NeuroImage

Neuroimaging faces a reproducibility crisis, where studies on small, heterogeneous datasets produce unreliable brain-wide associations and AI models that fail to generalize. To address this, we introduce GenBrain, a generative foundation model pretrained on approximately 1.2 million 3D scans from over 44,000 individuals across 34 imaging modalities to learn a population prior of brain structure and function. Crucially, GenBrain enables rapid, data-efficient adaptation, allowing any targeted stud...

AO_SCPLOWBSTRACTC_SCPLOWMagnetic resonance imaging (MRI) is a cornerstone of modern neuroimaging, where accurate segmentation of brain structures and lesions is essential for diagnosis, treatment planning, and clinical research. However, most current foundation models are trained on mixed-organ datasets, while the anatomical structures of the brain differ substantially from those of other organs such as the lungs and kidneys. As a result, these models often struggle to adapt to the distinctive c...

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...

We present PANDORA (Population Archive of Neuroimaging Data Organized for Rapid Analysis), a huge brain imaging data archive and analysis resource for UK Biobank neuroimaging data. PANDORA UKBv1 contains 81,939 subjects voxel-level images, created by the core UKB brain image processing pipeline. PANDORA also includes highly efficient supervoxel versions of the data - much smaller and faster to work with than the full voxelwise representation while losing virtually no signal or spatial detail and...

BackgroundDeep grey matter structures such as the thalamus and basal nuclei are implicated in numerous neurological disorders, yet accurate segmentation of these structures from standard T1-weighted MRI remains challenging due to poor intra-subcortical contrast, long preprocessing pipelines, and fragmented toolsets. MethodsWe introduce THOMASINA a deep learning pipeline for comprehensive subcortical segmentation from standard T1-weighted (T1w) as well as white-matter-nulled (WMn) MRI. The metho...

BackgroundSpeech cortical mapping (SCM) conducted with widely available functional MRI (fMRI) can yield divergent results compared to the more commonly used navigated TMS (nTMS). The impact of specific fMRI task paradigms and preprocessing choices on reaching similarity with nTMS has not been explored before. ObjectiveTo test how the fMRI experimental task and spatial smoothing of the data compare with nTMS-based results, to subsequently increase the reliability of object naming fMRI for SCM. ...

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 ...

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...

In this study, we subject the conventional physiological interpretation of independent component analysis (ICA) applied to EEG, the small-patch model, to systematic falsification, and propose an alternative large-patch model. The small-patch model assumes that ICs correspond to localized cortical patches with < 1 cm{superscript 2}. However, this assumption has remained unvalidated. The small-patch model predicts that approximately 70% of sources are localized within sulci up to 15 mm deep, with ...

Diffuse correlation spectroscopy (DCS) is a promising technique for noninvasive measurement of blood flow, especially for cerebral blood flow where other noninvasive techniques have shortcomings. Conventional DCS often requires multiple simultaneous measurements to enhance the signal-to-noise ratio (SNR) especially when probing deep into the brain with large source-detector separations where photons are scarce. However, this limits scalability when using discrete optical detectors. This study de...

Background and PurposeThe magnetic resonance imaging (MRI) access for patients with active and passive implants is limited by radiofrequency (RF) safety. The time-averaged root-mean-square RF field (B1+rms) and specific absorption rate (SAR) are being evaluated to monitor and control RF-induced heating near conductive metallic implants, such as deep brain stimulation (DBS) leads, during MRI. However, experimental methods to assess the relationship between RF power, B1+rms, and SAR are lacking fo...

Large pretrained models developed and shared by actors with privileged access to data and compute have played a central role in the democratisation of deep learning in a range of domains. Here, we contribute to this endeavour in the field of neuroimaging, by compiling a large dataset of structural magnetic resonance imaging scans (n=114,257) and using them to pretrain a multi-task convolutional neural network to predict age, sex, handedness, BMI, fluid intelligence and neuroticism. Subsequent an...

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...

Focused ultrasound can be delivered through the temporal window to modulate heterogeneously located brain areas. Acoustic simulations allow for safety assessments when dynamically targeting brain structures, but the mismatch between simulation and measured focal pressure can vary across the steerable range due to mechanically inaccurate assumptions made about the skull and transducer. Here, we describe efficient methods for simulation-measurement calibration using axisymmetric projections and sp...

Brain tumors are among the most lethal cancers with gliomas representing the most morphologically complex type. Precise and time efficient glioma segmentation and classification are essential for accurate diagnosis, treatment planning, and patient monitoring. Magnetic resonance imaging (MRI) remains the primary imaging modality for noninvasive glioma assessment. This review systematically analyzes deep learning (DL) and artificial intelligence (AI) approaches for brain tumor segmentation and cla...

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...

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...

PurposeTo externally evaluate three binary classification models designed to differentiate the molecular subtype of pediatric low-grade glioma (pLGG) between BRAF Fusion, BRAF Mutation, and Wild Type on T2-weighted magnetic resonance imaging using self-supervised transfer learning, which enables effective performance in a low data setting. Materials and methodsThis retrospective study evaluates pLGG molecular subtyping models, pre-trained using data collected at Dana Farber Cancer Institute/Bos...

ObjectivesConventional gadolinium-enhanced cardiac magnetic resonance imaging (MRI) typically evaluates myocardial tissues at a single post-contrast time point. In contrast, dynamic T1 mapping enables the estimation of contrast agent concentrations and subsequent pharmacokinetic modeling. This study compared a normal composite two-compartment model incorporating myocardial vascular components with the conventional Brix model. Materials and MethodsThis retrospective study included 107 participan...

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...