A multiple indicators, multiple causes (MIMIC) model identified predictors of latent factors identified by CFA. Results supported an intercorrelated model with three factors: executive function, incentive salience, and emotionality. All factors were associated with current AUD, and incentive salience was uniquely associated with past 30‐day drinking frequency. MIMIC results identified multiple significant predictors of these latent factors, including history of alcohol use disorder, positive family history of alcohol dependence, earlier age of first drink, and a history of childhood emotional abuse and physical neglect. Our results support an intercorrelated 3‐factor model of neurofunctional domains in alcohol use models, consistent with published findings. Because childhood physical neglect was a significant predictor of all latent factors, these results also highlight the significant negative impact of childhood neglect on later addiction development.

Autism spectrum disorder (ASD) and schizophrenia spectrum disorders (SZ) are characterized by difficulty with social cognition and atypical reception of facial communication—a key area in the Research Domain Criteria framework. To identify areas of overlap and dissociation between ASD and SZ, we review studies of event-related potentials (ERP) to faces across ASD and SZ populations, focusing on ERPs implicated in social perception: P100, N170, N250, and P300. There were many inconsistent findings across studies; however, replication was strongest for delayed N170 latency in ASD and attenuated N170 amplitude in SZ. These results highlight the challenges of replicating research findings in heterogeneous clinical populations and the need for transdiagnostic research that continuously quantifies behavior and neural activity across neurodevelopmental disorders.

Impairments in spatial working memory (sWM) have been well documented in schizophrenia. Here we provide a comprehensive test of a microcircuit model of WM performance in schizophrenia that predicts enhanced effects of increasing delay duration and distractors based on a hypothesized imbalance of excitatory and inhibitory processes.

Alcohol use disorder (AUD) is a chronic, relapsing condition with poor treatment outcomes. Both alcohol craving and negative affect increase alcohol drinking, and—in healthy adults—can be attenuated using cognitive strategies, which rely on the prefrontal cortex (PFC). However, AUD is associated with cognitive impairments and PFC disruptions. Thus, we tested whether individuals with AUD can successfully recruit the PFC to effectively regulate craving and negative emotions, whether neural mechanisms are shared between the two types of regulation, and whether individual differences influence regulation success.

Extensive research indicates that having a positive family history of alcohol use disorder (FHP) and impulsivity are 2 risk factors for problem drinking. To our knowledge, no study has investigated which facets of impulsivity interact with family history to increase risk for problem drinking. The goal of this study was to: (i) examine whether FHP individuals with higher levels of impulsivity are more likely to engage in problematic drinking, and (ii) identify which facets of impulsivity interact with FHP to increase risk for problems. The data consisted of a combined sample of 757 participants (50% female, 73% White, mean age = 32.85, SD = 11.31) drawn from the Transdisciplinary Tobacco Use Research Center and the Center for the Translational Neuroscience of Alcohol. Analyses of covariance and cumulative logistic regression models investigated the association of family history and impulsivity‐related traits with drinking quantity, frequency, and alcohol‐related problems. Models were adjusted for age, sex, race, ethnic group, education level, and data source. Significant interactions between impulsivity and family history were found for measures of alcohol‐related problems. Specifically, there was a stronger positive association of Barratt Impulsiveness Scale (BIS) poor self‐regulation with interpersonal, F(1, 504) = 6.27, p= 0.01, and impulse control alcohol‐related problems, F(1, 504) = 6.00, p= 0.01, among FHP compared to FHN individuals. Main effects of family history and impulsivity on alcohol quantity and frequency of use and problems were also found. These findings suggest that having both a family history of AUD and high BIS poor self‐regulation is more strongly associated with alcohol‐related consequences in the interpersonal and impulse control domains. Given the heterogeneity of impulsivity, these findings highlight the need for additional research to examine which facets of impulsivity are associated with which alcohol outcomes to narrow phenotypic risk for alcohol misuse. This study provides insight into the synergistic effects of impulsivity‐related traits and family history of alcohol use disorder (FHP) on alcohol outcomes. We found partial support for our hypothesis suggesting traits reflecting deficits in self‐control interacted with FHP to increase risk for alcohol problems. Specifically, we found a stronger association between Barratt Impulsiveness (BIS) poor self‐regulation and impulse control alcohol‐related consequences (displayed) and interpersonal alcohol‐related consequences among FHP. These findings suggest prevention efforts should consider the compounding effects of poor self‐regulation and FHP. The shaded regions in the figure represent confidence intervals.

