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Influential theories of complex behaviour invoke the notion of cognitive control modulated by conflict between counterfactual actions. Medial frontal cortex, notably the anterior cingulate cortex, has been variously posited as critical to such conflict detection, resolution, or monitoring, largely based on correlative data from functional imaging. Examining performance on the most widely used "conflict" task-Stroop-in a large cohort of patients with focal brain injury (N = 176), we compare anatomical patterns of lesion-inferred neural substrate dependence to those derived from functional imaging, meta-analytically summarised. Our results show that whereas performance is sensitive to the integrity of left lateral frontal regions implicated by functional imaging, it does not depend on medial frontal cortex, despite sampling adequate to reveal robust medial effects in the context of phonemic fluency. We suggest that medial frontal cortex is not critically invoked by Stroop and proceed to review the conceptual grounds for rejecting the core notion of conflict-driven cognitive control., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Intracranial pressure (ICP) is a physiological parameter that conventionally requires invasive monitoring for accurate measurement. Utilising multivariate predictive models, we sought to evaluate the utility of non-invasive, widely accessible MRI biomarkers in predicting ICP and their reversibility following cerebrospinal fluid (CSF) diversion. The retrospective study included 325 adult patients with suspected CSF dynamic disorders who underwent brain MRI scans within three months of elective 24-h ICP monitoring. Five MRI biomarkers were assessed: Yuh sella grade, optic nerve vertical tortuosity (VT), optic nerve sheath distension, posterior globe flattening and optic disc protrusion (ODP). The association between individual biomarkers and 24-h ICP was examined and reversibility of each following CSF diversion was assessed. Multivariate models incorporating these radiological biomarkers were utilised to predict 24-h median intracranial pressure. All five biomarkers were significantly associated with median 24-h ICP (p < 0.0001). Using a pair-wise approach, the presence of each abnormal biomarker was significantly associated with higher median 24-h ICP (p < 0.0001). On multivariate analysis, ICP was significantly and positively associated with Yuh sella grade (p < 0.0001), VT (p < 0.0001) and ODP (p = 0.003), after accounting for age and suspected diagnosis. The Bayesian multiple linear regression model predicted 24-h median ICP with a mean absolute error of 2.71 mmHg. Following CSF diversion, we found pituitary sella grade to show significant pairwise reversibility (p < 0.001). ICP was predicted with clinically useful precision utilising a compact Bayesian model, offering an easily interpretable tool using non-invasive MRI data. Brain MRI biomarkers are anticipated to play a more significant role in the screening, triaging, and referral of patients with suspected CSF dynamic disorders., (© 2024. The Author(s).)

The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vulnerability to failure of specificity and generalisability. Evaluating a compact version of Raven's Advanced Progressive Matrices (RAPM), a widely used clinical test of fluid intelligence, we show that LaMa, a self-supervised artificial neural network trained solely on the completion of partially masked images of natural environmental scenes, achieves representative human-level test scores a prima vista, without any task-specific inductive bias or training. Compared with cohorts of healthy and focally lesioned participants, LaMa exhibits human-like variation with item difficulty, and produces errors characteristic of right frontal lobe damage under degradation of its ability to integrate global spatial patterns. LaMa's narrow training and limited capacity suggest matrix-style tests may be open to computationally simple solutions that need not necessarily invoke the substrates of reasoning., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: A high prevalence of disorders of gut-brain interaction (DGBI) exist in patients with hypermobile Ehlers-Danlos Syndrome (hEDS) and hypermobility spectrum disorders (HSD). However, it is unknown if clusters of hEDS/HSD patients exist which overlap with different DGBIs and whether this overlap influences presence of comorbidities and quality of life. We aimed to study these knowledge gaps., Methods: A prospectively collected hEDS/HSD cohort of 1044 individuals were studied. We undertook Uniform Manifold Approximation and Projection-enabled (UMAP) dimension reduction to create a representation of nonlinear interactions between hEDS/HSD and DGBIs, from which individuals were stratified into clusters. Somatization, Postural Tachycardia Syndrome (PoTS), autonomic symptoms, psychological factors and quality of life were statistically compared between clusters., Key Results: The mean age of patients was 40 ± 13.2 years; 87.8% were female. Patients segregated into three clusters: Cluster 0 (n = 466): hEDS/HSD+ functional foregut disorders (FFD) + irritable bowel syndrome (IBS); Cluster 1 (n = 180): hEDS/HSD+ IBS and Cluster 2 (n = 337): hEDS/HSD alone. In cluster 0, we demonstrated increased somatization (p <0.0001), anxiety (p <0.0001), depression (p <0.0001), PoTS prevalence (p = 0.003), autonomic symptoms (p <0.0001) and reduced quality of life (p <0.0001) compared to cluster 2. Cluster 0 had greater comorbidity burden than cluster 1., Conclusions: Within hEDS/HSD, subgroups exist with a high prevalence of FFD and IBS. These subgroups have a higher prevalence of psychological disorders, dysautonomia and poorer quality of life compared with hEDS/HSD alone. Further research should focus on healthcare utilization, management and prognosis in hEDS/HSD and DGBI overlap., (© 2024 The Author(s). Neurogastroenterology & Motility published by John Wiley & Sons Ltd.)

