A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method.

The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.

Functional and structural effects of repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations in schizophrenia: A systematic review.

BACKGROUND: Auditory verbal hallucinations (AVH) are a disabling symptom for people with schizophrenia (SCZ), and do not always respond to antipsychotics. Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy for medication-refractory AVH, though the underlying neural mechanisms by which rTMS produces these effects remain unclear. This systematic review evaluated the structural and functional impact of rTMS for AVH in SCZ, and its association with clinical outcomes. METHODS: A systematic search was conducted in Medline, PsychINFO, and PubMed using terms for four key concepts: AVH, SCZ, rTMS, neuroimaging. Using PRISMA guidelines, 18 studies were identified that collected neuroimaging data of an rTMS intervention for AVH in SCZ. Risk of bias assessments was conducted. RESULTS: Low frequency (<5 Hz) rTMS targeting left hemispheric language processing regions may normalize brain abnormalities in AVH patients at structural, functional, electrophysiological, and topological levels, with concurrent symptom improvement. Amelioration of aberrant neural activity in frontotemporal networks associated with speech and auditory processing was commonly observed, as well as in cerebellar and emotion regulation regions. Neuroimaging analyses identified neural substrates with direct correlations to post-rTMS AVH severity, propounding their use as therapeutic targets. DISCUSSION: Combined rTMS-neuroimaging highlights the multidimensional alterations of rTMS on brain activity and structure in treatment-resistant AVH, which may be used to develop more efficacious therapies. Larger randomized, sham-controlled studies are needed. Future studies should explore alternate stimulation targets, investigate the neural effects of high-frequency rTMS and evaluate long-term neuroimaging outcomes.

Characterizing Cannabis Use and Perceived Benefit in a Tertiary Headache Center Patient Sample.

Background and Objectives: Research suggests a potential role for cannabinoids in the etiology and treatment of migraine. However, there is a paucity of research on usage patterns and perceived benefits of cannabis use in clinical headache patient populations. Methods: Patients from a tertiary headache center completed a 1-time online survey regarding cannabis use patterns and perceived benefits of cannabis-based products in treating migraine symptoms, clinical features, and risk factors (e.g., depression, sleep disturbance). Descriptive analyses were performed. Results: Data were collected from 1373 patients (response rate 25.4% [1,373/5,400]), with 55.7% reporting cannabis-based product use in the past 3 years and 32.5% indicating current use. The most frequently cited reasons for cannabis-based product use were treating headache (65.8%) and sleep concerns (50.8%). Inhaled products (i.e., smoked/vaped) and edibles were the most commonly reported delivery methods, with THC/CBD (∆9 tetrahydrocannabinol/cannabidiol) blends as the most-cited product composition. A majority of participants reported cannabis-related improvements in migraine headache characteristics (i.e., intensity: 78.1%; duration: 73.4%; frequency: 62.4%), nausea (56.3%), and risk factors (sleep disturbance: 81.2%; anxiety: 71.4%; depression: 57.0%). Over half (58.0%) of the respondents reported only using cannabis products when experiencing a headache, while 42.0% used cannabis most days/daily for prevention. Nearly half (48.9%) of the respondents reported that cannabis use contributed to a reduction in medication amount for headache treatment, and 14.5% reported an elimination of other medications. A minority (20.9%) of participants reported experiencing side effects when using cannabis products for headache, most commonly fatigue/lethargy. For those participants who reported no use of cannabis-based products in the previous 3 years, approximately half indicated not knowing what cannabis product to take or the appropriate dosage. Discussion: This is the largest study to date to document cannabis product usage patterns and perceived benefits for migraine management in a clinical headache patient sample. A majority of patients surveyed reported using cannabis products for migraine management and cited perceived improvements in migraine characteristics, clinical features, and associated risk factors. The findings warrant experimental trials to confirm the perceived benefits of cannabis products for migraine prevention and treatment.

Enlarged pituitary gland volume: a possible state rather than trait marker of psychotic disorders.

