Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Microstructural Characterization of Short Association Fibers Related to Long-Range White Matter Tracts in Normative Development

Short association fibers (SAFs) in the superficial white matter play a key role in mediating local cortical connections but have not been well-studied as innovations in whole-brain diffusion tractography have only recently been developed to study superficial white matter. Characterizing SAFs and their relationship to long-range white matter tracts is crucial to advance our understanding of neurodevelopment during the period from childhood to young adulthood. This study aims to (1) map SAFs in relation to long-range white matter tracts, (2) characterize typical neurodevelopmental changes across these white matter pathways, and (3) investigate the relationship between microstructural changes in SAFs and long-range white matter tracts. Leveraging a cohort of 616 participants ranging in age from 5.6 to 21.9 years old, we performed quantitative diffusion tractography and advanced diffusion modeling with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Robust linear regression models were applied to analyze microstructural features, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index (ODI). Our results reveal that both SAFs and long-range tracts exhibit similar overall developmental patterns, characterized by negative associations of MD, AD, and RD with age and positive associations of FA, ICVF, ISOVF, and ODI with age. Notably, FA, AD, and ODI exhibit significant differences between SAFs and long-range tracts, suggesting distinct neurodevelopmental trajectories between superficial and deep white matter. In addition, significant differences were found in MD, RD, and ICVF between males and females, highlighting variations in neurodevelopment. This normative study provides insights into typical microstructural changes of SAFs and long-range white matter tracts during development, laying a foundation for future research to investigate atypical development and dysfunction in disease pathology.

White matter structure-function coupling alteration is associated with plasma biomarkers and cognition in Alzheimer's disease

OBJECTIVES

There were white matter (WM) microstructural abnormalities in Alzheimer's disease (AD), and it may affect information transmission along WM tracts. We aim to investigate AD-related changes in the structure-function coupling of WM and their associations with AD plasma biomarkers and cognitive performance.

METHODS

This retrospective study evaluated participants who provided blood samples, underwent MR brain scans, and completed neuropsychological assessments. Plasma biomarker levels of β-amyloid (Aβ), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) were measured using the Single Molecule Array platform. We used a structure-function coupling approach, combining spatial-temporal correlations of functional signals with diffusion tensor orientations in the WM circuit derived from functional and diffusion magnetic resonance imaging (MRI). Association and mediation analyses were conducted to explore the relationships among plasma biomarkers, structure-function coupling, and cognitive performance in AD.

RESULTS

Compared with the cognitively normal (CN) and mild cognitive impairment (MCI) groups, the AD dementia group showed a widespread increase in structure-function coupling within WM regions, including the body and splenium of the corpus callosum. Additionally, structure-function coupling in WM tracts was significantly associated with the levels of p-tau181 and NfL. Moreover, structure-function coupling mediated the relationship between memory and the level of p-tau181 or NfL.

CONCLUSION

Our findings suggest that there is altered information transmission along WM tracts in patients with AD. The brain structure-function coupling is associated with plasma biomarkers and cognitive scores, suggesting that abnormal signal transfer of neuronal fiber pathways could be a potential mechanism of AD neuropathology.

KEY POINTS

Question White matter (WM) microstructural abnormalities may affect information transmission along WM tracts, resulting in cognitive decline in Alzheimer's disease (AD). Findings The AD dementia group showed a widespread increase in structure-function coupling within WM regions, which was associated with plasma biomarkers and cognitive scores. Clinical relevance The brain structure-function coupling is associated with plasma biomarkers and cognitive scores, suggesting that abnormal signal transfer of neuronal fiber pathways could be a potential mechanism of the neuropathology of AD.

Quantitative MRI Tractography of White Matter Tracts After Tumor Craniotomy Surgery: Comparative Analysis Between Tubular Retractor and Open Craniotomy Surgery

BACKGROUND AND OBJECTIVES

Tubular retraction has been a technique used to minimize the extent of cerebral retraction injury; however, only qualitative imaging assessments exist in the literature comparing this technique with open craniotomies using spatula retraction. This study uses quantitative MRI tractography to analyze the extent of cerebral retraction injury using tubular retraction (TR) compared with open craniotomies (OC).

METHODS

This study performed a retrospective analysis of a cohort of 20 patients who underwent cranial tumor surgery for deep-seated brain tumors. Ten patients who underwent surgery with TR were case-control matched with 10 patients who underwent an OC with spatula retraction. Quantitative metrics evaluating white matter tract integrity (fractional anisotropy (FA), geodesic anisotropy (GA), mean diffusivity, radial diffusivity, axial diffusivity, and tract volume), extent of resection, and neurological outcome were compared between the groups.

