Multi-modal deep learning from imaging genomic data for schizophrenia classification.

Background: Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual's cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises. Methods: Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC). Results: Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%. Conclusion: We proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.

Structural white matter abnormalities in Schizophrenia and associations with neurocognitive performance and symptom severity.

Schizophrenia is associated with robust white matter (WM) abnormalities but influences of potentially confounding variables and relationships with cognitive performance and symptom severity remain to be fully determined. This study was designed to evaluate WM abnormalities based on diffusion tensor imaging (DTI) in individuals with schizophrenia, and their relationships with cognitive performance and symptom severity. Data from individuals with schizophrenia (SZ; n=138, mean age±SD=39.02±11.82; 105 males) and healthy controls (HC; n=143, mean age±SD=37.07±10.84; 102 males) were collected as part of the Function Biomedical Informatics Research Network Phase 3 study. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were compared between individuals with schizophrenia and healthy controls, and their relationships with neurocognitive performance and symptomatology assessed. Individuals with SZ had significantly lower FA in forceps minor and the left inferior fronto-occipital fasciculus compared to HC. FA in several tracts were associated with speed of processing and attention/vigilance and the severity of the negative symptom alogia. This study suggests that regional WM abnormalities are fundamentally involved in the pathophysiology of schizophrenia and may contribute to cognitive performance deficits and symptom expression observed in schizophrenia.

Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia.

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.

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.

Uptake of self-management education programmes for people with type 2 diabetes in primary care through the embedding package: a cluster randomised control trial and ethnographic study.

BACKGROUND: Self-management education programmes are cost-effective in helping people with type 2 diabetes manage their diabetes, but referral and attendance rates are low. This study reports on the effectiveness of the Embedding Package, a programme designed to increase type 2 diabetes self-management programme attendance in primary care. METHODS: Using a cluster randomised design, 66 practices were randomised to: (1) a wait-list group that provided usual care for nine months before receiving the Embedding Package for nine months, or (2) an immediate group that received the Embedding Package for 18 months. 'Embedders' supported practices and self-management programme providers to embed programme referral into routine practice, and an online 'toolkit' contained embedding support resources. Patient-level HbA1c (primary outcome), programme referral and attendance data, and clinical data from 92,977 patients with type 2 diabetes were collected at baseline (months - 3-0), step one (months 1-9), step 2 (months 10-18), and 12 months post-intervention. An integrated ethnographic study including observations, interviews, and document analysis was conducted using interpretive thematic analysis and Normalisation Process Theory. RESULTS: No significant difference was found in HbA1c between intervention and control conditions (adjusted mean difference [95% confidence interval]: -0.10 [-0.38, 0.18] mmol/mol; -0.01 [-0.03, 0.02] %). Statistically but not clinically significantly lower levels of HbA1c were found in people of ethnic minority groups compared with non-ethnic minority groups during the intervention condition (-0.64 [-1.08, -0.20] mmol/mol; -0.06% [-0.10, -0.02], p = 0.004), but not greater self-management programme attendance. Twelve months post-intervention data showed statistically but not clinically significantly lower HbA1c (-0.56 [95% confidence interval: -0.71, -0.42] mmol/mol; -0.05 [-0.06, -0.04] %; p < 0.001), and higher self-management programme attendance (adjusted odds ratio: 1.13; 95% confidence interval: 1.02, 1.25; p = 0.017) during intervention conditions. Themes identified through the ethnographic study included challenges for Embedders in making and sustaining contact with practices and providers, and around practices' interactions with the toolkit. CONCLUSIONS: Barriers to implementing the Embedding Package may have compromised its effectiveness. Statistically but not clinically significantly improved HbA1c among ethnic minority groups and in longer-term follow-up suggest that future research exploring methods of embedding diabetes self-management programmes into routine care is warranted. TRIAL REGISTRATION: ISRCTN23474120, registered 05/04/2018.

Differences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset.

