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P4‐594: FUNCTIONAL CONNECTIVITY PATTERNS OF THE LESIONED DEFAULT MODE NETWORK
Author(s) -
Rivera-Dompenciel Adriana M.,
Bruss Joel E.,
Tranel Daniel,
Voss Michelle W.
Publication year - 2019
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 - 10.1016/j.jalz.2019.08.142
Subject(s) - default mode network , neuroscience , lesion , prefrontal cortex , temporal lobe , cortex (anatomy) , psychology , analysis of variance , functional magnetic resonance imaging , medicine , cognition , psychiatry , epilepsy
0.748 on the testing CN cohort and MCI cohort respectively, significantly better than those built upon regional GMvolumemeasures, demographic and cognitive measures, and their combination (Table 2). The deep learning prediction model also identified informative brain regions, mainly located in the lateral temporal, temporoparietal, and prefrontal cortices (Fig. 2). The predicted risk of cognitive decline obtained by the deep learning model also clustered the CN and MCI subjects into subgroups with significant differences in their rapidity of cognitive decline (Fig. 3). Conclusions: The deep learning prediction model could achieve promising performance for early predicting rapidity of cognitive decline of individual older adults based on their baseline MRI images and demographic and cognitive measures. P4-594 FUNCTIONAL CONNECTIVITY PATTERNS OF THE LESIONED DEFAULT MODE NETWORK