RT Journal Article
SR Electronic
T1 Consistent inter-protocol differences in resting-state functional connectomes between normal aging and mild cognitive impairment
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 019646
DO 10.1101/019646
A1 Angela Tam
A1 Christian Dansereau
A1 AmanPreet Badhwar
A1 Pierre Orban
A1 Sylvie Belleville
A1 Howard Chertkow
A1 Alain Dagher
A1 Alexandru Hanganu
A1 Oury Monchi
A1 Pedro Rosa-Neto
A1 Amir Shmuel
A1 Seqian Wang
A1 John Breitner
A1 Pierre Bellec
A1 Alzheimer’s Disease Neuroimaging Initiative
YR 2015
UL http://biorxiv.org/content/early/2015/07/18/019646.abstract
AB Resting-state functional connectivity is a promising biomarker for Alzheimer’s disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer’s disease and mild cognitive impairment (MCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from MCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore pooled four independent datasets, including 112 healthy controls and 143 patients with MCI, systematically testing multiple brain connections for consistent differences. The largest effects associated with MCI involved the ventromedial and dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with MCI exhibited significantly decreased connectivity within the frontal lobe, between frontal and temporal areas, and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified robust MCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 150 to 400 total subjects to achieve adequate statistical power. If our findings can be replicated and associated with other established biomarkers of Alzheimer’s disease (e.g. amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimer’s disease.