Formal thought disorder (FTD) is a clinical key factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, the relationship between FTD symptom dimensions and patterns of regional brain volume loss in schizophrenia remains to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles by enrolling a large multi-site cohort acquired by the ENIGMA Schizophrenia Working Group (752 schizophrenia patients and 1256 controls), to unravel the neuroanatomy of FTD in schizophrenia and using virtual histology tools on implicated brain regions to investigate the cellular basis. Based on the findings of previous clinical and neuroimaging studies, we decided to separately explore positive, negative and total formal thought disorder. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but positive and negative FTD demonstrated a dissociation: negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD also showed associations with microglial cell types. These results provide an important step towards linking FTD to brain structural changes and their cellular underpinnings, providing an avenue for a better mechanistic understanding of this syndrome.

A custom-built step exergame training programme to prevent falls in people with multiple sclerosis: A multicentre randomised controlled trial.

BACKGROUND: Cognitive-motor step training can improve stepping, balance and mobility in people with multiple sclerosis (MS), but effectiveness in preventing falls has not been demonstrated. OBJECTIVES: This multisite randomised controlled trial aimed to determine whether 6 months of home-based step exergame training could reduce falls and improve associated risk factors compared with usual care in people with MS. METHODS: In total, 461 people with MS aged 22-81 years were randomly allocated to usual care (control) or unsupervised home-based step exergame training (120 minutes/week) for 6 months. The primary outcome was rate of falls over 6 months from randomisation. Secondary outcomes included physical, cognitive and psychosocial function at 6 months and falls over 12 months. RESULTS: Mean (standard deviation (SD)) weekly training duration was 70 (51) minutes over 6 months. Fall rates did not differ between intervention and control groups (incidence rates (95% confidence interval (CI)): 2.13 (1.57-2.69) versus 2.24 (1.35-3.13), respectively, incidence rate ratio: 0.96 (95% CI: 0.69-1.34, p = 0.816)). Intervention participants performed faster in tests of choice-stepping reaction time at 6 months. No serious training-related adverse events were reported. CONCLUSION: The step exergame training programme did not reduce falls among people with MS. However, it significantly improved choice-stepping reaction time which is critical to ambulate safely in daily life environment.

A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry.

Psychosis (including symptoms of delusions, hallucinations, and disorganized conduct/speech) is a main feature of schizophrenia and is frequently present in other major psychiatric illnesses. Studies in individuals with first-episode (FEP) and early psychosis (EP) have the potential to interpret aberrant connectivity associated with psychosis during a period with minimal influence from medication and other confounds. The current study uses a data-driven whole-brain approach to examine patterns of aberrant functional network connectivity (FNC) in a multi-site dataset comprising resting-state functional magnetic resonance images (rs-fMRI) from 117 individuals with FEP or EP and 130 individuals without a psychiatric disorder, as controls. Accounting for age, sex, race, head motion, and multiple imaging sites, differences in FNC were identified between psychosis and control participants in cortical (namely the inferior frontal gyrus, superior medial frontal gyrus, postcentral gyrus, supplementary motor area, posterior cingulate cortex, and superior and middle temporal gyri), subcortical (the caudate, thalamus, subthalamus, and hippocampus), and cerebellar regions. The prominent pattern of reduced cerebellar connectivity in psychosis is especially noteworthy, as most studies focus on cortical and subcortical regions, neglecting the cerebellum. The dysconnectivity reported here may indicate disruptions in cortical-subcortical-cerebellar circuitry involved in rudimentary cognitive functions which may serve as reliable correlates of psychosis.

The Benefits of Cochlear Implantation for Adults: A Systematic Umbrella Review.

