Biology subjects

Kochunov, P.

Publications and source records attributed to Kochunov, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Thalamic Dysconnectivity and Negative Symptoms in Schizophrenia Spectrum Disorder

Background and Hypothesis Negative symptoms in schizophrenia spectrum disorder (SSD) are associated with poor functional outcomes and reduced quality of life; however, effective treatments remain limited. A better understanding of the underlying brain mechanisms is needed to develop better treatments. Neuroimaging studies implicate multiple brain networks in negative symptoms, though findings remain heterogeneous, potentially reflecting the multidimensionality of both symptoms and brain networks. Larger, more systematic dissections are required to identify the key hub(s) underlying negative symptoms. Study Design Structural MRI data from 394 participants (210 SSD, 184 healthy controls [HC]) were analyzed to identify cortical thickness and subcortical volume demonstrating both SSD-HC differences and associations with negative symptoms measured by the Brief Negative Symptom Scale (BNSS). Regions meeting both criteria were selected as seeds for resting-state functional connectivity analyses in 143 SSD patients to examine association with negative symptoms and their subdomains. Study Results The thalamus was the only region demonstrating significant structural abnormalities both in SSD and an association with BNSS total score (p-FDR<0.05). Functional-network-defined thalamic subregions were subsequently examined. Several thalamic hypoconnectivities were significantly corresponded with negative symptoms, involving thalamic subregions associated with the cortical default mode, somatosensory, medial temporal, and dorsal attention networks (p=0.001, corrected<0.05). Blunted affect showed the most extensive thalamic connectivity alterations, involving the striatum, cerebellum, and temporoparietal junction/superior temporal sulcus (TPJ-STS). Thalamic hypoconnectivity with TPJ-STS was also associated with overall negative symptoms and anhedonia. Conclusions This study identifies the thalamus as a central hub of negative symptom-related dysfunction in SSD and highlights specific thalamic subnetworks as potential targets for circuit-based interventions.

neuroscience↗

bioRxiv2026

Trustworthy ML/AI for Aging Clocks: Preventing Systematic Prediction Bias in Biological Age Estimation

Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from omics and medical imaging data as distinct from chronological age. Here, we characterize a fundamental but underappreciated statistical limitation of commonly used ML/AI regression models for continuous outcomes: systematic prediction bias and its propagation to downstream association estimates. This issue becomes more challenging when the true outcome, biological age, is latent and therefore unobserved during ML/AI model training. We demonstrate that systematic prediction bias can distort and, in some cases, even reverse downstream association analyses that use aging clocks as ML/AI-predicted outcomes to assess their associations with exposures or clinical factors. For example, it can produce spurious associations suggesting that older predicted brain age is linked to better cognitive performance, or that older epigenetic age is associated with better kidney function. To address this problem, we introduce a principled and broadly applicable ML/AI regression framework based on constrained optimization, yielding better calibrated aging-clock estimates and valid downstream inference.

bioinformatics↗

bioRxiv2026

The Regional Vulnerability Index (RVI) as a Neuroimaging-Based Biomarker for Autism: Associations with Likelihood, Cognition, and Longitudinal Social Outcomes