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Dysfunction in the autonomic nervous system is common throughout many functional gastrointestinal diseases (FGIDs) that have been historically difficult to treat. In recent years, transcutaneous vagal nerve stimulation (tVNS) has shown promise for improving FGID symptoms. However, the brain effects of tVNS remain unclear, which we investigated by neuroimaging meta-analysis., Methods: A total of 157 studies were identified, 4 of which were appropriate for inclusion, encompassing 60 healthy human participants. Using activation likelihood analysis estimation, we statistically quantified functional brain activity changes across three domains: (1) tVNS vs. null stimulation, (2) tVNS vs. sham stimulation, and (3) sham stimulation vs. null stimulation., Key Results: tVNS significantly increased activity in the insula, anterior cingulate, inferior and superior frontal gyri, caudate and putamen, and reduced activity in the hippocampi, occipital fusiform gyri, temporal pole, and middle temporal gyri, when compared to null stimulation (all corrected p < 0.005). tVNS increased activity in the anterior cingulate gyrus, left thalamus, caudate, and paracingulate gyrus and reduced activity in right thalamus, posterior cingulate cortex, and temporal fusiform cortex, when compared to sham stimulation (all corrected p < 0.005). Sham stimulation significantly increased activity in the insula and reduced activity in the posterior cingulate and paracingulate gyrus (all corrected p < 0.001), when contrasted to null stimulation., Conclusions: Brain effects of tVNS localize to regions associated with both physiological autonomic regulation and regions whose activity is modulated across numerous FGIDs, which may provide a neural basis for efficacy of this treatment. Functional activity differences between sham and null stimulation illustrate the importance of robust control procedures for future trials., (© 2022 The Authors. Neurogastroenterology & Motility published by John Wiley & Sons Ltd.)

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R 2 0.859), and weight (mean absolute error 2.609Kg, R 2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available., Competing Interests: Declaration of competing interest None to declare., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Disorders of gut-brain interaction (DGBI) are common in patients with hypermobile Ehlers-Danlos syndrome/hypermobility spectrum disorder (hEDS/HSD). Food is a known trigger for DGBI symptoms, which often leads to dietary alterations and, increasingly, nutrition support. We aimed to explore dietary behaviors and influencing factors in patients with hEDS/HSD., Methods: In a cross-sectional study, patients with hEDS/HSD were recruited from Ehlers-Danlos Support UK (nontertiary) and tertiary neurogastroenterology clinics to complete questionnaires characterizing the following: dietary behaviors, nutrition support, DGBI (Rome IV), gastrointestinal symptoms, anxiety, depression, avoidant restrictive food intake disorder (ARFID), mast cell activation syndrome, postural tachycardia syndrome (PoTS), and quality of life. We used stepwise logistic regression to ascertain which factors were associated with dietary behaviors and nutrition support., Results: Of 680 participants (95% female, median age 39 years), 62.1% altered their diet in the last year and 62.3% regularly skipped meals. Altered diet was associated with the following: reflux symptoms ( P < 0.001), functional dyspepsia ( P = 0.008), reported mast cell activation syndrome ( P < 0.001), and a positive screen for ARFID, specifically fear of eating and low interest ( P < 0.001). Approximately 31.7% of those who altered their diet required nutrition support. The strongest predictor of requiring nutrition support was a positive screen for ARFID, specifically fear of eating (OR: 4.97, 95% CI: 2.09-11.8, P < 0.001)., Discussion: Altered diet is very common in the patients with hEDS/HSD we studied and influenced by functional dyspepsia, reflux symptoms, and ARFID. Those with ARFID have a 4-fold increased risk of requiring nutrition support, and therefore, it is paramount that psychological support is offered in parallel with dietary support in the management of DGBI in hEDS/HSD., (Copyright © 2023 by The American College of Gastroenterology.)

