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Computer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized cross-correlation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, O a max N , as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity O a max N log N , where N is the number of voxels in the image and a max is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to O N . We test our methods on one synthetic and two real multiple-sclerosis databases, and compare their performances in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed methods for brain lesion detection and their comparable performance with existing techniques.

•The BOLD signal in white matter has an anisotropic correlation structure that has not been accounted for in standard fMRI pipelines.•We present a graph-based spatial smoothing approach that yields filters adapted to the underlying diffusion MRI data of the subject.•The proposed smoothing method outperforms isotropic Gaussian filtering in simulated phantom experiments.•Single-subject and group activation maps from task fMRI data smoothed with the proposed approach manifest slender, streamline-like activation patterns that are generally absent when the data are smoothed with isotropic Gaussian filters. Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detectability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject’s unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project’s 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

The inability to visualize the initiation and progression of type-1 diabetes (T1D) noninvasively in humans is a major research and clinical stumbling block. We describe an advanced, exportable method for imaging the pancreatic inflammation underlying T1D, based on MRI of the clinically approved magnetic nanoparticle (MNP) ferumoxytol. The MNP-MRI approach, which reflects nanoparticle uptake by macrophages in the inflamed pancreatic lesion, has been validated extensively in mouse models of T1D and in a pilot human study. The methodological advances reported here were enabled by extensive optimization of image acquisition at 3T, as well as by the development of improved MRI registration and visualization technologies. A proof-of-principle study on patients recently diagnosed with T1D versus healthy controls yielded two major findings: First, there was a clear difference in whole-pancreas nanoparticle accumulation in patients and controls; second, the patients with T1D exhibited pronounced inter- and intrapancreatic heterogeneity in signal intensity. The ability to generate noninvasive, 3D, high-resolution maps of pancreatic inflammation in autoimmune diabetes should prove invaluable in assessing disease initiation and progression and as an indicator of response to emerging therapies. Significance We describe a readily exportable method for noninvasive imaging of the pancreatic inflammation underlying type-1 diabetes (T1D), based on MRI of the clinically approved magnetic nanoparticle ferumoxytol. This approach, which reflects nanoparticle uptake by macrophages in the inflamed pancreatic lesion, has been validated rigorously in mouse T1D models. Methodological advances reported here include extensive optimization of image acquisition and improved MRI registration and visualization technologies. A proof-of-principle study revealed a clear difference in whole-pancreas nanoparticle accumulation in patients with recent-onset T1D versus healthy controls and pronounced intra- and interpancreatic signal heterogeneity in patients. Noninvasive generation of 3D, high-resolution maps of pancreatic inflammation should prove invaluable in assessing T1D progression and as an indicator of response to therapy.

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T 1 -weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

•We reconstructed cubic centimeters of human cerebellar samples at micrometer resolution in five subjects.•Thickness of the granular layer varies greater than that of the molecular layer.•Cross-subject variability is higher in optical property than cortical morphology.•Our results suggest homogenous cell and myelin density in the cortical layers of human cerebellum despite the highly convoluted folding patterns. The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases.

Introduction Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers. Methods We used four diffusion‐MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non‐MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi‐synaptic neural pathways. Results We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity. Discussion Our findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.

Despite their importance for visual perception, functional connectivity between ocular dominance columns in human primary visual cortex remains largely unknown. Using high-resolution functional MRI, we localize ocular dominance columns and assess their resting-state functional connectivity in 12 human adults. Consistent with anatomical studies in animals, we find stronger connectivity in middle compared to deep and superficial cortical depths and selectively stronger connectivity between columns with alike compared to unalike ocular polarity. Beyond what was known from animal models, and consistent with human perceptual biases, intra- and interhemispheric connectivity is stronger in peripheral (compared to central) and in dorsal (compared to ventral) subregions. Lastly, connectivity patterns correlate with ocular dominance column maps, suggesting that they can be predicted from connectivity patterns within primary visual cortex. These results highlight the heterogeneity in functional connectivity between ocular dominance columns across cortical depths and visual field representations, underscoring their likely association with perceptual features. High-resolution fMRI uncovers stronger functional connectivity between alike ocular dominance columns in human V1, varying across depth and visual subfields, providing new insights into the mesoscale functional organization of the visual system.

In diffusion-weighted magnetic resonance imaging (dMRI), the coordinate systems where the image and the diffusion gradients are represented may be inconsistent, thus impacting the quality of subsequent fiber tracking and connectivity analysis. Empirical verification of the reconstructed fiber orientations and subsequent correction of the gradient table (by permutation and flipping), both time-consuming tasks, are therefore often necessary. To save manual labor in studies involving dMRI, we introduce a new automatic gradient-table verification approach, which we propose to include in the dMRI processing pipeline. To that end, we exploit the concept of fiber continuity – the assumption that, in the fibrous tissue (such as the brain white matter), fiber bundles vary smoothly along their own orientations. Our tractography-free method tries all possible permutation and flip configurations of the gradient table, and in each case, assesses the consistency of the reconstructed fiber orientations with fiber continuity. Our algorithm then suggests the correct gradient table by choosing the configuration with the most consistent fiber orientations. We validated our method in 185 experiments on human brain dMRI data form three public data sources. The proposed algorithm identified the correct permutation and flip configuration for the gradient table in all the experiments.

The increasing prevalence of multi‐site diffusion‐weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power to investigate brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods for dMRI data harmonization exist, few specifically address structural brain connectivity. We introduce a new distribution‐matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from three distinct datasets (OASIS‐3, ADNI‐2, and PREVENT‐AD), comparing its performance to the widely used ComBat method and the more recent CovBat approach. We examine the impact of harmonization on the correlation of brain connectivity with the Mini‐Mental State Examination score and age. Our results demonstrate that our distribution‐matching technique effectively harmonizes structural brain connectivity while maintaining non‐negativity of the connectivity values and produces correlation strengths and significance levels competitive with alternative approaches. Qualitative assessments illustrate the desired distributional alignment across datasets, while quantitative evaluations confirm competitive performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research. Our distribution‐matching approach to harmonization outperforms ComBat in harmonizing structural brain connectivity across multi‐site dMRI studies, enhancing correlation with cognitive scores and age. This method improves reliability and comparability in neuroscientific research combining data from different sources.

Image segmentation is a critical step in numerous medical imaging studies, which can be facilitated by automatic computational techniques. Supervised methods, although highly effective, require large training datasets of manually labeled images that are labor-intensive to produce. Unsupervised methods, on the contrary, can be used in the absence of training data to segment new images. We introduce a new approach to unsupervised image segmentation that is based on the computation of the local center of mass. We propose an efficient method to group the pixels of a one-dimensional signal, which we then use in an iterative algorithm for two- and three-dimensional image segmentation. We validate our method on a 2D X-ray image, a 3D abdominal magnetic resonance (MR) image and a dataset of 3D cardiovascular MR images.