Biology subjects

Orban, P.

Publications and source records attributed to Orban, P..

7 recordsLinked to original sources

bioRxiv2020

Subtypes of functional connectivity associate robustly with ASD diagnosis

Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes this neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose this heterogeneity into subtypes of distinguishable connectivity types and promises an unbiased framework to investigate behavioural symptoms and causative genetic factors. Yet the robustness and generalizability of these imaging subtypes is unknown. Here, we show that unsupervised functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and that these associations generalize to independent replication data. We found that subtypes identified robust patterns of functional connectivity, but that a discrete assignment of individuals to these subtypes was not supported by the data. Our results support the use of data driven subtyping as a data dimensionality reduction technique, rather than to establish clinical categories.

neuroscience↗

bioRxiv2019

Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia

16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Deficit-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. We analyzed resting-state functional magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We used CNV FC-signatures to identify dimensions contributing to complex idiopathic conditions. CNVs had large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions played a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibited worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.

neuroscience↗

bioRxiv2018

Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors

Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. The main objective of this study was to assess the long-term consistency of rsfMRI connectivity maps derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to ten minutes of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. The consistency (spatial Pearsons correlation) of rsfMRI connectivity maps for seven canonical networks ranged from about 0.4-0.8 (intra-site) to 0.3-0.8 (inter-vendor), with a negligible effect of time. We noted systematic differences in data quality across vendors, which may also explain some of these results. We also pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match two scans of the same subjects. In this \"fingerprinting\" experiment, we found that scans from the Canadian subject (Csub) could be matched with high accuracy intra-site (>95% for some networks), but that the accuracy decreased substantially for scans drawn from different sites and vendors, while still remaining in the range of accuracies observed in HNU1. Overall, our results demonstrate good multivariate stability of rsfMRI measures over several years, but substantial impact of scanning site and vendors. How detrimental these effects are will depend on the application, yet improving methods for harmonizing multisite analysis is an important area for future work.\n\nHIGHLIGHTSO_LIConsistency of rsfMRI connectivity over 2.5 years, 13 sites and 3 scanner vendors\nC_LIO_LITime elapsed between scans had negligible effect on consistency\nC_LIO_LIConsistency decreased due to site and vendor differences\nC_LIO_LIAccuracy of connectivity fingerprints decreased due to site and vendor differences\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=181 HEIGHT=200 SRC=\"FIGDIR/small/497743v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@109bab7org.highwire.dtl.DTLVardef@1c1720corg.highwire.dtl.DTLVardef@12678e2org.highwire.dtl.DTLVardef@ca494d_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG

neuroscience↗

bioRxiv2018

A signature of cognitive deficits and brain atrophy that is highly predictive of progression to Alzheimer’s dementia

Patients with mild cognitive impairment (MCI) are at risk of progressing to Alzheimers dementia, yet only a fraction of them do. We explore here whether a very high-risk MCI subgroup can be identified using cognitive assessments and structural neuroimaging. A multimodal signature of Alzheimers dementia was first extracted using machine learning tools in the ADNI1 sample, and was comprised of cognitive deficits across multiple domains as well as atrophy in temporal, parietal and occipital regions. We then validated the predictive value of this signature on two MCI cohorts. In ADNI1 (N=235), the presence of the signature predicted progression to dementia over three years with 80.4% positive predictive value, adjusted for a \"typical\" MCI baseline rate of 33% (95.6% specificity, 55.1% sensitivity). These results were replicated in ADNI2 (N=235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity). Our results demonstrate that, even for widely used markers, marked improvement in positive predictive value over the literature can be achieved by focusing on a subgroup of individuals with similar brain characteristics. The signature can be readily applied for the enrichment of clinical trials.

neuroscience↗

bioRxiv2017

Subtypes of functional brain connectivity as early markers of neurodegeneration in Alzheimer’s disease

HighlightsO_LIReliable functional brain network subtypes accompany cognitive impairment in AD\nC_LIO_LISymptom-related subtypes exist in the default-mode, limbic and salience networks\nC_LIO_LIA limbic subtype is associated with a familial risk of AD in healthy older adults\nC_LIO_LILimbic subtypes also associate with beta amyloid deposition and ApoE4\nC_LI\n\nIn BriefWe found reliable subtypes of functional brain connectivity networks in older adults, associated with AD-related clinical symptoms in patients as well as several AD risk factors/biomarkers in asymptomatic individuals.\n\nSummaryThe heterogeneity of brain degeneration has not been investigated yet for functional brain network connectivity, a promising biomarker of Alzheimers disease. We coupled cluster analysis with resting-state functional magnetic resonance imaging to discover connectivity subtypes in healthy older adults and patients with cognitive disorders related to Alzheimers disease, noting associations between subtypes and cognitive symptoms in the default-mode, limbic and salience networks. In an independent asymptomatic cohort with a family history of Alzheimers dementia, the connectivity subtypes had good test-retest reliability across all tested networks. We found that a limbic subtype was overrepresented in these individuals, which was previously associated with symptoms. Other limbic subtypes showed associations with cerebrospinal fluid A{beta}1-42 levels and ApoE4 genotype. Our results demonstrate the existence of reliable subtypes of functional brain networks in older adults and support future investigations in limbic connectivity subtypes as early biomarkers of Alzheimers degeneration.

neuroscience↗

bioRxiv2017

Multisite Generalizability Of Schizophrenia Diagnosis Classification Based On Functional Brain Connectivity

Our objective was to assess the generalizability, across sites and cognitive contexts, of schizophrenia classification based on functional brain connectivity. We tested different training-test scenarios combining fMRI data from 191 schizophrenia patients and 191 matched healthy controls obtained at 6 scanning sites and under different task conditions. Diagnosis classification accuracy generalized well to a novel site and cognitive context provided data from multiple sites were used for classifier training. By contrast, lower classification accuracy was achieved when data from a single distinct site was used for training. These findings indicate that it is beneficial to use multisite data to train fMRI-based classifiers intended for large-scale use in the clinical realm.

neuroscience↗

bioRxiv2017

Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis

INTRODUCTIONWe performed a systematic review and meta-analysis of the Alzheimers disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment (MCI), using resting-state functional magnetic resonance imaging (rsfMRI).\n\nMETHODSStudies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies.\n\nRESULTSThirty-four studies were included (1,363 participants, average 40 per study). Consistent alterations in connectivity were found in the default-mode, salience and limbic networks in patients with AD dementia, MCI, or in both groups. We also identified a bias in the literature towards specific examination of the default-mode network.\n\nDISCUSSIONConvergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that metabolically expensive hub regions in the brain might be an early target of AD.

neuroscience↗