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Brain–computer interfaces (BCIs) can be used in real-time fMRI neurofeedback (rtfMRI NF) investigations to provide feedback on brain activity to enable voluntary regulation of the blood-oxygen-level dependent (BOLD) signal from localized brain regions. However, the temporal pattern of successful self-regulation is dynamic and complex. In particular, the general linear model (GLM) assumes fixed temporal model functions and misses other dynamics. We propose a novel data-driven analyses approach for rtfMRI NF using intersubject covariance (ISC) analysis. The potential of ISC was examined in a reanalysis of data from 21 healthy individuals and nine patients with post-traumatic stress-disorder (PTSD) performing up-regulation of the anterior cingulate cortex (ACC). ISC in the PTSD group differed from healthy controls in a network including the right inferior frontal gyrus (IFG). In both cohorts, ISC decreased throughout the experiment indicating the development of individual regulation strategies. ISC analyses are a promising approach to reveal novel information on the mechanisms involved in voluntary self-regulation of brain signals and thus extend the results from GLM-based methods. ISC enables a novel set of research questions that can guide future neurofeedback and neuroimaging investigations.

The present study combined neuropsychological and electrophysiological measures to obtain a comprehensive profile of the everyday attentional and memory dysfunction reported in PTSD. The event-related potential (ERP) literature has consistently found abnormalities in late components (N2, P3) reflecting working memory (WM) function. However, the neuropsychological profile reported in the literature has considerable variation. The present study examined ERP activity in 33 PTSD participants and matched controls during a standard two-tone auditory oddball task. Neuropsychological assessment was carried out using a task battery assessing a wide range of cognitive functions. Consistent with previous work, the PTSD group showed delayed N2 latency and reduced P3 target amplitude, together with slower and less accurate target detection. Scalp topography provided evidence of widespread abnormality during WM function, but with strongest effects broadly over the left hemisphere. Neuropsychological testing found concomitant difficulties on factorial measures of verbal memory retention/access and sustained attention but enhanced performance on measures of immediate recall. This integrative pattern of effects reflects a specific impairment in the operation of working memory systems that guide ongoing, planned behavior and that facilitate the acquisition and retention of new memories.

To investigate the conflict processing in posttraumatic stress disorder (PTSD) patients, we conducted the classical Stroop task by recording event-related potentials. Although the reaction time was overall slower for PTSD patients than healthy age-matched control group, the Stroop-interference effect of reaction time did not differ between the two groups. Compared with normal controls, the interference effects of N2 and N450 components were larger and the interference effect of slow potential component disappeared in PTSD. These data indicated the dysfunction of conflict processing in individuals with PTSD.

In the COVID-19 era, the use of social media platforms has significantly increased leading to misinformation being produced whose management is quite necessary for the domain experts, such as the Reddit social platform where people disseminate extensive information about their health issues using relevant posts and comments. The management of misinformation about COVID-19 impact on mental illness could be quite beneficial for the domain experts. In this regard, we proposed a two-step methodology which could aid domain experts to manage and group the posts and comments information with respect to COVID-19 impact on mental illness. First, we extract the information of well-known mental illnesses (such as depression, anxiety, OCD and PTSD) from the Raddit platform. Second, we leverage the capabilities of unsupervised learning algorithms and text categorisation approach to manage the information. We also proposed the evaluation model to assess the efficacy of the proposed method according to expert opinion. The experimental results indicate the efficacy of the proposed method. Moreover, we observed fuzzy c-means as an outperformed learner (with $\mathrm{\text{ARI}}=0.76$) as compared to K-means ($\mathrm{\text{ARI}}=0.70$) and Agglomerative ($\mathrm{\text{ARI}}=0.69$).