BACKGROUND: Enlarged pituitary gland volume could be a marker of psychotic disorders. However, previous studies report conflicting results. To better understand the role of the pituitary gland in psychosis, we examined a large transdiagnostic sample of individuals with psychotic disorders. METHODS: The study included 751 participants (174 with schizophrenia, 114 with schizoaffective disorder, 167 with psychotic bipolar disorder, and 296 healthy controls) across six sites in the Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium. Structural magnetic resonance images were obtained, and pituitary gland volumes were measured using the MAGeT brain algorithm. Linear mixed models examined between-group differences with controls and among patient subgroups based on diagnosis, as well as how pituitary volumes were associated with symptom severity, cognitive function, antipsychotic dose, and illness duration. RESULTS: Mean pituitary gland volume did not significantly differ between patients and controls. No significant effect of diagnosis was observed. Larger pituitary gland volume was associated with greater symptom severity (F = 13.61, p = 0.0002), lower cognitive function (F = 4.76, p = 0.03), and higher antipsychotic dose (F = 5.20, p = 0.02). Illness duration was not significantly associated with pituitary gland volume. When all variables were considered, only symptom severity significantly predicted pituitary gland volume (F = 7.54, p = 0.006). CONCLUSIONS: Although pituitary volumes were not increased in psychotic disorders, larger size may be a marker associated with more severe symptoms in the progression of psychosis. This finding helps clarify previous inconsistent reports and highlights the need for further research into pituitary gland-related factors in individuals with psychosis.

Differentiating Biomarker Features and Familial Characteristics of B-SNIP Psychosis Biotypes.

Idiopathic psychosis shows considerable biological heterogeneity across cases. B-SNIP used psychosis-relevant biomarkers to identity psychosis Biotypes, which will aid etiological and targeted treatment investigations. Psychosis probands from the B-SNIP consortium (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognition, saccades, and auditory EEG measurements. ERP quantifications were substantially modified from previous iterations of this approach. Multivariate integration reduced multiple biomarker outcomes to 11 "bio-factors". Twenty-four different approaches indicated bio-factor data among probands were best distributed as three subgroups. Numerical taxonomy with k-means constructed psychosis Biotypes, and rand indices evaluated consistency of Biotype assignments. Psychosis subgroups, their non-psychotic first-degree relatives, and healthy individuals were compared across bio-factors. The three psychosis Biotypes differed significantly on all 11 bio-factors, especially prominent for general cognition, antisaccades, ERP magnitude, and intrinsic neural activity. Rand indices showed excellent consistency of clustering membership when samples included at least 1100 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons and captured neural dysregulation, neural vigor, and stimulus salience variates. Neural dysregulation captured Biotype-2, low neural vigor captured Biotype-1, and deviations of stimulus salience captured Biotype-3. First-degree relatives showed similar patterns as their Biotyped proband relatives on general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. Results extend previous efforts by the B-SNIP consortium to characterize biologically distinct psychosis Biotypes. They also show that at least 1100 observations are necessary to achieve consistent outcomes. First-degree relative data implicate specific bio-factor deviations to the subtype of their proband and may inform studies of genetic risk.

A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants.

BACKGROUND: Accurate psychiatric risk assessment requires biomarkers that are both stable and adaptable to development. Functional network connectivity (FNC), which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. However, the absence of suitable analytical methodologies has constrained this area of investigation. METHODS: We investigated the brainwide risk score (BRS), a novel FNC-based metric that contrasts the relative distances of an individual's FNC to that of psychiatric disorders versus healthy control references. To generate group-level disorder and healthy control references, we utilized a large brain imaging dataset containing 5231 total individuals diagnosed with schizophrenia, autism spectrum disorder, major depressive disorder, and bipolar disorder and their corresponding healthy control individuals. The BRS metric was employed to assess the psychiatric risk in 2 new datasets: Adolescent Brain Cognitive Development (ABCD) Study (n = 8191) and Human Connectome Project Early Psychosis (n = 170). RESULTS: The BRS revealed a clear, reproducible gradient of FNC patterns from low to high risk for each psychiatric disorder in unaffected adolescents. We found that low-risk ABCD Study adolescent FNC patterns for each disorder were strongly present in over 25% of the ABCD Study participants and homogeneous, whereas high-risk patterns of each psychiatric disorder were strongly present in about 1% of ABCD Study participants and heterogeneous. The BRS also showed its effectiveness in predicting psychosis scores and distinguishing individuals with early psychosis from healthy control individuals. CONCLUSIONS: The BRS could be a new image-based tool for assessing psychiatric vulnerability over time and in unaffected individuals, and it could also serve as a potential biomarker, facilitating early screening and monitoring interventions.

Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.

AIM: To harmonize two ascertainment and severity rating instruments commonly used for the clinical high risk syndrome for psychosis (CHR-P): the Structured Interview for Psychosis-risk Syndromes (SIPS) and the Comprehensive Assessment of At-Risk Mental States (CAARMS). METHODS: The initial workshop is described in the companion report from Addington et al. After the workshop, lead experts for each instrument continued harmonizing attenuated positive symptoms and criteria for psychosis and CHR-P through an intensive series of joint videoconferences. RESULTS: Full harmonization was achieved for attenuated positive symptom ratings and psychosis criteria, and modest harmonization for CHR-P criteria. The semi-structured interview, named Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS (PSYCHS), generates CHR-P criteria and severity scores for both CAARMS and SIPS. CONCLUSIONS: Using the PSYCHS for CHR-P ascertainment, conversion determination, and attenuated positive symptom severity rating will help in comparing findings across studies and in meta-analyses.

Identification of a Composite Latent Dimension of Reward and Impulsivity Across Clinical, Behavioral, and Neurobiological Domains Among Youth.

BACKGROUND: Individual differences in reward processing are central to heightened risk-taking behaviors during adolescence, but there is inconsistent evidence for the relationship between risk-taking phenotypes and the neural substrates of these behaviors. METHODS: Here, we identify latent features of reward in an attempt to provide a unifying framework linking together aspects of the brain and behavior during early adolescence using a multivariate pattern learning approach. Data (N = 8295; n male = 4190; n female = 4105) were acquired as part of the Adolescent Brain Cognitive Development (ABCD) Study and included neuroimaging (regional neural activity responses during reward anticipation) and behavioral (e.g., impulsivity measures, delay discounting) variables. RESULTS: We revealed a single latent dimension of reward driven by shared covariation between striatal, thalamic, and anterior cingulate responses during reward anticipation, negative urgency, and delay discounting behaviors. Expression of these latent features differed among adolescents with attention-deficit/hyperactivity disorder and disruptive behavior disorder, compared with those without, and higher expression of these latent features was negatively associated with multiple dimensions of executive function and cognition. CONCLUSIONS: These results suggest that cross-domain patterns of anticipatory reward processing linked to negative features of impulsivity exist in both the brain and in behavior during early adolescence and that these are representative of 2 commonly diagnosed reward-related psychiatric disorders, attention-deficit/hyperactivity disorder and disruptive behavior disorder. Furthermore, they provide an explicit baseline from which multivariate developmental trajectories of reward processes may be tracked in later waves of the ABCD Study and other developmental cohorts.

Estradiol effects on an emotional interference task in adolescents with current and remitted depression.

