Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease

Abstract

Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent performance in differentiating individuals with Alzheimer's disease (AD). However, the value of DL in detecting progressive structural MRI (sMRI) abnormalities linked to AD pathology has yet to be established. In this study, an interpretable DL algorithm named the Ensemble of 3-dimensional convolutional neural network (Ensemble 3DCNN) with enhanced parsing techniques is proposed to investigate the longitudinal trajectories of whole-brain sMRI changes denoting AD onset and progression. A set of 2369 T1-weighted images from the multi-centre Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies cohorts are applied to model derivation, validation, testing, and pattern analysis. An Ensemble-3DCNN-based P-score is generated, based on which multiple brain regions, including amygdala, insular, parahippocampal, and temporal gyrus, exhibit early and connected progressive neurodegeneration. Complex individual variability in the sMRI is also observed. This study combining non-invasive sMRI and interpretable DL in detecting patterned sMRI changes confirmed AD pathological progression, shedding new light on predicting AD progression using whole-brain sMRI.

A Survey of Deep Learning for Alzheimer’s Disease

“…sMRI is a non-invasive brain imaging technique that can investigate changes in brain structure [150]. Changes in brain structure due to worsening cognitive impairment may include atrophy of specific brain regions, loss of brain tissue, and changes in the shape and size of certain brain structures [151,152].…”

Relationship Between Excessive Daytime Sleepiness and Caudate Nucleus Volume in Patients with Subjective Cognitive Decline: A Study from the SILCODE Using the Volbrain

“…Structural magnetic resonance imaging (sMRI) is a widely accessible, non-invasive, and commonly used clinical method [21] that can track subcortical changes provoking disease progression [22]. A quantitative volumetric analysis of the caudate nucleus using sMRI can provide valuable information in early diagnosis and prognosis of patients with AD [17].…”

Cross-ancestry and sex-stratified genome-wide association analyses of amygdala and subnucleus volumes

“…The amygdala is a small but important structure in the human brain, which is composed of multiple interconnected subnuclei with unique cytoarchitectures, myeloarchitectures, and chemoarchitectures 1 . The amygdala shows extensive connections with the primary sensory cortices, medial prefrontal cortex, orbitofrontal cortex, nucleus accumbens, hippocampus, hypothalamus, and other nuclei [2][3][4] , indicating that the structure involves multiple parallel circuits to support distinct functions, including emotion, motivation, memory, and social interaction 4,5 , which may explain why the structural and functional impairments of amygdala are observed in clinically distinct neuropsychiatric disorders, such as major depressive disorder 6 , anxiety [7][8][9][10] , schizophrenia [11][12][13][14][15] , bipolar disorder 13,15 , autism spectrum disorder 14 , Alzheimer's disease 16,17 , and multiple sclerosis 18 . Although interindividual differences are observed in the sizes of the amygdala and its subnuclei, the underlying neurobiological mechanisms remain largely unknown.…”