Biology subjects

Dagher, A.

Publications and source records attributed to Dagher, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Molecular and connectomic vulnerability shape cross-disorder cortical abnormalities

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21 000 patients and N = 26 000 controls, collected using a harmonized processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find that regional molecular vulnerability and macroscale brain network architecture interact to drive the spatial patterning of cortical abnormalities in multiple disorders. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, medial temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local biological attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

neuroscience↗

bioRxiv2021

Correspondence between gene expression and neurotransmitter receptor and transporter density in the human brain

Neurotransmitter receptors modulate the signaling between neurons. Thus, neurotransmitter receptors and transporters play a key role in shaping brain function. Due to the lack of comprehensive neurotransmitter receptor/transporter density datasets, microarray gene expression is often used as a proxy for receptor densities. In the present report, we comprehensively test the expression-density association for a total of 27 neurotransmitter receptors, receptor binding-sites, and transporters across 9 different neurotransmitter systems, using both PET and autoradiography imaging modalities. We find poor spatial correspondences between gene expression and density for all neurotransmitter receptors and transporters except four single-protein metabotropic receptors (5-HT1A, D2, CB1, and MOR). These expression-density associations are related to population variance and change across different classes of laminar differentiation. Altogether, we recommend using direct measures of receptor and transporter density when relating neurotransmitter systems to brain structure and function.

neuroscience↗

bioRxiv2021

Multilevel neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

AO_SCPLOWBSTRACTC_SCPLOWIt is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we assessed i) shared dimensions of alterations in cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression, obsessive-compulsive disorder, bipolar disorder, schizophrenia) and ii) carried out a multiscale neural contextualization, by cross-referencing shared anomalies against cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we computed a shared disease dimension on cortical morphology using principal component analysis that described a sensory-fugal pattern with paralimbic regions showing the most consistent abnormalities across conditions. The shared disease dimension was closely related to cortical gradients of microstructure and intrinsic connectivity, as well as neurotransmitter systems, specifically serotonin and dopamine. Our findings embed the shared effects of major psychiatric conditions on brain structure in multiple scales of brain organization and may provide novel insights into neural mechanisms into transdiagnostic vulnerability.

neuroscience↗

bioRxiv2021

Mapping neurotransmitter systems to the structural and functional organization of the human neocortex

Neurotransmitter receptors support the propagation of signals in the human brain. How receptor systems are situated within macroscale neuroanatomy and how they shape emergent function remains poorly understood, and there exists no comprehensive atlas of receptors. Here we collate positron emission tomography data from >1 200 healthy individuals to construct a whole-brain 3-D normative atlas of 19 receptors and transporters across 9 different neurotransmitter systems. We find that receptor profiles align with structural connectivity and mediate function, including neurophysiological oscillatory dynamics and resting state hemodynamic functional connectivity. Using the Neurosynth cognitive atlas, we uncover a topographic gradient of overlapping receptor distributions that separates extrinsic and intrinsic psychological processes. Finally, we find both expected and novel associations between receptor distributions and cortical thinning patterns across 13 disorders. We replicate all findings in an independently collected autoradiography dataset. This work demonstrates how chemoarchitecture shapes brain structure and function, providing a new direction for studying multi-scale brain organization.

neuroscience↗

bioRxiv2021

Investigating the genetic and environmental basis of head micromovements during MRI

IntroductionHead motion during magnetic resonance imaging is heritable. Further, it shares phenotypical and genetic variance with body mass index (BMI) and impulsivity. Yet, to what extent this trait is related to single genetic variants and physiological or behavioral features is unknown. We investigated the genetic basis of head motion in a meta-analysis of genome-wide association studies. Further, we tested whether physiological or psychological measures, such as respiratory rate or impulsivity, mediated the relationship between BMI and head motion. MethodsWe conducted a genome-wide association meta-analysis for mean and maximal framewise head displacement (FD) in seven population neuroimaging cohorts (UK Biobank, LIFE-Adult, Rotterdam Study cohort 1-3, Austrian Stroke Prevention Family Study, Study of Health in Pomerania; total N = 35.109). We performed a pre-registered analysis to test whether respiratory rate, respiratory volume, self-reported impulsivity and heart rate mediated the relationship between BMI and mean FD in LIFE-Adult. ResultsNo variant reached genome-wide significance for neither mean nor maximal FD. Neither physiological nor psychological measures mediated the relationship between BMI and head motion. ConclusionBased on these findings from a large meta-GWAS and pre-registered follow-up study, we conclude that the previously reported genetic correlation between BMI and head motion relies on polygenic variation, and that neither psychological nor simple physiological parameters explain a substantial amount of variance in the association of BMI and head motion. Future imaging studies should thus rigorously control for head motion at acquisition and during preprocessing.