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with symptoms spanning cognitive, social and behavioral domains and leading to lifelong challenges. Autism is heritable but specific genetic and environmental factors that shape its early neurodevelopmental trajectory remain unknown. Despite the clinical variability, neuroimaging findings from Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA)-ASD consortium identified stable and replicable patterns of cortical and subcortical differences that were consistent with those reported by an independent consortium, the Cognitive Genetics Collaborative Research Organization (COCORO) in Japan. Here we developed and evaluated a regional vulnerability index (RVI), a similarity metric that quantifies how closely a persons brain resembles the specific pattern of an individual with autism. RVI-ASD is based on combining the regional effect sizes for regional brain measurements published by the ENIGMA-ASD group with microstructural white matter integrity differences reported by COCORO. RVI-ASD showed significantly higher effect size for case-control differences relative to any individual brain measure (d=0.30 vs. d=0.01-0.21) in individuals with autism, particularly in the adolescent-to-adult sample (N=2,577; Mean age = 16.1; SD=5.0). We next calculated RVI-ASD in the baseline and follow-up (ages 10 and 12) data from normally developing participants of the ABCD study (N=4,201). We tested the longitudinal stability, heritability, genotype-by-age interaction and sensitivity of RVI-ASD to known factors and cognitive measurements. RVI-ASD were stable on the 2-year follow up (ICC=0.76-0.92); showed significant heritability (h{superscript 2}=0.55-0.83, p<10-16) but was not affected by gene-by-age interaction. RVI-ASD showed significant positive correlation with paternal age, while correlation with maternal age was non-significant. Baseline and follow-up RVI-ASD were negatively correlated with cognitive measures including total, fluid and crystallized intelligence (r=-0.09 to -0.11, p<10-6). RVI-ASD scores tracked with core autism phenotypes including poor eye contact and rigid routines (p < .01). In a sub-sample of children with symptoms of autism (N=20), we found that earlier age of symptom onset was strongly correlated with higher White Matter RVI (r = -0.61), linking early behavioral emergence to the neuroanatomical signature. Longitudinal changes in subcortical RVI-ASD are significantly correlated with change in social functioning. The RVI-ASD is a neuroimaging-based biomarker linked to stable and reproducible brain patterns in autism. RVI-ASD allows researchers to study associations with factors associated with the likelihood for autism and cognition across large and inclusive non-clinical samples, thus moving beyond simple case-control models to understand the biological pathways of autism.

neuroscience↗

bioRxiv2026

NeuroMark-SZ: A Holistic Resting-State-fMRI-Based Model for Divergent Functional Circuitry in Schizophrenia

BackgroundSchizophrenia is a severe neuropsychiatric disorder. Efforts to describe the underlying biology and establish diagnostic markers through non-invasive neuroimaging methods are ongoing, resulting in a range of theoretical brain-based frameworks. Prominent frameworks for aberrant schizophrenia-associated functional connectivity in resting-state functional magnetic resonance imaging (rsfMRI) include the dysconnectivity hypothesis, theory of cognitive dysmetria, and triple network theory. Although informative, prior work can be improved by increasing sample size, avoiding confirmation bias, and accounting for individual variability and the effects of medication and chronicity. MethodsWith these recommendations in mind, we employed a data-driven, whole-brain approach using a large multi-site rsfMRI dataset (N = 2,656; schizophrenia = 1,248). We used reference-guided independent component analysis to generate subject-specific whole-brain functional network connectivity (FNC) and extract imaging markers of similarity to schizophrenia patterns. We modeled the relationship between medication dosage, age of onset, chronicity, symptom severity, and cognitive performance and FNC. ResultsOur analysis identified a reliable schizophrenia-FNC signature characterized by aberrantly stronger negative cerebellothalamic and positive thalamocortical connectivity, implicating sensory, motor, and associative cortical circuits. While medication and chronicity were significantly associated with these signatures, the core cerebellothalamic disruptions remained a robust marker of schizophrenia. ConclusionsThis work represents the largest schizophrenia-specific rsfMRI study to date, refines existing theoretical frameworks with a more nuanced map of how clinical variables interact with brain connectivity, and provides a high-fidelity template of schizophrenia-related connectivity. We have released this template as an open-access resource to facilitate reproducibility and accelerate the development of reliable rsfMRI-based schizophrenia biomarkers.

neuroscience↗

bioRxiv2026

Identification of a miRNA signature for schizophrenia in plasma-derived extracellular vesicles

Background and Hypothesis: Extracellular vesicles (EVs) are phospholipid bilayer vesicles released from cells containing proteins, lipids and nucleic acids derived from the parent cell. Alterations in miRNA expression within blood-derived EVs have been proposed as potential biomarkers of disease. Specifically, identification of differentially expressed miRNAs in patients with schizophrenia (SZ) compared to healthy individuals could be used as a "miRNA signature" to aid in diagnosis and treatment. We therefore aimed to identify differentially expressed miRNAs in plasma-derived EVs between people living with SZ and healthy controls and to correlate miRNA levels with SZ-relevant clinical measures. Study Design: Plasma-derived EVs were isolated from a cohort of 33 individuals with SZ and 34 controls. Expression of 84 miRNAs was examined using a RT-qPCR panel. Study Results: Three miRNAs (hsa-miR-30e-5p, hsa-miR-103a-3p, hsa-miR-200b-3p) were differentially expressed between controls and patients. Gene ontology analysis of putative target genes shared between these miRNAs revealed enrichment of biological process terms related to neurogenesis. Analysis of miRNA expression compared to clinical measures showed that hsa-miR-103a-3p expression was associated with working memory and negatively correlated with white matter integrity in the combined patient-control group. Conclusions: We have identified a miRNA signature for SZ in plasma-derived EVs and shown for the first time that hsa-miR-30e-5p expression is significantly increased in plasma-derived EVs in SZ. The genetic links between differentially expressed miRNAs and neurogenesis, along with the correlations of hsa-miR-103a-3p with working memory and white matter integrity may underlie the functional importance of altered expression of the identified miRNAs in SZ.

