Neuroimaging studies have consistently shown functional brain abnormalities in patients with Bipolar Disorder (BD) and Major Depressive Disorder (MDD). However, the extent to which these two disorders are associated with similar or distinct neural changes remains unclear. We conducted a systematic review of functional magnetic resonance imaging studies comparing BD and MDD patients to healthy participants using facial affect processing paradigms. Relevant spatial coordinates from twenty original studies were subjected to quantitative Activation Likelihood Estimation meta-analyses based on 168 BD and 189 MDD patients and 344 healthy controls. We identified common and distinct patterns of neural engagement for BD and MDD within the facial affect processing network. Both disorders were associated with increased engagement of limbic regions. Diagnosis-specific differences were observed in cortical, thalamic and striatal regions. Decreased ventrolateral prefrontal cortical engagement was associated with BD while relative hypoactivation of the sensorimotor cortices was seen in MDD. Increased responsiveness in the thalamus and basal ganglia were associated with BD. These findings were modulated by stimulus valence. These data suggest that whereas limbic overactivation is reported consistently in patients with mood disorders, future research should consider the relevance of a wider network of regions in formulating conceptual models of BD and MDD.

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.

Genome-wise association studies have identified a number of common single-nucleotide polymorphisms (SNPs), each of small effect, associated with risk to bipolar disorder (BD). Several risk-conferring SNPs have been individually shown to influence regional brain activation thus linking genetic risk for BD to altered brain function. The current study examined whether the polygenic risk score method, which models the cumulative load of all known risk-conferring SNPs, may be useful in the identification of brain regions whose function may be related to the polygenic architecture of BD. We calculated the individual polygenic risk score for BD (PGR-BD) in forty-one patients with the disorder, twenty-five unaffected first-degree relatives and forty-six unrelated healthy controls using the most recent Psychiatric Genomics Consortium data. Functional magnetic resonance imaging was used to define task-related brain activation patterns in response to facial affect and working memory processing. We found significant effects of the PGR-BD score on task-related activation irrespective of diagnostic group. There was a negative association between the PGR-BD score and activation in the visual association cortex during facial affect processing. In contrast, the PGR-BD score was associated with failure to deactivate the ventromedial prefrontal region of the default mode network during working memory processing. These results are consistent with the threshold-liability model of BD, and demonstrate the usefulness of the PGR-BD score in identifying brain functional alternations associated with vulnerability to BD. Additionally, our findings suggest that the polygenic architecture of BD is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions.

Impaired social cognition is a cardinal feature of Autism Spectrum Disorders (ASD) and Schizophrenia (SZ). However, the functional neuroanatomy of social cognition in either disorder remains unclear due to variability in primary literature. Additionally, it is not known whether deficits in ASD and SZ arise from similar or disease-specific disruption of the social cognition network.

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

Although previous studies have highlighted associations of cannabis use with cognition and brain morphometry, critical questions remain with regard to the association between cannabis use and brain structural and functional connectivity. In a cross-sectional community sample of 205 African Americans (age 18-70) we tested for associations of cannabis use disorder (CUD, n = 57) with multi-domain cognitive measures and structural, diffusion, and resting state brain-imaging phenotypes. Post hoc model evidence was computed with Bayes factors (BF) and posterior probabilities of association (PPA) to account for multiple testing. General cognitive functioning, verbal intelligence, verbal memory, working memory, and motor speed were lower in the CUD group compared with non-users (p < .011; 1.9 < BF < 3,217). CUD was associated with altered functional connectivity in a network comprising the motor-hand region in the superior parietal gyri and the anterior insula (p < .04). These differences were not explained by alcohol, other drug use, or education. No associations with CUD were observed in cortical thickness, cortical surface area, subcortical or cerebellar volumes (0.12 < BF < 1.5), or graph-theoretical metrics of resting state connectivity (PPA < 0.01). In a large sample collected irrespective of cannabis used to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non-CUD. We did not find evidence for or against a disruption of structural connectivity, whereas we did find localized resting state functional dysconnectivity in CUD. There was sufficient proof, however, that organization of functional connectivity as determined via graph metrics does not differ between CUD and non-user group.

Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this gap, the aim of the study was to construct a reliable brain atlas derived only from older participants. We leveraged resting-state functional magnetic resonance imaging data from three cohorts of healthy older adults (total N = 563; age = 55-95 years) and a younger-adult cohort (N = 128; age = 18-35 years). We identified the major RSNs and their subdivisions across all older-adult cohorts. We demonstrated high spatial reproducibility of these RSNs with an average spatial overlap of 67%. Importantly, the RSNs derived from the older-adult cohorts were spatially different from those derived from the younger-adult cohort (P = 2.3 × 10-3). Lastly, we constructed a novel brain atlas, called Atlas55+, which includes the consensus of the major RSNs and their subdivisions across the older-adult cohorts. Thus, Atlas55+ provides a reliable age-appropriate template for RSNs in late adulthood and is publicly available. Our results confirm the need for age-appropriate functional atlases for studies investigating aging-related brain mechanisms.

The default mode network (DMN) dysfunction has emerged as a consistent biological correlate of multiple psychiatric disorders. Specifically, there is evidence of alterations in DMN cohesiveness in schizophrenia, mood and anxiety disorders. The aim of this study was to synthesize at a fine spatial resolution the intra-network functional connectivity of the DMN in adults diagnosed with schizophrenia, mood and anxiety disorders, capitalizing on powerful meta-analytic tools provided by activation likelihood estimation.

The habenula (Hb), a bilateral nucleus located next to the dorsomedial thalamus, is of particular relevance to psychiatric disorders based on preclinical evidence linking the Hb to depressive and amotivational states. However, studies in clinical samples are scant because segmentation of the Hb in neuroimaging data is challenging due to its small size and low contrast from the surrounding tissues. Negative affective states dominate the clinical course of schizophrenia and bipolar disorder and represent a major cause of disability. Diagnosis-related alterations in the volume of Hb in these disorders have therefore been hypothesized but remain largely untested. To probe this question, we used a recently developed objective and reliable semi-automated Hb segmentation method based on myelin-sensitive magnetic resonance imaging (MRI) data. We ascertained case-control differences in Hb volume from high resolution structural MRI data obtained from patients with schizophrenia (n = 95), bipolar disorder (n = 44) and demographically matched healthy individuals (n = 52). Following strict quality control of the MRI data, the final sample comprised 68 patients with schizophrenia, 32 with bipolar disorder and 40 healthy individuals. Regardless of diagnosis, age, sex, and IQ were not correlated with Hb volume. This was also the case for age of illness onset and medication (i.e., antipsychotic dose and lithium-treatment status). Case-control differences in Hb volume did not reach statistical significance; their effect size (Cohen's d) was negligible on the left (schizophrenia: 0.14; bipolar disorder: -0.03) and small on the right (schizophrenia: 0.34; bipolar disorder: 0.26). Nevertheless, variability in the volume of the right Hb was associated with suicidality in the entire patient sample (ρ = 0.29, p = 0.004) as well as in each patient group (bipolar disorder: ρ = 0.34, p = 0.04; schizophrenia: ρ = 0.25, p = 0.04). These findings warrant replication in larger samples and longitudinal designs and encourage more comprehensive characterization of Hb connectivity and function in clinical populations.

To characterize the association between the protracted biopsychosocial coronavirus disease 2019 (COVID-19) pandemic exposures and incident suicide attempt rates.

Repetitive transcranial magnetic stimulation (TMS) is increasingly used to treat neurocognitive symptoms in mood disorders. Intermittent theta burst stimulation (iTBS) is a brief version of TMS that may preferentially target cognitive functions. This study evaluated whether iTBS leads to cognitive improvements and associated increased hippocampal volumes in bipolar depression.

Earlier pubertal timing is associated with higher rates of depressive disorders in adolescence. Neuroimaging studies report brain structural associations with both pubertal timing and depression. However, whether brain structure mediates the relationship between pubertal timing and depression remains unclear.

The COVID-19 pandemic has been associated with increased levels of depression and anxiety with implications for the use of antidepressant medications.

Emotion regulation strategies based on suppressing behavioral expressions of emotion have been considered maladaptive. However, this may not apply to suppressing the emotional experience (experiential suppression). The aim of this study was to define the effect of experiential suppression on subjective and physiological emotional responses.