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bioRxiv2021

A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD

Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset - 560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r=0.30, p<0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.

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bioRxiv2021

Global network structure and local transcriptomic vulnerability shape atrophy in sporadic and genetic behavioral variant frontotemporal dementia

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioral variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). We first identify distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbors, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicenter of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and anteromedial temporal areas. Finally, we find that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, effectively modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability.

neuroscience↗

bioRxiv2021

Human brain anatomy reflects separable genetic and environmental components of socioeconomic status

Recent studies report that socioeconomic status (SES) correlates with brain structure. Yet, such findings are variable and little is known about underlying causes. We present a well-powered voxel-based analysis of grey matter volume (GMV) across levels of SES, finding many small SES effects widely distributed across the brain, including cortical, subcortical and cerebellar regions. We also construct a polygenic index of SES to control for the additive effects of common genetic variation related to SES, which attenuates observed SES-GMV relations, to different degrees in different areas. Remaining variance, which may be attributable to environmental factors, is substantially accounted for by body mass index, a marker for lifestyle related to SES. In sum, SES affects multiple brain regions through measurable genetic and environmental effects. One-sentence SummarySocioeconomic status is linked with brain anatomy through a varying balance of genetic and environmental influences.

neuroscience↗

bioRxiv2021

Representational learning of brain responses in executive function and higher-order cognition using deep graph convolutions

Brain decoding aims to infer human cognition from recordings of neural activity using modern neuroimaging techniques. Studies so far often concentrated on a limited number of cognitive states and aimed to classifying patterns of brain activity within a local area. This procedure demonstrated a great success on classifying motor and sensory processes but showed limited power over higher cognitive functions. In this work, we investigate a high-order graph convolution model, named ChebNet, to model the segregation and integration organizational principles in neural dynamics, and to decode brain activity across a large number of cognitive domains. By leveraging our prior knowledge on brain organization using a graph-based model, ChebNet graph convolution learns a new representation from task-evoked neural activity, which demonstrates a highly predictive signature of cognitive states and task performance. Our results reveal that between-network integration significantly boosts the decoding of high-order cognition such as visual working memory tasks, while the segregation of localized brain activity is sufficient to classify motor and sensory processes. Using twin and family data from the Human Connectome Project (n = 1,070), we provide evidence that individual variability in the graph representations of working-memory tasks are under genetic control and strongly associated with participants in-scanner behaviors. These findings uncover the essential role of functional integration in brain decoding, especially when decoding high-order cognition other than sensory and motor functions. TeaserO_LIModelling functional integration through graph convolution is a necessary step towards decoding high-order human cognition. C_LI Significance statementOver the past two decades, many studies have applied multivariate pattern analysis to decode what task a human participant is performing, based on a scan of her brain. The vast majority of these studies have however concentrated on select regions and a specific domain, because of the computational complexity of handling full brain data in a multivariate model. With the fast progress in the field of deep learning, it is now possible to decode a variety of cognitive domains simultaneously using a full-brain model. By leveraging our prior knowledge on brain organization using a graph-based model, we uncovered different organizational principles in brain decoding for motor execution and high-order cognition by modelling functional integration through graph convolution.

neuroscience↗

bioRxiv2021

Schizophrenia polygenic risk during typical development reflects multiscale cortical organization

Schizophrenia is widely recognized as a neurodevelopmental disorder. Abnormal cortical development may by revealed using polygenic risk scoring for schizophrenia (PRS-SCZ). We assessed PRS-SCZ and cortical morphometry in typically developing children (3-21 years) using whole genome genotyping and T1-weighted MRI (n=390) from the Pediatric Imaging, Neurocognition and Genetics (PING) cohort. We contextualise the findings using (i) age-matched transcriptomics, (ii) histologically-defined cytoarchitectural types and functionally-defined networks, (iii) case-control differences of schizophrenia and other major psychiatric disorders. Higher PRS-SCZ was associated with greater cortical thickness, which was most prominent in areas with heightened gene expression of dendrites and synapses. PRS-SCZ related increases in vertex-wise cortical thickness were especially focused in the ventral attention network, while koniocortical type cortex (i.e. primary sensory areas) was relatively conserved from PRS-SCZ related differences. The large-scale pattern of cortical thickness increases related to PRS-SCZ mirrored the pattern of cortical thinning in schizophrenia and mood-related psychiatric disorders. Age group models illustrate a possible trajectory from PRS-SCZ associated cortical thickness increases in early childhood towards thinning in late adolescence, which resembles the adult brain phenotype of schizophrenia. Collectively, combining imaging-genetics with multi-scale mapping, our work provides novel insight into how genetic risk for schizophrenia impacts the cortex early in life.

neuroscience↗

bioRxiv2021

Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce multivariate genomic-relatedness restricted maximum likelihood (MGREML) and provide estimates of the heritability of grey-matter volume in 74 regions of interest (ROIs) in the brain. We map genetic correlations between these ROIs and health-relevant behavioral outcomes including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs.