genetics↗

bioRxiv2025

Mitochondrial Response to Psychological Stress and Its Medial Prefrontal Biomarker Correlates

BackgroundStress response obligates increased mitochondrial activities to meet stress-induced high energy requirement. This stress-mitochondrial response process involves glucocorticoid but also multiple alternative pathways that are top-down regulated by the medial prefrontal cortex (mPFC). These pathways are important for many neuropsychiatric conditions that are sensitive to stress. However, the field lacks a reliable, clinically accessible stress-mitochondrial response paradigm to study the process in humans. MethodWe used an established psychological stress challenge combined with assaying salivary cell-free mitochondrial DNA (cf-mtDNA), thought to reflect heightened mitochondrial changes or disruptions, in 35 healthy individuals (21 males). We also explored if these stress-induced cf-mtDNA marker elevations were associated brain metabolites as measured by magnetic resonance spectroscopy (MRS), as well as high-resolution brain imaging based cortical thickness focusing on the mPFC. ResultsWe found that salivary cf-mtDNA was significant elevated immediately after the stress challenge (p=2.0x10-7) and gradually declined after. Exploratory causal analysis showed that this cf-mtDNA response was not primarily driven by cortisol response. Instead, individuals with higher baseline dACC lactate+ levels, thought to in part reflect mitochondrial dysfunctions, was significantly associated with the cf-mtDNA response (r=0.80, p<0.001). Higher mtDNA response was also significantly associated with thinner dorsomedial prefrontal cortex (r=-0.52, p=0.01). Age had a U-shape effect such that cf-mtDNA response trended lower in earlier adulthood but higher in older people, explaining 33.8% of the ct-mtDNA response variance (p=0.003). ConclusionThis stress challenge-salivary cf-mtDNA assay paradigm may offer a new, non-invasive approach to evaluate the stress-mitochondrial pathway functioning in aging, psychopharmacology, and neuropsychiatric conditions where psychological stress plays a role.

neuroscience↗

bioRxiv2025

Blood and Neuronal Extracellular Vesicle Mitochondrial Disruptions in Schizophrenia

The high energy demand of the human brain obligates robust mitochondrial energy metabolism, while mitochondrial dysfunctions have been linked to neuropsychiatric disorders including schizophrenia spectrum disorders (SSD). However, in vivo assessments that can directly inform brain mitochondrial functioning and its etiopathophysiological path to SSD remain difficult to obtain. We hypothesized that system and brain mitochondrial dysfunctions in SSD may be indexed by elevated cell-free mitochondrial DNA (cf-mtDNA) levels in the blood and in neuronal extracellular vesicles (nEVs). We also explored if these mtDNA marker elevations were associated brain metabolites as measured by magnetic resonance spectroscopy (MRS). We examined blood cf-mtDNA in 58 SSD patients and 33 healthy controls, followed by assessing nEV mtDNA and metabolite levels using MRS in a subgroup of patients and controls. We found that people with SSD had significantly elevated cf-mtDNA levels in both the blood (p=0.0002) and neuronal EVs (p=0.003) compared to controls. These mtDNA abnormalities can be linked back to brain lactate+ levels such that higher blood and nEV mtDNA levels were significantly associated with higher lactate+ levels measured at the anterior cingulate cortex (r=0.53, 0.53; p=0.008, 0.03, respectively) in SSD patients. Furthermore, higher developmental stress and trauma were significantly associated with higher cf-mtDNA levels in both the blood and neuronal EVs in SSD patients (r=0.29, 0.49; p=0.01, 0.03, respectively). In conclusion, if replicated and fully developed, blood and neuronal EV-based cell free mtDNA may provide a clinically accessible biomarker to more directly evaluate the mitochondrial hypothesis and the abnormal bioenergetics pathways in schizophrenia.