RESULTS

Twenty patients underwent cranial surgery for deep-seated brain lesions. Preoperative neurological and tumor characteristics were comparable between the 2 cohorts. Postoperative extent of resection was found to be 90.4% in the TR group and 94.8% in the OC group (P = .395). Significant improvement was seen in the change in Karnofsky Performance Score from preoperative to postoperative status in the TR group, an 11-point increase, compared with the OC group, no change in score (P = .035). Quantitative metrics evaluating overall axonal status (FA) and compression (GA) showed significant signs of improvement in the TR group, with an FA of 0.322 vs 0.029 in the OC group (P = .011). GA was found to increase in the TR group (0.441) and decrease in the OC group (0.411, P = .0.012). Diffusivity metrics, evaluating axonal integrity were comparable between the 2 groups.

CONCLUSION

Tubular retraction surgery provides a viable surgical option for deep-seated tumors that provides comparable extent of resection outcomes while mitigating the effects of some components of retraction injury.

Diffusion tensor imaging and fiber tractography of the epididymis in men with non-obstructive azoospermia

BACKGROUND

Scrotal magnetic resonance imaging, including diffusion tensor imaging and fiber tractography has emerged as a valuable, non-invasive method in the evaluation of non-obstructive azoospermia. The epididymis has a crucial role in male infertility.

OBJECTIVES

To evaluate the role of diffusion tensor imaging and fiber tractography of the epididymis in the work-up of non-obstructive azoospermia.

MATERIALS AND METHODS

This prospective study included 22 men with non-obstructive azoospermia and 15 controls. Scrotal magnetic resonance imaging, including diffusion tensor imaging, was performed. The epididymal apparent diffusion coefficient and fractional anisotropy were measured. Fiber tractography reconstructions were created. Non-parametric statistics compared apparent diffusion coefficient and fractional anisotropy of the epididymis between: (1) non-obstructive azoospermia and normal men; (2) histologic phenotypes of non-obstructive azoospermia; (3) non-obstructive azoospermia men, with positive and negative sperm retrieval; and (4) non-obstructive azoospermia men, with idiopathic and non-genetic etiology. Visual assessment of the epididymal fiber tracts was performed.

RESULTS

Lower epididymal fractional anisotropy (p = 0.027) was observed in men with non-obstructive azoospermia in comparison to normal population. Fractional anisotropy decreased (p = 0.033) in cases with idiopathic non-obstructive azoospermia in comparison to men with non-genetic etiology. Fiber tractography showed abnormalities in epididymal fiber tracts in men with non-obstructive azoospermia, including decrease in number and/or thickness and disorganization. However, diffusion tensor imaging parameters were unable to differentiate the histologic types of non-obstructive azoospermia and to predict the results of sperm retrieval (p > 0.05).

DISCUSSION AND CONCLUSION

Our preliminary observations showed that diffusion tensor imaging and fiber tractography of the epididymis provide valuable, non-invasive biomarkers in the work-up of non-obstructive azoospermia, although the clinical significance of these findings is yet to be determined. However, diffusion tensor imaging data were not predictive for the presence of spermatozoa before microdissection testicular sperm extraction.

Imaging the structural connectome with hybrid MRI-microscopy tractography

Mapping how neurons are structurally wired into whole-brain networks can be challenging, particularly in larger brains where 3D microscopy is not available. Multi-modal datasets combining MRI and microscopy provide a solution, where high resolution but 2D microscopy can be complemented by whole-brain but lowresolution MRI. However, there lacks unified approaches to integrate and jointly analyse these multi-modal data in an insightful way. To address this gap, we introduce a data-fusion method for hybrid MRI-microscopy fibre orientation and connectome reconstruction. Specifically, we complement precise "in-plane" orientations from microscopy with "through-plane" information from MRI to construct 3D hybrid fibre orientations at resolutions far exceeding that of MRI whilst preserving microscopy's myelin specificity, resulting in superior fibre tracking. Our method is openly available, can be deployed on standard 2D microscopy, including different microscopy contrasts, and is species agnostic, facilitating neuroanatomical investigation in both animal models and human brains.

