Dataset Information

Non-monotonic reorganization of brain networks with Alzheimer's disease progression.

ABSTRACT:

Background

Identification of stage-specific changes in brain network of patients with Alzheimer's disease (AD) is critical for rationally designed therapeutics that delays the progression of the disease. However, pathological neural processes and their resulting changes in brain network topology with disease progression are not clearly known.

Methods

The current study was designed to investigate the alterations in network topology of resting state fMRI among patients in three different clinical dementia rating (CDR) groups (i.e., CDR = 0.5, 1, 2) and amnestic mild cognitive impairment (aMCI) and age-matched healthy subject groups. We constructed density networks from these 5 groups and analyzed their network properties using graph theoretical measures.

Results

The topological properties of AD brain networks differed in a non-monotonic, stage-specific manner. Interestingly, local and global efficiency and betweenness of the network were rather higher in the aMCI and AD (CDR 1) groups than those of prior stage groups. The number, location, and structure of rich-clubs changed dynamically as the disease progressed.

Conclusions

The alterations in network topology of the brain are quite dynamic with AD progression, and these dynamic changes in network patterns should be considered meticulously for efficient therapeutic interventions of AD.

SUBMITTER: Kim H

PROVIDER: S-EPMC4460428 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Non-monotonic reorganization of brain networks with Alzheimer's disease progression.

Kim HyoungKyu H Yoo Kwangsun K Na Duk L DL Seo Sang Won SW Jeong Jaeseung J Jeong Yong Y

Frontiers in aging neuroscience 20150609

<h4>Background</h4>Identification of stage-specific changes in brain network of patients with Alzheimer's disease (AD) is critical for rationally designed therapeutics that delays the progression of the disease. However, pathological neural processes and their resulting changes in brain network topology with disease progression are not clearly known.<h4>Methods</h4>The current study was designed to investigate the alterations in network topology of resting state fMRI among patients in three diff ...[more]

PMID: 26106325

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Project description:Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a "preprocessing" issue, by imputing the missing data using the previous timepoint ("forward filling") or linear interpolation ("linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing ("model filling"). Our analyses suggest that the minimalRNN with "model filling" compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.

Dataset Information

Non-monotonic reorganization of brain networks with Alzheimer's disease progression.

Background

Methods

Results

Conclusions

Publications

Non-monotonic reorganization of brain networks with Alzheimer's disease progression.

Similar Datasets

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