×
  • Select the area you would like to search.
  • ACTIVE INVESTIGATIONS Search for current projects using the investigator's name, institution, or keywords.
  • EXPERTS KNOWLEDGE BASE Enter keywords to search a list of questions and answers received and processed by the ADNI team.
  • ADNI PDFS Search any ADNI publication pdf by author, keyword, or PMID. Use an asterisk only to view all pdfs.
Principal Investigator  
Principal Investigator's Name: Obaï Bin Ka'b Ali
Institution: Concordia University
Department: Physics
Country:
Proposed Analysis: The functional connectivity large-scale network analysis of resting-state functional Magnetic Resonance Imaging (fMRI) data regularly involves the choice of a brain atlas. Such brain atlases (or parcellations) are useful to facilitate comparison and improve reproducibility across studies. In addition, they are convenient to reduce computational complexity as well as to increase signal-to-noise ratio (e.g., through voxel timeseries aggregations within regions of interest). The past decade has seen an increased interest in the use of multiresolution atlases (e.g., ranging from 100 to 1000 parcels) as an attempt to better characterize the multiscale spatial organization of the functional architecture of the brain. However, it remains to be established what significant information may be brought forth by estimating, across multiple spatial resolutions, large-scale functional connectivity networks and the network architecture of their coordinated activity. In this study, we investigate the spatial multiscale profile of intrinsic functional communication within and between canonical resting-state networks in older adults at risk for or afflicted with Alzheimer Disease (AD). We use the resting-state fMRI data of a clinic-based cohort, the AD Neuroimaging Initiative, comprising cognitively unimpaired older adults, and patients with mild cognitive impairment and AD. As a measure of functional interactions, we use indices of information theory, the hierarchical integration measures, allowing the quantification of the informational relationships between any pair of fMRI resting-state networks as well as among the functional assemblies constituting any given such network. We use a multiresolution group-level functional atlas, the Multiresolution Intrinsic Segmentation Template, in order to systematically define each individual’s a-priori whole-brain resting-state-fMRI-based functional parcellation. This atlas provides a hierarchical decomposition tree of large-scale functional brain networks across nine resolutions (from 7 to 444 functional parcels). We systematically compare the profiles of hierarchical integration measures across resolutions and across the populations using indices of graph theory including modularity, clustering coefficient, efficiency, and centrality. We further relate such profiles to neuropsychological measures of cognition across multiple domains using a general linear model. We propose that a spatial multiresolution quantification of the informational relationships between large-scale neural networks significantly correlates with the decline in various cognitive domains and allows to better capture the heterogeneity in Mild Cognitive Impairment and AD populations. Along these lines, such a multiscale quantification may offer a viable neural marker of cognitive states in older adults at risk for or afflicted with AD.
Additional Investigators  
Investigator's Name: Habib Benali
Proposed Analysis: The functional connectivity large-scale network analysis of resting-state functional Magnetic Resonance Imaging (fMRI) data regularly involves the choice of a brain atlas. Such brain atlases (or parcellations) are useful to facilitate comparison and improve reproducibility across studies. In addition, they are convenient to reduce computational complexity as well as to increase signal-to-noise ratio (e.g., through voxel timeseries aggregations within regions of interest). The past decade has seen an increased interest in the use of multiresolution atlases (e.g., ranging from 100 to 1000 parcels) as an attempt to better characterize the multiscale spatial organization of the functional architecture of the brain. However, it remains to be established what significant information may be brought forth by estimating, across multiple spatial resolutions, large-scale functional connectivity networks and the network architecture of their coordinated activity. In this study, we investigate the spatial multiscale profile of intrinsic functional communication within and between canonical resting-state networks in older adults at risk for or afflicted with Alzheimer Disease (AD). We use the resting-state fMRI data of a clinic-based cohort, the AD Neuroimaging Initiative, comprising cognitively unimpaired older adults, and patients with mild cognitive impairment and AD. As a measure of functional interactions, we use indices of information theory, the hierarchical integration measures, allowing the quantification of the informational relationships between any pair of fMRI resting-state networks as well as among the functional assemblies constituting any given such network. We use a multiresolution group-level functional atlas, the Multiresolution Intrinsic Segmentation Template, in order to systematically define each individual’s a-priori whole-brain resting-state-fMRI-based functional parcellation. This atlas provides a hierarchical decomposition tree of large-scale functional brain networks across nine resolutions (from 7 to 444 functional parcels). We systematically compare the profiles of hierarchical integration measures across resolutions and across the populations using indices of graph theory including modularity, clustering coefficient, efficiency, and centrality. We further relate such profiles to neuropsychological measures of cognition across multiple domains using a general linear model. We propose that a spatial multiresolution quantification of the informational relationships between large-scale neural networks significantly correlates with the decline in various cognitive domains and allows to better capture the heterogeneity in MCI and AD populations. Along these lines, such a multiscale quantification may offer a viable neural marker of cognitive states in older adults at risk for or afflicted with AD.