The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used clinically. We sought to resolve this problem with software automation and machine learning. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software applied to open-source lesion masks and an openly available tumour segmentation model. Consultant neuroradiologists independently quantified VASARI features in 100 held-out glioblastoma cases. We quantified 1) agreement across neuroradiologists and VASARI-auto, 2) software equity, 3) an economic workforce analysis, and 4) fidelity in predicting survival. Tumour segmentation was compatible with the current state of the art and equally performant regardless of age or sex. A modest inter-rater variability between in-house neuroradiologists was comparable to between neuroradiologists and VASARI-auto, with far higher agreement between VASARI-auto methods. The time for neuroradiologists to derive VASARI was substantially higher than VASARI-auto (mean time per case 317 vs. 3 s). A UK hospital workforce analysis forecast that three years of VASARI featurisation would demand 29,777 consultant neuroradiologist workforce hours and >£1.5 ($1.9) million, reducible to 332 hours of computing time (and £146 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features instead of those derived by neuroradiologists. VASARI-auto is a highly efficient and equitable automated labelling system, a favourable economic profile if used as a decision support tool, and non-inferior survival prediction. Future work should iterate upon and integrate such tools to enhance patient care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Progress in neuro-oncology is increasingly recognized to be obstructed by the marked heterogeneity-genetic, pathological, and clinical-of brain tumours. If the treatment susceptibilities and outcomes of individual patients differ widely, determined by the interactions of many multimodal characteristics, then large-scale, fully-inclusive, richly phenotyped data-including imaging-will be needed to predict them at the individual level. Such data can realistically be acquired only in the routine clinical stream, where its quality is inevitably degraded by the constraints of real-world clinical care. Although contemporary machine learning could theoretically provide a solution to this task, especially in the domain of imaging, its ability to cope with realistic, incomplete, low-quality data is yet to be determined. In the largest and most comprehensive study of its kind, applying state-of-the-art brain tumour segmentation models to large scale, multi-site MRI data of 1251 individuals, here we quantify the comparative fidelity of automated segmentation models drawn from MR data replicating the various levels of completeness observed in real life. We demonstrate that models trained on incomplete data can segment lesions very well, often equivalently to those trained on the full completement of images, exhibiting Dice coefficients of 0.907 (single sequence) to 0.945 (complete set) for whole tumours and 0.701 (single sequence) to 0.891 (complete set) for component tissue types. This finding opens the door both to the application of segmentation models to large-scale historical data, for the purpose of building treatment and outcome predictive models, and their application to real-world clinical care. We further ascertain that segmentation models can accurately detect enhancing tumour in the absence of contrast-enhancing imaging, quantifying the burden of enhancing tumour with an R 2 > 0.97, varying negligibly with lesion morphology. Such models can quantify enhancing tumour without the administration of intravenous contrast, inviting a revision of the notion of tumour enhancement if the same information can be extracted without contrast-enhanced imaging. Our analysis includes validation on a heterogeneous, real-world 50 patient sample of brain tumour imaging acquired over the last 15 years at our tertiary centre, demonstrating maintained accuracy even on non-isotropic MRI acquisitions, or even on complex post-operative imaging with tumour recurrence. This work substantially extends the translational opportunity for quantitative analysis to clinical situations where the full complement of sequences is not available and potentially enables the characterization of contrast-enhanced regions where contrast administration is infeasible or undesirable., Competing Interests: The authors report no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects., (© 2023. The Author(s).)