Working memory (WM) is impaired in psychotic disorders and linked to functional outcome. Most neurobiological models emphasize prefrontal cortex (PFC) dysfunction in the etiology of WM impairment. However, WM is composed of multiple processes, including encoding and maintenance, and the delineation of the neurobiology of these sub-processes has not been well characterized in schizophrenia and psychotic bipolar disorder. Functional MRI was obtained during an event-related spatial delayed match-to-sample task from 58 healthy individuals, 72 individuals with schizophrenia and 41 people with bipolar I disorder with psychotic features in order to: 1) characterize neural responses during encoding, maintenance and retrieval stages of WM using complementary region-of-interest and whole brain approaches; 2) determine whether schizophrenia and psychotic bipolar disorder exhibit similar abnormalities in WM-related brain function; and 3) elucidate the associations between WM-related brain function, task performance, and neuropsychological functioning. Both schizophrenia and psychotic bipolar disorder groups showed encoding- and maintenance-related impairments in the posterior parietal cortex (PPC) and frontal eye fields (FEF). BOLD response in the PPC and FEF, during encoding and maintenance respectively, was associated with task performance independent of group. Additionally, encoding-related activation in the PPC correlated with general neuropsychological functioning independent of group. Only encoding-related activation in the right ventral striatum differed between schizophrenia and psychotic bipolar disorder; individuals with schizophrenia showed significantly lower activation than both psychotic bipolar disorder and healthy groups. Our results are consistent with emerging evidence implicating PPC dysfunction in WM impairment and suggest interventions targeting neural activation in PPC may improve WM and neuropsychological functioning across psychotic disorders.

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

Converging evidence implicates abnormal thalamocortical interactions in the pathophysiology of schizophrenia. This evidence includes consistent findings of increased resting-state functional connectivity of the thalamus with somatosensory and motor cortex during wake and reduced spindle activity during sleep. We hypothesized that these abnormalities would be correlated, reflecting a common mechanism: reduced inhibition of thalamocortical neurons by the thalamic reticular nucleus (TRN). The TRN is the major inhibitory nucleus of the thalamus and is abnormal in schizophrenia. Reduced TRN inhibition would be expected to lead to increased and less filtered thalamic relay of sensory and motor information to the cortex during wake and reduced burst firing necessary for spindle initiation during sleep.

Structural and functional cortico-striatal–thalamic–cortical (CSTC) circuit abnormalities have been observed in schizophrenia and the clinical high-risk state. However, this circuit is sexually dimorphic and changes across neurodevelopment. We examined effects of sex and age on structural and functional properties of the CSTC circuit in a large sample of youth with and without psychosis spectrum symptoms (PSS) from the Philadelphia Neurodevelopmental Cohort. T1-weighted and resting-state functional MRI scans were collected on a 3T Siemens scanner, in addition to participants’ cognitive and psychopathology data. After quality control, the total sample (aged 11–21) was n= 1095 (males = 485, females = 610). Structural subdivisions of the striatum and thalamus were identified using the MAGeT Brain segmentation tool. Functional seeds were segmented based on brain network connectivity. Interaction effects among PSS group, sex, and age on striatum, thalamus, and subdivision volumes were examined. A similar model was used to test effects on functional connectivity of the CSTC circuit. A sex by PSS group interaction was identified, whereby PSS males had higher volumes and PSS females had lower volumes in striatal and thalamic subdivisions. Reduced functional striato-cortical connectivity was found in PSS youth, primarily driven by males, whereby younger male PSS youth also exhibited thalamo-cortical hypo-connectivity (compared to non-PSS youth), vs. striato-cortical hyper-connectivity in older male PSS youth (compared to non-PSS youth). Youth with PSS demonstrate sex and age-dependent differences in striatal and thalamic subdivision structure and functional connectivity. Further efforts at biomarker discovery and early therapeutic intervention targeting the CSTC circuit in psychosis should consider effects of sex and age.