neuroscience↗

bioRxiv2025

aNy-way ICA and its application to estimate cortico-thalamo-cerebellar functional links in schizophrenia

Disrupted large-scale brain network interactions are a hallmark of schizophrenia, yet the circuit-level mechanisms underlying cognitive deficits remain incompletely understood. In particular, cortico-thalamo-cerebellar circuitry has been implicated in "cognitive dysmetria," but its functional organization across subdomains has not been fully characterized. Here, we apply a flexible multimodal data fusion framework (aNy-way independent component analysis) to integrate 4D domain-specific resting-state fMRI data from the cortex, thalamus, and cerebellum. Using a large discovery cohort (FBIRN, N=311) and two independent replication datasets (COBRE, N=148; MPRC, N=364), we identify a robust cortico-thalamo-cerebellar circuit involving higher-order thalamic nuclei, visual cortex, default mode network, and cerebellar posterior regions. This circuit shows altered connectivity in schizophrenia, characterized by reduced anticorrelation between thalamus and cortex, and is consistently associated with cognitive impairments, including processing speed, working memory, and reasoning deficits across cohorts. Importantly, these findings are largely replicated across independent cohorts. Together, these results provide data-driven evidence for a cortico-thalamo-cerebellar mechanism underlying cognitive dysfunction in schizophrenia, supporting the cognitive dysmetria hypothesis. This work also demonstrates the utility of aNy-way independent component analysis for identifying clinically relevant brain circuits in psychiatric disorders. HighlightsO_LIIdentified cortico-thalamo-cerebellar circuits in schizophrenia C_LIO_LIAltered thalamocortical interactions differentiated schizophrenia C_LIO_LICircuit alterations were associated with cognitive impairment C_LIO_LIFindings are replicated across three independent multisite datasets C_LIO_LIMultidomain fusion enabled subdomain-specific circuit characterization C_LI

neuroscience↗

bioRxiv2024

Evidence for white matter intrinsic connectivity networks at rest and during a task: a large-scale study and templates

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

neuroscience↗

bioRxiv2024

Sex differences in deep brain shape and asymmetry persist across schizophrenia and healthy individuals: A meta-analysis from the ENIGMA-Schizophrenia Working Group

