Biology subjects

Morgan, S. E.

Publications and source records attributed to Morgan, S. E..

10 recordsLinked to original sources

bioRxiv2026

Functional connectivity correlates of the hierarchical p-factor model in youth at neurodevelopmental risk

BackgroundEmotional and cognitive difficulties often co-occur in neurodevelopmental conditions. While transdiagnostic, dimensional approaches offer a more precise framework for understanding mental health than diagnostic categories, their neural correlates in youth with learning difficulties remain poorly understood. This study investigates associations between transdiagnostic mental health dimensions and resting-state functional connectivity in struggling learners. MethodsCross-sectional behavioural data from the Centre for Attention, Learning and Memory (CALM) for struggling learners (N = 378) was used to replicate a hierarchical model of mental health from the Conners Parent Rating Short Form, the Revised Childrens Anxiety and Depression Scale and the Strengths and Difficulties Questionnaire. Functional connectomes were derived from resting-state fMRI data (N = 67), and partial least squares regression related mental health dimensions to connectivity within and between large-scale brain networks. ResultsThe replicated model comprised a general p-factor, two broad domains (internalising and externalising), and three specific dimensions (specific internalising, neurodevelopmental and social maladjustment). Symptom severity was associated with two connectivity patterns: greater default mode network coupling to frontoparietal and attention networks, and reduced connectivity between visual and somatomotor systems. These effects were strongest for the neurodevelopmental and social maladjustment dimensions, respectively. ConclusionsThese findings align with population-level evidence linking mental health dimensions to brain network organization, extending it to struggling learners and offering new insight into the neural basis of mental health vulnerability in neurodevelopmentally at-risk youth.

neuroscience↗

bioRxiv2024

The genetic architecture of cortical similarity networks

The genetic architecture of human brain networks is central to understanding the organization and evolution of the cortex, the causal relationships between brain structure and function, and the pathogenesis of heritable neuropsychiatric disorders. However, the current understanding of the genetics of brain networks remains fragmented. Here, we investigated common genetic effects on Morphometric INverse Divergence (MIND), a biologically-validated, heritable, and multimodal MRI metric of inter-areal similarity and connectivity. Using a discovery dataset (N>30,000 adults), we estimated subject-specific MIND networks from the multivariate distributions of four MRI features at each of 23 cortical areas and performed genome-wide association studies (GWAS) at each of the 276 inter-areal edges. These edge-level genetic effects were highly replicated by parallel GWAS of an independent validation dataset (N>18,000 adults). We found that strong genetic correlations between multiple edges were largely reducible to two gradients of genetically-determined cortical similarity, each of which was aligned with geodesic distance from one of the two phylogenetically primitive areas (paleocortex and archicortex) predicted by the dual origin theory of cortical evolution. Genetic MIND gradients were more heritable than comparable gradients derived from GWAS of functional MRI connectivity networks; and the paleocortical trend was genetically correlated with, and causally predictive of, functional connectivity. Finally, we identified multiple global and local genetic correlations between both MIND gradients and nine clinical diagnoses or biomedical traits, indicating that the normative genetic architecture of human brain networks is pleiotropically associated with inherited risk of neuropsychiatric disorders and systemic metabolic and immune traits. These results provide fresh insight into the dual origins of the human cortex and their implications for brain function and health.

neuroscience↗

bioRxiv2024

Amyloid peptide - synthetic polymer blends with enhanced mechanical and biological properties

Interest in utilizing amyloids to develop biomaterials is increasing due to their potential for biocompatibility, unique assembling morphology, mechanical stability, and biophysical properties. However, challenges include the complexity of peptide chemistry and the practical techniques required for processing amyloids into bulk materials. In this work, two decapeptides with fibrillar and globular morphologies were selected, blended with poly(ethylene oxide), and fabricated into composite mats via electrospinning. Notable enhancements in mechanical properties were observed, attributed to the uniform distribution of the decapeptide assemblies within the PEO matrix. Morphological differences, such as the production of thinner nanofibers, are attributed to the increased conductivity from the zwitterionic nature of the decapeptides. Blend rheology and post-processing analysis revealed how processing might affect the amyloid aggregation and secondary structure of the peptides. Both decapeptides demonstrated good biocompatibility and strong antioxidant activity, indicating their potential for safe and effective use as biomaterials. By evaluating these interdependencies, this research lays the foundation for understanding the structure-property-processing relationships of peptide-polymer blends and highlights the strong potential for developing applications in biotechnology.

