henhaobio
Sign in

bioRxivNeurosciencePreprint

Resting-state functional MRI derivatives: A dataset derived from the The Comprehensive Assessment of Neurodegeneration and Dementia Study

Natasha ClarkeiD, Hao-Ting WangiD, Désirée LussieriD, Arnaud BoréiD, Loïc TetreliD, Camille Beaudoin, Samir DasiD, Randi Pilon, Alan Charles EvansiD, Howard Mark ChertkowiD, Roger A. Dixon, AmanPreet BadhwariD, Simon DuchesneiD, Pierre BelleciD

PDFDOI

Abstract

Resting-state functional connectivity (RSFC) holds promise for the detection and characterisation of dementia. The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) Study, by the Canadian Consortium on Neurodegeneration in Aging (CCNA), provides a unique resource to study deeply phenotyped neurodegenerative conditions. We present RSFC derivatives for 784 participants (data release 7 of the cohort) who were either cognitively unimpaired or diagnosed primarily with Alzheimers disease (AD), mixed dementia (AD with a vascular component), mild cognitive impairment (MCI), vascular MCI, frontotemporal dementia, Parkinsons disease with or without MCI or dementia, Lewy body disease or subjective cognitive impairment. Functional MRI scans were preprocessed using fMRIPrep, and time-series and whole-brain connectomes generated using three atlases at multiple resolutions, denoised using seven different techniques. High-motion artifacts were managed using a liberal quality control threshold appropriate for an older clinical population, resulting in data from 680 participants. These derivatives are made available to the research community to accelerate research on RSFC biomarkers of neurodegenerative disease, reducing duplication of effort, saving computational resources, and improving standardisation across studies.

Citations
0

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISTam '15Brown '25Hassanzadeh '24Badhwar '17Jin '23Scheel '25
65% semantic matchNeuroscience
Angela Tam, Christian Dansereau, AmanPreet Badhwar+12 more·bioRxiv·Jul 18, 2015