Technological advances in artificial intelligence (AI) represent an enticing opportunity to benefit gastroenterological practice. Moreover, AI, through machine or deep learning, permits the ability to develop predictive models from large datasets. Possibilities of predictive model development in machine learning are numerous dependent on the clinical question. For example, binary classifiers aim to stratify allocation to a categorical outcome, such as the presence or absence of a gastrointestinal disease. In addition, continuous variable fitting techniques can be used to predict quantity of a therapeutic response, thus offering a tool to predict which therapeutic intervention may be most beneficial to the given patient. Namely, this permits an important opportunity for personalization of medicine, including a movement from guideline-specific treatment algorithms to patient-specific ones, providing both clinician and patient the capacity for data-driven decision making. Furthermore, such analyses could predict the development of GI disease prior to the manifestation of symptoms, raising the possibility of prevention or pre-treatment. In addition, computer vision additionally provides an exciting opportunity in endoscopy to automatically detect lesions. In this review, we overview the recent developments in healthcare-based AI and machine learning and describe promises and pitfalls for its application to gastroenterology.

The transcription factor ΔFosB is upregulated in numerous brain regions following repeated drug exposure. This induction is likely to, at least in part, be responsible for the mechanisms underlying addiction, a disorder in which the regulation of gene expression is thought to be essential. In this review, we describe and discuss the proposed role of ΔFosB as well as the implications of recent findings. The expression of ΔFosB displays variability dependent on the administered substance, showing region-specificity for different drug stimuli. This transcription factor is understood to act via interaction with Jun family proteins and the formation of activator protein-1 (AP-1) complexes. Once AP-1 complexes are formed, a multitude of molecular pathways are initiated, causing genetic, molecular and structural alterations. Many of these molecular changes identified are now directly linked to the physiological and behavioral changes observed following chronic drug exposure. In addition, ΔFosB induction is being considered as a biomarker for the evaluation of potential therapeutic interventions for addiction.

The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods—and data scales—hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system—a multidimensional, generative network—that renders its richness tractable within future models of its function in health and disease.

Conditioned pain modulation is significantly diminished in patients with IBS vs healthy controls. These data suggest that abnormal descending pathways may play an important pathophysiological role in IBS, which could represent an investigation and a therapeutic target in IBS.

The autonomic nervous system (ANS) is a brain body interface which serves to maintain homeostasis by influencing a plethora of physiological processes, including metabolism, cardiorespiratory regulation and nociception. Accumulating evidence suggests that ANS function is disturbed in numerous prevalent clinical disorders, including irritable bowel syndrome and fibromyalgia. While the brain is a central hub for regulating autonomic function, the association between resting autonomic activity and subcortical morphology has not been comprehensively studied and thus was our aim. In 27 healthy subjects [14 male and 13 female; mean age 30 years (range 22-53 years)], we quantified resting ANS function using validated indices of cardiac sympathetic index (CSI) and parasympathetic cardiac vagal tone (CVT). High resolution structural magnetic resonance imaging scans were acquired, and differences in subcortical nuclei shape, that is, 'deformation', contingent on resting ANS activity were investigated. CSI positively correlated with outward deformation of the brainstem, right nucleus accumbens, right amygdala and bilateral pallidum (all thresholded to corrected P < 0.05). In contrast, parasympathetic CVT negatively correlated with inward deformation of the right amygdala and pallidum (all thresholded to corrected P < 0.05). Left and right putamen volume positively correlated with Additional Supporting Information may be found in the online version of this article. CVT (r 5 0.62, P 5 0.0047 and r 5 0.59, P 5 0.008, respectively), as did the brainstem (r 5 0.46, P 5 0.049). These data provide novel evidence that resting autonomic state is associated with differences in the shape and volume of subcortical nuclei. Thus, subcortical morphological brain differences in various disorders may partly be attributable to perturbation in autonomic function. Further work is warranted to investigate these findings in clinical populations. Hum Brain Mapp 39:381-392, 2018.