Fluid intelligence is arguably the defining feature of human cognition. Yet the nature of its relationship with the brain remains a contentious topic. Influential proposals drawing primarily on functional imaging data have implicated 'multiple demand' frontoparietal and more widely distributed cortical networks, but extant lesion-deficit studies with greater causal power are almost all small, methodologically constrained, and inconclusive. The task demands large samples of patients, comprehensive investigation of performance, fine-grained anatomical mapping, and robust lesion-deficit inference, yet to be brought to bear on it. We assessed 165 healthy controls and 227 frontal or non-frontal patients with unilateral brain lesions on the best-established test of fluid intelligence, Raven's Advanced Progressive Matrices, employing an array of lesion-deficit inferential models responsive to the potentially distributed nature of fluid intelligence. Non-parametric Bayesian stochastic block models were used to reveal the community structure of lesion deficit networks, disentangling functional from confounding pathological distributed effects. Impaired performance was confined to patients with frontal lesions [F(2,387) = 18.491; P < 0.001; frontal worse than non-frontal and healthy participants P < 0.01, P <0.001], more marked on the right than left [F(4,385) = 12.237; P < 0.001; right worse than left and healthy participants P < 0.01, P < 0.001]. Patients with non-frontal lesions were indistinguishable from controls and showed no modulation by laterality. Neither the presence nor the extent of multiple demand network involvement affected performance. Both conventional network-based statistics and non-parametric Bayesian stochastic block modelling heavily implicated the right frontal lobe. Crucially, this localization was confirmed on explicitly disentangling functional from pathology-driven effects within a layered stochastic block model, prominently highlighting a right frontal network involving middle and inferior frontal gyrus, pre- and post-central gyri, with a weak contribution from right superior parietal lobule. Similar results were obtained with standard lesion-deficit analyses. Our study represents the first large-scale investigation of the distributed neural substrates of fluid intelligence in the focally injured brain. Combining novel graph-based lesion-deficit mapping with detailed investigation of cognitive performance in a large sample of patients provides crucial information about the neural basis of intelligence. Our findings indicate that a set of predominantly right frontal regions, rather than a more widely distributed network, is critical to the high-level functions involved in fluid intelligence. Further they suggest that Raven's Advanced Progressive Matrices is a useful clinical index of fluid intelligence and a sensitive marker of right frontal lobe dysfunction., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Equity is widely held to be fundamental to the ethics of healthcare. In the context of clinical decision-making, it rests on the comparative fidelity of the intelligence - evidence-based or intuitive - guiding the management of each individual patient. Though brought to recent attention by the individuating power of contemporary machine learning, such epistemic equity arises in the context of any decision guidance, whether traditional or innovative. Yet no general framework for its quantification, let alone assurance, currently exists. Here we formulate epistemic equity in terms of model fidelity evaluated over learnt multidimensional representations of identity crafted to maximise the captured diversity of the population, introducing a comprehensive framework for Representational Ethical Model Calibration. We demonstrate the use of the framework on large-scale multimodal data from UK Biobank to derive diverse representations of the population, quantify model performance, and institute responsive remediation. We offer our approach as a principled solution to quantifying and assuring epistemic equity in healthcare, with applications across the research, clinical, and regulatory domains., (© 2022. The Author(s).)

Hematological malignancies place individuals at risk of CNS involvement from their hematological disease and opportunistic intracranial infection secondary to disease-/treatment-associated immunosuppression. Differentiating CNS infection from hematological disease infiltration in these patients is valuable but often challenging. We sought to determine if statistical models might aid discrimination between these processes. Neuroradiology, clinical and laboratory data for patients with hematological malignancy at our institution between 2007 and 2017 were retrieved. MRI were deep-phenotyped across anatomical distribution, presence of pathological enhancement, diffusion restriction and hemorrhage and statistically modelled with Bayesian-directed probability networks and multivariate logistic regression. 109 patients were studied. Irrespective of a diagnosis of CNS infection or hematological disease, the commonest anatomical distributions of abnormality were multifocal-parenchymal (34.9%), focal-parenchymal (29.4%) and leptomeningeal (11.9%). Pathological enhancement was the most frequently observed abnormality (46.8%), followed by hemorrhage (22.9%) and restricted diffusion (19.3%). Logistic regression could differentiate CNS infection from hematological disease infiltration with an AUC of 0.85 where, with OR &gt; 1 favoring CNS infection and &lt; 1 favoring CNS hematological disease, significantly predictive imaging features were hemorrhage (OR 24.61, p = 0.02), pathological enhancement (OR 0.17, p = 0.04) and an extra-axial location (OR 0.06, p = 0.05). In conclusion, CNS infection and hematological disease are heterogeneous entities with overlapping radiological appearances but a multivariate interaction of MR imaging features may assist in distinguishing them., (© 2022. The Author(s).)