Toward Principles of Brain Network Organization and Function

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of behaviors. Understanding patterns of these complex interactions and how they are coordinated to support collective neural function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities to distill underlying principles of brain organization and function. Here, we take stock of recent progress and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory, and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine organizing principles and constraints that shape the biological structure and function of neural circuits. We conclude with an overview of several critical frontiers, including expanding current models, fostering tighter feedback between theory and experiment, and leveraging perturbative approaches to understand neural systems. Alongside these efforts, we highlight the importance of contextualizing their contributions by linking them to formal accounts of explanation and causation.

The Variation of White Matter Connectome After Surgery Revealed Factors Affecting Supplementary Syndrome Recovery Time in Low-Grade Glioma Patients

OBJECTIVE

Supplementary motor area (SMA) syndrome is a common complication after SMA glioma resection. The compensatory mechanism of the structural sensorimotor network (SMN) and the factors influencing the recovery time of SMA syndrome have not been investigated.

METHODS

Pre- and postoperative diffusion tensor images of 42 low-grade glioma patients with SMA syndrome were processed to construct white matter connectomes. Patients were classified into fast and slow-recovery groups according to whether postoperative motor disorder recovers within 7 days. Fiber counts between nodes and graph theory topological properties were calculated. The shortest distance from the surgical region to the corticospinal tract (dCST) and the upper limb region of Brodmann area 4 (A4ul) was measured to find correlations with recovery time. Cox regressions were conducted to identify factors associated with SMA syndrome recovery time. A general linear model was formed using significant factors in multivariate Cox analysis to predict recovery time.

RESULTS

Decrease of fiber number between lesioned-hemispheric A4ul and contralateral SMN is correlated with prolongation of recovery time. Compared with the slow-recovery group, a higher increase of nodal degree centrality and nodal efficiency of ipsilateral A4ul was found in the fast-recovery group (nodal efficiency: left pre-op: 0.182 ± 0.009, left post-op: 0.231 ± 0.008, p < 0.0001; right pre-op: 0.157 ± 0.021, right post-op: 0.195 ± 0.018, p = 0.0011); (nodal degree centrality: left pre-op: 1.985 ± 0.166; left post-op: 3.195 ± 0.230, p < 0.0001; right pre-op: 1.620 ± 0.389; right post-op: 2.411 ± 0.452, p = 0.0005). Multivariate Cox analysis indicated that the increase in nodal efficiency of A4ul and dCST were protective factors for SMA syndrome recovery time. A significant negative correlation between the predict score and recovery time was found in the left lesion group (r = -0.756, p < 0.0001), and the same trend was found in the right lesion group (r = -0.531, p = 0.076).

CONCLUSIONS

This study revealed an increase in lesioned-hemispheric A4ul nodal efficiency and long dCST as protective factors in SMA syndrome recovery. A decrease in the number of interhemispheric fibers connecting lesioned-hemispheric A4ul to nodes on the contralateral hemisphere was correlated with the long recovery time of SMA syndrome.

Fiber Microstructure Quantile (FMQ) Regression: A Novel Statistical Approach for Analyzing White Matter Bundles from Periphery to Core

The structural connections of the brain's white matter are critical for brain function. Diffusion MRI tractography enables the in-vivo reconstruction of white matter fiber bundles and the study of their relationship to covariates of interest, such as neurobehavioral or clinical factors. In this work, we introduce Fiber Microstructure Quantile (FMQ) Regression, a new statistical approach for studying the association between white matter fiber bundles and scalar factors (e.g., cognitive scores). Our approach analyzes tissue microstructure measures based on quantile-specific bundle regions . These regions are defined in a data-driven fashion according to the quantiles of fractional anisotropy (FA) of a population fiber bundle, which pools all individuals' bundles. The FA quantiles induce a natural subdivision of a fiber bundle, defining regions from the periphery (low FA) to the core (high FA) of the population fiber bundle. To investigate how fiber bundle tissue microstructure relates to covariates of interest, we employ the statistical technique of quantile regression. Unlike ordinary regression, which only models a conditional mean, quantile regression models the conditional quantiles of a response variable. This enables the proposed analysis, where a quantile regression is fitted for each quantile-specific bundle region. To demonstrate FMQ Regression, we perform an illustrative study in a large healthy young adult tractography dataset derived from the Human Connectome Project-Young Adult (HCP-YA), focusing on particular bundles expected to relate to particular aspects of cognition and motor function. In comparison with traditional regression analyses based on FA Mean and Automated Fiber Quantification (AFQ), we find that FMQ Regression provides a superior model fit with the lowest mean squared error. This demonstrates that FMQ Regression captures the relationship between scalar factors and white matter microstructure more effectively than the compared approaches. Our results suggest that FMQ Regression, which enables FA analysis in data-driven regions defined by FA quantiles, is more powerful for detecting brain-behavior associations than AFQ, which enables FA analysis in regions defined along the trajectory of a bundle. FMQ Regression finds significant brain-behavior associations in multiple bundles, including findings unique to males or to females. In both males and females, language performance is significantly associated with FA in the left arcuate fasciculus, with stronger associations in the bundle's periphery. In males only, memory performance is significantly associated with FA in the left uncinate fasciculus, particularly in intermediate regions of the bundle. In females only, motor performance is significantly associated with FA in the left and right corticospinal tracts, with a slightly lower relationship at the bundle periphery and a slightly higher relationship toward the bundle core. No significant relationships are found between executive function and cingulum bundle FA. Our study demonstrates that FMQ Regression is a powerful statistical approach that can provide insight into associations from bundle periphery to bundle core. Our results also identify several brain-behavior relationships unique to males or to females, highlighting the importance of considering sex differences in future research.

