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Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

BackgroundDysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.MethodsA subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.ResultsMultimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.ConclusionsAdolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.

Prior studies have reported long-term differences in brain structure (brain morphometry) as being associated with cumulative concussion and contact sport participation. There is emerging evidence to suggest that similar effects of prior concussion and contact sport participation on brain morphometry may be present in younger cohorts of active athletes. We investigated the relationship between prior concussion and primary sport participation with subcortical and cortical structures in active collegiate contact sport and non-contact sport athletes. Contact sport athletes (CS; N = 190) and matched non-contact sport athletes (NCS; N = 95) completed baseline clinical testing and participated in up to four serial neuroimaging sessions across a 6-months period. Subcortical and cortical structural metrics were derived using FreeSurfer. Linear mixed-effects (LME) models examined the effects of years of primary sport participation and prior concussion (0, 1+) on brain structure and baseline clinical variables. Athletes with prior concussion across both groups reported significantly more baseline concussion and psychological symptoms (all ps < 0.05). The relationship between years of primary sport participation and thalamic volume differed between CS and NCS (p = 0.015), driven by a significant inverse association between primary years of participation and thalamic volume in CS (p = 0.007). Additional analyses limited to CS alone showed that the relationship between years of primary sport participation and dorsal striatal volume was moderated by concussion history (p = 0.042). Finally, CS with prior concussion had larger hippocampal volumes than CS without prior concussion (p = 0.015). Years of contact sport exposure and prior concussion(s) are associated with differences in subcortical volumes in young-adult, active collegiate athletes, consistent with prior literature in retired, primarily symptomatic contact sport athletes. Longitudinal follow-up studies in these athletes are needed to determine clinical significance of current findings.

There has been a recent call for longitudinal cohort studies to track the physiological recovery of sport-related concussion (SRC) and its relationship with clinical recovery. Resting-state functional magnetic resonance imaging (rs-fMRI) has shown potential for detecting subtle changes in brain function after SRC. We investigated the effects of SRC on rs-fMRI metrics assessing local connectivity (regional homogeneity; REHO), global connectivity (average nodal strength), and the relative amplitude of slow oscillations of rs-fMRI (fractional amplitude of low-frequency fluctuations; fALFF). Athletes diagnosed with SRC (n = 92) completed visits with neuroimaging at 24-48 h post-injury (24 h), after clearance to begin the return-to-play (RTP) progression (asymptomatic), and 7 days following unrestricted RTP (post-RTP). Non-injured athletes (n = 82) completed visits yoked to the schedule of matched injured athletes and served as controls. Concussed athletes had elevated symptoms, worse neurocognitive performance, greater balance deficits, and elevated psychological symptoms at the 24-h visit relative to controls. These deficits were largely recovered by the asymptomatic visit. Concussed athletes still reported elevated psychological symptoms at the asymptomatic visit relative to controls. Concussed athletes also had elevated REHO in the right middle and superior frontal gyri at the 24-h visit that returned to normal levels by the asymptomatic visit. Additionally, REHO in these regions at 24 h predicted psychological symptoms at the asymptomatic visit in concussed athletes. Current results suggest that SRC is associated with an acute alteration in local connectivity that follows a similar time course as clinical recovery. Our results do not indicate strong evidence that concussion-related alterations in rs-fMRI persist beyond clinical recovery.

ObjectiveWe propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia.MethodsIt uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa.ResultsFindings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively.ConclusionN-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications.SignificanceThe use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

Previous studies have shown that mild traumatic brain injury (mTBI) can cause abnormalities in clinically relevant magnetic resonance imaging (MRI) sequences. No large-scale study, however, has prospectively assessed this in athletes with sport-related concussion (SRC). The aim of the current study was to characterize and compare the prevalence of acute, trauma-related MRI findings and clinically significant, non-specific MRI findings in athletes with and without SRC. College and high-school athletes were prospectively enrolled and participated in scanning sessions between January 2015 through August 2017. Concussed contact sport athletes (n = 138; 14 female [F]; 19.5 ± 1.6 years) completed up to four scanning sessions after SRC. Non-concussed contact (n = 135; 15 F; 19.7 ± 1.6) and non-contact athletes (n = 96; 15 F; 20.0 ± 1.7) completed similar scanning sessions and served as controls. Board-certified neuroradiologists, blinded to SRC status, reviewed T1-weighted and T2-weighted fluid-attenuated inversion recovery and T2*-weighted and T2-weighted images for acute (i.e., injury-related) or non-acute findings that prompted recommendation for clinical follow-up. Concussed athletes were more likely to have MRI findings relative to contact (30.4% vs. 15.6%; odds ratio [OR] = 2.32; p = 0.01) and non-contact control athletes (19.8%; OR = 2.11; p = 0.04). Female athletes were more likely to have MRI findings than males (43.2% vs. 19.4%; OR = 2.62; p = 0.01). One athlete with SRC had an acute, injury-related finding; group differences were largely driven by increased rate of non-specific white matter hyperintensities in concussed athletes. This prospective, large-scale study demonstrates that

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45 ± 8.9%, sensitivity 0.74 ± 0.11, specificity 0.79 ± 0.11) than STR (accuracy 67.45 ± 8.13%, sensitivity 0.65 ± 0.11, specificity 0.71 ± 0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

