Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings

The overlap across psychotic disorders: A functional network connectivity analysis

Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).

Cortical morphology variations during the menstrual cycle in individuals with and without premenstrual dysphoric disorder

BACKGROUND

Premenstrual dysphoric disorder (PMDD) is hypothesized to stem from maladaptive neural sensitivity to ovarian steroid hormone fluctuations. Recently, we found thinner cortices in individuals with PMDD, compared to healthy controls, during the symptomatic phase. Here, we aimed at investigating whether such differences illustrate state-like characteristics specific to the symptomatic phase, or trait-like features defining PMDD.

METHODS

Patients and controls were scanned using structural magnetic resonance imaging during the mid-follicular and late-luteal phase of the menstrual cycle. Group-by-phase interaction effects on cortical architecture metrics (cortical thickness, gyrification index, cortical complexity, and sulcal depth) were assessed using surface-based morphometry.

RESULTS

Independently of menstrual cycle phase, a main effect of diagnostic group on surface metrics was found, primarily illustrating thinner cortices (0.3 < Cohen's d > 1.1) and lower gyrification indices (0.4 < Cohen's d > 1.0) in patients compared to controls. Furthermore, menstrual cycle-specific effects were detected across all participants, depicting a decrease in cortical thickness (0.4 < Cohen's d > 1.7) and region-dependent changes in cortical folding metrics (0.4 < Cohen's d > 2.2) from the mid-follicular to the late luteal phase.

LIMITATIONS

Small effects (d = 0.3) require a larger sample size to be accurately characterized.

CONCLUSIONS

These findings provide initial evidence of trait-like cortical characteristics of the brain of individuals with premenstrual dysphoric disorder, together with indications of menstrual cycle-related variations in cortical architecture in patients and controls. Further investigations exploring whether these differences constitute stable vulnerability markers or develop over the years may help understand PMDD etiology.

Increased grey matter volumes in the temporal lobe and its relationship with cognitive functioning in euthymic patients with bipolar disorder

BACKGROUND

Bipolar disorder (BD) is characterized by episodic mood dysregulation, although a significant portion of patients suffer persistent cognitive impairment during euthymia. Previous magnetic resonance imaging (MRI) research suggests BD patients may have accelerated brain aging, observed as lower grey matter volumes. How these neurostructural alterations are related to the cognitive profile of BD is unclear.

METHODS

We aim to explore this relationship in euthymic BD patients with multimodal structural neuroimaging. A sample of 27 euthymic BD patients and 24 healthy controls (HC) underwent structural grey matter MRI and diffusion-weighted imaging (DWI). BD patient's cognition was also assessed. FreeSurfer algorithms were used to obtain estimations of regional grey matter volumes. White matter pathways were reconstructed using TRACULA, and four diffusion metrics were extracted. ANCOVA models were performed to compare BD patients and HC values of regional grey matter volume and diffusion metrics. Global brain measures were also compared. Bivariate Pearson correlations were explored between significant brain results and five cognitive domains.

RESULTS

Euthymic BD patients showed higher ventricular volume (F(1, 46) = 6.04; p = 0.018) and regional grey matter volumes in the left fusiform (F(1, 46) = 15.03; pFDR = 0.015) and bilateral parahippocampal gyri compared to HC (L: F(1, 46) = 12.79, pFDR = 0.025/ R: F(1, 46) = 15.25, pFDR = 0.015). Higher grey matter volumes were correlated with greater executive function (r = 0.53, p = 0.008).

LIMITATIONS

We evaluated a modest sample size with concurrent pharmacological treatment.

CONCLUSIONS

Higher medial temporal volumes in euthymic BD patients may be a potential signature of brain resilience and cognitive adaptation to a putative illness neuroprogression. This knowledge should be integrated into further efforts to implement imaging into BD clinical management.

