Mouse brain magnetic resonance microscopy: Applications in Alzheimer disease

A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects' features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs' input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph's imaging features and edge-assigning functions can both significantly affect classification accuracy.

Voluntary wheel running delays brain atrophy in aged mice

BACKGROUND:

Physical exercises have been shown to be a surprisingly effective strategy to take advantage of the brain’s natural capacity for plasticity, and prevent brain degeneration in mouse histological studies. In vivo magnetic resonance microscopy (MRM) provides highly resolved anatomical images and allows quantitative assessment of brain atrophy in the aged mouse model.

OBJECTIVE:

The aim of the present study was to investigate, through the effects of 10 weeks voluntary wheel running, the mouse’s brain atrophy.

METHODS:

Sixteen C57BL/6J mice, aged 21 months, were randomized to the exercise or sedentary group. Each mouse was scanned in a 7.0-T MRM scanner at two time points: 22 months old baseline and a follow-up three months later. Multi-atlas based brain segmentation approach was used to obtain volumes of 39 brain regions.

RESULTS:

The results showed that mice in the exercise group had less brain atrophy compared with the mice in the sedentary group.

CONCLUSIONS:

The results provide new insights into exercise induced brain plasticity in aged animals.

Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks

Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.