OBJECTIVES: The uptake of cochlear implants among adults who could benefit (based on pure-tone audiometry) in developed countries is estimated to be less than 10%. Concerns about potential surgical complications, fear of losing residual hearing, and limited awareness about the benefits of this intervention contribute to the low adoption rate. To enhance quality of life and improve the uptake of cochlear implants, it is essential to have a clear understanding of their benefits. DESIGN: This umbrella review aims to summarize the major benefits of cochlear implant usage in adults, by synthesizing findings from published review articles. A comprehensive search of databases including MEDLINE, EMBASE, PsycINFO, and Google Scholar, was conducted. The search was limited to English-language review articles published between 1990 and 2022, focusing on cochlear implant outcomes in at least 5 adults (aged ≥18 years). Two independent reviewers screened titles, abstracts, and full-text articles, and conducted a quality assessment using the Joanna Briggs Checklist for Systematic Reviews and Research Syntheses. RESULTS: Forty-two articles were included in this review. There were 15 systematic reviews with meta-analysis, 25 systematic reviews without meta-analysis, and 2 systematic scoping reviews. All 42 articles underwent quality assessment using the Joanna Briggs Institute Checklist for Systematic Reviews and Research Syntheses, of which 40% (n = 17) satisfied 9 out of 11 quality criteria. This umbrella review shows that cochlear implants are associated with improvements in speech perception and recognition as well as improved quality of life and cognition. These benefits are observed in a significant proportion of adults undergoing the procedure, highlighting its effectiveness as a viable intervention for individuals with severe to profound hearing loss. CONCLUSIONS: The potential benefits of cochlear implantation appear to outweigh the risks and complications associated with the procedure. It is recommended that adults with severe to profound hearing loss in particular, engage in informed discussions with healthcare professionals to consider cochlear implantation as a viable treatment option.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.

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

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers.

Fibromyalgia is a chronic pain syndrome that presents with a constellation of broad symptoms, including decreased physical function, fatigue, cognitive disturbances, and other somatic complaints. Available therapies are often insufficient in treating symptoms, with inadequate pain control commonly leading to opioid usage for attempted management. Cranial electrical stimulation (CES) is a promising non-pharmacologic treatment option for pain conditions that uses pulsed electrical current stimulation to modify brain function via transcutaneous electrodes. These neural mechanisms and the applications of CES in fibromyalgia symptom relief require further exploration. A total of 50 participants from the Atlanta Veterans Affairs Healthcare System (VAHCS) diagnosed with fibromyalgia were enrolled and then block-randomized into either a placebo plus standard therapy or active CES plus standard therapy group. Baseline assessments were obtained prior to the start of treatment. Both interventions occurred over 12 weeks, and participants were assessed at 6 weeks and 12 weeks after treatment initiation. The primary outcome investigated whether pain and functional improvements occur with the application of CES. Additionally, baseline and follow-up resting state functional connectivity magnetic resonance imaging (rs-fcMRI) were obtained at the 6-week and 12-week time points to assess for clinical applications of neural connectivity biomarkers and the underlying neural associations related to treatment effects. This is a randomized, placebo-controlled trial to determine the efficacy of CES for improving pain and function in fibromyalgia and further develop rs-fcMRI as a clinical tool to assess the neural correlates and mechanisms of chronic pain and analgesic response.

Exergame and cognitive training for preventing falls in community-dwelling older people: a randomized controlled trial.

Exergame training, in which video games are used to promote exercise, can be tailored to address cognitive and physical risk factors for falls and is a promising method for fall prevention in older people. Here, we performed a randomized clinical trial using the smart±step gaming system to examine the effectiveness of two home-based computer game interventions, seated cognitive training and step exergame training, for fall prevention in community-dwelling older people, as compared with a minimal-intervention control group. Participants aged 65 years or older (n = 769, 71% female) living independently in the community were randomized to one of three arms: (1) cognitive training using a computerized touchpad while seated, (2) exergame step training on a computerized mat or (3) control (provided with an education booklet on healthy ageing and fall prevention). The rate of falls reported monthly over 12 months-the primary outcome of the trial-was significantly reduced in the exergame training group compared with the control group (incidence rate ratio = 0.74, 95% confidence interval = 0.56-0.98), but was not statistically different between the cognitive training and control groups (incidence rate ratio = 0.86, 95% confidence interval = 0.65-1.12). No beneficial effects of the interventions were found for secondary outcomes of physical and cognitive function, and no serious intervention-related adverse events were reported. The results of this trial support the use of exergame step training for preventing falls in community-dwelling older people. As this intervention can be conducted at home and requires only minimal equipment, it has the potential for scalability as a public health intervention to address the increasing problem of falls and fall-related injuries. Australian and New Zealand Clinical Trial Registry identifier: ACTRN12616001325493 .

Civilian Moral Injury and Amygdala Functional Connectivity During Attention to Threat.