A framework for a brain-derived nosology of psychiatric disorders

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Psychosis superspectrum II: neurobiology, treatment, and implications

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Robust and replicable functional brain signatures of 22q11.2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study

A major genetic risk factor for psychosis is 22q11.2 deletion (22q11.2DS). However, robust and replicable functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis remain elusive due to small sample sizes and a focus on small single-site cohorts. Here, we identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis, and their links with idiopathic early psychosis, using one of the largest multi-cohort data to date. We obtained multi-cohort clinical phenotypic and task-free fMRI data from 856 participants (101 22q11.2DS, 120 idiopathic early psychosis, 101 idiopathic autism, 123 idiopathic ADHD, and 411 healthy controls) in a case-control design. A novel spatiotemporal deep neural network (stDNN)-based analysis was applied to the multi-cohort data to identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis. Next, stDNN was used to test the hypothesis that the functional brain signatures of 22q11.2DS-associated psychosis overlap with idiopathic early psychosis but not with autism and ADHD. stDNN-derived brain signatures distinguished 22q11.2DS from controls, and 22q11.2DS-associated psychosis with very high accuracies (86-94%) in the primary cohort and two fully independent cohorts without additional training. Robust distinguishing features of 22q11.2DS-associated psychosis emerged in the anterior insula node of the salience network and the striatum node of the dopaminergic reward pathway. These features also distinguished individuals with idiopathic early psychosis from controls, but not idiopathic autism or ADHD. Our results reveal that individuals with 22q11.2DS exhibit a highly distinct functional brain organization compared to controls. Additionally, the brain signatures of 22q11.2DS-associated psychosis overlap with those of idiopathic early psychosis in the salience network and dopaminergic reward pathway, providing substantial empirical support for the theoretical aberrant salience-based model of psychosis. Collectively, our findings, replicated across multiple independent cohorts, advance the understanding of 22q11.2DS and associated psychosis, underscoring the value of 22q11.2DS as a genetic model for probing the neurobiological underpinnings of psychosis and its progression.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Neuroanatomical Correlates of Cognitive Dysfunction in 22q11.2 Deletion Syndrome

22q11.2 Deletion Syndrome (22q11.2DS), the most common chromosomal microdeletion, presents as a heterogeneous phenotype characterized by an array of anatomical, behavioral, and cognitive abnormalities. Individuals with 22q11.2DS exhibit extensive cognitive deficits, both in overall intellectual capacity and focal challenges in executive functioning, attentional control, perceptual abilities, motor skills, verbal processing, as well as socioemotional operations. Heterogeneity is an intrinsic factor of the deletion's clinical manifestation in these cognitive domains. Structural imaging has identified significant changes in volume, thickness, and surface area. These alterations are closely linked and display region-specific variations with an overall increase in abnormalities following a rostral-caudal gradient. Despite the extensive literature developing around the neurocognitive and neuroanatomical profiles associated with 22q11.2DS, comparatively little research has addressed specific structure-function relationships between aberrant morphological features and deficient cognitive processes. The current review attempts to categorize these limited findings alongside comparisons to populations with phenotypic and structural similarities in order to answer to what degree structural findings can explain the characteristic neurocognitive deficits seen in individuals with 22q11.2DS. In integrating findings from structural neuroimaging and cognitive assessments, this review seeks to characterize structural changes associated with the broad neurocognitive challenges faced by individuals with 22q11.2DS.

Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia

Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues' magnetic susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume and magnetic susceptibility and determines specific differences in these measures among patients with major depressive disorder (MDD), patients with schizophrenia, and healthy controls (HCs). This was a cross-sectional study. Sex- and age- matched patients with MDD (n = 49), patients with schizophrenia (n = 24), and HCs (n = 50) were included. Magnetic resonance imaging was conducted using quantitative susceptibility mapping and T1-weighted imaging to measure subcortical susceptibility and volume. The acquired brain measurements were compared among groups using analyses of variance and post hoc comparisons. Finally, a general linear model examined the susceptibility-volume relationship. Significant group-level differences were found in the magnetic susceptibility of the nucleus accumbens and amygdala (p = 0.045). Post-hoc analyses indicated that the magnetic susceptibility of the nucleus accumbens and amygdala for the MDD group was significantly higher than that for the HC group (p = 0.0054, p = 0.0065, respectively). However, no significant differences in subcortical volume were found between the groups. The general linear model indicated a significant interaction between group and volume for the nucleus accumbens in MDD group but not schizophrenia or HC groups. This study showed susceptibility alterations in the nucleus accumbens and amygdala in MDD patients. A significant relationship was observed between subcortical susceptibility and volume in the MDD group's nucleus accumbens, which indicated abnormalities in myelination and the dopaminergic system related to iron deposition.

Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people

Background

Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people.

Methods

T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches.

Results

Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60-0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85-0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness.

Conclusion

Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

Behavioral stress and antidepressant treatments altered hippocampal expression of Nogo signal-related proteins in rats

Some immune molecules including neurite outgrowth inhibitor (Nogo) ligands and their receptor（Nogo receptor-1: NgR1）are expressed at the neuronal synaptic sites. Paired immunoglobulin-like receptor B (PirB) is another Nogo receptor that also binds to major histocompatibility complex I and β-amyloid and suppresses dendritic immune cell functions and neuronal plasticity in the central nervous system. Augmenting structural and functional neural plasticity by manipulating the Nogo signaling pathway is a novel promising strategy for treating brain ischemia and degenerative processes such as Alzheimer's disease. In recent decades psychiatric research using experimental animals has focused on the attenuation of neural plasticity by stress loadings and on the enhanced resilience by psychopharmacological treatments. In the present study, we examined possible expressional alterations in Nogo signal-related proteins in the rat hippocampus after behavioral stress loadings and antidepressant treatments. To validate the effectiveness of the procedures, previously reported increase in brain-derived neurotrophic factor (BDNF) by ECS or ketamine administration and decrease of BDNF by stress loadings are also shown in the present study. Significant increases in hippocampal NgR1 and PirB expression were observed following chronic variable stress, and a significant increase in NgR1 expression was observed under a single prolonged stress paradigm. These results indicate a possible contribution of enhanced Nogo signaling to the attenuation of neural plasticity in response to stressful experiences. Additionally, the suppression of hippocampal NgR1 expression using electroconvulsive seizure treatment and administration of subanesthetic dose of ketamine supported the increased neural plasticity induced by the antidepressant treatments.

Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering

BACKGROUND

Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences.

METHODS

455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives.

RESULTS

Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms.

CONCLUSIONS

These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.

Advances in the understanding of the pathophysiology of schizophrenia and bipolar disorder through induced pluripotent stem cell models

The pathophysiology of schizophrenia and bipolar disorder involves a complex interaction between genetic and environmental factors that begins in the early stages of neurodevelopment. Recent advancements in the field of induced pluripotent stem cells (iPSCs) offer a promising tool for understanding the neurobiological alterations involved in these disorders and, potentially, for developing new treatment options. In this review, we summarize the results of iPSC-based research on schizophrenia and bipolar disorder, showing disturbances in neurodevelopmental processes, imbalance in glutamatergic-GABAergic transmission and neuromorphological alterations. The limitations of the reviewed literature are also highlighted, particularly the methodological heterogeneity of the studies, the limited number of studies developing iPSC models of both diseases simultaneously, and the lack of in-depth clinical characterization of the included samples. Further studies are needed to advance knowledge on the common and disease-specific pathophysiological features of schizophrenia and bipolar disorder and to promote the development of new treatment options.

Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Estimating multimodal brain variability in schizophrenia spectrum disorders: A worldwide ENIGMA study

Objective

Schizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia.

Methods

This meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy.

Results

We found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region.

