Euler Elastica Regularized Logistic Regression for Whole-Brain Decoding of fMRI Data

Abnormal Brain State in Major Depressive Disorder: A Resting-State Magnetic Resonance Study

Background: Respective changes in resting-state linear and nonlinear measures in major depressive disorder (MDD) have been reported. However, few studies have used integrated measures of linear and nonlinear brain dynamics to explore the pathological mechanisms underlying MDD. Method: Forty-two patients with MDD and 42 sex- and age-matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging to calculate multiscale entropy (MSE) and regional homogeneity (ReHo). The MSE-ReHo coupling of the whole gray matter and the MSE/ReHo ratio (the complexity of intensity homogeneity per unit time series) of each voxel were compared between the two groups. To evaluate the discriminative capacity of ratio features between patients with MDD and HC, we employed the support vector machine (SVM) learning method. Results: We observed that patients with MDD displayed increased MSE/ReHo ratio mainly in the orbitofrontal cortex, sensorimotor areas, and visual cortex. Moreover, significant correlations were observed between MSE/ReHo ratio and clinical indicators, including depression severity and cognitive function tests. The SVM model demonstrated high accuracy in differentiating patients with MDD from HC, highlighting the potential of the MSE/ReHo ratio as a diagnostic and prognostic tool. Conclusions: The aberrant MSE/ReHo ratio implicated the underlying mechanisms of depressive symptoms and cognitive impairment in patients with MDD. It may represent a critical state of the brain region, reflecting the degree of chaos and order in the brain region. Integrating linear and nonlinear combinations of brain signals holds promise for diagnosing psychiatric disorders.

A within-subject voxel-wise constant-block partial least squares correlation method to explore MRI-based brain structure-function relationship

The brain structure-function relationship is crucial to how the human brain works under normal or diseased conditions. Exploring such a relationship is challenging when using the 3-dimensional magnetic resonance imaging (MRI) functional dataset which is temporal dynamic and the structural MRI which is static. Partial Least Squares Correlation (PLSC) is one of the classical methods for exploring the joint spatial and temporal relationship. The goal of PLSC is to identify covarying patterns via linear voxel-wise combinations in each of the structural and functional data sets to maximize the covariance. However, existing PLSC cannot adequately deal with the unmatched temporal dimensions between structural and functional data sets. We proposed a new alternative variant of the PLSC, termed within-subject, voxel-wise, and constant-block PLSC, to address this problem. To validate our method, we used two data sets with weak and strong relationships in simulated data. Additionally, the analysis of real brain data was carried out based on gray matter volume hubs derived from sMRI and whole-brain voxel-wise measures from resting-state fMRI for aging effect based on healthy subjects aged 16-85 years. Our results showed that our constant-block PLSC can detect weak structure-function relationships and has better robustness to noise. In fact, it adequately unearthed the true simulated number of significant and more accurate latent variables for the simulated data and more meaningful LVs for the real data, with covariance improvement from 16.19 to 41.48% (simulated) and 13.29-53.68% (real data), respectively. More interestingly in the real data analysis, our method identified simultaneously the well-known brain networks such as the default mode, sensorimotor, auditory, and dorsal attention networks both functionally and structurally, implying the hubs we derived from gray matter volumes are the basis of brain function, supporting diverse functions. Constant-block PLSC is a feasible tool for analyzing the brain structure-function relationship.

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75-96.9% of channels) with a 1.65-5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2-15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain-computer interface (BCI).

Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.

Image categorization from functional magnetic resonance imaging using functional connectivity

BACKGROUND

Previous studies have attempted to infer the category of objects in a stimulus image from functional magnetic resonance imaging (fMRI) data recoded during image-viewing. Most studies focus on extracting activity patterns within a given region or across multiple voxels, and utilize the relationships among voxels to decipher the category of a stimulus image. Yet, the functional connectivity (FC) patterns across regions of interest in response to image categories, and their potential contributions to category classification are largely unknown.

NEW METHOD

We investigated whole-brain FC patterns in response to 4 image category stimuli (cats, faces, houses, and vehicles) using fMRI in healthy adult volunteers, and classified FC patterns using machine learning framework (Support Vector Machine [SVM] and Random Forest). We further examined the FC robustness and the influence of the window length on FC patterns for neural decoding.

RESULTS

The average one-vs.-one classification accuracy of the two classification models were 74% within subjects and 80% between subjects, which are higher than the chance level (50%). The Random Forest results were better than SVM results, and the 48-s FC results were better than the 24-s FC results.

COMPARISON WITH EXISTING METHOD(S)

We compared the classification performance of our FC patterns with two other existing methods, inter-block and intra-block, without overlapping temporal information.

CONCLUSIONS

Whole-brain FC patterns for different window lengths (24 and 48 s) can predict images categories with high accuracy. These results reveal novel mechanisms underlying the representation of categorical information in large-scale FC patterns in the human brain.