Human Brain Mapping

Deep-learning based super-resolution has shown promise for enhancing the spatial resolution of brain magnetic resonance images, which may help visualize small anatomical structures more clearly. However, when only limited training data are available, it remains uncertain which model assessment method provides the most reliable estimate of out-of-sample performance. In this study, three widely used assessment strategies (three-way holdout, k-fold cross-validation, and nested cross-validation) wer...

BackgroundMotor threshold (MT) estimation is fundamental to transcranial magnetic stimulation (TMS), guiding individualized stimulation intensity in research and therapy. Conventional methods such as the 5-out-of-10 rule require many stimuli, while adaptive approaches like Parameter Estimation by Sequential Testing (PEST) improve efficiency but can exhibit poor convergence under certain conditions. ObjectiveThis study introduces the Bayesian Uncertainty Dynamic Algorithm for Parameter Estimatio...

Lesion network mapping (LNM) links focal brain lesions to distributed neural circuits by projecting lesion locations through a normative functional connectome. van den Heuvel and colleagues recently showed how commonly used LNM procedures generate maps that converge on nonspecific, low-dimensional properties of the connectome, introducing a bias. Consequently, many published maps of different conditions appear strikingly similar. Here, we offer an alternative approach that does highlight distinc...

Magnetic Resonance Spectroscopy Imaging (MRSI) offers spatially-resolved, neurometabolic information, acquired non-invasively at whole-brain scales from human subjects. Analysis of MRSI however, is extremely challenging. The metabolic information is highly convolved, and sparsely distributed across millions of spatial-spectral datapoints, allowing for little direct human interpretation. Conversely, the overall low signal-to-noise with high-intensity artifacts can confound unsupervised machine le...

Brain tumors are one of the most life-threatening diseases, requiring precise and timely detection for effective treatment. Traditional methods for brain tumor detection rely heavily on manual analysis of MRI scans, which is time-consuming, subjective, and prone to human error. With advancements in deep learning, Convolutional Neural Networks (CNNs) have become popular for medical image analysis. However, CNNs are limited in their ability to capture spatial hierarchies and pose variations, which...

Background and ObjectivesWhite matter hyperintensities (WMH) of presumed vascular origin are a neuroimaging hallmark of cerebral small vessel disease (CSVD). Their spatial heterogeneity may reflect different clinical phenotypes. Most prior studies relied on principal component analysis to characterise such heterogeneity, which has limited ability to stratify individuals into discrete and interpretable WMH subtypes. We therefore propose a data-driven framework to identify WMH spatial subtypes, ch...

Lifestyle and environmental factors such as diet, physical activity, residential greenspace exposure, alcohol consumption, and sleep are increasingly promoted as modifiable targets for maintaining cognitive health and mitigating age-related decline. Yet, it remains unclear how well they predict cognitive functioning and, importantly, to what extent their associations with cognition are reflected in brain and bodily health. Here, we applied machine learning to multimodal data from over 10,000 UK ...

Oscillatory coupling between respiration, heart rate, and cortical function is fundamental to physiological regulation yet remains poorly characterized in humans. Diminished respiratory heart rate variability (RespHRV)--the rhythmic heart rate modulation accompanying respiration--has emerged as a transdiagnostic biomarker of mental and physical health, reduced in anxiety, depression, cardiovascular disease, and aging (Beauchaine & Thayer, 2015; Menuet & Gourine et al., 2025). However, the cortic...

Traumatic brain injury (TBI), particularly sports- and recreational activity related mild TBI (mTBI), is common in young adults and can be followed by persistent attentional and executive complaints. This study investigated chronic ([≥]6 months post-injury) structural brain alterations in gray matter (GM) and white matter (WM) and their associations with self-reported inattentive and hyperactive/impulsive symptoms, with a focus on sex-differentiated patterns. Structural brain properties in gr...

PurposeNeonatal imaging is particularly challenging because newborns have a high likelihood of head motion, which can degrade image quality and complicate interpretation. Improving MRI brain image quality may help reduce diagnostic uncertainty and facilitate the nuanced assessment of early myelinating structures in the neonatal brain. Although deep learning reconstruction algorithms designed to improve MRI image quality have been evaluated in pediatric imaging, they have not been specifically st...

