Bae, Jinhyeong;
Stocks, Jane;
Heywood, Ashley;
Jung, Youngmoon;
Katsaggelos, Aggelos;
Jenkins, Lisanne M;
Karteek, Popuri;
Beg, Mirza Faisal;
Wang, Lei
Relating occlusion maps obtained through deep learning to functional impairment in dementia of Alzheimer’s type : Neuroimaging / Optimal neuroimaging measures for early detection
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Medientyp:
E-Artikel
Titel:
Relating occlusion maps obtained through deep learning to functional impairment in dementia of Alzheimer’s type : Neuroimaging / Optimal neuroimaging measures for early detection
:
Neuroimaging / Optimal neuroimaging measures for early detection
Beschreibung:
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Predicting the conversion from Mild Cognitive Impairment (MCI) into Dementia of the Alzheimer’s type (DAT) and functional change is crucial to patient care and treatment. In order to visualize brain regions which are significant in the prediction, we implemented an occlusion map based on deep learning.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>Using T1‐weighted structural MRI data from ADNI, 3D convolutional neural network was trained to predict the conversion from MCI to DAT through a transfer learning pipeline. The model resulted in an 82.4% classification accuracy on an independent test set. An occlusion map was subsequently generated as follows. Each brain scan was occluded by 2x2x2 voxel patch iterated through every position in the brain. The model produced a prediction score corresponding to the location of the occlusion patch to identify important voxels for the model’s prediction at the subject level. Mean intensity value within the occlusion map was obtained in Gray Matter. This was used to correlate to clinical scores’ rate of change, which include Clinical Dementia Rating‐Sum of Boxes (CDRSB), Alzheimer’s Disease Assessment Scale – cognitive 11 item (ADAS11) and cognitive 13 item (ADAS13), Mini Mental State Exam (MMSE), Rey Auditory Verbal Learning Test (RAVLT) – RAVLT Immediate (IMD), RAVLT Learning (LRN), RAVLT Forgetting (FRG), RAVLT Percent Forgetting (PCF), and Functional Activities Questionnaire (FAQ).</jats:p></jats:sec><jats:sec><jats:title>Result</jats:title><jats:p>The occlusion map identified regions important to the prediction of conversion. Mean intensity values of the gray matter within the occlusion map showed significant correlation with behavior measures: decreased intensity (indicating gray matter loss) was associated with decreased memory performance as measured by RAVLT immediate recall performance, increased cognitive dysfunction as measured by ADAS11, ADAS13, and increased daily living deficits as measured by FAQ.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>These results indicate brain regions associated with cognitive change during conversion from MCI to DAT. These regions provide validity of the deep learning model’s results and provide insights on patient functional changes during conversion.</jats:p></jats:sec>