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bioRxiv2021

Spontaneous Neural Activity Changes after Bariatric Surgery: a resting-state fMRI study

1.BackgroundMetabolic disorders associated with obesity could lead to alterations in brain structure and function. Whether these changes can be reversed after weight loss is unclear. Bariatric surgery provides a unique opportunity to address these questions because it induces marked weight loss and metabolic improvements which in turn may impact the brain in a longitudinal fashion. Previous studies found widespread changes in grey matter (GM) and white matter (WM) after bariatric surgery. However, findings regarding changes in spontaneous neural activity following surgery, as assessed with the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity of neural activity (ReHo), are scarce and heterogenous. In this study, we used a longitudinal design to examine the changes in spontaneous neural activity after bariatric surgery (comparing pre- to post-surgery), and to determine whether these changes are related to cardiometabolic variables. MethodsThe study included 57 participants with severe obesity (mean BMI=43.1{+/-}4.3kg/m2) who underwent sleeve gastrectomy (SG), biliopancreatic diversion with duodenal switch (BPD), or Roux-en-Y gastric bypass (RYGB), scanned prior to bariatric surgery and at follow-up visits of 4 months (N=36), 12 months (N=29), and 24 months (N=14) after surgery. We examined fALFF and ReHo measures across 1022 cortical and subcortical regions (based on combined Schaeffer-Xiao parcellations) using a linear mixed effect model. Voxel-based morphometry (VBM) based on T1-weighted images was also used to measure GM density in the same regions. We also used an independent sample from the Human Connectome Project (HCP) to assess regional differences between individuals who had normal-weight (N=46) or severe obesity (N=46). ResultsWe found a global increase in the fALFF signal with greater increase within dorsolateral prefrontal cortex, precuneus, inferior temporal gyrus, and visual cortex. This effect was more significant 4 months after surgery. The increase within dorsolateral prefrontal cortex, temporal gyrus, and visual cortex was more limited after 12 months and only present in the visual cortex after 24 months. These increases in neural activity measured by fALFF were also significantly associated with the increase in GM density following surgery. Furthermore, the increase in neural activity was significantly related to post-surgery weight loss and improvement in cardiometabolic variables, such as insulin resistance index and blood pressure. In the independent HCP sample, normal-weight participants had higher global and regional fALFF signals, mainly in dorsolateral/medial frontal cortex, precuneus and middle/inferior temporal gyrus compared to the obese participants. These BMI-related differences in fALFF were associated with the increase in fALFF 4 months post-surgery especially in regions involved in control, default mode and dorsal attention networks. ConclusionsBariatric surgery-induced weight loss and improvement in metabolic factors are associated with widespread global and regional increases in neural activity, as measured by fALFF signal. These findings alongside the higher fALFF signal in normal-weight participants compared to participants with severe obesity in an independent dataset suggest an early recovery in the neural activity signal level after the surgery.

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bioRxiv2020

Heritability of cortical morphology reflects a sensory-fugal plasticity gradient

Human brain plastically adapts to environmental demands. Here, we propose that naturally occuring plasticity in certain brain areas should be reflected by higher environmental influence and therefore lower heritability of the structure of those brain areas. Mesulams (1998) seminal overview proposed a hierarchy of plasticity, where higher-order multimodal areas should be more plastic than lower-order sensory areas. Using microstructural and functional gradients as proxies for Mesulams hierarchy, we seek to test whether these gradients predict heritability of brain structure. We test this model simultaneously across multiple measures of cortical structure and microstructure derived from structural magnet resonance imaging. We also account for multiple other explanations of heritability differences, such as signal-to-noise ratio and spatial autocorrelation. We estimated heritability of brain areas using 984 participants from the Human Connectome Project. Multi-level modelling of heritability differences demonstrated that heritability is explained by both signal quality, as well as by the primary microstructural gradient. Namely, sensory areas had higher heritability and limbic/heteromodal areas had lower heritability. Given the increasing availability of genetically informed imaging data, heritability could be a quick method assess brain plasticity. Highlights (up to 85 chars)Cortical areas vary in heritability. This is seen across structural measures. Heritability differences could be explained by plasticity, topography, or noise. We build a comprehensive model testing many explanations across 5 measures. Heritability is explained by noise and 1st structural gradient reflecting plasticity. Heritability could be a method to study brain plasticity.

neuroscience↗

bioRxiv2020

Evidence and urgency-related EEG signals during dynamic decision-making in humans