biophysics↗

bioRxiv2023

De novo amyloid peptides with subtle sequence variations differ in their self-assembly and nanomechanical properties

Proteinaceous amyloids are well known for their widespread pathological roles but lately have emerged also as key components in several biological functions. The remarkable ability of amyloid fibers to form tightly packed conformations in a cross {beta}-sheet arrangement manifests in their robust enzymatic and structural stabilities. These characteristics of amyloids make them attractive for designing proteinaceous biomaterials for various biomedical and pharmaceutical applications. In order to design customizable and tunable amyloid nanomaterials, it is imperative to understand the sensitivity of the peptide sequence for subtle changes based on amino acid position and chemistry. Here we report our results from four rationally-designed amyloidogenic decapeptides that subtly differ in hydrophobicity and polarity at positions 5 and 6. We show that making the two positions hydrophobic renders the peptide with enhanced aggregation and material properties while the introduction of polar residues in position 5 dramatically changes the structure and nanomechanical properties of the fibrils formed. A charged residue at position 6, however, completely abrogates amyloid formation. In sum, we show that subtle changes in the sequence do not make the peptide innocuous but rather sensitive to aggregation, reflected in the biophysical and nanomechanical properties of the fibrils. We conclude that tolerance of peptide amyloid for subtle changes in the sequence should not be neglected for the effective design of customizable amyloid nanomaterials.

biophysics↗

bioRxiv2022

MIND Networks: Robust Estimation of Structural Similarity from Brain MRI

Structural similarity networks are a central focus of magnetic resonance imaging (MRI) research into human brain connectomes in health and disease. We present Morphometric INverse Divergence (MIND), a robust method to estimate within-subject structural similarity between cortical areas based on the Kullback-Leibler divergence between the multivariate distributions of their structural features. Compared to the prior approach of morphometric similarity networks (MSNs) on N>10,000 data from the ABCD cohort, MIND networks were more consistent with known cortical symmetry, cytoarchitecture, and (in N=19 macaques) gold-standard tract-tracing connectivity, and were more invariant to cortical parcellation. Importantly, MIND networks were remarkably coupled with cortical gene co-expression, providing fresh evidence for the unified architecture of brain structure and transcription. Using kinship (N=1282) and genetic data (N=4085), we characterized the heritability of MIND phenotypes, identifying stronger genetic influence on the relationship between structurally divergent regions compared to structurally similar regions. Overall, MIND presents a biologically-validated lens for analyzing the structural organization of the cortex using readily-available MRI measurements.

neuroscience↗

bioRxiv2020

Prion-like C-terminal domain of TDP-43 and α-Synuclein interact synergistically to generate neurotoxic hybrid fibrils

Aberrant aggregation and amyloid formation of tar DNA binding protein (TDP-43) and -synuclein (S) underlie frontotemporal dementia (FTD) and Parkinsons disease (PD), respectively. Amyloid inclusions of TDP-43 and S are also commonly co-observed in amyotrophic lateral sclerosis (ALS), dementia with Lewy bodies (DLB) and Alzheimer disease (AD). Emerging evidence from cellular and animal models show colocalization of the TDP-43 and S aggregates, raising the possibility of direct interactions and co-aggregation between the two proteins. In this report, we set out to answer this question by investigating the interactions between S and prion-like pathogenic C-terminal domain of TDP-43 (TDP-43 PrLD). PrLD is an aggregation-prone fragment generated both by alternative splicing as well as aberrant proteolytic cleavage of full length TDP-43. Our results indicate that two proteins interact in a synergistic manner to augment each others aggregation towards hybrid fibrils. While monomers, oligomers and sonicated fibrils of S seed TDP-43 PrLD monomer aggregation, TDP-43 PrLD fibrils failed to seed S monomers indicating selective interactions. Furthermore, S modulates liquid droplets formed by TDP-43 PrLD and RNA to promote insoluble amyloid aggregates. Importantly, the cross-seeded hybrid aggregates show greater cytotoxicity as compared to the individual homotypic aggregates suggesting that the interactions between the two proteins have a discernable impact on cellular functions. Together, these results bring forth insights into TDP-43 PrLD - S interactions that could help explain clinical and pathological presentations in patients with co-morbidities involving the two proteins.

biophysics↗

bioRxiv2019

Transcriptomic and Cellular Decoding of Regional Brain Vulnerability to Neurodevelopmental Disorders

