Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

Background: Health professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. Objective: This study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. Methods: A set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. Results: Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. Conclusions: Our results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.

Traumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.

In this study we explored the robustness of machine learning algorithms for the classification of Neuropsychiatric systemic lupus erythematosus (NPSLE) patients and healthy controls using resting-state fMRI functional connectivity matrices. NPSLE, which is driven by systemic autoimmune inflammation in the context of lupus, involves a wide range of focal and diffuse central and peripheral nervous system symptoms and poses significant diagnostic challenges. Machine learning applications on clinical data may enhance the existing workflow for NPSLE classification as there is no established method of applying neuroimaging data to the diagnosis of NPSLE. Feature selection methods were applied prior to the classification process in order to perform the classification process on a lower dimension feature space. The Connectivity Matrix used consisted of pairwise regional functional associations of the fMRI signals (ROI to ROI correlations) within each of three predetermined brain networks in 41 NPSLE patients and 31 healthy control subjects. Support Vector Machines (SVM) was utilized in the final model. Results were evaluated using a nested cross validation methodology to prevent overfitting, and enhance generalization. Regions of Interest (ROI's) that contributed most in the final model were: Right Inferior Temporal, Thalamus, Left Angular Gyrus, Right Precuneus, Left Primary Motor Cortex, SMA, Left and Right Primary Motor Cortex. With a final F1 score of up to 77%, the results demonstrate the potential for the future implementation of similar methods in the diagnosis of NPSLE.

Mind-blanking (MB) is the inability to report mental events during unconstraint thinking. Previous work shows that MB is linked to decreased levels of cortical arousal, indicating dominance of cerebral mechanisms when reporting mental states. What remains inconclusive is whether MB can also ensue from autonomic arousal manipulations, pointing to the implication of peripheral physiology to mental events. Using experience-sampling, neural, and physiological measurements in 26 participants, we first show that MB was reported more frequently in low arousal conditions, elicited by sleep deprivation. Also, there was partial evidence for a higher number of MB reports in high arousal conditions, elicited by intense physical exercise. Transition probabilities revealed that, after sleep deprivation, mind-wandering was more likely to be followed by MB and less likely to be followed by more mind-wandering reports. Using classification schemes, we show higher performance of a balanced random forest classifier trained on both neural and physiological markers in comparison to performance when solely neural or physiological were used. Collectively, we show that both cortical and autonomic arousal affect MB report occurrences. Our results establish that MB is supported by combined brain-body configurations, and, by linking mental and physiological states they pave the way for novel, embodied accounts of spontaneous thinking.