Human Brain Mapping

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...

The reliability of MRI-derived measures critically depends on image quality. Poor-quality scans can obscure anatomical detail and compromise the accuracy of automated image analysis, underscoring the need for robust quality control (QC) procedures. Automated QC offers scalability for large neuroimaging datasets, yet the comparative performance of different approaches for detecting specific artifact types remains poorly understood. We systematically compared rule-based (RB), classical machine le...

Accurate detection of anatomical landmarks in brain Magnetic Resonance Imaging (MRI) scans is essential for reliable spatial normalization, image alignment, and quantitative neuroimaging analyses. In this study, we introduce BrainSignsNET, a deep learning framework designed for robust three-dimensional (3D) landmark detection. Our approach leverages a multi-task 3D convolutional neural network that integrates an attention decoder branch with a multi-class decoder branch to generate precise 3D he...

Perivascular spaces (PVS) are cerebrospinal fluid-filled tunnels around brain blood vessels, crucial for the functions of the glymphatic system. Changes in PVS have been linked to vascular diseases and aging, necessitating accurate segmentation for further study. PVS segmentation poses challenges due to their small size, varying MRI appearances, and the scarcity of annotated data. We present a finely segmented PVS dataset from T2-weighted MRI scans, sourced from the Human Connectome Project Agin...

Alzheimers disease (AD) causes progressive structural brain changes that precede clinical symptoms by years. Detecting these changes using structural MRI remains challenging, especially in early stages and when relying on visual interpretation alone. Automated semantic segmentation methods offer anatomical precision and objective measurements, but their outputs are rarely used to support human visual assessment. In this study, we explored whether such segmentation outputs can be used to guide a...

0.This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate whole-brain parcellation in T1-weighted brain MRI, which aims to overcome the limitations of conventional normalization-to-atlas-based approaches and multi-atlas label-fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization-to-atlas-based methods have been employed for this ta...

PurposeFunctional connectivity magnetic resonance imaging (fcMRI) is a widely utilized tool for analyzing functional connectivity (FC) in both healthy and diseased brains. However, patients with brain disorders are particularly susceptible to head movement during scanning, which can introduce substantial noise and compromise data quality. Therefore, identifying optimal denoising strategies is essential to ensure reliable and accurate downstream data analysis for both lesional and non-lesional br...

We present an open, vendor-neutral BOLD SMS-EPI protocol tailored for multi-site fMRI studies, intended as a drop-in replacement for conventional vendor-specific, black-box acquisition and reconstruction pipelines. Built on Pulseq--an emerging standard for cross-platform MRI pulse sequence development--our protocol ensures identical SMS-EPI pulse sequences and image reconstruction across scanner vendors. This provides, for the first time, known and consistent experimental conditions across sites...

Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant challenges for computational analysis. While models proposed for brain MRIs analyses yield encouraging results, the high complexity of neuroimaging data hinders generalizability and clinical application. We introduce DUNE, a neuroimaging-oriented encoder designed to extract deep-features from multisequence brain MRIs, thereby enabling their processing by basic machine learning algorithms. A UNet-based ...

1Quadrantanopia caused by inadvertent severing of Meyers Loop of the optic radiation is a well-recognised complication of temporal lobectomy for conditions such as epilepsy. Dissection studies indicate that the anterior extent of Meyers Loop varies considerably between individuals. Quantifying this for individual patients is thus an important step to improve the safety profile of temporal lobectomies. Previous attempts to delineate Meyers Loop using diffusion MRI tractography have had difficulty...

Sex differences in the size of specific brain structures have been extensively studied but careful and reproducible statistical hypothesis testing to identify them produced overall small effect sizes and differences brains of males and females. On the other hand, multivariate statistical or machine learning methods that analyse MR images of the whole brain have reported respectable accuracies for the task of distinguishing males from females. However, most existing studies lacked a careful contr...

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 propose a new perceptual super resolution (PSR) method for 3D neuroimaging and evaluate its performance in detecting brain changes due to neurodegenerative disease. The method, concurrent super resolution and segmentation (CSRS), is trained on volumetric brain data to consistently upsample both an image intensity channel and associated segmentation labels. The simultaneous nature of the method improves not only the resolution of the images but also the resolution of associated segmentations t...

Background and Significance18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is commonly used to measure regional metabolism for diagnosis and monitoring of Alzheimers Disease (AD). However, FDG-PET is expensive and not widely available. Regional cerebral blood flow (CBF) is coupled to regional glucose metabolism and can be imaged noninvasively using Arterial Spin Labeling (ASL) perfusion MRI. Previously, we developed FlowGAN to synthesize FDG-PET images from ASL CBF and T1w MRI for ...

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train a...

BackgroundGenerative adversarial networks (GAN) can generate images of improved quality but their ability to augment image-based classification tasks is not fully explored. PurposeWe evaluated if a modified GAN can learn from MRI scans of multiple magnetic field strength to enhance Alzheimers disease (AD) classification performance. Materials and methodsT1-weighted brain MRI scans from 151 participants of the Alzheimers Disease Neuroimaging Initiative (ADNI), who underwent both 1.5 Tesla (1.5T...

Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies on metrics either coming from the tractography process itself or from each voxel along the bundle. Statistical detection of abnormal voxels in the context of disease usually relies on univariate and multivariate statistics models, such as the Genera...

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia pati...

BackgroundAccurate thalamic nuclei segmentation is critical for neuroscience research and clinical interventions such as deep brain stimulation and magnetic resonance guided focused ultrasound. Connectivity based parcellation has been widely used for two decades, yet its anatomical validity remains uncertain compared with newer imaging approaches. MethodsWe analyzed high resolution diffusion magnetic resonance imaging (MRI) and T1 weighted data from 67 healthy young adults in the Human Connecto...

BackgroundMultimodal MRI (sMRI, dMRI, rsfMRI) encodes complementary aspects of brain structure and function; principled joint representations promise more sensitive and interpretable markers of brain health than single-modality features. MethodsWe evaluate Normative Neurological Health Embedding (NNHEmbed), a flexible multi-view framework that uses constrained cross-modal similarity objectives to learn low-dimensional embeddings. Models were trained and tested on the UK Biobank (n = 21,300) and...