Schizophrenia (SZ) and bipolar disorder (BD) share clinical features, genetic risk factors and neuroimaging abnormalities. There is evidence of disrupted connectivity in resting state networks in patients with SZ and BD and their unaffected relatives. Resting state networks are known to undergo reorganization during youth coinciding with the period of increased incidence for both disorders. We therefore focused on characterizing resting state network connectivity in youth at familial risk for SZ or BD to identify alterations arising during this period. We measured resting-state functional connectivity in a sample of 106 youth, aged 7-19 years, comprising offspring of patients with SZ (N = 27), offspring of patients with BD (N = 39) and offspring of community control parents (N = 40). We used Independent Component Analysis to assess functional connectivity within the default mode, executive control, salience and basal ganglia networks and define their relationship to grey matter volume, clinical and cognitive measures. There was no difference in connectivity within any of the networks examined between offspring of patients with BD and offspring of community controls. In contrast, offspring of patients with SZ showed reduced connectivity within the left basal ganglia network compared to control offspring, and they showed a positive correlation between connectivity in this network and grey matter volume in the left caudate. Our findings suggest that dysconnectivity in the basal ganglia network is a robust correlate of familial risk for SZ and can be detected during childhood and adolescence.

Accumulating evidence suggest a life-long impact of disease related mechanisms on brain structure in schizophrenia which may be modified by antipsychotic treatment. The aim of the present study was to investigate in a large sample of patients with schizophrenia the effect of illness duration and antipsychotic treatment on brain structure. Seventy-one schizophrenic patients and 79 age and gender matched healthy participants underwent brain magnetic resonance imaging (MRI). All images were processed with voxel based morphometry, using SPM5. Compared to healthy participants, patients showed decrements in gray matter volume in the left medial and left inferior frontal gyrus. In addition, duration of illness was negatively associated with gray matter volume in prefrontal regions bilaterally, in the temporal pole on the left and the caudal superior temporal gyrus on the right. Cumulative exposure to antipsychotics correlated positively with gray matter volumes in the cingulate gyrus for typical agents and in the thalamus for atypical drugs. These findings (a) indicate that structural abnormalities in prefrontal and temporal cortices in schizophrenia are progressive and, (b) suggest that antipsychotic medication has a significant impact on brain morphology.

Early identification of Bipolar Disorder (BD) remains poor despite the high levels of disability associated with the disorder.

Patients with Bipolar Disorder (BD) perform poorly on tasks of selective attention and inhibitory control. Although similar behavioural deficits have been noted in their relatives, it is yet unclear whether they reflect dysfunction in the same neural circuits. We used functional magnetic resonance imaging and the Stroop Colour Word Task to compare task related neural activity between 39 euthymic BD patients, 39 of their first-degree relatives (25 with no Axis I disorders and 14 with Major Depressive Disorder) and 48 healthy controls. Compared to controls, all individuals with familial predisposition to BD, irrespective of diagnosis, showed similar reductions in neural responsiveness in regions involved in selective attention within the posterior and inferior parietal lobules. In contrast, hypoactivation within fronto-striatal regions, implicated in inhibitory control, was observed only in BD patients and MDD relatives. Although striatal deficits were comparable between BD patients and their MDD relatives, right ventrolateral prefrontal dysfunction was uniquely associated with BD. Our findings suggest that while reduced parietal engagement relates to genetic risk, fronto-striatal dysfunction reflects processes underpinning disease expression for mood disorders.

Resilient adaptation can be construed in different ways, but as used here it refers to adaptive brain responses associated with avoidance of psychopathology despite expressed genetic predisposition to Bipolar Disorder (BD). Although family history of BD is associated with elevated risk of affective morbidity a significant proportion of first-degree relatives remain free of psychopathology. Examination of brain structure and function in these individuals may inform on adaptive responses that pre-empt disease expression.

Genetic, neuroimaging, and molecular neurobiological evidence support the hypothesis that the disconnectivity syndrome in schizophrenia (SZ) could arise from failures of saltatory conduction and abnormalities at the nodes of Ranvier (NOR) interface where myelin and axons interact.