Biology subjects

Ballem, R.

Publications and source records attributed to Ballem, R..

5 recordsLinked to original sources

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↗

bioRxiv2025

Spatiotemporal Network Dynamics Reveal Alzheimer's Disease Progression

Alzheimers disease (AD) is characterized by progressive disruptions in large-scale brain networks that precede cognitive decline, yet conventional functional connectivity analyses often fail to detect disruptions in coordination among large-scale brain networks that may be critical for early detection. This study leverages quasi periodic patterns (QPPs) and complex principal component analysis (cPCA) to characterize spatiotemporal network alterations across longitudinally stable (normal cognitive, mild cognitive impairment, dementia of Alzheimers type) and transitioning (normal cognitive to mild cognitive impairment, mild cognitive impairment to dementia of Alzheimers type) cohorts from the Alzheimers Disease Neuroimaging Initiative using resting state fMRI. QPPs were used to derive recurrent spatiotemporal templates and network integrity measures at the intrinsic connectivity network level, while cPCA decomposed Hilbert transformed time series into complex valued patterns that capture amplitude and phase relationships. Nonparametric group comparisons revealed a structured trajectory in which limbic, subcortical, and higher cognition networks, including triple network components, are affected early, followed by progressive disruption in visual, cerebellar, sensorimotor, and additional triple network systems. Transitioning cohorts showed many of these alterations before formal diagnostic conversion, indicating that spatiotemporal signatures carry preclinical information. QPP based metrics were particularly sensitive to limbic and subcortical degradation, whereas cPCA emphasized changes in higher order, visual, and cerebellar patterns, revealing complementary aspects of the same underlying pathology. These findings extend prior QPP only work and highlight the utility of combining QPP and cPCA based measures as a dynamic, network-level biomarker framework for AD progression. with potential applications in early detection, characterizing disease trajectories, and treatment monitoring.

neuroscience↗

bioRxiv2025

Toward Granular Brain Intrinsic Connectivity Networks and Insights into Schizophrenia

Spatial group independent component analysis (sgr-ICA) has become a crucial method to understand brain function in functional magnetic resonance imaging (fMRI) research, especially in resting-state fMRI (rsfMRI) studies. Early studies identified large-scale brain networks using sgr-ICA with lower order (e.g., 20 - 45 components); however, more recent studies have employed higher model orders (e.g., 200 components) to reveal more refined intrinsic connectivity networks (ICNs), offering a more detailed representation of functional brain architecture. This increased granularity has encouraged researchers to explore even higher model orders to better capture the brains function. Although previous studies explored higher model orders, small datasets often limited them. In this study, we addressed this gap by assessing an sgr-ICA model with 500 components using a large rsfMRI dataset of over 100,000 subjects. This extensive data set allowed us to provide a robust estimation of fine-grained ICNs. We further assessed diagnostic effects and cognitive performance using whole-brain functional network connectivity (FNC) of 502 individuals with schizophrenia and 640 typical controls from these ICNs. We also compared the results with ICNs obtained using a lower-order, multi-spatial-scale template. Results demonstrate that our approach yields a large set of reliable and fine-grained ICNs, enhancing characterization of schizophrenia related dysconnectivity patterns. Specifically, we observed a relatively large number of ICNs within the cerebellar and paralimbic area. We detected significant hypoconnectivity between the cerebellar and subcortical domains, including the basal ganglia and thalamic regions. We also found hyperconnectivity between the cerebellar domain and the visual, sensorimotor, and higher cognitive domains, as well as between the sensorimotor and subcortical domains. Our finding revealed that granular ICNs can detect significant FNC differences between cohorts which are missed in larger scale ICNs. This work highlights the capability of higher model order ICA to capture distinct, fine-grained ICNs, enriching our understanding of FNC and serving as a valuable addition to current multiscale ICN templates. The ICNs derived from this study may serve as valuable references for future research, with the potential to improve the clinical utility of rsfMRI and advance the study of psychiatric disorders.

neuroscience↗

bioRxiv2025

LINKING MULTI-SCALE BRAIN CONNECTIVITY WITH VIGILANCE, WORKING MEMORY, AND BEHAVIOR IN ADOLESCENTS

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in "the fronto-thalamo-cerebellar (FTC) circuitry" and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD).

neuroscience↗

bioRxiv2025

Identifying Neurobiological Psychosis Biotypes Using Multi-Scale Functional Network Connectivity and its Latent Independent Subspace

This study aims to identify Psychosis Imaging Neurosubtypes (PINs)-- homogeneous subgroups of individuals with psychosis characterized by distinct neurobiology derived from imaging features. Specifically, we utilized resting-state fMRI data from 2103 B-SNIP 1&2 participants (1127 with psychosis, 350 relatives, 626 controls) to compute subject-specific multiscale functional network connectivity (msFNC). We then derived a low-dimensional neurobiological subspace, termed Latent Network Connectivity (LNC), which captured system-wide interconnected multiscale information across three components (cognitive-related, typical, psychosis-related). Projections of psychosis participants msFNC onto this subspace revealed three PINs through unsupervised learning, each with distinct cognitive, clinical, and connectivity profiles, spanning all DSM diagnoses (Schizophrenia, Bipolar, Schizoaffective). PIN-1, the most cognitively impaired, showed Cerebellar-Subcortical and Visual-Sensorimotor hypoconnectivity, alongside Visual-Subcortical hyperconnectivity. Most cognitively preserved PIN-2 showed Visual-Subcortical, Subcortical-Sensorimotor, and Subcortical-Higher Cognition hypoconnectivity. PIN-3 exhibited intermediate cognitive function, showing Cerebellar-Subcortical hypoconnectivity alongside Cerebellar-Sensorimotor and Subcortical-Sensorimotor hyperconnectivity. Notably, 55% of relatives aligned with the same neurosubtype as their affected family members--a significantly higher rate than random chance (p-valueRelatives-to-PIN-1 < 0.001, p-valueRelatives-to-PIN-2 < 0.05, p-valueRelatives-to-PIN-3 < 0.001) compared to a non-significant 37% DSM-based classification, supporting a biological basis of these neurosubtypes. Cognitive performance reliably aligns with distinct brain connectivity patterns, which are also evident in relatives, supporting their construct validity. Our PINs differed from original B-SNIP Biotypes, which were determined from electrophysiological, cognitive, and oculomotor data. These findings underscore the limitations of DSM-based classifications in capturing the biological complexity of psychotic disorders and highlight the potential of imaging-based neurosubtypes to enhance our understanding of the psychosis spectrum.

neuroscience↗