The value of biomedical research-a $1.7 trillion annual investment-is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative predictability of future real-world translation-as indexed by inclusion in patents, guidelines, or policy documents-from complex models of title/abstract-level content versus citations and metadata alone. We quantify predictive performance out of sample, ahead of time, across major domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990-2019, encompassing 43.3 million papers. We show that citations are only moderately predictive of translational impact. In contrast, high-dimensional models of titles, abstracts, and metadata exhibit high fidelity (area under the receiver operating curve [AUROC] > 0.9), generalize across time and domain, and transfer to recognizing papers of Nobel laureates. We argue that content-based impact models are superior to conventional, citation-based measures and sustain a stronger evidence-based claim to the objective measurement of translational potential., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

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., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Patients with haematological malignancy are at increased risk of developing central nervous system (CNS) infections, which are associated with significant morbidity and mortality. Neuroimaging plays a pivotal role in the diagnostic pathway of these patients; however, layers of complexity are added to image interpretation by the heterogeneity in imaging manifestations of haematological malignancies in the CNS, overlapping imaging features of CNS infection, treatment-related parenchymal changes and the presence of intracranial comorbidity. In this article, we review important intracranial findings of CNS infection cases accrued in 1,855 studies over more than a decade at a specialist tertiary centre. We offer schema to identify common and important neuroimaging features, discuss key differential diagnoses and frequent diagnostic pitfalls., (Copyright © 2021 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Introduction: Chronic constipation is classified into 2 main syndromes, irritable bowel syndrome with constipation (IBS-C) and functional constipation (FC), on the assumption that they differ along multiple clinical characteristics and are plausibly of distinct pathophysiology. Our aim was to test this assumption by applying machine learning to a large prospective cohort of comprehensively phenotyped patients with constipation., Methods: Demographics, validated symptom and quality of life questionnaires, clinical examination findings, stool transit, and diagnosis were collected in 768 patients with chronic constipation from a tertiary center. We used machine learning to compare the accuracy of diagnostic models for IBS-C and FC based on single differentiating features such as abdominal pain (a "unisymptomatic" model) vs multiple features encompassing a range of symptoms, examination findings and investigations (a "syndromic" model) to assess the grounds for the syndromic segregation of IBS-C and FC in a statistically formalized way., Results: Unisymptomatic models of abdominal pain distinguished between IBS-C and FC cohorts near perfectly (area under the curve 0.97). Syndromic models did not significantly increase diagnostic accuracy (P > 0.15). Furthermore, syndromic models from which abdominal pain was omitted performed at chance-level (area under the curve 0.56). Statistical clustering of clinical characteristics showed no structure relatable to diagnosis, but a syndromic segregation of 18 features differentiating patients by impact of constipation on daily life., Discussion: IBS-C and FC differ only about the presence of abdominal pain, arguably a self-fulfilling difference given that abdominal pain inherently distinguishes the 2 in current diagnostic criteria. This suggests that they are not distinct syndromes but a single syndrome varying along one clinical dimension. An alternative syndromic segregation is identified, which needs evaluation in community-based cohorts. These results have implications for patient recruitment into clinical trials, future disease classifications, and management guidelines., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Background: The vagus nerve exerts an anti-nociceptive effect on the viscera., Aim: To investigate whether transcutaneous vagal nerve stimulation (t-VNS) prevents the development of and/or reverses established visceral hypersensitivity in a validated model of acid-induced oesophageal pain., Methods: Before and after a 30-minute infusion of 0.15M hydrochloric acid into the distal oesophagus, pain thresholds to electrical stimulation were determined in the proximal non-acid exposed oesophagus. Validated sympathetic (cardiac sympathetic index) and parasympathetic (cardiac vagal tone [CVT]) nervous system measures were recorded. In study 1, 15 healthy participants were randomised in a blinded crossover design to receive either t-VNS or sham for 30 minutes during acid infusion. In study 2, 18 different healthy participants were randomised in a blinded crossover design to receive either t-VNS or sham, for 30 minutes after acid infusion., Results: Study 1: t-VNS increased CVT (31.6% ± 58.7 vs -9.6 ± 20.6, P = 0.02) in comparison to sham with no effect on cardiac sympathetic index. The development of acid-induced oesophageal hypersensitivity was prevented with t-VNS in comparison to sham (15.5 mA per unit time (95% CI 4.9 - 26.2), P = 0.004). Study 2: t-VNS increased CVT (26.3% ± 32.7 vs 3 ± 27.1, P = 0.03) in comparison to sham with no effect on cardiac sympathetic index. t-VNS reversed established acid-induced oesophageal hypersensitivity in comparison to sham (17.3mA/unit time (95% CI 9.8-24.7), P = 0.0001)., Conclusions: t-VNS prevents the development of, and reverses established, acid-induced oesophageal hypersensitivity. These results have therapeutic implications for the management of visceral pain hypersensitivity., (© 2020 John Wiley & Sons Ltd.)