People with schizophrenia (PSZ) show impaired accuracy in spatial working memory (sWM), which is thought to reflect abnormalities in the sustained firing of feature selective neurons that are critical for successful encoding and maintenance processes. Recent research has documented a new source of variance in the accuracy of sWM: In healthy adults, sWM representations are unconsciously biased by previous trials such that current-trial responses are attracted to previous-trial responses (serial dependence). This opens a new window to examine how schizophrenia impacts both the sustained neural firing representing the current-trial target and the longer-term synaptic plasticity that stores previous-trial information.

Pharmacological and nonpharmacological methods of inducing altered states of consciousness (ASCs) are becoming increasingly relevant in the treatment of psychiatric disorders. While comparisons between them are often drawn, to date no study has directly compared their neural correlates.

Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet microscale bases of specialization across human cortex are poorly understood. Anatomical hierarchy is conventionally informed by invasive tract-tracing measurements, creating a need for a principled proxy measure in humans. Moreover, cortex exhibits marked interareal variation in gene expression, yet organizing principles of cortical transcription remain unclear. We hypothesized that specialization of cortical microcircuitry involves hierarchical gradients of gene expression. We found that a noninvasive neuroimaging measure—MRI-derived T1-weighted/T2-weighted (T1w/T2w) mapping—reliably indexes anatomical hierarchy, and it captures the dominant pattern of transcriptional variation across human cortex. We found hierarchical gradients in expression profiles of genes related to microcircuit function, consistent with monkey microanatomy, and implicated in neuropsychiatric disorders. Our findings identify a hierarchical axis linking cortical transcription and anatomy, along which gradients of microscale properties may contribute to the macroscale specialization of cortical function. Burt et al. analyze patterns of gene expression across human cortex and show expression primarily varies along a sensory-association hierarchy captured by noninvasive neuroimaging, suggesting an organizing principle for microcircuit specialization.

Noninvasive neuroimaging has revolutionized the study of the organization of the human brain and how its structure and function are altered in psychiatric disorders. A critical explanatory gap lies in our mechanistic understanding of how systems-level neuroimaging biomarkers emerge from underlying synaptic-level perturbations associated with a disease state. We describe an emerging computational psychiatry approach leveraging biophysically based computational models of large-scale brain dynamics and their potential integration with clinical and pharmacological neuroimaging. In particular, we focus on neural circuit models, which describe how patterns of functional connectivity observed in resting-state functional magnetic resonance imaging emerge from neural dynamics shaped by inter-areal interactions through underlying structural connectivity defining long-range projections. We highlight the importance of local circuit physiological dynamics, in combination with structural connectivity, in shaping the emergent functional connectivity. Furthermore, heterogeneity of local circuit properties across brain areas, which impacts large-scale dynamics, may be critical for modeling whole-brain phenomena and alterations in psychiatric disorders and pharmacological manipulation. Finally, we discuss important directions for future model development and biophysical extensions, which will expand their utility to link clinical neuroimaging to neurobiological mechanisms.

Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC–vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder.

