Applications of generative adversarial networks in neuroimaging and clinical neuroscience.

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.

Magnetic Resonance Spectroscopy of Hypoxic-Ischemic Encephalopathy After Cardiac Arrest.

BACKGROUND AND OBJECTIVES: To correlate brain metabolites with clinical outcome using magnetic resonance spectroscopy (MRS) in patients undergoing targeted temperature management (TTM) after cardiac arrest and assess their relationships to MRI and EEG variables. METHODS: A prospective cohort of 50 patients was studied. The primary outcome was coma recovery to follow commands. Comparison of MRS measures in the posterior cingulate gyrus, parietal white matter, basal ganglia, and brainstem were also made to 25 normative controls. RESULTS: Fourteen of 50 patients achieved coma recovery before hospital discharge. There was a significant decrease in total N-acetylaspartate (NAA/Cr) and an increase in lactate/creatine (Lac/Cr) in patients who did not recover, with changes most prominent in the posterior cingulate gyrus. Patients who recovered had decrease in NAA/Cr as compared to controls. NAA/Cr had a strong monotonic relationship with MRI cortical apparent diffusion coefficient (ADC); Lac level exponentially increased with decreasing ADC. EEG suppression/burst suppression was strongly associated with Lac elevation. DISCUSSION: NAA and Lac changes are associated with clinical/MRI/EEG changes consistent with hypoxic-ischemic encephalopathy (HIE) and are most prominent in the posterior cingulate gyrus. NAA/Cr decrease observed in patients with good outcomes suggests mild HIE in patients asymptomatic at hospital discharge. The appearance of cortical Lac represents a deterioration of aerobic energy metabolism and is associated with EEG background suppression, synaptic transmission failure, and severe, potentially irreversible HIE. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that in patients undergoing TTM after cardiac arrest, brain MRS-determined decrease in total NAA/Cr and an increase in Lac/Cr are associated with an increased risk of not recovering.

Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge-based cut-offs for each component of ATN and compared it to two data-driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data-driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia-like and normal groups. Both data-driven classification methods performed better than the empirical cut-offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data-driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

Tai Chi Improves Brain Metabolism and Muscle Energetics in Older Adults.

BACKGROUND AND PURPOSE: Tai Chi is a mind-body exercise that has been shown to improve both mental and physical health. As a result, recent literature suggests the use of Tai Chi to treat both physical and psychological disorders. However, the underlying physiological changes have not been characterized. The aim of this pilot study is to assess the changes in brain metabolites and muscle energetics after Tai Chi training in an aging population using a combined brain-muscle magnetic resonance spectroscopy (MRS) examination. METHODS: Six healthy older adults were prospectively recruited and enrolled into a 12-week Tai Chi program. A brain 1 H MRS and a muscle 31 P MRS were scanned before and after the training, and postprocessed to measure N-acetylaspartate to creatine (NAA/Cr) ratios and phosphocreatine (PCr) recovery time. Wilcoxon-signed rank tests were utilized to assess the differences between pre- and post-Tai Chi training. RESULTS: A significant within-subject increase in both the NAA/Cr ratios (P = .046) and the PCr recovery time (P = .046) was observed between the baseline and the posttraining scans. The median percentage changes were 5.38% and 16.51% for NAA/Cr and PCr recovery time, respectively. CONCLUSIONS: Our pilot study demonstrates significant increase of NAA/Cr ratios in posterior cingulate gyrus and significantly improved PCr recovery time in leg muscles in older adults following short-term Tai Chi training, and thus provides insight into the beneficial mechanisms.