Functional connectivity in reward-related networks is associated with individual differences in gambling strategies in male Lister hooded rats.

Individuals with gambling disorder display deficits in decision-making in the Iowa Gambling Task. The rat Gambling Task (rGT) is a rodent analogue that can be used to investigate the neurobiological mechanisms underlying gambling behaviour. The aim of this explorative study was to examine individual strategies in the rGT and investigate possible behavioural and neural correlates associated with gambling strategies. Thirty-two adult male Lister hooded rats underwent behavioural testing in the multivariate concentric square field™ (MCSF) and the novel cage tests, were trained on and performed the rGT and subsequently underwent resting-state functional magnetic resonance imaging (R-fMRI). In the rGT, stable gambling strategies were found with subgroups of rats that preferred the suboptimal safest choice as well as the disadvantageous choice, that is, the riskiest gambling strategy. R-fMRI results revealed associations between gambling strategies and brain regions central for reward networks. Moreover, rats with risky gambling strategies differed from those with strategic and intermediate strategies in brain functional connectivity. No differences in behavioural profiles, as assessed with the MCSF and novel cage tests, were observed between the gambling strategy groups. In conclusion, stable individual differences in gambling strategies were found. Intrinsic functional connectivity using R-fMRI provides novel evidence to support the notion that individual differences in gambling strategies are associated with functional connectivity in brain regions important for reward networks.

CHANGES IN FUNCTIONAL CONNECTIVITY FOLLOWING INTENSIVE ATTENTION TRAINING IN PATIENTS WITH TRAUMATIC BRAIN INJURY. A PILOT STUDY.

Objective: To explore functional connectivity after intensive attention training in the chronic phase after traumatic brain injury as clinical evidence indicates that intensive attention training improves attention dysfunction in persons with traumatic brain injury. Design and subjects: A case series study. Two young adults, 13- and 18-months post traumatic brain injury, with traumatic brain injury induced attention deficits were assigned to 20 h of intensive attention training and neuroimaging. Methods: Functional magnetic resonance imaging during a psychomotor vigilance test was conducted pre- and post-intervention. Results: The neuroimaging indicated both increased and decreased connectivity density in frontal, posterior and subcortical brain regions, for some regions with separate change patterns for left and right hemisphere respectively, and an overall reduction in variability in functional connectivity. Conclusion: The changed and decreased variability of functional connectivity in various brain regions, captured by fMRI during a psychomotor vigilance test after direct attention training in a small sample of persons with traumatic brain injury, suggests further studies of functional connectivity changes in neural networks.

Automatic recognition of schizophrenia from brain-network features using graph convolutional neural network.

Schizophrenia is a severe mental illness that imposes considerable economic burden on families and society. However, its clinical diagnosis primarily relies on scales and doctors' clinical experience and lacks an objective and accurate diagnostic approach. In recent years, graph convolutional neural networks (GCN) have been used to assist in psychiatric diagnosis owing to their ability to learn spatial-association information. Therefore, this study proposes a schizophrenia automatic recognition model based on graph convolutional neural network. Herein, the resting-state electroencephalography (EEG) data of 103 first-episode schizophrenia patients and 92 normal controls (NCs) were obtained. The automatic recognition model was trained with a nodal feature matrix that comprised the time and frequency-domain features of the EEG signals and local features of the brain network. The most significant regions that contributed to the model classification were identified, and the correlation between the node topological features of each significant region and clinical evaluation metrics was explored. Experiments were conducted to evaluate the performance of the model using 10-fold cross-validation. The best performance in the theta frequency band with a 6 s epoch length and phase-locked value. The recognition accuracy was 90.01%. The most significant region for identifying with first-episode schizophrenia patients and NCs was located in the parietal lobe. The results of this study verify the applicability of the proposed novel method for the identification and diagnosis of schizophrenia.

Assessment of dynamic functional connectivity in resting-state fMRI using the sliding window technique.

INTRODUCTION: Recent studies related to assessing functional connectivity (FC) in resting-state functional magnetic resonance imaging have revealed that the resulting connectivity patterns exhibit considerable fluctuations (dynamic FC [dFC]). A widely applied method for quantifying dFC is the sliding window technique. According to this method, the data are divided into segments with the same length (window size) and a correlation metric is employed to assess the connectivity within these segments, whereby the window size is often empirically chosen. METHODS: In this study, we rigorously investigate the assessment of dFC using the sliding window approach. Specifically, we perform a detailed comparison between different correlation metrics, including Pearson, Spearman and Kendall correlation, Pearson and Spearman partial correlation, Mutual Information (MI), Variation of Information (VI), Kullback-Leibler divergence, Multiplication of Temporal Derivatives and Inverse Covariance. RESULTS: Using test-retest datasets, we show that MI and VI yielded the most consistent results by achieving high reliability with respect to dFC estimates for different window sizes. Subsequent hypothesis testing, based on multivariate phase randomization surrogate data generation, allowed the identification of dynamic connections between the posterior cingulate cortex and regions in the frontal lobe and inferior parietal lobes, which were overall in agreement with previous studies. CONCLUSIONS: In the case of MI and VI, a window size of at least 120 s was found to be necessary for detecting dFC for some of the previously identified dynamically connected regions.