Mapping the coupling between tract reachability and cortical geometry of the human brain

The study of cortical geometry and connectivity is prevalent in research on the human brain. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aimed to elucidate fundamental links between cortical geometry and white matter tract connectivity. We developed the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. Specifically, spectral analyses of the cortical surface yielded a set of geometrical eigenmodes, which were then used to explain the locations on the cortical surface reached by specific white matter tracts, referred to as tract reachability. In two independent datasets, we confirmed that tract reachability was well characterized by cortical geometry. We further observed that TGC had high test-retest ability and was specific to each individual. Interestingly, low-frequency TGC was found to be heritable and more informative than the high-frequency components in behavior prediction. Finally, we found that TGC could reproduce task-evoked cortical activation patterns. Collectively, our study provides a new approach to mapping coupling between cortical geometry and connectivity, highlighting how these two aspects jointly shape the connected brain.

An Investigation of Corticospinal Tract Microstructural Integrity in ARSACS Using a Profilometry MRI Analysis: Results From the PROSPAX Study

BACKGROUND

Spasticity represents a core clinical feature of Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) patients. Nonetheless, its pathophysiological substrate is poorly investigated. We assessed the microstructural integrity of the corticospinal tract (CST) using diffusion MRI (dMRI) via profilometry analysis to understand its possible role in the development of spasticity in ARSACS.

MATERIALS AND METHODS

In this multi-center prospective study, data of 37 ARSACS (M/F = 21/16; 33.4 ± 12.4 years) and 29 controls (M/F = 13/16; 42.1 ± 17.2 years) acquired within the PROSPAX consortium were collected from January 2021 to October 2022 and analyzed. Differences in terms of global CST microstructural integrity were probed, as well as a possible spatial distribution of the damage along the tract via profilometry analysis. Possible correlations between clinical severity, including the Spastic Paraplegia Rating Scale (SPRS), were also tested.

RESULTS

A significant global involvement of the CST was found in ARSACS compared to controls (all tests with p < 0.001), with a spatially defined pattern of more pronounced microstructural integrity loss occurring right below and above the pons, a structure that was also confirmed to be thickened in these patients (p < 0.001). A bilateral negative correlation emerged between the microstructural integrity of the CST and clinical indices of spasticity expressed via SPRS (p = 0.02 for both CSTs).

CONCLUSION

A clinically meaningful microstructural involvement of CST is present in ARSACS patients, with a spatially defined pattern of damage occurring right below and above a thickened pons. An evaluation of the microstructure of this bundle might serve as a possible biomarker in this condition.

Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks

The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity for precise striatal segmentation. In this study, we introduce a novel deep clustering pipeline for automated, fine-scale parcellation of the striatum using diffusion MRI (dMRI) tractography. Initially, we employ a voxel-based probabilistic fiber tractography algorithm combined with a fiber-tract embedding technique to capture intricate dMRI connectivity patterns. To maintain critical inter-voxel relationships, our approach employs Graph Neural Networks (GNNs) to create accurate graph representations of the striatum. This involves encoding probabilistic fiber bundle characteristics as node attributes and refining edge weights using activation functions to enhance the graph's interpretability and accuracy. The methodology incorporates a Transformer-based GraphConv autoencoder in the pre-training phase to extract critical spatial features while minimizing reconstruction loss. In the fine-tuning phase, a novel joint loss mechanism markedly improves segmentation precision and anatomical fidelity. Integration of traditional clustering techniques with multi-head self-attention mechanisms further elevates the accuracy and robustness of our segmentation approach. This methodology provides new insights into the striatum's role in cognition and behavior and offers potential clinical applications for neurological disorders.

TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography

The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network design. We introduce TractGraphFormer, a hybrid Graph CNN-Transformer deep learning framework tailored for diffusion MRI tractography. This model leverages local anatomical characteristics and global feature dependencies of white matter structures. The Graph CNN module captures white matter geometry and grey matter connectivity to aggregate local features from anatomically similar white matter connections, while the Transformer module uses self-attention to enhance global information learning. Additionally, TractGraphFormer includes an attention module for interpreting predictive white matter connections. We apply TractGraphFormer to tasks of sex and age prediction. TractGraphFormer shows strong performance in large datasets of children (n = 9345) and young adults (n = 1065). Overall, our approach suggests that widespread connections in the WM are predictive of the sex and age of an individual. For each prediction task, consistent predictive anatomical tracts are identified across the two datasets. The proposed approach highlights the potential of integrating local anatomical information and global feature dependencies to improve prediction performance in machine learning with diffusion MRI tractography.

The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study

The shape of the brain's white matter connections is relatively unexplored in diffusion magnetic resonance imaging (dMRI) tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement two machine learning models (1D-CNN and Least Absolute Shrinkage and Selection Operator [LASSO]) to predict individual cognitive performance scores. We study a large-scale database from the Human Connectome Project Young Adult study (n = 1065). We apply an atlas-based fiber cluster parcellation (953 fiber clusters) to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models (using fivefold cross-validation) to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHapley Additive exPlanations (SHAP), to assess the importance of each fiber cluster for prediction. Our results demonstrate that fiber cluster shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are generally as effective for prediction as traditional microstructure and connectivity measures. The 1D-CNN model generally outperforms the LASSO method for prediction. Further interpretation and analysis using SHAP values from the 1D-CNN suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.

TractCloud-FOV: Deep Learning-Based Robust Tractography Parcellation in Diffusion MRI With Incomplete Field of View

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial, or truncated fiber tracts. To address this challenge, we introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV. We propose a novel training strategy, FOV-Cut Augmentation (FOV-CA), in which we synthetically cut tractograms to simulate a spectrum of real-world inferior FOV cutoff scenarios. This data augmentation approach enriches the training set with realistic truncated streamlines, enabling the model to achieve superior generalization. We evaluate the proposed TractCloud-FOV on both synthetically cut tractography and two real-life datasets with incomplete FOV. TractCloud-FOV significantly outperforms several state-of-the-art methods on all testing datasets in terms of streamline classification accuracy, generalization ability, tract anatomical depiction, and computational efficiency. Overall, TractCloud-FOV achieves efficient and consistent tractography parcellation in diffusion MRI with incomplete FOV.

Structural Connectome Analysis using a Graph-based Deep Model for Age and Dementia Prediction

We tackle the prediction of age and mini-mental state examination (MMSE) score based on structural brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple branches, thereby disentangling the input node and graph features. The novelty of our work lies in the model architecture, especially the connectivity attention module, which learns an embedding representation of brain graphs while providing graph-level attention. We show experiments on publicly available datasets of PREVENT-AD and OASIS3. Through our experiments, we validate our model by comparing it to existing methods and via ablations. This quantifies the degree to which the connectome varies depending on the task, which is important for improving our understanding of health and disease across the population. The proposed model generally demonstrates higher performance especially for age prediction compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component.

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI. Using DDEvENet, we obtained accurate parcellation and uncertainty estimates across different datasets from healthy and clinical populations and with different imaging acquisitions. The overall network includes five parallel subnetworks, where each is dedicated to learning the FreeSurfer parcellation for a certain diffusion MRI parameter. An evidence-based ensemble methodology is then proposed to fuse the individual outputs. We perform experimental evaluations on large-scale datasets from multiple imaging sources, including high-quality diffusion MRI data from healthy adults and clinically diffusion MRI data from participants with various brain diseases (schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, Parkinson's disease, cerebral small vessel disease, and neurosurgical patients with brain tumors). Compared to several state-of-the-art methods, our experimental results demonstrate highly improved parcellation accuracy across the multiple testing datasets despite the differences in dMRI acquisition protocols and health conditions. Furthermore, thanks to the uncertainty estimation, our DDEvENet approach demonstrates a good ability to detect abnormal brain regions in patients with lesions that are consistent with expert-drawn results, enhancing the interpretability and reliability of the segmentation results.

