Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment

Zhang, Yingteng; Liu, Shenquan; Yu, Xinbing

doi:10.1002/ima.22390

Cited by 6 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a few researchers have analyzed longitudinal data to predict neurological disorders via machine learning [24]. Among the aforementioned databases, ABIDE II [23] provides longitudinal samples.…”

Section: Introductionmentioning

confidence: 99%

Outlier-Based Autism Detection Using Longitudinal Structural MRI

et al. 2022

View full text Add to dashboard Cite

Diagnosis of Autism Spectrum Disorder (ASD) using clinical evaluation (cognitive tests) is challenging due to wide variations amongst individuals. Since no effective treatment exists, prompt and reliable ASD diagnosis can enable the effective preparation of treatment regimens. This paper proposes structural Magnetic Resonance Imaging (sMRI)-based ASD diagnosis via an outlier detection approach. To learn spatio-temporal patterns in structural brain connectivity, a Generative Adversarial Network (GAN) is trained exclusively with sMRI scans of healthy subjects. Given a stack of three adjacent slices as input, the GAN generator reconstructs the next three adjacent slices; the GAN discriminator then identifies ASD sMRI scan reconstructions as outliers. This model is compared against two other baselines-a simpler UNet and a sophisticated Self-Attention GAN. Axial, Coronal and Sagittal sMRI slices from the multi-site ABIDE II dataset are used for evaluation. Extensive experiments reveal that our ASD detection framework performs comparably with the state-of-the-art with far fewer training data. Furthermore, longitudinal data (two scans per subject over time) achieve 17-28% higher accuracy than cross-sectional data (one scan per subject). Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Outlier-Based Autism Detection Using Longitudinal Structural MRI

et al. 2022

View full text Add to dashboard Cite

show abstract

“…With the aging population becoming grim worldwide, the cognitive level of the old people has especially garnered massive attention [ 1 – 4 ] because it is related to the quality of life. Determining the cognitive ability of the elderly as early as possible is a prerequisite for prevention or intervention for slowing down the time course of degenerative or pathological cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

Optimized Multiscale Entropy Model Based on Resting-State fMRI for Appraising Cognitive Performance in Healthy Elderly

Yang

Zhang

Belkacem

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance imaging (rfMRI) signals. However, there are problems of subjectivity and lack of uniform standards in the selection of model parameters relying on experience when using the entropy model to analyze rfMRI. To address this issue, an optimized multiscale entropy (MSE) model was proposed to confirm the parameters objectively. All healthy elderly volunteers were divided into two groups, namely, excellent and poor, by the scores estimated through traditional scale tests before the rfMRI scan. The parameters of the MSE model were optimized with the help of sensitivity parameters such as receiver operating characteristic (ROC) and area under the ROC curve (AUC) in a comparison study between the two groups. The brain regions with significant differences in entropy values were considered biomarkers. Their entropy values were regarded as feature vectors to use as input for the probabilistic neural network in the classification of cognitive scores. Classification accuracy of 80.05% was obtained using machine learning. These results show that the optimized MSE model can accurately select the brain regions sensitive to cognitive performance and objectively select fixed parameters for MSE. This work was expected to provide the basis for entropy to test the cognitive scores of the healthy elderly.

show abstract