Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review.

Background: At present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis. Objectives: According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls). Methods: We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors. Results: All ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis. Conclusion: In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

Brain Activation Evoked by Motor Imagery in Pediatric Patients with Complete Spinal Cord Injury.

BACKGROUND AND PURPOSE: Currently, there is no effective treatment for pediatric patients with complete spinal cord injury. Motor imagery has been proposed as an alternative to physical training for patients who are unable to move voluntarily. Our aim was to reveal the potential mechanism of motor imagery in the rehabilitation of pediatric complete spinal cord injury. MATERIALS AND METHODS: Twenty-six pediatric patients with complete spinal cord injury and 26 age- and sex-matched healthy children as healthy controls were recruited. All participants underwent the motor imagery task-related fMRI scans, and additional motor execution scans were performed only on healthy controls. First, we compared the brain-activation patterns between motor imagery and motor execution in healthy controls. Then, we compared the brain activation of motor imagery between the 2 groups and compared the brain activation of motor imagery in pediatric patients with complete spinal cord injury and that of motor execution in healthy controls. RESULTS: In healthy controls, compared with motor execution, motor imagery showed increased activation in the left inferior parietal lobule and decreased activation in the left supplementary motor area, paracentral lobule, middle cingulate cortex, and right insula. In addition, our results revealed that the 2 groups both activated the bilateral supplementary motor area, middle cingulate cortex and left inferior parietal lobule, and supramarginal gyrus during motor imagery. Compared with healthy controls, higher activation in the bilateral paracentral lobule, supplementary motor area, putamen, and cerebellar lobules III-V was detected in pediatric complete spinal cord injury during motor imagery, and the activation of these regions was even higher than that of healthy controls during motor execution. CONCLUSIONS: Our study demonstrated that part of the motor imagery network was functionally preserved in pediatric complete spinal cord injury and could be activated through motor imagery. In addition, higher-level activation in sensorimotor-related regions was also found in pediatric complete spinal cord injury during motor imagery. Our findings may provide a theoretic basis for the application of motor imagery training in pediatric complete spinal cord injury.

The Synthesis and Properties of Solution Processable a Red Phosphorescent Iridium(III) Complex with Alkyl Group.

The (TMP-HT)2Ir(acac) was synthesized with 2-bromo-4-(trifluoromethyl)pyridine and 5-hexyl-2-thiopheneboronic acid pinacol ester for organic light-emitting diodes (OLEDs). This material was designed on the basis of Gaussian modeling program results. The ligands have both the electron donor and acceptor in a molecule. There are pyridyl group which decrease electron density and thiophene group which increase electron density. Therefore, it showed intramolecular charge transfer (ICT) property. For solution process, the ligand have a alkyl group which has hydrophobic property. The (TMP-HT)21r(acac) was synthesized by Suzuki coupling reaction and Nonoyama reaction. The (TMP-HT)21r(acac) emitted at approximately 600 nm. The device structures were ITO/PEDOT (500 Å)/TFB (170 Å)/PVK:PBD (40%): (TMPHT)21r(acac) (300 Å:10%)/BH:BD5% (150 Å)/L201(50 Å)/Liq(200 Å)/Al. Electroluminescent properties were observed with devices doped with various doping concentrations.