Attentional biases to emotional stimuli are thought to reflect vulnerability for mood disorder onset and maintenance. This study examined the association between the endogenous sex hormone estradiol and emotional attentional biases in adolescent females with either current or remitted depression. Three groups of participants (mean age ± SD) completed the Emotional Interrupt Task: 1) 20 adolescent females (15.1 ± 1.83 years) currently diagnosed with Major Depressive Disorder (MDD), 2) 16 adolescent females (16.4 ± 1.31 years) who had experienced at least one episode of MDD in their lifetime but currently met criteria for MDD in remission, and 3) 30 adolescent female (15.4 ± 1.83 years) healthy controls. Attentional interference (AI) scores were calculated as differences in target response reaction time between trials with emotional facial expressions versus neutral facial expressions. Estradiol levels were assayed by Salimetrics LLC using saliva samples collected within 30 min of waking on assessment days. Robust multiple regression with product terms evaluated estradiol's main effect on AI scores, as well as hypothesized estradiol × diagnostic group interactions. Although neither mean estradiol levels nor mean AI scores in the current-MDD and remitted-MDD groups differed from controls, the relationship between estradiol and overall AI score differed between control adolescents and the remitted-MDD group. Specifically, the remitted-MDD adolescents performed worse (i.e., showed greater attentional interference) when they had higher estradiol; no significant relationship existed in the current-MDD group. Because this finding was driven by angry and not happy stimuli, it appears higher estradiol levels were associated with greater susceptibility to the attention-capturing effects of negatively-valenced emotional content in girls at risk for MDD from prior history.

Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study.

BACKGROUND: Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored. METHODS: We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders. Particularly, we examined the predictive power of brain functional network connectivity (FNC) and 41 environmental exposures for 23 behaviors related to cognitive ability and mental health in 7655 children selected from the Adolescent Brain Cognitive Development (ABCD) Study at both baseline and follow-up. RESULTS: FNC demonstrated more predictability for cognitive abilities than for mental health, with cross-validation from the UK Biobank study (N = 20,852), highlighting the importance of thalamus and hippocampus in longitudinal prediction, while FNC+environment demonstrated more predictive power than FNC in both cross-sectional and longitudinal prediction of all behaviors, especially for mental health (r = 0.32-0.63). We found that family and neighborhood exposures were common critical environmental influencers on cognitive ability and mental health, which can be mediated by FNC significantly. Healthy perinatal development was a unique protective factor for higher cognitive ability, whereas sleep problems, family conflicts, and adverse school environments specifically increased risk of poor mental health. CONCLUSIONS: This work revealed comprehensive environment-brain-behavior triple interactions based on the ABCD Study, identified cognitive control and default mode networks as the most predictive functional networks for a wide repertoire of behaviors, and underscored the long-lasting impact of critical environmental exposures on childhood development, in which sleep problems were the most prominent factors affecting mental health.

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.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk.

BACKGROUND: Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification. METHODS: We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. RESULTS: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. CONCLUSIONS: Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.

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.

Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia.

PURPOSE OF REVIEW: Over the last decade, evidence suggests that a combination of behavioral and neuroimaging findings can help illuminate changes in functional dysconnectivity in schizophrenia. We review the recent connectivity literature considering several vital models, considering connectivity findings, and relationships with clinical symptoms. We reviewed resting state fMRI studies from 2017 to 2023. We summarized the role of two sets of brain networks (cerebello-thalamo-cortical (CTCC) and the triple network set) across three hypothesized models of schizophrenia etiology (neurodevelopmental, vulnerability-stress, and neurotransmitter hypotheses). RECENT FINDINGS: The neurotransmitter and neurodevelopmental models best explained CTCC-subcortical dysfunction, which was consistently connected to symptom severity and motor symptoms. Triple network dysconnectivity was linked to deficits in executive functioning, and the salience network (SN)-default mode network dysconnectivity was tied to disordered thought and attentional deficits. This paper links behavioral symptoms of schizophrenia (symptom severity, motor, executive functioning, and attentional deficits) to various hypothesized mechanisms.

4D DYNAMIC SPATIAL BRAIN NETWORKS AT REST LINKED TO COGNITION SHOW ATYPICAL VARIABILITY AND COUPLING IN SCHIZOPHRENIA.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain net-works in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan. We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxelwise changes within and between brain networks.

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Age-related, multivariate associations between white matter microstructure and behavioral performance in three executive function domains.