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

Neuronal communication relies on the anatomy of the brain, yet it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints on effective connectivity. Here, we assess a hierarchical empirical Bayes model that builds on a well-established dynamic causal model by integrating structural connectivity into resting-state effective connectivity via priors. In silico analyses show that the model successfully recovers ground-truth effective connectivity and compares favorably with a prominent alternative. Analyses of empirical data reveal that a positive, monotonic relationship between structural connectivity and the prior probability of group-level effective connectivity generalizes across sessions and samples. Finally, attesting to the model's biological plausibility, we show that inter-network differences in the coupling between structural and effective connectivity recapitulate a well-known unimodal-transmodal hierarchy. These findings underscore the value of integrating structural and effective connectivity to enhance the understanding of functional integration in health and disease.

Characterizing Brain-Cardiovascular Aging Using Multiorgan Imaging and Machine Learning

The structure and function of the brain and cardiovascular system change over the lifespan. In this study, we aim to establish the extent to which age-related changes in these two vital organs are linked. Utilizing normative models and data from the UK Biobank, we estimate biological ages for the brain and heart for 2,904 middle-aged and older healthy adults, including both males and females. Biological ages were based on multiple structural, morphological, and functional features derived from brain and cardiovascular imaging modalities. We find that cardiovascular aging, particularly aging of its functional capacity and physiology, is selectively associated with the aging of specific brain networks, including the salience, default mode, and somatomotor networks as well as the subcortex. Our work provides unique insight into brain-heart relationships and may facilitate an improved understanding of the increased co-occurrence of brain and heart diseases in aging.

Consistent cerebellar pathway-cognition associations across pre-adolescents & young adults: a diffusion MRI study of 9000+ participants

The cerebellum, long implicated in movement, is now recognized as a contributor to higher-order cognition. The cerebellar pathways provide key structural links between the cerebellum and cerebral regions integral to language, memory, and executive function. Here, we present a large-scale, cross-sectional diffusion MRI (dMRI) analysis investigating the relationships between cerebellar pathway microstructure and cognitive performance in over 9,000 participants spanning pre-adolescence (n>8,000 from the ABCD dataset) and young adulthood (n>900 from the HCP-YA dataset). We assessed the microstructure of five cerebellar pathways-the inferior, middle, and superior cerebellar peduncles; the parallel fibers; and input/Purkinje fibers-using three dMRI measures of fractional anisotropy, mean diffusivity, and number of streamlines. Cognitive performance was evaluated using seven NIH Toolbox assessments of language, executive function, and memory. In both datasets, we found numerous significant associations between cerebellar pathway microstructure and cognitive performance. These associations showed a strong correlation across the two datasets (r = 0.47, p < 0.0001), underscoring the reliability of cerebellar dMRI-cognition relationships in pre-adolescents and young adults. In both datasets, the strongest associations were found between the superior cerebellar peduncle and performance on language assessments, suggesting this pathway plays an important role in language function across age groups. In young adults, but not pre-adolescents, parallel fiber microstructure was linked to inhibitory control, suggesting that contributions to attentional processes may emerge or strengthen with maturation. Overall, our findings highlight the important role of cerebellar pathways in cognition and the utility of large-scale datasets for advancing our understanding of brain-cognition relationships.

A systematic review of diffusion tensor imaging and tractography in dementia with Lewy bodies and Parkinson's disease dementia

We reviewed studies using diffusion tensor imaging (DTI) and tractography to characterise white matter changes in Dementia with Lewy Bodies (DLB) and Parkinson's Disease Dementia (PDD). The search included MEDLINE and EMBASE, and we used a narrative strategy to synthesise the evidence. Data was extracted from 57 studies, of which the majority were considered 'good quality'. Subjects with DLB and PDD had widespread white matter changes compared to healthy controls and Parkinson's disease without cognitive impairment, with a relative sparing of the hippocampus. Compared to subjects with Alzheimer's disease (AD), DLB had greater changes in thalamic connectivity and in the nigroputaminal tract, while AD had greater changes in the parahippocampal white matter and fornix. Cognition was associated with widespread white matter changes, visual hallucinations with thalamic and cholinergic connectivity, and parkinsonism with changes in structures involved in motor control. DTI and tractography may therefore be well suited for discriminating DLB and PDD from other types of dementia, and for studying the aetiology of common symptoms.