Competing Interests: Competing interests: None declared.

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.

Key Points: Nausea is an adverse experience characterised by alterations in autonomic and cerebral function. Susceptibility to nausea is difficult to predict, but machine learning has yet to be applied to this field of study. The severity of nausea that individuals experience is related to the underlying morphology (shape) of the subcortex, namely of the amygdala, caudate and putamen; a functional brain network related to nausea severity was identified, which included the thalamus, cingulate cortices (anterior, mid- and posterior), caudate nucleus and nucleus accumbens. Sympathetic nervous system function and sympathovagal balance, by heart rate variability, was closely related to both this nausea-associated anatomical variation and the functional connectivity network, and machine learning accurately predicted susceptibility or resistance to nausea. These novel anatomical and functional brain biomarkers for nausea severity may permit objective identification of individuals susceptible to nausea, using artificial intelligence/machine learning; brain data may be useful to identify individuals more susceptible to nausea., Abstract: Nausea is a highly individual and variable experience. The central processing of nausea remains poorly understood, although numerous influential factors have been proposed, including brain structure and function, as well as autonomic nervous system (ANS) activity. We investigated the role of these factors in nausea severity and if susceptibility to nausea could be predicted using machine learning. Twenty-eight healthy participants (15 males; mean age 24 years) underwent quantification of resting sympathetic and parasympathetic nervous system activity by heart rate variability. All were exposed to a 10-min motion-sickness video during fMRI. Neuroanatomical shape differences of the subcortex and functional brain networks associated with the severity of nausea were investigated. A machine learning neural network was trained to predict nausea susceptibility, or resistance, using resting ANS data and detected brain features. Increasing nausea scores positively correlated with shape variation of the left amygdala, right caudate and bilateral putamen (corrected P = 0.05). A functional brain network linked to increasing nausea severity was identified implicating the thalamus, anterior, middle and posterior cingulate cortices, caudate nucleus and nucleus accumbens (corrected P = 0.043). Both neuroanatomical differences and the functional nausea-brain network were closely related to sympathetic nervous system activity. Using these data, a machine learning model predicted susceptibility to nausea with an overall accuracy of 82.1%. Nausea severity relates to underlying subcortical morphology and a functional brain network; both measures are potential biomarkers in trials of anti-nausea therapies. The use of machine learning should be further investigated as an objective means to develop models predicting nausea susceptibility., (© 2019 The Authors. The Journal of Physiology © 2019 The Physiological Society.)