Capitalizing on recent advances in resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) and the distinctive paradigm of rapid mood normalization following ketamine treatment, the current study investigated intrinsic brain networks in major depressive disorder (MDD) during a depressive episode and following treatment with ketamine. Medication-free patients with MDD and healthy control subjects (HC) completed baseline rs-fcMRI. MDD patients received a single infusion of ketamine and underwent repeated rs-fcMRI at 24 h posttreatment. Global brain connectivity with global signal regression (GBCr) values were computed as the average of correlations of each voxel with all other gray matter voxels in the brain. MDD group showed reduced GBCr in the prefrontal cortex (PFC) but increased GBCr in the posterior cingulate, precuneus, lingual gyrus, and cerebellum. Ketamine significantly increased GBCr in the PFC and reduced GBCr in the cerebellum. At baseline, 2174 voxels of altered GBCr were identified, but only 310 voxels significantly differed relative to controls following treatment (corrected α<0.05). Responders to ketamine showed increased GBCr in the lateral PFC, caudate, and insula. Follow-up seed-based analyses illustrated a pattern of dysconnectivity between the PFC/subcortex and the rest of the brain in MDD, which appeared to normalize postketamine. The extent of the functional dysconnectivity identified in MDD and the swift and robust normalization following treatment suggest that GBCr may serve as a treatment response biomarker for the development of rapid acting antidepressants. The data also identified unique prefrontal and striatal circuitry as a putative marker of successful treatment and a target for antidepressants’ development.

Emerging behavioral and neuroimaging studies in schizophrenia (SCZ) and major depressive disorder (MD) are mapping mechanisms of co-occurring and distinct affective disturbances across these disorders. This constitutes a critical goal towards developing rationally guided therapies for upstream neural pathways that contribute to comorbid symptoms across disorders. We highlight the current state of the art in our understanding of emotional dysregulation in SCZ versus MD by focusing on broad domains of behavioral function that can map onto underlying neural systems, namely deficits in hedonics, anticipatory behaviors, computations underlying value and effort, and effortful goal-directed behaviors needed to pursue rewarding outcomes. We highlight unique disturbances in each disorder that may involve dissociable neural systems, but also possible interactions between affect and cognition in MD versus SCZ. Finally, we review computational and translational approaches that offer mechanistic insight into how cellular-level disruptions can lead to complex affective disturbances, informing development of therapies across MD and SCZ.

Schizophrenia is an illness with a remarkably complex symptom presentation that has thus far been out of reach of neuroscientific explanation. This presents a fundamental problem for developing better treatments that target specific symptoms or root causes. One promising path forward is the incorporation of computational neuroscience, which provides a way to formalize experimental observations and, in turn, make theoretical predictions for subsequent studies. We review three complementary approaches: (a) biophysically based models developed to test cellular-level and synaptic hypotheses, (b) connectionist models that give insight into large-scale neural-system-level disturbances in schizophrenia, and (c) models that provide a formalism for observations of complex behavioral deficits, such as negative symptoms. We argue that harnessing all of these modeling approaches represents a productive approach for better understanding schizophrenia. We discuss how blending these approaches can allow the field to progress toward a more comprehensive understanding of schizophrenia and its treatment.

Despite a well-established relationship between amygdala activation during learning and long-term memory, contributions of amygdala to immediate memory have not been studied to date. We used functional magnetic resonance imaging at 1.5-T field strength to determine whether amygdala responses to emotional pictures would predict performance on an immediate recognition memory test. Sex differences in amygdala activation were related to false-positive error rates in recognizing unpleasant pictures. Specifically, increased right amygdala activation during unpleasant picture viewing was related to lower false-positive rates for men and higher false-positive rates for women. Our results indicate that increased amygdala activation while viewing unpleasant pictures may preferentially facilitate immediate recognition memory in men relative to women.

Thalamocortical white matter connectivity is disrupted in psychosis and is hypothesized to play a role in its etiology and associated cognitive impairment. Attenuated cognitive symptoms often begin in adolescence, during a critical phase of white matter and cognitive development. However, little is known about the development of thalamocortical white matter connectivity and its association with cognition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.