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IC‐P‐166: BASELINE DIFFERENCES IN BRAIN MORPHOMETRY AND IMAGE GRADING OF INDIVIDUALS ON THE CONTINUUM FROM SUBJECTIVE COGNITIVE DECLINE TO AD: RESULTS FROM THE CIMA‐Q STUDY
Author(s) -
Potvin Olivier,
Marcotte Christine,
Collins Louis,
Duchesne Simon
Publication year - 2018
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.2018.06.2233
Subject(s) - voxel , cognition , effects of sleep deprivation on cognitive performance , cognitive decline , cognitive impairment , medicine , entorhinal cortex , imaging biomarker , magnetic resonance imaging , psychology , audiology , radiology , neuroscience , dementia , disease , hippocampus
disease, many years before clinical onset, pathological changes are widespread through the brain. Combining the most informative features may help track early decline. However, strong correlations between changes in brain structures could hinder the ability to reliably identify such features in multivariate analyses. Even though two highly correlated regions may be equally discriminative, some methods could consistently mark one as important and the other as irrelevant. Methods:Data comes from the tenth cutoff of the Dominantly Inherited Alzheimer Network (DIAN). This analysis included 92 mutation carriers (mean age (standard deviation) 42.7(8.5) years), 73 presymptomatic and 19 affected, and 44 non-carriers (age: 42.3(8.0) years). Volumetric T1 brain scans were parcellated into 162 unique brain structures using the Geodesic Information Flow (GIF) algorithm (Cardoso et al., IEEE Trans Med Im:34:1976-1988, 2015). Within-subject atrophy rates were calculated from two MRIs approximately 12 months apart and adjusted for total intracranial volume. To rank the most discriminative regions between ADAD mutation carriers and non-carriers, a random forest classifier was employed. Each classifier used a random subsample of 80% of the participants as the training set, from which a ranking of the features was extracted. The global ranking was obtained by averaging the rankings for each label over all the classifiers. Pairwise Pearson correlations between all structures were calculated to assess where the redundancy of information could influence the ranking. Results:High left-right correlation, particularly (r>0.9) in the lateral ventricles, thalami and white matter (frontal, parietal, insula), indicates strong hemispheric symmetry in atrophy patterns. High correlations were also present in some of the highest-ranking features for detecting disease progression. For example, CSF regions (including lateral ventricles) ranked amongst the most discriminative of features, and the correlation matrix (figure) highlights that these features are strongly correlated with many regions throughout the brain. Conclusions:Our analysis revealed strong correlations across regional atrophy rates. These correlations must be considered when examining the most relevant features extracted by common multivariate techniques.