Neurodevelopmental disorders are highly heritable and associated with spatially-selective disruptions of brain anatomy. The logic that translates genetic risks into spatially patterned brain vulnerabilities remains unclear but is a fundamental question in disease pathogenesis. Here, we approach this question by integrating (i) in vivo neuroimaging data from patient subgroups with known causal genomic copy number variations (CNVs), and (ii) bulk and single-cell gene expression data from healthy cortex. First, for each of six different CNV disorders, we show that spatial patterns of cortical anatomy change in youth are correlated with spatial patterns of expression for CNV region genes in bulk cortical tissue from typically-developing adults. Next, by transforming normative bulk-tissue cortical expression data into cell-type expression maps, we further link each disorders anatomical change map to specific cell classes and specific CNV-region genes that these cells express. Finally, we establish convergent validity of this "transcriptional vulnerability model" by inter-relating patient neuroimaging data with measures of altered gene expression in both brain and blood-derived patient tissue. Our work clarifies general biological principles that govern the mapping of genetic risks onto regional brain disruption in neurodevelopmental disorders. We present new methods that can harness these principles to screen for potential cellular and molecular determinants of disease from readily available patient neuroimaging data.

neuroscience↗

bioRxiv2018

Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia related genes

Schizophrenia has been conceived as a disorder of brain connectivity but it is unclear how this network phenotype is related to the emerging genetics. We used morphometric similarity analysis of magnetic resonance imaging (MRI) data as a marker of inter-areal cortical connectivity in three prior case-control studies of psychosis: in total, N=185 cases and N=227 controls. Psychosis was associated with globally reduced morphometric similarity (MS) in all 3 studies. There was also a replicable pattern of case-control differences in regional MS which was significantly reduced in patients in frontal and temporal cortical areas, but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case-control differences in MS was spatially correlated with cortical expression of a weighted combination of genes enriched for neu-robiologically relevant ontology terms and pathways. In addition, genes that were normally over-expressed in cortical areas with reduced MS were significantly up-regulated in a prior post mortem study of schizophrenia. We propose that this combination of neuroimaging and transcriptional data provides new insight into how previously implicated genes and proteins, as well as a number of unreported proteins in their vicinity on the protein interaction network, may interact to drive structural brain network changes in schizophrenia.

neuroscience↗

bioRxiv2018

Schizotypy-related magnetization of cortex in healthy adolescence is co-located with expression of schizophrenia risk genes

BackgroundGenetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits but the underlying changes in brain structure that mechanistically link genomic variation to schizotypal experience and behaviour are unclear. MethodsWe assessed schizotypy using a self-reported questionnaire, and measured magnetization transfer (MT), as a putative micro-structural MRI marker of intra-cortical myelination, in 68 brain regions, in 248 healthy young people (aged 14-25 years). We used normative adult brain gene expression data, and partial least squares (PLS) analysis, to find the weighted gene expression pattern that was most co-located with the cortical map of schizotypy-related magnetization (SRM). ResultsMagnetization was significantly correlated with schizotypy in bilateral posterior cingulate cortex and precuneus (and for disorganized schizotypy also in medial prefrontal cortex; all FDR-corrected P < 0.05), which are regions of the default mode network specialized for social and memory functions. The genes most positively weighted on the whole genome expression map co-located with SRM were enriched for genes that were significantly down-regulated in two prior case-control histological studies of brain gene expression in schizophrenia. Conversely, the most negatively weighted genes were enriched for genes that were transcriptionally up-regulated in schizophrenia. Positively weighted (down-regulated) genes were enriched for neuronal, specifically inter-neuronal, affiliations and coded a network of proteins comprising a few highly interactive "hubs" such as parvalbumin and calmodulin. ConclusionsMicrostructural MRI maps of intracortical magnetization can be linked to both the behavioural traits of schizotypy and to prior histological data on dysregulated gene expression in schizophrenia.

neuroscience↗

bioRxiv2017

Low dimensional morphospace of topological motifs in human fMRI brain networks

We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC corresponds to the small-world axis and correlates strongly with the networks global efficiency. There is also some evidence that PC1 correlates with the average length of the 5% of longest edges in the network. Hence this axis represents the trade-off between the cost of long distance edges and their topological benefits. The second PC correlates with the networks assortativity. Finally, we show that the economical clustering generative model proposed by Vertes et al. can approximately reproduce the motif PC space of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualise the relationships between network properties and to study the driving forces behind the topology of fMRI brain networks.

neuroscience↗