DTI fiber-tracking parameters adjacent to gliomas: the role of tract irregularity value in operative planning, resection, and outcome

PURPOSE

The goal of glioma surgery is maximal tumor resection associated with minimal post-operative morbidity. Diffusion tensor imaging-tractography/fiber tracking (DTI-FT) is a valuable white-matter (WM) visualization tool for diagnosis and surgical planning. Still, it assumes a descriptive role since the main DTI metrics and parameters showed several limitations in clinical use. New applications and quantitative measurements were recently applied to describe WM architecture that surround the tumor area. The brain adjacent tumor area (BAT) is defined as the region adjacent to the gross tumor volume, which contains signal abnormalities on T2-weighted or FLAIR sequences. The DTI-FT analysis of the BAT can be adopted as predictive values and a guide for safe tumor resection.

METHODS

This is an observational prospective study on an extensive series of glioma patients who performed magnetic resonance imaging (MRI) with pre-operative DTI-FT analyzed on the BAT by two different software. We examined DTI parameters of Fractional anisotropy (FA mean, min-max), Mean diffusivity (MD), and the shape-metric "tract irregularity" (TI) grade, comparing it with the surgical series' clinical, radiological, and outcome data.

RESULTS

The population consisted of 118 patients, with a mean age of 60.6 years. 82 patients suffering from high-grade gliomas (69.5%), and 36 from low-grade gliomas (30.5%). A significant inverse relationship exists between the FA mean value and grading (p = 0.001). The relationship appears directly proportional regarding MD values (p = 0.003) and TI values (p = 0.005). FA mean and MD values are susceptible to significant variations with tumor and edema volume (p = 0.05). TI showed an independent relationship with grading regardless of tumor radiological features and dimensions, with a direct relationship with grading, ki67% (p = 0,05), PFS (p < 0.001), and EOR (p < 0.01).

CONCLUSION

FA, MD, and TI are useful predictive measures of the clinical behavior of glioma, and TI could be helpful for tumor grading identification and surgical planning.

Structurally informed models of directed brain connectivity

Understanding how one brain region exerts influence over another in vivo is profoundly constrained by models used to infer or predict directed connectivity. Although such neural interactions rely on the anatomy of the brain, it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints on models of directed connectivity. Here, we review the current state of research on this question, highlighting a key distinction between inference-based effective connectivity and prediction-based directed functional connectivity. We explore the methods via which structural connectivity has been integrated into directed connectivity models: through prior distributions, fixed parameters in state-space models and inputs to structure learning algorithms. Although the evidence suggests that integrating structural connectivity substantially improves directed connectivity models, assessments of reliability and out-of-sample validity are lacking. We conclude this Review with a strategy for future research that addresses current challenges and identifies opportunities for advancing the integration of structural and directed connectivity to ultimately improve understanding of the brain in health and disease.

White Matter Tract Crossing and Bottleneck Regions in the Fetal Brain

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been investigated for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 62 fetal brain scans and extracted a set of 51 distinct white matter tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20%-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75% and 80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. These results highlight the relevance of these regions to key developmental processes, specifically, the dispersion of projection fibers, the protracted growth of commissural pathways, and the emergence of association tracts that contribute to the formation of complex intersection regions. These developmental interactions lead to a high prevalence of crossing fibers and bottleneck areas, reflecting the intricate organization required for establishing structural and functional connectivity. Additionally, our results highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

New Graphs at the braingraph.org Website for Studying the Aging Brain Circuitry

Human braingraphs or connectomes are widely studied in the last decade to understand the structural and functional properties of our brain. In the last several years our research group has computed and deposited thousands of human braingraphs to the braingraph.org site, by applying public structural (diffusion) MRI data from young and healthy subjects. Here we describe a recent addition to the braingraph.org site, which contains connectomes from healthy and demented subjects between 42 and 95 years of age, based on the public release of the OASIS-3 dataset. The diffusion MRI data was processed with the Connectome Mapper Toolkit v.3.1. We believe that the new addition to the braingraph.org site will become a useful resource for enlightening the aging circuitry of the human brain in healthy and diseased subjects, including those with Alzheimer's disease in several stages.

