Deep Learning for Multimodal Brain Data Processing and Analysis

Editorial

18 August 2023

Editorial: Deep learning for multimodal brain data processing and analysis

Liansheng Wang

Lequan Yu

and

Baptiste Magnier

1,539 views

0 citations

Editors

Liansheng Wang

Xiamen University

Lequan Yu

The University of Hong Kong

Baptiste Magnier

Mines-Telecom Institute Alès

Impact

Original Research

09 June 2023

A comparison of the five-minute cognitive test with the mini-mental state examination in the elderly for cognitive impairment screening

Xiaoli Pan

, 4 more and

Guoqiang Fei

The five-minute cognitive test (FCT) is a novel cognitive screening method with the quick and reliable merit for detecting cognitive impairment at an early stage. The diagnostic power of FCT in differentiating subjects with cognitive impairment from people with cognition in a normal range was demonstrated effective as that of the Mini-Mental Status Evaluation (MMSE) in a previous cohort study. Here, we analyzed the effect of sociodemographic and health-related factors on FCT performance and further investigated the consistency of FCT. Then, we compared the correlation of subitem scores of FCT or MMSE with a comprehensive battery of neuropsychological tests that focus on specific domains of cognition. Finally, the association of the total FCT scores with the volumes of brain subregions was investigated. There were 360 subjects aged 60 years or above enrolled in this study, including 226 adults with cognitive abilities in normal range, 107 subjects with mild cognitive impairment (MCI) and 27 mild Alzheimer’s disease (AD). The results showed that the total FCT scores was negatively associated with increasing age (β = −0.146, p < 0.001), and positively associated with education attainment (β = 0.318, p < 0.001), dwelling condition with family (β = 0.153, p < 0.001) and the Body Mass Index (β = 1.519, p < 0.01). The internal consistency of the FCT (Cronbach’s α) was 0.644. The sub-scores of FCT showed a significant correlation with other specific neuropsychological tests. Impressively, the total FCT scores showed a significantly positive association with the volumes of hippocampus related subregions (r = 0.523, p < 0.001) and amygdala (r = 0.479, p < 0.001), but not with cerebellum (r = 0.158, p > 0.05) or subcortical subregions (r = 0.070, p > 0.05). Combining with previous data, FCT is a reliable and valid cognitive screening test for detecting cognitive impairment in a community setting.

8,380 views

2 citations

Original Research

14 March 2023

Unified ICH quantification and prognosis prediction in NCCT images using a multi-task interpretable network

Kai Gong

, 7 more and

Zhanxiang Wang

With the recent development of deep learning, the regression, classification, and segmentation tasks of Computer-Aided Diagnosis (CAD) using Non-Contrast head Computed Tomography (NCCT) for spontaneous IntraCerebral Hematoma (ICH) have become popular in the field of emergency medicine. However, a few challenges such as time-consuming of ICH volume manual evaluation, excessive cost demanding patient-level predictions, and the requirement for high performance in both accuracy and interpretability remain. This paper proposes a multi-task framework consisting of upstream and downstream components to overcome these challenges. In the upstream, a weight-shared module is trained as a robust feature extractor that captures global features by performing multi-tasks (regression and classification). In the downstream, two heads are used for two different tasks (regression and classification). The final experimental results show that the multi-task framework has better performance than single-task framework. And it also reflects its good interpretability in the heatmap generated by Gradient-weighted Class Activation Mapping (Grad-CAM), which is a widely used model interpretation method, and will be presented in subsequent sections.

2,181 views

7 citations

Comparison of dice coefficients obtained from Freesurfer and Neuro I using paired t-test. IQR, interquartile range. *Statistically significant difference at p < 0.05.

Original Research

12 May 2023

Development and validation of deep learning-based automatic brain segmentation for East Asians: A comparison with Freesurfer

Chung-Man Moon

, 6 more and

Seul Kee Kim

2,967 views

2 citations

Original Research

24 February 2023

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network

Peng Lv

, 6 more and

Jianjun Zhou

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.

2,674 views

7 citations

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