BackgroundSchizophrenia (SCZ) is characterized by a disconnect from reality that manifests as various clinical and cognitive symptoms, and persistent neurobiological abnormalities. Sex-related differences in clinical presentation imply separate brain substrates. The present study characterized deep brain morphology using shape features to understand the independent effects of diagnosis and sex on the brain, and to determine whether the neurobiology of schizophrenia varies as a function of sex. MethodsThis study analyzed multi-site archival data from 1,871 male (M) and 955 female (F) participants with SCZ, and 2,158 male and 1,877 female healthy controls (CON) from twenty-three cross-sectional samples from the ENIGMA Schizophrenia Workgroup. Harmonized shape analysis protocols were applied to each sites data for seven deep brain regions obtained from T1-weighted structural MRI scans. Effect sizes were calculated for the following main contrasts: 1) Sex effects;2) Diagnosis-by-Sex interaction; 3) within sex tests of diagnosis; 4) within diagnosis tests of sex differences. Meta-regression models between brain structure and clinical variables were also computed separately in men and women with schizophrenia. ResultsMass univariate meta-analyses revealed more concave-than-convex shape differences in all regions for women relative to men, across diagnostic groups (d = -0.35 to 0.20, SE = 0.02 to 0.07); there were no significant diagnosis-by-sex interaction effects. Within men and women separately, we identified more-concave-than-convex shape differences for the hippocampus, amygdala, accumbens, and thalamus, with more-convex-than-concave differences in the putamen and pallidum in SCZ (d = -0.30 to 0.30, SE = 0.03 to 0.10). Within CON and SZ separately, we found more-concave-than-convex shape differences in the thalamus, pallidum, putamen, and amygdala among females compared to males, with mixed findings in the hippocampus and caudate (d = -0.30 to 0.20, SE = 0.03 to 0.09). Meta-regression models revealed similarly small, but significant relationships, with medication and positive symptoms in both SCZ-M and SCZ-F. ConclusionsSex-specific variation is an overriding feature of deep brain shape regardless of disease status, underscoring persistent patterns of sex differences observed both within and across diagnostic categories, and highlighting the importance of including it as a critical variable in studies of neurobiology. Future work should continue to explore these dimensions independently to determine whether these patterns of brain morphology extend to other aspects of neurobiology in schizophrenia, potentially uncovering broader implications for diagnosis and treatment. Key PointsO_LIStatistical analyses revealed significant main effects for diagnosis and sex in deep brain shape morphology. Among patients with schizophrenia, there was a pattern of thinning and surface contraction in the bilateral hippocampus, amygdala, accumbens, and thalamus, and a pattern of significant thickening and surface expansion in the bilateral putamen and pallidum compared to healthy control participants. Between males and females, there was a pattern of significant thinning and surface contraction in the bilateral thalamus, pallidum, putamen, and amygdala in females compared to males. C_LIO_LIThere was no significant interaction between diagnosis and biological sex, suggesting that sex differences in deep brain shape and asymmetry among patients with schizophrenia reflect those observed in healthy individuals. C_LIO_LISmall but statistically significant relationships exist between brain structure and clinical correlates of schizophrenia were similar for both men and women with the disease, such that higher CPZ was associated with shape-derived thinning and surface contraction in the caudate, accumbens, hippocampus, amygdala, and thalamus, and elevated positive symptoms were associated with shape-derived thinning and surface contraction in the bilateral caudate, right hippocampus, and right amygdala. C_LI

neuroscience↗

bioRxiv2024

Multi-modal Spatial-modality Attentive Fusion for Studying Neurodegenerative Disorders

Multi-modal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domains-specific, leading to data being represented in dissimilar subspaces, and (ii) fused data is often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multi-modal artificial intelligence (AI) model with a novel fusion module inspired by a bottleneck attention module (BAM). We use deep neural networks (DNN) to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multi-modal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (P < 0.0001), outperforming state-of-the-art unimodal and multi-modal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural networks decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multi-modal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to schizophrenia. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies.

neuroscience↗

bioRxiv2024

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults

The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

neuroscience↗

bioRxiv2024

Reference curves for harmonizing multi-site regional diffusion MRI metrics across the lifespan

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).

neuroscience↗

bioRxiv2024

A site-wise reliability analysis of the ABCD diffusion fractional anisotropy data, impact of the scanner and analytical pipeline

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce non-biological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61{+/-}0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76{+/-}0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared to those using GE/Philips scanners for both FA (AICC=0.71{+/-}0.12 vs 0.46{+/-}0.17, p<0.001) and CT (AICC=0.80{+/-}0.10 vs 0.69{+/-}0.11, p<0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

neuroscience↗

bioRxiv2024

Age-Related Decline in BBB Function is More Pronounced in Males than Females

The blood-brain barrier (BBB) plays a pivotal role in protecting the central nervous system (CNS), shielding it from potential harmful entities. A natural decline of BBB function with aging has been reported in both animal and human studies, which may contribute to cognitive decline and neurodegenerative disorders. Limited data also suggest that being female may be associated with protective effects on BBB function. Here we investigated age and sex-dependent trajectories of perfusion and BBB water exchange rate (kw) across the lifespan in 186 cognitively normal participants spanning the ages of 8 to 92 years old, using a non-invasive diffusion prepared pseudo-continuous arterial spin labeling (DP-pCASL) MRI technique. We found that the pattern of BBB kw decline with aging varies across brain regions. Moreover, results from our DP-pCASL technique revealed a remarkable decline in BBB kw beginning in the early 60s, which was more pronounced in males. In addition, we observed sex differences in parietal and temporal regions. Our findings provide in vivo results demonstrating sex differences in the decline of BBB function with aging, which may serve as a foundation for future investigations into perfusion and BBB function in neurodegenerative and other brain disorders. Significance statementThe blood-brain barrier (BBB) serves as a critical protection mechanism for the CNS. A natural decline of BBB function with aging has been reported in both animal and human studies, with possible differences in BBB function by sex. Using our MRI technique, DP-pCASL that measures water exchange rate (kw) without contrast in 186 participants from diverse race and age groups, we identified age and sex-specific patterns in BBB kw especially in parietal and temporal regions. We observed of a decline in kw beginning in the early 60s, especially in males. Our study unveils the dynamic spatiotemporal pattern of kw differences with age and sex, which serve as a foundation for understanding aberrations of BBB function in neurodegenerative and other brain disorders.