BACKGROUND: Moral injury references emotional and spiritual/existential suffering that may emerge following psychological trauma. Despite being linked to adverse mental health outcomes, little is known about the neurophysiological mechanisms of this phenomenon. In this study, we examined neural correlates of moral injury exposure and distress using the Moral Injury Exposure and Symptom Scale for Civilians. We also examined potential moderation of these effects by race (Black vs. White individuals) given the likely intersection of race-related stress with moral injury. METHODS: Forty-eight adults ages 18 to 65 years (mean age = 30.56, SD = 11.93) completed the Moral Injury Exposure and Symptom Scale for Civilians and an affective attentional control measure, the affective Stroop task (AS), during functional magnetic resonance imaging; the AS includes presentation of threat-relevant and neutral distractor stimuli. Voxelwise functional connectivity of the bilateral amygdala was examined in response to threat-relevant versus neutral AS distractor trials. RESULTS: Functional connectivity between the right amygdala and left postcentral gyrus/primary somatosensory cortex was positively correlated with the Moral Injury Exposure and Symptom Scale for Civilians exposure score (voxelwise p < .001, cluster false discovery rate-corrected p < .05) in response to threat versus neutral AS distractor trials. Follow-up analyses revealed significant effects of race; Black but not White participants demonstrated this significant pattern of amygdala-left somatosensory cortex connectivity. CONCLUSIONS: Increased exposure to potentially morally injurious events may lead to emotion-somatosensory pathway disruptions during attention to threat-relevant stimuli. These effects may be most potent for individuals who have experienced multilayered exposure to morally injurious events, including racial trauma. Moral injury appears to have a distinct neurobiological signature that involves abnormalities in connectivity of emotion-somatosensory paths, which may be amplified by race-related stress.

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.

White matter predictors of PTSD: Testing different machine learning models in a sample of Black American women.

BACKGROUND: Machine learning neuroimaging studies of posttraumatic stress disorder (PTSD) show promise for identifying neurobiological signatures of PTSD. However, studies to date, have largely evaluated a single machine learning approach, and few studies have examined white matter microstructure as a predictor of PTSD. Further, individuals from minoritized racial groups, specifically, Black individuals, who experience disproportionate trauma frequency, and have relatively higher rates of PTSD, have been underrepresented in these studies. We used four different machine learning models to test white matter microstructure classifiers of PTSD in a sample of trauma-exposed Black American women with and without PTSD. METHOD: Participants included 45 Black women with PTSD and 89 trauma-exposed controls recruited from an ongoing trauma study. Current PTSD presence was estimated using the Clinician-Administered PTSD Scale. Average fractional anisotropy of 53 white matter tracts served as input features. Additional exploratory analysis incorporated estimates of interpersonal and structural racism exposure. Classification models included linear support vector machine, radial basis function support vector machine, multilayer perceptron, and random forest. RESULTS: Performance varied notably between models. With white matter features along, linear support vector machine demonstrated the best model fit and reached an average AUC = 0.643. Inclusion of estimates of exposure to racism increased linear support vector machine performance (AUC = 0.808). CONCLUSIONS: White matter microstructure had limited ability to predict PTSD presence in this sample. These results may indicate that the relationship between white matter microstructure and PTSD may be nuanced across race and gender spectrums.

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.

Neural Correlates of Positive and Negative Formal Thought Disorder in Individuals with Schizophrenia: An ENIGMA Schizophrenia Working Group Study.

Formal thought disorder (FTD) is a key clinical factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, relationship between FTD symptom dimensions and patterns of regional brain volume deficiencies in schizophrenia remain to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles based on a large multi-site cohort through the ENIGMA Schizophrenia Working Group (752 individuals with schizophrenia and 1256 controls), to unravel the neuroanatomy of positive, negative and total FTD in schizophrenia and their cellular bases. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks for positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD was also linked to microglial cell types. These findings relate different dimensions of FTD to distinct brain structural changes and their cellular underpinnings, improve our mechanistic understanding of these key psychotic symptoms.

Auditory oddball hypoactivation in schizophrenia.

Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts.

NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data.

Introduction: Open science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies. Methods: The NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles. Results: The NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section "Methods" of the article. Articles returned from NeuroQuery based on the same search are also presented. Conclusion: The NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user's research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering "enough data of the right kind," ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis.

BACKGROUND: Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM. METHODS: 313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM. RESULTS: We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety. CONCLUSION: Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.