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[P2–412]: A FUNCTIONAL RESTING STATE STUDY OF BASAL FOREBRAIN FUNCTIONAL CONNECTIVITY IN ASYMPTOMATIC AT‐RISK INDIVIDUALS FOR AD: THE INSIGHT‐PREAD STUDY
Author(s) -
Chiesa Patrizia A.,
Cavedo Enrica,
Teipel Stefan J.,
Grothe Michel J.,
Habert MarieOdile,
Lista Simone,
Dubois Bruno,
Hampel Harald
Publication year - 2017
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.2017.06.1068
Subject(s) - precuneus , nucleus basalis , basal forebrain , neuroscience , cholinergic , resting state fmri , atrophy , cholinergic neuron , psychology , entorhinal cortex , cortex (anatomy) , pittsburgh compound b , posterior parietal cortex , biology , pathology , medicine , hippocampus , cognition , cognitive impairment
cognitive testing for five years. DSEG provides a vector of 16 discrete segments describing brain microstructure. Segments represent diffusion signatures related to healthy grey matter (GM) and white matter (WM) cerebrospinal fluid or microstructural damage. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG q) describing the patients’ brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Either individual DSEG segments or the SVD severity DSEG q score can be used to assess brain changes related to risk of dementia. Dementia diagnosis was based on the criteria from the Diagnostic and Statistical Manual for mental disorders 5. Stepwise Cox regression was used to identify which DSEG parameters and vascular risk factors were related to increased risk of dementia. Linear discriminant analysis was used to assess accuracy of models capable of identifying individuals who developed dementia. Results:18 (18.4 %) patients converted to dementia. Model One included one DSEG segment related to healthy WM microstructure and one segment representing microstructural damage. It accurately predicted dementia with a balanced classification rate (BCR) of 79.65 % and a C statistic of 0.84. Model Two included the SVD severity score (DSEG q) and predicted dementia with a BCR of 81.50 % and a C statistic of 0.88. Vascular risk factors, age, gender and premorbid IQ did not improve either model. Conclusions:DSEG provides two different methods for assessing brain microstructural damage in SVD that can be used to identify those at risk of developing dementia over a five-year period. This automatic segmentation technique may be applied in the monitoring of SVD patients.