neuroscience↗

bioRxiv2023

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Functional magnetic resonance imaging (fMRI) studies often estimate brain intrinsic connectivity networks (ICNs) from temporal relationships between hemodynamic signals using approaches such as independent component analysis (ICA). While ICNs are thought to represent functional sources that play important roles in various psychological phenomena, current approaches have been tailored to identify ICNs that mainly reflect linear statistical relationships. However, the elements comprising neural systems often exhibit remarkably complex nonlinear interactions that may be involved in cognitive operations and altered in psychiatric conditions such as schizophrenia. Consequently, there is a need to develop methods capable of effectively capturing ICNs from measures that are sensitive to nonlinear relationships. Here, we advance a novel approach to estimate ICNs from explicitly nonlinear whole-brain functional connectivity (ENL-wFC) by transforming resting-state fMRI (rsfMRI) data into the connectivity domain, allowing us to capture unique information from distance correlation patterns that would be missed by linear whole-brain functional connectivity (LIN-wFC) analysis. Our findings provide evidence that ICNs commonly extracted from linear (LIN) relationships are also reflected in explicitly nonlinear (ENL) connectivity patterns. ENL ICN estimates exhibit higher reliability and stability, highlighting our approachs ability to effectively quantify ICNs from rsfMRI data. Additionally, we observed a consistent spatial gradient pattern between LIN and ENL ICNs with higher ENL weight in core ICN regions, suggesting that ICN function may be subserved by nonlinear processes concentrated within network centers. We also found that a uniquely identified ENL ICN distinguished individuals with schizophrenia from healthy controls while a uniquely identified LIN ICN did not, emphasizing the valuable complementary information that can be gained by incorporating measures that are sensitive to nonlinearity in future analyses. Moreover, the ENL estimates of ICNs associated with auditory, linguistic, sensorimotor, and self-referential processes exhibit heightened sensitivity towards differentiating between individuals with schizophrenia and controls compared to LIN counterparts, demonstrating the translational value of our approach and of the ENL estimates of ICNs that are frequently reported as disrupted in schizophrenia. In summary, our findings underscore the tremendous potential of connectivity domain ICA and nonlinear information in resolving complex brain phenomena and revolutionizing the landscape of clinical FC analysis.

neuroscience↗

bioRxiv2023

APOE4 poses opposite effects of plasma LDL on white matter integrity in older adults

INTRODUCTIONAPOE4 is a strong genetic risk factor of Alzheimers disease and is associated with changes in metabolism. However, the interactive relationship between APOE4 and plasma metabolites on the brain remains largely unknown. MEHODSIn the UK Biobank, we investigated the moderation effects of APOE4 on the relationship between 249 plasma metabolites derived from nuclear magnetic resonance spectroscopy on whole-brain white matter integrity, measured by fractional anisotropy using diffusion magnetic resonance imaging. RESULTSThe increase in the concentration of metabolites, mainly LDL and VLDL, is associated with a decrease in white matter integrity (b= -0.12, CI= [-0.14, -0.10]) among older APOE4 carriers, whereas an increase (b= 0.05, CI= [0.04, 0.07]) among non-carriers, implying a significant moderation effect of APOE4 (b= -0.18, CI= [-0.20,-0.15]). DISCUSSIONThe results suggest that lipid metabolism functions differently in APOE4 carriers compared to non-carriers, which may inform the development of targeted interventions for APOE4 carriers to mitigate cognitive decline.

neuroscience↗

bioRxiv2023

Estimating multimodal brain variability in schizophreniaspectrum disorders: A worldwide ENIGMA study

ObjectiveSchizophrenia 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. MethodsThis 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. ResultsWe 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. ConclusionsHigher 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.

neuroscience↗