Biology subjects

Woods, S. W.

Publications and source records attributed to Woods, S. W..

5 recordsLinked to original sources

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2024

Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis

Background22q11.2 Deletion Syndrome (22qDel) is a copy number variant (CNV) associated with psychosis and other neurodevelopmental disorders. Adolescents at clinical high risk for psychosis (CHR) have subthreshold psychosis symptoms without known genetic risk factors. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped results to biological pathways. MethodsWe analyzed two large multi-site cohorts with resting-state functional MRI (rs-fMRI): 1) 22qDel (n=164, 47% female) and typically developing (TD) controls (n=134, 56% female); 2) CHR individuals (n=244, 41% female) and TD controls (n=151, 46% female) from the North American Prodrome Longitudinal Study-2. We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions, testing case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation. ResultsBSV, LC, and GBC are significantly disrupted in 22qDel compared with TD controls (False Discovery Rate q<0.05). Spatial maps of BSV and LC differences are highly correlated with each other, unlike GBC. In CHR, only LC is significantly altered versus controls, with a different spatial pattern compared to 22qDel. Group differences map onto biological gradients, with 22qDel effects strongest in regions with high predicted blood flow and metabolism. Conclusion22qDel and CHR exhibit divergent effects on fMRI temporal variability and multi-scale functional connectivity. In 22qDel, strong and convergent disruptions in BSV and LC not seen in CHR individuals suggest distinct functional brain alterations.

neuroscience↗

bioRxiv2022

Linking Choroid Plexus Enlargement with Plasma Analyte and Structural Phenotypes in Clinical High Risk for Psychosis: A Multisite Neuroimaging Study

BackgroundChoroid plexus (ChP) enlargement exists in first-episode and chronic psychosis, but whether enlargement occurs before psychosis onset is unknown. This study investigated whether ChP volume is enlarged in individuals with clinical high-risk (CHR) for psychosis and whether these changes are related to clinical, neuroanatomical, and plasma analytes. MethodsClinical and neuroimaging data from the North American Prodrome Longitudinal Study 2 (NAPLS2) was used for analysis. 509 participants (169 controls, 340 CHR) were recruited. Conversion status was determined after 2-years of follow-up, with 36 psychosis converters. The lateral ventricle ChP was manually segmented from baseline scans. A subsample of 31 controls and 53 CHR had plasma analyte and neuroimaging data. ResultsCompared to controls, CHR (d=0.23, p=0.017) and non-converters (d=0.22, p=0.03) demonstrated higher ChP volumes, but not in converters. In CHR, greater ChP volume correlated with lower cortical (r=-0.22, p<0.001), subcortical gray matter (r=-0.21, p<0.001), and total white matter volume (r=-0.28,p<0.001), as well as larger lateral ventricle volume (r=0.63,p<0.001). Greater ChP volume correlated with makers functionally associated with the lateral ventricle ChP in CHR [CCL1 (r=-0.30, p=0.035), ICAM1 (r=0.33, p=0.02)], converters [IL1{beta} (r=0.66, p=0.004)], and non-converters [BMP6 (r=-0.96, p<0.001), CALB1 (r=-0.98, p<0.001), ICAM1 (r=0.80, p=0.003), SELE (r=0.59, p=0.026), SHBG (r=0.99, p<0.001), TNFRSF10C (r=0.78, p=0.001)]. ConclusionsCHR and non-converters demonstrated significantly larger ChP volumes compared to controls. Enlarged ChP was associated with neuroanatomical alterations and analyte markers functionally associated with the ChP. These findings suggest that the ChP may be a key explanatory biomarker in CHR for psychosis.

neuroscience↗

bioRxiv2019

Genetic and clinical analyses of psychosis spectrum symptoms in a large multi-ethnic youth cohort reveal significant link with ADHD.

ObjectivePsychotic symptoms are an important feature of severe neuropsychiatric disorders, but are also common in the general population, especially in youth. The genetic etiology of psychosis symptoms in youth remains poorly understood. To characterize genetic risk for psychosis spectrum symptoms (PS), we leverage a community-based multi-ethnic sample of children and adolescents aged 8-22 years, the Philadelphia Neurodevelopmental Cohort (n = 7,225, 20% PS).\n\nMethodsUsing an elastic net regression model, we aim to classify PS status using polygenic scores (PGS) based on a range of heritable psychiatric and brain-related traits in a multi-PGS model. We also perform univariate PGS associations and evaluate age-specific effects.\n\nResultsThe multi-PGS analyses do not improve prediction of PS status over univariate models, but reveal that the attention deficit hyperactivity disorder (ADHD) PGS is robustly and uniquely associated with PS (OR 1.12 (1.05, 1.18) P = 0.0003). This association is: i) driven by subjects of European ancestry (OR=1.23 (1.14, 1.34), P=4.15x10-7) but is not observed in African American subjects (P=0.65) and ii) independent of phenotypic overlap. We also find a significant interaction with age (P=0.01), with a stronger association in younger children. In an independent sample, we replicate an increased ADHD PGS in 328 youth at clinical high risk for psychosis, compared to 216 unaffected controls (OR 1.06, CI(1.01, 1.11), P= 0.02).\n\nConclusionsOur findings suggest that PS in youth may reflect a different genetic etiology than psychotic symptoms in adulthood, one more akin to ADHD, and shed light on how genetic risk can be investigated across early disease trajectories.

genetics↗

bioRxiv2019

Reliability of Mismatch Negativity Event-Related Potentials in a Multisite, Traveling Subjects Study

ObjectiveMismatch negativity (MMN) is an auditory event-related potential (ERP) used to study schizophrenia and psychosis risk. MMN reliability from a multisite, traveling subjects study was compared using different ERP referencing, averaging, and scoring techniques.\n\nMethodsReliability of frequency, duration, and double (frequency+duration) MMN was determined from eight traveling subjects, tested on two occasions at eight EEG laboratory sites. Deviant-specific variance components were estimated for MMN peak amplitude and latency measures using different ERP processing methods. Generalizability (G) coefficients were calculated using two-facet (site, occasion), fully-crossed models and single-facet (occasion) models within each laboratory to assess MMN reliability.\n\nResultsG-coefficients calculated from two-facet models indicated fair (0.4 0.5). Reliability of MMN amplitude was greater than latency, and reliability with mastoid referencing significantly outperformed nose-referencing.\n\nConclusionsEEG preprocessing methods have a significant impact on the reliability of MMN amplitude. Within site MMN reliability can be excellent, consistent with prior single site studies.\n\nSignificanceWith standardized data collection and ERP processing, MMN can be reliably obtained in multisite studies, providing larger samples sizes within rare patient groups.

neuroscience↗