Quantum kernels for unattributed graphs using discrete-time quantum walks

In this paper, we develop a new family of graph kernels where the graph structure is probed by means of a discrete-time quantum walk. Given a pair of graphs, we let a quantum walk evolve on each graph and compute a density matrix with each walk. With the density matrices for the pair of graphs to hand, the kernel between the graphs is defined as the negative exponential of the quantum Jensen–Shannon divergence between their density matrices. In order to cope with large graph structures, we propose to construct a sparser version of the original graphs using the simplification method introduced in Qiu and Hancock (2007). To this end, we compute the minimum spanning tree over the commute time matrix of a graph. This spanning tree representation minimizes the number of edges of the original graph while preserving most of its structural information. The kernel between two graphs is then computed on their respective minimum spanning trees. We evaluate the performance of the proposed kernels on several standard graph datasets and we demonstrate their effectiveness and efficiency.

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.

Systems 1 and 2 thinking processes and cognitive reflection testing in medical students

Background: Diagnostic decision-making is made through a combination of Systems 1 (intuition or pattern-recognition) and Systems 2 (analytic) thinking. The purpose of this study was to use the Cognitive Reflection Test (CRT) to evaluate and compare the level of Systems 1 and 2 thinking among medical students in pre-clinical and clinical programs. Methods: The CRT is a three-question test designed to measure the ability of respondents to activate metacognitive processes and switch to System 2 (analytic) thinking where System 1 (intuitive) thinking would lead them astray. Each CRT question has a correct analytical (System 2) answer and an incorrect intuitive (System 1) answer. A group of medical students in Years 2 & 3 (pre-clinical) and Years 4 (in clinical practice) of a 5-year medical degree were studied. Results: Ten percent (13/128) of students had the intuitive answers to the three questions (suggesting they generally relied on System 1 thinking) while almost half (44%) answered all three correctly (indicating full analytical, System 2 thinking). Only 3-13% had incorrect answers (i.e. that were neither the analytical nor the intuitive responses). Non-native English speaking students (n = 11) had a lower mean number of correct answers compared to native English speakers (n = 117: 1.0 s 2.12 respectfully: p < 0.01). As students progressed through questions 1 to 3, the percentage of correct System 2 answers increased and the percentage of intuitive answers decreased in both the pre-clinical and clinical students. Conclusions: Up to half of the medical students demonstrated full or partial reliance on System 1 (intuitive) thinking in response to these analytical questions. While their CRT performance has no claims to make as to their future expertise as clinicians, the test may be used in helping students to understand the importance of awareness and regulation of their thinking processes in clinical practice.

Impact of exercise on innate immunity in multiple sclerosis progression and symptomatology

Multiple Sclerosis (MS), an idiopathic progressive immune-mediated neurological disorder of the central nervous system (CNS), is characterized by recurrent episodes of inflammatory demyelination and consequent axonal deterioration. It accounts for functional deterioration and lasting disability among young adults. A body of literature demonstrates that physical activity counteracts fatigue and depression and may improve overall quality of life in MS patients. Furthermore, much data indicates that exercise ameliorates chronic neuroinflammation and its related pathologies by tipping cytokine profiles toward an anti-inflammatory signature. Recent data has focused on the direct impact of exercise training on the innate immune system by targeting toll-like receptors (TLRs), signaling pattern recognition receptors that govern the innate immune response, shedding light on the physiological role of TLRs in health and disease. Indeed, TLRs continue to emerge as players in the neuroinflammatory processes underpinning MS. This review will highlight evidence that physical activity and exercise are potential immunomodulatory therapies, targeting innate signaling mechanism(s) to modulate MS symptom development and progression.