A successful class of models link decision-making to brain signals by assuming that evidence accumulates to a decision threshold. These evidence accumulation models have identified neuronal activity that appears to reflect sensory evidence and decision variables that drive behavior. More recently, an additional evidence-independent and time-variant signal, named urgency, has been hypothesized to accelerate decisions in the face of insufficient evidence. However, most decision-making paradigms tested with fMRI or EEG in humans have not been designed to disentangle evidence accumulation from urgency. Here we use a face-morphing decision-making task in combination with EEG and a hierarchical Bayesian model to identify neural signals related to sensory and decision variables, and to test the urgency-gating model. We find that an evoked potential time-locked to the decision, the centroparietal positivity, reflects the decision variable from the computational model. We further show that the unfolding of this signal throughout the decision process best reflects the product of sensory evidence and an evidence-independent urgency signal. Urgency varied across subjects, suggesting that it may represent an individual trait. Our results show that it is possible to use EEG to distinguish neural signals related to sensory evidence accumulation, decision variables, and urgency. These mechanisms expose principles of cognitive function in general and may have applications to the study of pathological decision-making as in impulse control and addictive disorders. Significance StatementPerceptual decisions are often described by a class of models that assumes sensory evidence accumulates gradually over time until a decision threshold is reached. In the present study, we demonstrate that an additional urgency signal impacts how decisions are formed. This endogenous signal encourages one to respond as time elapses. We found that neural decision signals measured by EEG reflect the product of sensory evidence and an evidence-independent urgency signal. A nuanced understanding of human decisions, and the neural mechanisms that support it, can improve decision-making in many situations and potentially ameliorate dysfunction when it has gone awry.

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bioRxiv2020

Body mass variations relate to fractionated functional brain hierarchies

AO_SCPLOWBSTRACTC_SCPLOWVariations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined association between functional connectome organization and BMI variations. We capitalized on connectome manifold learning techniques, which represent macroscale functional connectivity patterns along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of an increasingly segregated modular architecture and a disruption in the hierarchical integration of different brain system. Transcriptomic decoding and subsequent gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings provide novel insights for functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.

neuroscience↗

bioRxiv2020

Obesity impairs cognitive function via metabolic syndrome and cerebrovascular disease: an SEM analysis in 15,000 adults from the UK Biobank.

Chronic obesity is associated with several complications, including cognitive impairment and dementia. However, we have piecemeal knowledge of the mechanisms linking obesity to central nervous system damage. Adiposity leads to the metabolic syndrome, consisting of inflammation, hypertension, dyslipidemia and insulin resistance. In turn, these metabolic abnormalities cause cerebrovascular dysfunction, which may cause white and grey matter tissue loss and consequent cognitive impairment. While there have been several neuroimaging studies linking adiposity to changes in brain morphometry, a comprehensive investigation of the relationship has so far not been done. Here we use structural equation modelling applied to over 15,000 individuals from the UK Biobank to identify the causal chain that links adiposity to cognitive dysfunction. We found that body mass index and waist-to-hip ratio were positively related to higher plasma C-reactive protein, dyslipidemia, occurrence of hypertension and diabetes, all of which were in turn related to cerebrovascular disease as measured by volume of white matter hyperintensities on magnetic resonance imaging. White mater hyperintensities were associated with lower cortical thickness and volume and higher subcortical volumes, which were associated with cognitive deficits on tests of visuospatial memory, fluid intelligence, and working memory among others. In follow-up analyses we found that inflammation, hypertension and diabetes mediated 20% of the relationship between obesity and cerebrovascular disease and that cerebrovascular disease mediated a significant proportion of the relationship between obesity and cortical thickness and volume. We also showed that volume of white matter hyperintensities was related to decreased fractional anisotropy and increased mean diffusivity in the majority of white matter tracts, pointing to white matter dysconnectivity as a major cause of impaired cognition. Our results have clinical implications, supporting a role for the management of adiposity in the prevention of late-life dementia and cognitive decline.

neuroscience↗

bioRxiv2020

Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses

The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MR imaging data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Here we use an open structural MR imaging dataset (ABIDE), supplemented with the Human Connectome Project (HCP), to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of 1) software tool (e.g. ANTs, CIVET, FreeSurfer), 2) cortical parcellation (DKT, Destrieux, Glasser), and 3) quality control procedure (manual, automatic). We divide our statistical analyses by 1) method type, i.e. task-free (unsupervised) versus task-driven (supervised), and 2) inference objective, i.e. neurobiological group differences versus individual prediction. Results show that software, parcellation, and quality control significantly impact task-driven neurobiological inference. Additionally, software selection strongly impacts neurobiological and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

Integrated morphometric, molecular, and clinical characterization of Parkinson's disease pathology

Individuals with Parkinsons disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the "average" patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method--similarity network fusion--to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinsons disease from the Parkinsons Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.

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