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The application of the Human Connectome Project in large scale brain network imaging: A potential biomarker for Alzheimer's disease and related dementias
Author(s) -
Rosenbloom Michael H.,
Hanson Leah R.,
Kashyap Bhavani,
Erickson Lauren O.,
Sughrue Michael Edward
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.050749
Subject(s) - connectome , diffusion mri , connectomics , neuroimaging , human connectome project , computer science , functional magnetic resonance imaging , artificial intelligence , resting state fmri , neuroscience , magnetic resonance imaging , psychology , medicine , functional connectivity , radiology
Background Alzheimer’s disease and related dementias (ADRD) result in progressive dysfunction of large scale brain networks (LSBNs) including the default mode network (DMN). Resting state functional MRI (rs‐fMRI) is an imaging tool offering the potential for tracking LSBN degeneration, but been underutilized as an imaging biomarker. Potential reasons include complexity of imaging protocols and analyzing imaging data. If these steps were simplified and operationalized, further integration of rs‐fMRI into clinical trials as a surrogate endpoint may be feasible. We describe an automated connectome mapping program (Infinitome) that leverages rs‐fMRI scans with standardized protocols, Human Connectome Project (HCP) data, and machine learning to understand longitudinal LSBN degeneration in dementia (figure 1). Method The Infinitome program creates a subject specific version of the Human Connectome Project Multimodal Parcellation (HCP‐MMP1) atlas using diffusion tractography (figure 2). Analytics are performed on both diffusion tensor imaging and rs‐fMRI. Outlier detection using a tangent space connectivity matrix is performed by comparing results with a subset of 300 normal HCP subject fMRI samples to determine the range of normal correlations for each regions of interest in a LSBN. Abnormal connectivity is determined as a 3‐sigma outlier for that correlation (figure 3). The imaging protocol for rs‐fMRI is limited to 8 minutes, enabling each study to be readily added on to structural MRI sequences. The program also provides automated image processing and analysis, thus simplifying the process of accessing imaging data and incorporating rs‐fMRI into routine practice. Result Preliminary work has shown that connectomic analysis using this software in ADRD is feasible and can detect functional anomalies involving regions of interest described by the HCP. Conclusion By providing support for fMRI processing through a cloud‐based server and incorporating normal control data from the HCP, the Infinitome program addresses the challenges associated with the analysis and interpretation of rs‐fMRI data. Thus, it has the potential to simplify the incorporation of rs‐fMRI into clinical trials as a biomarker for progressive neurodegeneration of vulnerable networks in ADRD. Further studies are planned evaluating LSBNs in AD and dementia with Lewy bodies.