Cognitive control & the anterior cingulate cortex: Necessity & coherence.

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.

A machine learning approach to stratify patients with hypermobile Ehlers-Danlos syndrome/hypermobility spectrum disorders according to disorders of gut brain interaction, comorbidities and quality of life.

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.

The utility of MRI radiological biomarkers in determining intracranial pressure.

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.

VASARI-auto: Equitable, efficient, and economical featurisation of glioma MRI.

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.

The minimal computational substrate of fluid intelligence.

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.

Compressed representation of brain genetic transcription.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).