Biology subjects

Dansereau, C.

Publications and source records attributed to Dansereau, C..

5 recordsLinked to original sources

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↗

bioRxiv2018

Data-driven approaches improve Tau-PET biomarkers in Alzheimer’s disease

Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published \"pathology-driven\" ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimers disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18F]AV1451 scans. We performed linear models comparing [18F]AV1451 signal across all 40 ROIs to several tests of global cognition, adjusting for age, sex and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

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↗