Salience network resting-state activity prediction of frontotemporal dementia progression

Gregory S. Day, Norman A.S. Farb, David F. Tang-Wai, Mario Masellis, Sandra E. Black, Morris Freedman, Bruce G. Pollock, Tiffany W. Chow

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Importance Noninvasive measures of activity within intrinsic brain networks may be clinically relevant, providing a marker of neurodegenerative disease and predicting clinical behaviors. Objective To correlate baseline resting-state measures within the salience network and changes in behavior among patients with frontotemporal dementia. DESIGN Baseline resting-state functional magnetic resonance imaging data and longitudinal clinical measures were obtained from prospectively accrued patients during 8 weeks. SETTING Tertiary academic care center specializing in the assessment and management of patients with neurodegenerative disease. PARTICIPANTS Fifteen patients with clinically diagnosed frontotemporal dementia (5 behavioral variant and 10 semantic dementia). Main Outcomes and Measures Baseline resting-state functional magnetic resonance imaging data measured within regions of interest were regressed on serial behavioral measures from prospectively accrued patients with frontotemporal dementia to determine the ability of baseline resting-state activity to account for changes in behavior. Results Low-frequency fluctuations in the left insula significantly predicted changes in Frontal Behavioral Inventory scores (standard â = 0.51, P =.049), accounting for 28%of the change variance. The trend was driven by changes in measures of apathy independent of dementia severity. CONCLUSION AND RELEVANCE Baseline measures of salience network connectivity involving the left insula may predict behavioral changes in patients with frontotemporal dementia.

Original languageEnglish
Pages (from-to)1249-1253
Number of pages5
JournalJAMA Neurology
Issue number10
StatePublished - Oct 2013


Dive into the research topics of 'Salience network resting-state activity prediction of frontotemporal dementia progression'. Together they form a unique fingerprint.

Cite this