Background: Linaclotide is efficacious in the management of irritable bowel syndrome with constipation (IBS-C), yet relatively little is known regarding its effect on human gastrointestinal physiology. The primary aim of the study was to examine the effect of linaclotide on change in pH across the ileocecal junction (ICJ), a proposed measure of cecal fermentation, and its relationship to symptoms and quality of life (QoL) in IBS-C., Methods: A total of 13 participants with Rome III IBS-C underwent a standardized wireless motility capsule (WMC). Stool consistency was measured using the Bristol stool form scale (BSFS) and frequency with spontaneous bowel movements (SBM). Gastrointestinal symptoms and QoL were assessed using validated questionnaires. The WMC and questionnaires were repeated after 28 days of linaclotide 290 g po od., Key Results: Linaclotide reduced the change in pH across the ICJ (-2.4 ± 0.2 vs -2.1 ± 0.4, P = 0.01) as a function of a relative alkalinization of the cecum (5.2 ± 0.2 vs 5.5 ± 0.3, P = 0.02). Linaclotide accelerated colonic transit time (2650 minutes (2171-4038) vs. 1757 (112-3011), P = 0.02), increased colonic log motility index (15 ± 1.8 vs. 16.5 ± 1.8, P = 0.004) but had no effect of gastric emptying or small bowel transit. Change in pH across the ICJ correlated with improvement in symptom intensity, unpleasantness, and visceral sensitivity index (r = 0.62, P = 0.03, r = 0.63, P = 0.02, r = 0.62, P = 0.02) and with increases in BSFS type and SBM (r = 0.9, P < 0.0001, r = 0.6, P = 0.02)., Conclusions & Inferences: Linaclotide's effects are confined to the colon where it increases cecal pH, potentially representing a reduction in cecal fermentation and accelerates colonic motility., (© 2018 John Wiley & Sons Ltd.)

Background: Irritable bowel syndrome (IBS) is common and is characterised by recurrent abdominal pain, which is a major contributor to healthcare seeking. The neurobiological basis of this pain is incompletely understood. Conditioned pain modulation is a neuromodulatory mechanism through which the brain inhibits the nociceptive afferent barrage through the descending pathways. Reduced conditioned pain modulation has been implicated in the pathophysiology of IBS, although to date only in studies with relatively small sample sizes., Aim: To clarify the relationship between conditioned pain modulation and IBS by undertaking a systemic review and meta-analysis METHODS: A systematic review of MEDLINE and Web of Science databases was searched (up to 10 May 2018). We included studies examining conditioned pain modulation in adults with IBS and healthy subjects. Data were pooled for meta-analysis to calculate the odds ratio and effect size of abnormal conditioned pain modulation in IBS, with 95% confidence intervals (CI)., Results: The search strategy identified 645 studies, of which 13 were relevant and 12 met the inclusion criteria. Conditioned pain modulation in IBS patients vs healthy subjects was significantly reduced, odds ratio 4.84 (95% CI: 2.19-10.71, P < 0.0001), Hedges' g effect size of 0.85 (95% CI: 0.42-1.28, P < 0.001). There was significant heterogeneity in effect sizes (Q-test χ 2 = 52, P < 0.001, I 2 = 78.8%) in the absence of publication bias., Conclusion: 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., (© 2018 John Wiley & Sons Ltd.)

The mechanisms that underpin the anti-nociceptive effect of the parasympathetic nervous system (PNS) on visceral pain remain incompletely understood. We sought to describe the effect of resting parasympathetic tone on functional brain networks during the anticipation and experience of oesophageal pain. 21 healthy participants had their resting cardiac vagal tone (CVT), a validated measure of the PNS, quantified, and underwent functional magnetic resonance imaging during the anticipation and experience of painful oesophageal distention. The relationship between resting CVT and functional brain networks was examined using 11 hypothesis-driven nodes and network-based statistics. A network comprising all nodes was apparent in individuals with high resting CVT, compared to those with low CVT, during oesophageal pain (family wise error rate (FWER)-corrected p < 0.048). Functional connections included the thalamus-amygdala, thalamus-hypothalamus, hypothalamus-nucleus accumbens, amygdala-pallidum, pallidum-nucleus accumbens and insula-pallidum. A smaller network was seen during pain anticipation, comprising the amygdala, pallidum and anterior insula (FWER-corrected p < 0.049). These findings suggest that PNS tone is associated with functional brain networks during the anticipation and experience of visceral pain. Given the role of these subcortical regions in the descending inhibitory modulation of pain, these networks may represent a potential neurobiological explanation for the anti-nociceptive effect of the PNS.

