Biology subjects

Haas, S. S.

Publications and source records attributed to Haas, S. S..

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↗

bioRxiv2024

Generalizability of Normative Models of Brain Morphometry Across Distinct Ethnoracial Groups

Normative modeling of brain morphometric data can inform about the clinical significance of deviations from typical patterns in brain structure. Their usefulness, however, is dependent on their applicability to diverse ethnoracial groups. With this in mind, we developed age- and sex-specific normative models for cortical thickness, surface area, and subcortical volumes using brain scans from 37,407 healthy individuals from a diverse international sample. Here we demonstrate the validity of these models in diverse and distinct populations. Specifically, we tested these pre-trained models on independent samples of healthy individuals that either self-identified as Black, South Asian, East Asian Chinese, East Asian Japanese, or we categorized as African, Admixed American, East Asian, and European based on their genetic ancestry. Regardless of ethnoracial definition, the performance of the pretrained models in these samples was exceptionally high; the relative mean absolute error for each regional brain morphometry measure was less than 10% across all the distinct ethnoracial groups. These findings affirm the broad applicability of our models, ensuring that brain morphometry assessments using these models are accurate and reliable for individuals regardless of background. This broad applicability has significant implications for advancing personalized medicine and improving health outcomes in diverse populations.

bioinformatics↗

bioRxiv2023

Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain: Algorithm Benchmarking and Model Optimization

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

bioinformatics↗

bioRxiv2023

Normative modeling of brain morphometry in Clinical High-Risk for Psychosis

ImportanceThe lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in the majority of individuals at psychosis risk may be nested within the range observed in healthy individuals. ObjectiveTo quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high-risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design, Setting, and ParticipantsClinical, IQ and FreeSurfer-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1,340 CHR-P individuals [47.09% female; mean age: 20.75 (4.74) years] and 1,237 healthy individuals [44.70% female; mean age: 22.32 (4.95) years] from 29 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. Main Outcomes and MeasuresFor each regional morphometric measure, z-scores were computed that index the degree of deviation from the normative means of that measure in a healthy reference population (N=37,407). Average deviation scores (ADS) for CT, SA, SV, and globally across all measures (G) were generated by averaging the respective regional z-scores. Regression analyses were used to quantify the association of deviation scores with clinical severity and cognition and two-proportion z-tests to identify case-control differences in the proportion of individuals with infranormal (z<-1.96) or supranormal (z>1.96) scores. ResultsCHR-P and healthy individuals overlapped in the distributions of the observed values, regional z-scores, and all ADS vales. The proportion of CHR-P individuals with infranormal or supranormal values in any metric was low (<12%) and similar to that of healthy individuals. CHR-P individuals who converted to psychosis compared to those who did not convert had a higher percentage of infranormal values in temporal regions (5-7% vs 0.9-1.4%). In the CHR-P group, only the ADSSA showed significant but weak associations (|{beta}|<0.09; PFDR<0.05) with positive symptoms and IQ. Conclusions and RelevanceThe study findings challenge the usefulness of macroscale neuromorphometric measures as diagnostic biomarkers of psychosis risk and suggest that such measures do not provide an adequate explanation for psychosis risk. Key pointsO_ST_ABSQuestionC_ST_ABSIs the risk of psychosis associated with brain morphometric changes that deviate significantly from healthy variation? FindingsIn this study of 1340 individuals high-risk for psychosis (CHR-P) and 1237 healthy participants, individual-level variation in macroscale neuromorphometric measures of the CHR-P group was largely nested within healthy variation and was not associated with the severity of positive psychotic symptoms or conversion to a psychotic disorder. MeaningThe findings suggest the macroscale neuromorphometric measures have limited utility as diagnostic biomarkers of psychosis risk.

neuroscience↗

bioRxiv2022

Sex differences in predictors and regional patterns of brain-age-gap estimates

BackgroundThe brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data, and is considered a biomarker of brain health. Understanding sex-differences in brainAGE is a significant step toward precision medicine. MethodsGlobal and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex-differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. ResultsL-brainAGE showed sex-specific differences in brain ageing. In females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex-differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception for poor sleep quality, which was common to both. The most important predictor of higher G-brainAGE was non-white race in males and systolic blood pressure in females. ConclusionsThe results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain ageing and provide a foundation for targeted interventions.

neuroscience↗