Computational limits to the legibility of the imaged human brain.

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, R2 0.859), and weight (mean absolute error 2.609Kg, R2 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.

Comprehensive Assessment of Nutrition and Dietary Influences in Hypermobile Ehlers-Danlos Syndrome-A Cross-Sectional Study.

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.

Brain tumour genetic network signatures of survival.

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 human cost of ethical artificial intelligence.

Foundational models such as ChatGPT critically depend on vast data scales the internet uniquely enables. This implies exposure to material varying widely in logical sense, factual fidelity, moral value, and even legal status. Whereas data scaling is a technical challenge, soluble with greater computational resource, complex semantic filtering cannot be performed reliably without human intervention: the self-supervision that makes foundational models possible at least in part presupposes the abilities they seek to acquire. This unavoidably introduces the need for large-scale human supervision-not just of training input but also model output-and imbues any model with subjectivity reflecting the beliefs of its creator. The pressure to minimize the cost of the former is in direct conflict with the pressure to maximise the quality of the latter. Moreover, it is unclear how complex semantics, especially in the realm of the moral, could ever be reduced to an objective function any machine could plausibly maximise. We suggest the development of foundational models necessitates urgent innovation in quantitative ethics and outline possible avenues for its realisation.

Brain tumour segmentation with incomplete imaging data.

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.

A framework for focal and connectomic mapping of transiently disrupted brain function.

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.

Graph lesion-deficit mapping of fluid intelligence.

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.

Representational ethical model calibration.

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.

Brain effect of transcutaneous vagal nerve stimulation: A meta-analysis of neuroimaging evidence.

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.

Differentiating central nervous system infection from disease infiltration in hematological malignancy.

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 > 1 favoring CNS infection and < 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.

Deep forecasting of translational impact in medical research.

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.

The autonomic brain: Multi-dimensional generative hierarchical modelling of the autonomic connectome.

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.

Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach.

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.

Transcutaneous vagus nerve stimulation prevents the development of, and reverses, established oesophageal pain hypersensitivity.

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.

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning.

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.

Artificial Intelligence-Assisted Gastroenterology- Promises and Pitfalls.

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.

Linaclotide increases cecal pH, accelerates colonic transit, and increases colonic motility in irritable bowel syndrome with constipation.

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.