Biology subjects

Ziolkowski, J.

Publications and source records attributed to Ziolkowski, J..

5 recordsLinked to original sources

bioRxiv2026

Algorithmic Geometry Shapes the Biological Meaning of Thalamocortical Structural Covariance

Neuroimaging studies often derive and discuss structural covariance (SC) as a representation of underlying brain circuitry that reflects coordinated maturation, anatomical connectivity, transcriptional similarity, and shared genetic influences. These associations motivate its use to study brain organization across development, aging, and disease. However, the algorithms used to derive SC networks impose constraints that may shape their biological interpretation. To understand the impact of algorithm choice, we used the thalamocortical system as an example and compared five common representations of voxelwise SC, namely, principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), orthogonal projective NMF (OPNMF), and \textit{k}-means clustering, in 755 participants aged 8--22 years from the Philadelphia Neurodevelopmental Cohort (PNC). SC networks derived from deformation-based morphometry (DBM) were evaluated against three complementary biological benchmarks: age-related individual differences, fractional-anisotropy (FA)-based microstructural connectivity of regionally-specific thalamocortical connections, and Allen Human Brain Atlas gene expression. Each algorithm demonstrated underlying sensitivity to differing aspects of neurobiology: PCA and OPNMF showed the strongest associations between participant-level component weights and age. NMF, OPNMF, and \textit{k}-means showed greater spatial overlap between positive cortical SC and tractography-derived projections. ICA and PCA showed stronger correspondence between thalamic and cortical gene-expression profiles across the transcriptome, whereas NMF, OPNMF, and \textit{k}-means more often identified thalamic components with relatively high core-matrix gene expression. This study emphasizes that algorithmic constraints effectively act as biological priors and decomposition methods do not provide neutral compression of the data. We conclude that it is critical to consider whether the algorithmic objective matches the representational geometry of the neurobiology being studied. Overall, our findings suggest that SC studies should select and justify algorithms according to the biological question, explicitly considering whether their constraints favour distributed gradients, overlapping regional parts, or discrete clusters. Meanwhile, interpretations of SC findings could also be informed by the specific algorithmic constraints used to derive them.

neuroscience↗

bioRxiv2025

Assigning Targetable Molecular Pathways to Transdiagnostic Subgroups Across Autism and Related Neurodevelopmental Disorders

The heterogeneity of autism and related neurodevelopmental conditions has impeded accurate prognoses and treatment discovery. Using translational neuroimaging across 135 mouse models (3,515 mice) and two human MRI datasets (n = 1,234 and n = 1,015), we derived participant subgroups from shared neuroanatomical features. These subgroups did not distinguish autism, ADHD, or OCD diagnoses and only modestly differentiated cognitive and behavioural phenotypes. Instead, they mapped onto four molecular pathways: (1) synaptic function; (2) MAPK and Wnt signalling; (3) chromatin modification and cellular stress responses; and (4) broader chromatin, immune, and second-messenger signalling pathways. This framework bridges preclinical models and idiopathic human neurodevelopmental conditions, linking patients to biologically relevant molecular mechanisms.

neuroscience↗

bioRxiv2022

Menopause, Brain Anatomy, Cognition and Alzheimer's Disease

The menopause transition has been repeatedly associated with decreased cognitive performance and increased incidence of Alzheimers Disease (AD), particularly when it is induced surgically 1,2 or takes place at a younger age 3,4. However, there are very few studies that use neuroimaging techniques to examine the effects of these variables in aggregate and in a large sample. Here, we use data from thousands of participants from the UK Biobank to assess the relationship between menopausal status, menopause type (surgical or natural), and age at menopause with cognition, AD, and neuroanatomical measures derived from magnetic resonance imaging. We find that for brain and cognitive measures, menopausal status, menopause type and age at surgical menopause do not impact the brain; but that there is a positive correlation between anatomy, cognition and age at non-surgical menopause. These results do not align with previous reports in the literature with smaller samples. However, we confirm that both early and surgical menopause are associated with a higher risk of developing AD, indicating that early and abrupt ovarian hormone deprivation might contribute to the development of the disorder.

neuroscience↗

bioRxiv2022

Human Brain Development: a cross-sectional and longitudinal study integrating multiple neuromorphological features

Brain maturation studies typically examine relationships linking a single morphometric feature with aspects of cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the long-running National Institute of Mental Health developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. We sought to use this technique longitudinally to examine covariance in rates of change, which highlighted a preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV; 96 % covariance explained) that recapitulated patterns of reduced CT, GI, and SA that are generally related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment. SignificanceThe complex anatomy of the cortical sheet is best characterized using multiple morphometric characteristics. We expanded on recent developments in matrix factorization to identify spatial patterns of covariance across the cortical sheet. Using a large, well-characterized dataset, we examined the differential contributions of neuroanatomical features to cortical covariation in a single analytical framework using both cross-sectional and longitudinal data. We identified dominant modes of covariance between cortical morphometric features and their coordinated pattern of change, demonstrating sexually differentiated patterns and a strong association with variability in age, socioeconomic status, and cognitive ability. This novel characterization of cortical morphometry provides an important understanding of the interdependencies between neuroanatomical measures in the brain and behavioral development context.

neuroscience↗

bioRxiv2020

The developing relations between networks of cortical myelin and neurophysiological connectivity

Recent work identified that patterns of distributed brain regions sharing similar myeloarchitecture are related to underlying functional connectivity, demonstrating cortical myelins plasticity to changes in functional demand. However, the changing relation between functional connectivity and structural architecture throughout child and adulthood is poorly understood. We show that structural covariance connectivity measured using T1-weighted/T2-weighted ratio and functional connectivity measured using magnetoencephalography exhibit nonlinear developmental changes. We then show significant relations between structural and functional connectivity, which have both shared and distinct characteristics dependent on the neural oscillatory frequency. Increases in structure-function coupling are visible during the protracted myelination observed throughout childhood and adolescence, and are followed by decreases near the onset of adulthood to potentially support increasing cognitive flexibility and functional specialization in adulthood. Our work lays the foundation for understanding the mechanisms by which myeloarchitecture supports brain function, enabling future investigations into how clinical populations may deviate from normative patterns.

neuroscience↗