Genetic factors are known to influence both risk for schizophrenia (SZ) and variation in brain structure. A pressing question is whether the genetic underpinnings of brain phenotype and the disorder overlap. Using multivariate analytic methods and focusing on 1,402 common single-nucleotide polymorphisms (SNPs) mapped from the Psychiatric Genomics Consortium (PGC) 108 regions, in 777 discovery samples, we identified 39 SNPs to be significantly associated with SZ-discriminating gray matter volume (GMV) reduction in inferior parietal and superior temporal regions. The findings were replicated in 609 independent samples. These 39 SNPs in chr6:28308034-28684183 (6p22.1), the most significant SZ-risk region reported by PGC, showed regulatory effects on both DNA methylation and gene expression of postmortem brain tissue and saliva. Furthermore, the regulated methylation site and gene showed significantly different levels of methylation and expression in the prefrontal cortex between cases and controls. In addition, for one regulated methylation site we observed a significant in vivo methylation-GMV association in saliva, suggesting a potential SNP-methylation-GMV pathway. Notably, the risk alleles inferred for GMV reduction from in vivo imaging are all consistent with the risk alleles for SZ inferred from postmortem data. Collectively, we provide evidence for shared genetic risk of SZ and regional GMV reduction in 6p22.1 and demonstrate potential molecular mechanisms that may drive the observed in vivo associations. This study motivates dissecting SZ-risk variants to better understand their associations with focal brain phenotypes and the complex pathophysiology of the illness.

Cerebrovascular activity is not only crucial to optimal cerebral perfusion, but also plays an important role in the glymphatic clearance of interstitial waste, including α‐synuclein. This highlights a need to evaluate how cerebrovascular activity is altered in Lewy body diseases. This review begins by discussing how vascular risk factors and cardiovascular autonomic dysfunction may serve as upstream or direct influences on cerebrovascular activity. We then discuss how patients with Lewy body disease exhibit reduced and delayed cerebrovascular activity, hypoperfusion, and reductions in measures used to capture cerebrospinal fluid flow, suggestive of a reduced capacity for glymphatic clearance. Given the lack of an existing framework, we propose a model by which these processes may foster α‐synuclein aggregation and neuroinflammation. Importantly, this review highlights several avenues for future research that may lead to treatments early in the disease course, prior to neurodegeneration. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Cerebral autoregulation is an intrinsic myogenic response of cerebral vasculature that allows for preservation of stable cerebral blood flow levels in response to changing systemic blood pressure. It is effective across a broad range of blood pressure levels through precapillary vasoconstriction and dilation. Autoregulation is difficult to directly measure and methods to indirectly ascertain cerebral autoregulation status inherently require certain assumptions. Patients with impaired cerebral autoregulation may be at risk of brain ischemia. One of the central mechanisms of ischemia in patients with metabolically compromised states is likely the triggering of spreading depolarization (SD) events and ultimately, terminal (or anoxic) depolarization. Cerebral autoregulation and SD are therefore linked when considering the risk of ischemia. In this scoping review, we will discuss the range of methods to measure cerebral autoregulation, their theoretical strengths and weaknesses, and the available clinical evidence to support their utility. We will then discuss the emerging link between impaired cerebral autoregulation and the occurrence of SD events. Such an approach offers the opportunity to better understand an individual patient's physiology and provide targeted treatments. [ABSTRACT FROM AUTHOR]

Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia. Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive performance in individuals with schizophrenia and healthy controls. A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia using multiple cognitive domain scores. Results highlight the salience network (gray matter, GM), corpus callosum (fractional anisotropy, FA), central executive and default-mode networks (fractional amplitude of low-frequency fluctuation, fALFF) as modality-specific biomarkers of generalized cognition. FALFF features are found to be more sensitive to cognitive domain differences, while the salience network in GM and corpus callosum in FA are highly consistent and predictive of multiple cognitive domains. These modality-specific brain regions define-in three separate cohorts-promising co-varying multimodal signatures that can be used as predictors of multi-domain cognition.

Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.

Background: Functional underpinnings of cognitive control deficits in unbiased samples (i.e., all comers) of patients with psychotic spectrum disorders (PSD) remain actively debated. While many studies suggest hypofrontality in the lateral prefrontal cortex (PFC) and greater deficits during proactive relative to reactive control, few have examined the full hemodynamic response. Methods: Patients with PSD (n = 154) and healthy controls (n = 65) performed the AX continuous performance task (AX-CPT) during rapid (460 ms) functional neuroimaging and underwent full clinical characterization. Results: Behavioural results indicated generalized cognitive deficits (slower and less accurate) across proactive and reactive control conditions in patients with PSD relative to healthy controls. We observed a delayed/prolonged neural response in the left dorsolateral PFC, the sensorimotor cortex and the superior parietal lobe during proactive control for patients with PSD. These proactive hemodynamic abnormalities were better explained by negative rather than by positive symptoms or by traditional diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition, Text Revision (DSM-IV-TR), with subsequent simulations unequivocally demonstrating how these abnormalities could be erroneously interpreted as hypoactivation. Conversely, true hypoactivity, unassociated with clinical symptoms or DSM-IV-TR diagnoses, was observed within the ventrolateral PFC during reactive control. Limitations: In spite of guidance for AX-CPT use in neuroimaging studies, one-third of patients with PSD could not perform the task above chance and were more clinically impaired. Conclusion: Current findings question the utility of the AX-CPT for neuroimaging-based appraisal of cognitive control across the full spectrum of patients with PSD. Previously reported lateral PFC 'hypoactivity' during proactive control may be more indicative of a delayed/prolonged neural response, important for rehabilitative purposes. Negative symptoms may better explain certain behavioural and hemodynamic abnormalities in patients with PSD relative to DSM-IV-TR diagnoses., Introduction Cognitive dysfunction represents a core deficit in patients with psychotic spectrum disorders (PSDs) and is associated with impairments in everyday functioning (1-2) as well as with increased negative symptoms. [...]