The contribution of hippocampal subfields to the progression of neurodegeneration

• Published in: bioRxiv
• Main Authors: Kwak, Kichang, Niethammer, Marc, Giovanello, Kelly S, Styner, Martin, Dayan, Eran, The Alzheimer’s Disease Neuroimaging Initiative
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 16.10.2020
• Subjects: Alzheimer's disease; Cognitive ability; Hippocampus; Morphometry; Neural networks; Neurodegeneration; Neurodegenerative diseases; Occlusion; Presubiculum; Subiculum
• DOI: 10.1101/2020.05.06.081034

Abstract Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer’s disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus, but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression using a total sample of 1157 subjects (349 in the training set, 427 in a validation set and 381 in the testing set). We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry. The proposed deep learning model predicted MCI progression with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI. Our predictive model uncovers pronounced differences in the contribution of hippocampal subfields to the progression of MCI. The results may reflect the sparing of hippocampal structure in individuals with a slower progression of neurodegeneration. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵† Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in analysis or writing of this article. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.