Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment

Abstract

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD).Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybridgranularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability. Source code is made available at https://github.com/thibault-wch/ HOPE-for-mild-cognitive-impairment.

Parkinson’s Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

“…where and indicate Lagrangian multipliers. According to the KKT condition, when equals to , (29) has the minimum value equal to zero. Thus, we can get:…”

“…In addition, in most existing studies, classification and regression are performed only based on the baseline data [27,28], while the longitudinal data (i.e., multi-time points data) are ignored. Owing to the persistent exacerbation of the disease, it is imperative to learn reliable classification and prediction models that meet multi-time points [29]. We highlight our contributions: 1) We propose a novel unsupervised learning method from longitudinal multimodal data for feature selection.…”

Towards Effective Classification of aMCI Based on Resting‐State Multiscale Brain Features and Machine Learning Approaches

“…Local features after fusion Our goal is to simulate the information processing pattern of human brain to extract structural information of BFN, which further improves the detection performance of the aMCI system. Consequently, mathematical modelling about BFN is implemented, which contains information about the interactions between brain regions [33][34][35]. We perform the rough feature extraction module to extract the structural features; that is, the information of brain regions with higher correlation will be maintained after dimensionality reduction.…”

Representation Learning of Resting State fMRI with Variational Autoencoder

“…Most applications are in the regime of supervised learning. Typically, a neural network takes an fMRI-based input data and is trained to generate an output that optimally matches the ground truth for a task, such as individual identification (Chen and Hu, 2018;Wang et al, 2019), prediction of gender, age, or intelligence (Fan et al, 2020;Gadgil et al, 2020;Plis et al, 2014), disease classification (Seo et al, 2019;Suk et al, 2016;Wang et al, 2020;Yang et al, 2019;Zou et al, 2017). The labels required for supervised learning are often orders of magnitude smaller in size than the fMRI data itself, which has a high dimension in both space and time.…”

“…The labels required for supervised learning are often orders of magnitude smaller in size than the fMRI data itself, which has a high dimension in both space and time. As a result, the prior studies often limit the model capacity by using a shallow network and/or limit the input data to activity at the region of interest (ROI) level (Chen and Hu, 2018;Dvornek et al, 2018;Koppe et al, 2019;Matsubara et al, 2019;Suk et al, 2016;Wang et al, 2019;Wang et al, 2020) or reduce it to functional connectivity (D'Souza et al, 2019;Fan et al, 2020;Kawahara et al, 2017;Kim and Lee, 2016;Riaz et al, 2020;Seo et al, 2019;Venkatesh et al, 2019;Yang et al, 2019;Zhao et al, 2018). It is also uncertain to what extent representations learned for a specific task would be generalizable to other tasks.…”