The executive function (EF) domains of working memory (WM), response inhibition (RI), and set shifting (SS) show maturational gains and are linked to neuroimaging-measured brain changes. This study explored ways in which maturation-linked differences in EF abilities are systematically associated with white matter microstructural differences from adolescence into young adulthood. Diffusion tensor imaging (DTI) and nine neurocognitive tests were collected from 120 healthy subjects ages 12-24. Analyses across the white matter skeleton were performed, focusing on fractional anisotropy (FA). Data were 'fused' using a multivariate technique (CCA+jICA), producing four independent components (ICs) depicting white matter FA values that covaried with test performance. Correlations between age and IC loading coefficients identified three EF-DTI profiles that may change developmentally. In one, SS performance was linked to greater reliance on the FA of ventral brain tracts, and less on dorsal tracts with age. In another, white matter microstructure was related to a pattern of strong WM and weak SS that became more pronounced with age. A final IC revealed that younger individuals with low RI and high WM/SS skills typically matured out of this cognitive imbalance, underscored by white matter changes with age. These novel multivariate results begin to emphasize the complexity of brain structure-cognition relationships in adolescents and young adults.

Double dissociation between P300 components and task switch error type in healthy but not psychosis participants.

Event-related potentials (ERPs) during oddball tasks and the behavioral performance on the Penn Conditional Exclusion Task (PCET) measure context-appropriate responding: P300 ERPs to oddball targets reflect detection of input changes and context updating in working memory, and PCET performance indexes detection, adherence, and maintenance of mental set changes. More specifically, PCET variables quantify cognitive functions including inductive reasoning (set 1 completion), mental flexibility (perseverative errors), and working memory maintenance (regressive errors). Past research showed that both P300 ERPs and PCET performance are disrupted in psychosis. This study probed the possible neural correlates of 3 PCET abnormalities that occur in participants with psychosis via the overlapping cognitive demands of the two study paradigms. In a two-tiered analysis, psychosis (n = 492) and healthy participants (n = 244) were first divided based on completion of set 1 - which measures subjects' ability to use inductive reasoning to arrive at the correct set. Results showed that participants who failed set 1 produced lower parietal P300, independent of clinical status. In the second tier of analysis, a double dissociation was found among healthy set 1 completers: frontal P300 amplitudes were negatively associated with perseverative errors, and parietal P300 was negatively associated with regressive errors. In contrast, psychosis participants showed global P300 reductions regardless of PCET performance. From this we conclude that in psychosis, overall activations evoked by the oddball task are reduced while the cognitive functions required by PCET are still somewhat supported, showing some level of independence or compensatory physiology in psychosis between neural activities underlying the two tasks.

Clinical characterization and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)-1.

Clinically defined psychosis diagnoses are neurobiologically heterogeneous. The B-SNIP consortium identified and validated more neurobiologically homogeneous psychosis Biotypes using an extensive battery of neurocognitive and psychophysiological laboratory measures. However, typically the first step in any diagnostic evaluation is the clinical interview. In this project, we evaluated if psychosis Biotypes have clinical characteristics that can support their differentiation in addition to obtaining laboratory testing. Clinical interview data from 1907 individuals with a psychosis Biotype were used to create a diagnostic algorithm. The features were 58 ratings from standard clinical scales. Extremely randomized tree algorithms were used to evaluate sensitivity, specificity, and overall classification success. Biotype classification accuracy peaked at 91 % with the use of 57 items on average. A reduced feature set of 28 items, though, also showed 81 % classification accuracy. Using this reduced item set, we found that only 10-11 items achieved a one-vs-all (Biotype-1 or not, Biotype-2 or not, Biotype-3 or not) area under the sensitivity-specificity curve of .78 to .81. The top clinical characteristics for differentiating psychosis Biotypes, in order of importance, were (i) difficulty in abstract thinking, (ii) multiple indicators of social functioning, (iii) conceptual disorganization, (iv) severity of hallucinations, (v) stereotyped thinking, (vi) suspiciousness, (vii) unusual thought content, (viii) lack of spontaneous speech, and (ix) severity of delusions. These features were remarkably different from those that differentiated DSM psychosis diagnoses. This low-burden adaptive algorithm achieved reasonable classification accuracy and will support Biotype-specific etiological and treatment investigations even in under-resourced clinical and research environments.