Conclusions

Higher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

FreeSurfer 7 quality control: Key problem areas and importance of manual corrections

We have studied the effects of manual quality control of brain Magnetic Resonance Imaging (MRI) images processed with Freesurfer. T1 images of first episode psychosis patients (N = 60) and healthy controls (N = 41) were inspected for gray matter boundary errors. The errors were fixed, and the effects of error correction on brain volume, thickness, and surface area were measured. It is commonplace to apply quality control to Freesurfer MRI recordings to ensure that the edges of gray and white matter are detected properly, as incorrect edge detection leads to changes in variables such as volume, cortical thickness, and cortical surface area. We find that while Freesurfer v7.1.1. does regularly make mistakes in identifying the edges of cortical gray matter, correcting these errors yields limited changes in the commonly measured variables listed above. We further find that the software makes fewer gray matter boundary errors when processing female brains. The results suggest that manually correcting gray matter boundary errors may not be worthwhile due to its small effect on the measurements, with potential exceptions for studies that focus on the areas that are more commonly affected by errors: the areas around the cerebellar tentorium, paracentral lobule, and the optic nerves, specifically the horizontal segment of the middle cerebral artery.

Dissociation of Cognitive Effort-Based Decision Making and Its Associations With Symptoms, Cognition, and Everyday Life Function Across Schizophrenia, Bipolar Disorder, and Depression

BACKGROUND

Anhedonia and amotivation are symptoms of many different mental health disorders that are frequently associated with functional disability, but it is not clear whether the same processes contribute to motivational impairments across disorders. This study focused on one possible factor, the willingness to exert cognitive effort, referred to as cognitive effort-cost decision making.

METHODS

We examined performance on the deck choice task as a measure of cognitive effort-cost decision making, in which people choose to complete an easy task for a small monetary reward or a harder task for larger rewards, in 5 groups: healthy control (n = 80), schizophrenia/schizoaffective disorder (n = 50), bipolar disorder with psychosis (n = 58), current major depression (n = 60), and past major depression (n = 51). We examined cognitive effort-cost decision making in relation to clinician and self-reported motivation symptoms, working memory and cognitive control performance, and life function measured by ecological momentary assessment and passive sensing.

RESULTS

We found a significant diagnostic group × reward interaction (F8,588 = 4.37, p < .001, ηp2 = 0.056). Compared with the healthy control group, the schizophrenia/schizoaffective and bipolar disorder groups, but not the current or past major depressive disorder groups, showed a reduced willingness to exert effort at the higher reward values. In the schizophrenia/schizoaffective and bipolar disorder groups, but not the major depressive disorder groups, reduced willingness to exert cognitive effort for higher rewards was associated with greater clinician-rated motivation impairments, worse working memory and cognitive control performance, and less engagement in goal-directed activities measured by ecological momentary assessment.

CONCLUSIONS

These findings suggest that the mechanisms contributing to motivational impairments differ among individuals with psychosis spectrum disorders versus depression.

Subcortical Brain Alterations in Carriers of Genomic Copy Number Variants

OBJECTIVE

Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs), including autism (ASD) and schizophrenia. Little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, the authors investigated gross volume, vertex-level thickness, and surface maps of subcortical structures in 11 CNVs and six NPDs.

METHODS

Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (CNVs at 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2; age range, 6-80 years; 340 males) and 782 control subjects (age range, 6-80 years; 387 males) as well as ENIGMA summary statistics for ASD, schizophrenia, attention deficit hyperactivity disorder, obsessive-compulsive disorder, bipolar disorder, and major depression.

RESULTS

All CNVs showed alterations in at least one subcortical measure. Each structure was affected by at least two CNVs, and the hippocampus and amygdala were affected by five. Shape analyses detected subregional alterations that were averaged out in volume analyses. A common latent dimension was identified, characterized by opposing effects on the hippocampus/amygdala and putamen/pallidum, across CNVs and across NPDs. Effect sizes of CNVs on subcortical volume, thickness, and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and schizophrenia.