Chemical shift-encoded magnetic resonance imaging using high-resolved 3D Dixon techniques enables the non-invasive and radiation-free assessment of whole-body adipose tissue and ectopic fat distribution. Automatic deep learning-based segmentation of metabolically relevant adipose tissue compartments and ectopic fat deposits in parenchymal tissue is the most important image processing step for the quantification of adipose tissue volumes and ectopic fat percentages from whole-body imaging. This ...

IntroductionIdiopathic normal pressure hydrocephalus (iNPH) is a partially reversible neurological disorder in which imaging biomarkers support diagnosis and surgical decision-making. The callosal angle (CA) is one of the most robust radiological markers of iNPH and has also been associated with postoperative shunt outcome. However, several manual measurement variants exist and artificial intelligence (AI)-based tools now enable automatic CA measurement. Materials and MethodsIn total 71 patient...

Quantifying muscle health at scale has been limited by the difficulty of segmenting individual muscles on MRI. We developed an automated 3D deep-learning framework that segments 20 bilateral hip and thigh muscles from Dixon MRI, enabling muscle level quantification of volume and relative fat fraction (rFF). Applied to 10,840 baseline and 2,766 longitudinal UK Biobank scans, this framework supports population-scale phenotyping across demographic, metabolic and treatment exposures. Segmentation ac...

Obesity and metabolic dysfunction are among the strongest risk factors for poor brain and mental health, yet the neural mechanisms linking metabolism, brain, and behaviour remains poorly understood. Here, we provide the first evidence for two distinct large-scale brain network configurations--one associated with metabolic health and another with obesity-- identified using resting-state fMRI data and metabolic phenotypes from a large community cohort (N = 564). While obesity was linked to enhance...

ObjectiveQuantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative MRI. Current methods for resection cavity masking are time-consuming and labour-intensive, while existing automated approaches exhibit variable segmentation accuracy, particularly on extra-temporal resections. We developed MELD-PostOp, a deep learning tool trained and evaluated on a large, international, heterogeneous cohort to automatic...

Executive dysfunction affects nearly 50% of individuals with traumatic brain injuries (TBI), yet interventions targeting the underlying neural mechanisms remain limited. This study examined whether aerobic exercise modulates functional connectivity to improve executive function in individuals with mild TBI and identified the neural pathways mediating these improvements. In this secondary analysis of a 12-week pilot randomized controlled trial, participants with mild TBI (n=24) were randomized to...

BackgroundPost-stroke cognitive impairment (PSCI) affects nearly 30% of stroke survivors and significantly impairs functional recovery. Brain-derived neurotrophic factor (BDNF)-tropomyosin receptor kinase-{beta} (Trk{beta}) signalling is crucial for synaptic plasticity and cognitive function. While altered expression of truncated TRK{beta}-T1 isoforms has been linked to stroke, the contribution of the TRK{beta}-SHC isoform to PSCI in humans remains poorly understood. ObjectivesThis study aimed ...

In 2024, approximately 30% of U.S. adolescents reported having consumed alcohol at least once in their lifetime, with about 25% of these individuals engaging in binge drinking. Adolescent alcohol use is associated with neurodevelopmental impairments, elevated risk of later alcohol use, and mental health disorders. These findings underscore the importance of identifying the variables driving adolescent alcohol use and leveraging them for early identification and targeted intervention. Previous st...

The UK Biobank (UKB) Brain Imaging cohort contains data from almost 100,000 subjects and has yielded invaluable understanding of the links between the brain and health outcomes and lifestyles. Much of the understanding of these links has come from exploring the association between Imaging Derived Phenotypes (IDPs) and other variables that are unrelated to brain imaging, so called non-Imaging Derived Phenotypes (nIDPs). When performing analysis of this kind, it is very important to control for we...

Visual Snow Syndrome (VSS) is a neurological condition characterized by continuous visual disturbances resembling television static across the visual field. Despite its significant impact on quality of life, objective assessment methods remain limited, with diagnosis relying primarily on subjective patient reports. Current understanding of VSS pathophysiology suggests cortical hyperexcitability, but precise mechanisms remain unclear. Here we developed an integrated protocol combining transcrania...