The contribution of hippocampal subfields to the progression of neurodegeneration

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. We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry 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, and subiculum showing the most central role.Moreover, the analysis reveals that more than 20% of the hippocampal volume was redundant in the differentiation between stable and progressive MCI.

Atrophy-centered subtyping of mild cognitive impairment

“…As our approach utilizes whole brain gray matter features, the major regional contributors to the model’s output remain unclear. We thus next examined the relative lobar (frontal, parietal, medial temporal, lateral temporal, occipital, and cingulate) contribution to the performance of the model, through occlusion analysis, as proposed previously (21) . Briefly, we retested the deep learning model iteratively, occluding a bilateral binary mask composed of each lobe from the model’s test-set input data (Fig.…”

“…We then comprehensibly validated the model-based subgroups, findings marked group differences in baseline CSF biomarker concentrations and PET uptake, along with baseline and longitudinal cognitive performance scores. Through occlusion analysis (21) we investigated lobar contribution to the performance of the deep learning model, reporting that it mostly relied on gray matter volume from the medial and lateral temporal lobes. Finally, we found a limited degree of overlap between the current subtyping approach and that based on neuropsychological examination.…”

“…The copyright holder for this this version posted November 30, 2020. ; https://doi.org/10.1101/2020.11.28.20238964 doi: medRxiv preprint 8 model, through occlusion analysis, as proposed previously (21). Briefly, we retested the deep learning model iteratively, occluding a bilateral binary mask composed of each lobe from the model's test-set input data ( Fig.…”

“…We then deploy the trained CNN to classify MCI subjects into two subgroups, MCI-AD and MCI-CN, corresponding to MCI subjects with AD-like and CN-like morphometric characteristics, respectively. We also identified the major regional atrophy patterns contributing to the differentiation between the two MCI subtypes through occlusion analysis (21) . The resulting labels were then validated against cerebrospinal fluid (CSF) biomarkers for β-amyloid (Aβ) and tau, baseline fluorodeoxyglucose (FDG) and amyloid positron emission tomography (PET), as well as baseline and longitudinal cognitive scores.…”

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

“…Histological studies have shown that lesions are not uniformly distributed within the hippocampus 91,93 . Neuronal loss results in a reduction of the thickness of the layers richer in neuronal bodies, while the loss of synapses results in the reduction of the layers poorer in neuronal bodies [94][95][96][97] and these changes are stage-dependent 98,99 .…”