Geon Ha Kim, Jieun E. Kim, Sandy Jeong Rhie and Sujung Yoon. Exp Neurobiol 2015;24:325-40. https://doi.org/10.5607/en.2015.24.4.325

Post-traumatic stress disorder (PTSD) is a chronic condition characterized by symptoms of physiological and psychosocial burden. While growing research demonstrated signs of inflammation in PTSD, specific biomarkers that may be representative of PTSD such as the detailed neural correlates underlying the inflammatory responses in relation to trauma exposure are seldom discussed. Here, we review recent studies that explored alterations in key inflammatory markers in PTSD, as well as neuroimaging-based studies that further investigated signs of inflammation within the brain in PTSD, as to provide a comprehensive summary of recent literature with a neurological perspective. A search was conducted on studies published from 2009 through 2019 in PubMed and Web of Science. Fifty original articles were selected. Major findings included elevated levels of serum proinflammatory cytokines in individuals with PTSD across various trauma types, as compared with those without PTSD. Furthermore, neuroimaging-based studies demonstrated that altered inflammatory markers are associated with structural and functional alterations in brain regions that are responsible for the regulation of stress and emotion, including the amygdala, hippocampus, and frontal cortex. Future studies that utilize both central and peripheral inflammatory markers are warranted to elucidate the underlying neurological pathway of the pathophysiology of PTSD.

Kim TD, et al. Exp Neurobiol. 2019 Feb;28(1):1-16. https://doi.org/10.5607/en.2019.28.1.1

Eleven patients with bipolar depression were given doses of up to 18 g per day of triacetyluridine (TAU) over 6 weeks to test the effect of uridine on symptoms of depression via Montgomery-Asberg Depression Rating Scale (MADRS; Asberg, Montgomery, Perris, Schalling, & Sedvall, 1978) scores and on cellular bioenergetics using phosphorus magnetic resonance spectroscopic imaging (31P-MRSI). All patients and comparison participants (n = 9) completed baseline 31P-MRSI scans, and 9 patients completed posttherapy scans. The percentage changes for MADRS scores (Week 2, -23.8; Week 3, -34.9; Week 4, -42.5) and the time effects of TAU on MADRS scores (Week 2, z = -2.07, p = .039; Week 3, z = -4.28, p < .001; Week 4, z = -4.54, p < .001) may reflect TAU effects on early symptom improvement. TAU responders (patients who had a 50% or greater reduction in MADRS scores from baseline at any time) demonstrated a significant difference from nonresponders in pH changes from baseline (effect size = 150). These results suggest that TAU treatment may decrease symptoms of depression and improve mitochondrial functioning.

The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer’s disease groups by identifying variances in brain age gaps between them, highlighting the algorithm’s potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.

Val66Met, a naturally occurring polymorphism in the human brain-derived neurotrophic factor (BDNF) gene resulting in a valine (Val) to methionine (Met) substitution at codon 66, plays an important role in neuroplasticity. While the effect of the BDNF Val66Met polymorphism on local brain structures has previously been examined, its impact on the configuration of the graph-based white matter structural networks is yet to be investigated. In the current study, we assessed the effect of the BDNF polymorphism on the network properties and robustness of the graph-based white matter structural networks. Graph theory was employed to investigate the structural connectivity derived from white matter tractography in two groups, Val homozygotes (n = 18) and Met-allele carriers (n = 55). Although there were no differences in the global network measures including global efficiency, local efficiency, and modularity between the two genotype groups, we found the effect of the BDNF Val66Met polymorphism on the robustness properties of the white matter structural networks. Specifically, the white matter structural networks of the Met-allele carrier group showed higher vulnerability to targeted removal of central nodes as compared with those of the Val homozygote group. These findings suggest that the central role of the BDNF Val66Met polymorphism in regards to neuroplasticity may be associated with inherent differences in the robustness of the white matter structural network according to the genetic variants. Furthermore, greater susceptibility to brain disorders in Met-allele carriers may be understood as being due to their limited stability in white matter structural connectivity.