CONCLUSIONS

The findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions, as well distinct effects, with some CNVs clustering with adult-onset conditions and others with ASD. These findings provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD and why a single CNV increases the risk for a diverse set of NPDs.

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.

Examining the shared etiology of psychopathology with genome-wide association studies

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Subcortical brain alterations in carriers of genomic copy number variants

Objectives

Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs) including autism (ASD) and schizophrenia (SZ). Overall, little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, we investigated gross volume, and vertex level thickness and surface maps of subcortical structures in 11 different CNVs and 6 different NPDs.

Methods

Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (at the following loci: 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2) and 782 controls (Male/Female: 727/730; age-range: 6-80 years) as well as ENIGMA summary-statistics for ASD, SZ, ADHD, Obsessive-Compulsive-Disorder, Bipolar-Disorder, and Major-Depression.

Results

Nine of the 11 CNVs affected volume of at least one subcortical structure. The hippocampus and amygdala were affected by five CNVs. Effect sizes of CNVs on subcortical volume, thickness and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and SZ. Shape analyses were able to identify subregional alterations that were averaged out in volume analyses. We identified a common latent dimension - characterized by opposing effects on basal ganglia and limbic structures - across CNVs and across NPDs.

Conclusion

Our findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions. We also observed distinct effects with some CNVs clustering with adult conditions while others clustered with ASD. This large cross-CNV and NPDs analysis provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD, as well as why a single CNV increases the risk for a diverse set of NPDs.

Genetic associations between bipolar disorder and brain structural phenotypes

Patients with bipolar disorder (BD) and their first-degree relatives exhibit alterations in brain volume and cortical structure, whereas the underlying genetic mechanisms remain unclear. In this study, based on the published genome-wide association studies (GWAS), the extent of polygenic overlap between BD and 15 brain structural phenotypes was investigated using linkage disequilibrium score regression and MiXeR tool, and the shared genomic loci were discovered by conjunctional false discovery rate (conjFDR) and expression quantitative trait loci (eQTL) analyses. MiXeR estimated the overall measure of polygenic overlap between BD and brain structural phenotypes as 4-53% on a 0-100% scale (as quantified by the Dice coefficient). Subsequent conjFDR analyses identified 54 independent loci (71 risk single-nucleotide polymorphisms) jointly associated with BD and brain structural phenotypes with a conjFDR < 0.05, among which 33 were novel that had not been reported in the previous BD GWAS. Follow-up eQTL analyses in respective brain regions both confirmed well-known risk genes (e.g. CACNA1C, NEK4, GNL3, MAPK3) and discovered novel risk genes (e.g. LIMK2 and CAMK2N2). This study indicates a substantial shared genetic basis between BD and brain structural phenotypes, and provides novel insights into the developmental origin of BD and related biological mechanisms.

Hippocampal and amygdala volumes vary with transdiagnostic psychopathological dimensions of distress, anxious arousal, and trauma

Reduced hippocampal and/or amygdala volumes have been reported in patients with a variety of different anxiety diagnoses, suggesting that structural alterations may vary transdiagnostically across the internalizing disorders. The current study measured hippocampal and amygdala volumes in anxiety and mood disorder patients assessing differences that vary dimensionally with transdiagnostic factors of distress, anxious arousal, and trauma, based on a principal components analysis of questionnaires relating to symptomology. High-resolution structural images were collected in a sample of 165 patients, and volumes extracted from the hippocampal formation (including CA1, CA2/3, CA4/DG, subiculum, and molecular layer) and the amygdala. Transdiagnostically, increasing distress was associated with reduced hippocampal CA1 volume, increasing anxious arousal was associated with reduced hippocampal CA4/DG volume, and increasing trauma severity was associated with reduced amygdala volume in women. Taken together, the data indicate that subcortical brain volumes decrease as the severity of transdiagnostic psychopathological symptomology increases.

Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective.

Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach

BACKGROUND

Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles.

MATERIALS AND METHODS

We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP.

RESULTS

Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences.

CONCLUSION

These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.