Background: Traditional psychometric measures aimed at characterizing the pain experience often show considerable overlap, due to interlinked affective and modulatory processes under central nervous system control. Neuroimaging studies have been employed to investigate this complexity of pain processing, in an attempt to provide a quantifiable, adjunctive description of pain perception. In this exploratory study, we examine psychometric and neuroimaging data from 38 patients with painful osteoarthritis of the carpometacarpal joint. We had two aims: first, to utilize principal component analysis (PCA) as a dimension reduction strategy across multiple self-reported endpoints of pain, cognitive and affective functioning; second, to investigate the relationship between identified dimensions and regional cerebral blood flow (rCBF) as an indirect measure of brain activity underpinning their ongoing pain experiences., Methods: Psychometric data were collected using validated questionnaires. Quantitative estimates of rCBF were acquired using pseudo-continuous arterial spin-labelled functional magnetic resonance imaging., Results: Two principal components were identified that accounted for 73% of data variance; one related to pain scores and a second to psychological traits. Voxel-wise multiple regression analysis revealed a significant negative association between the 'pain score' component and rCBF to a right temporal lobe cluster, including the amygdala and the parahippocampal cortex., Conclusion: We suggest this association may represent a coping mechanism that aims to reduce fear-related pain-anxiety. Further investigation of central brain processing mechanisms in osteoarthritis-related pain may offer insights into more effective therapeutic strategies., Significance: This study demonstrates that dimension reduction using PCA allows insight into pain perception and its affective components in relation to brain activation patterns in patients with painful hand osteoarthritis., (© 2018 The Authors. European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation - EFIC®.)

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 CVT (r = 0.62, P = 0.0047 and r = 0.59, P = 0.008, respectively), as did the brainstem (r = 0.46, P = 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. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

The Rome IV diagnostic criteria delineates 5 functional esophageal disorders which include functional chest pain, functional heartburn, reflux hypersensitivity, globus, and functional dysphagia. These are a heterogenous group of disorders which, despite having characteristic symptom profiles attributable to esophageal pathology, fail to demonstrate any structural, motility or inflammatory abnormalities on standard clinical testing. These disorders are associated with a marked reduction in patient quality of life, not least considerable healthcare resources. Furthermore, the pathophysiology of these disorders is incompletely understood. In this narrative review we provide the reader with an introductory primer to the structure and function of esophageal perception, including nociception that forms the basis of the putative mechanisms that may give rise to symptoms in functional esophageal disorders. We also discuss the provocative techniques and outcome measures by which esophageal hypersensitivity can be established.

Treatment of esophageal pain remains a major challenge for the clinician. Although many patients have heartburn and may respond to proton pump inhibitors, there in an unmet need for other treatment modalities in patients where there are no obvious pathological findings. Although analgesics are the mainstay in esophageal pain treatment, many patients are nonresponders to these drugs. The current concise review focuses on other systems affecting pain processing, where better understanding may serve as a framework for therapy. These are the parasympathetic nervous system, exercise, and personality profiles. Finally, treatment with analgesics for functional chest pain remains a challenge, and an overview of treatment with antidepressive drugs is provided., (© 2016 New York Academy of Sciences.)

Introduction: Chronic nausea and vomiting have a detrimental impact on quality of life. When standard diagnostic investigations fail to provide a definitive diagnosis, patients are often attributed as having a functional gastrointestinal disorder such as cyclic vomiting syndrome. Cannabinoid hyperemesis syndrome (CHS) is a relatively recently described entity presenting with symptoms similar to cyclic vomiting syndrome., Methods: We carried out a retrospective cohort study of all patients attending a tertiary neurogastroenterology and secondary care gastroenterology clinic from 2013 to 2015. Data were obtained by review of clinical notes, letters and electronic patient records., Results: We identified 10 cases of CHS (five men, mean age 27 years, range 19-51), who hitherto had been labelled with a variety of alternative diagnoses. All patients had symptoms that were episodic and refractory to medical therapy. Patients had experienced symptoms for a mean of 19.3±11.09 months before diagnosis. The median length of cannabinoid use was 42 months (interquartile range: 15-81.8). Eight patients (80%) had a history of compulsive hot water bathing (hydrophilia). The patients had a median follow-up of 9.5 months (range 1-20), during which symptoms recurred in three patients who returned to regular cannabis use., Conclusion: CHS is an underappreciated cause of recurrent nausea and vomiting and is frequently misdiagnosed. Healthcare providers should have a low index of suspicion for diagnosing CHS and the clinical history in such patients should routinely include direct questioning on cannabis use. The prognosis is very good upon cessation of cannabis intake.

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.