Tractography-Based Automated Identification of Retinogeniculate Visual Pathway With Novel Microstructure-Informed Supervised Contrastive Learning

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a new streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. In the experiments, we perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation. Furthermore, to assess the generalizability of the proposed RGVP method, we apply our method to dMRI tractography data from neurosurgical patients with pituitary tumors. In comparison with the state-of-the-art methods, we show superior RGVP identification results using DeepRGVP with significantly higher accuracy and F1 scores. In the patient data experiment, we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome

Diffusion MRI-driven tractography, a non-invasive technique that reveals how the brain is connected, is widely used in brain lateralization studies. To improve the accuracy of tractography in showing the underlying anatomy of the brain, various tractography filtering methods were applied to reduce false positives. Based on different algorithms, tractography filtering methods are able to identify the fibers most consistent with the original diffusion data while removing fibers that do not align with the original signals, ensuring the tractograms are as biologically accurate as possible. However, the impact of tractography filtering on the lateralization of the brain connectome remains unclear. This study aims to investigate the relationship between fiber filtering and laterality changes in brain structural connectivity. Three typical tracking algorithms were used to construct the raw tractography, and two popular fiber filtering methods(SIFT and SIFT2) were employed to filter the tractography across a range of parameters. Laterality indices were computed for six popular biological features, including four microstructural measures (AD, FA, RD, and T1/T2 ratio) and two structural features (fiber length and connectivity) for each brain region. The results revealed that tractography filtering may cause significant laterality changes in more than 10% of connections, up to 25% for probabilistic tracking, and deterministic tracking exhibited minimal laterality changes compared to probabilistic tracking, experiencing only about 6%. Except for tracking algorithms, different fiber filtering methods, along with the various biological features themselves, displayed more variable patterns of laterality change. In conclusion, this study provides valuable insights into the intricate relationship between fiber filtering and laterality changes in brain structural connectivity. These findings can be used to develop improved tractography filtering methods, ultimately leading to more robust and reliable measurements of brain asymmetry in lateralization studies.

Insulin resistance and white matter microstructural abnormalities in nondiabetic adult: A population-based study

BACKGROUND

Insulin resistance (IR) is of growing concern yet its association with white matter integrity remains controversial. We aimed to investigate the association between IR and white matter integrity in nondiabetic adults.

METHODS

This cross-sectional analysis was conducted based on the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study. A total of 1709 nondiabetic community-dwelling adults with available diffusion-weighted imaging based on brain magnetic resonance imaging and completed oral glucose tolerance test were included. IR was measured noninvasively by insulin sensitivity indices (ISI), including ISIcomposite and ISI0,120, as well as homeostasis model assessment of insulin resistance (HOMA-IR). White matter microstructure abnormalities were identified by diffusion-weighted imaging along with tract-based spatial statistical analysis to compare diffusion metrics between groups. The multivariable linear regression models were applied to measure the association between white matter microstructure abnormalities and IR.

RESULTS

A total of 1709 nondiabetic individuals with a mean age of 60.8 ± 6.4 years and 54.1% female were included. We found that IR was associated with a significant increase in mean diffusivity, axial diffusivity, and radial diffusivity extensively in cerebral white matter in regions such as the anterior corona radiata, superior corona radiata, anterior limb of internal capsule, external capsule, and body of corpus callosum. The pattern of associations was more marked for ISIcomposite and ISI0,120. However, the effect of IR on white matter integrity was attenuated after, in addition, adjustment for history of hypertension and cardiovascular disease and antihypertensive medication use.

CONCLUSION

Our findings indicate a significant association between IR and white matter microstructural abnormalities in nondiabetic middle-aged community residents, while these associations were greatly influenced by the history of hypertension and cardiovascular disease, and antihypertensive medication use. Further investigation is needed to clarify the role of IR in white matter integrity, whereas prophylactic strategies of maintaining a low IR status may ameliorate disturbances in white matter integrity.

DATA ACCESSIBILITY STATEMENT

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Reconstruction of intra- and extra-neurite conductivity tensors via conductivity at Larmor frequency and DWI data patterns

The developed magnetic resonance electrical properties tomography (MREPT) techniques visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data. In biological tissues, electrical conductivity is influenced by ion concentrations and mobility. To visualize the anisotropic conductivity tensor of biological tissues, we use the Einstein-Smoluchowski equation, which links the diffusion coefficient to particle mobility. By assuming a correlation between ion mobility and water diffusivity, we aim to decompose the internal isotropic conductivity at Larmor frequency into anisotropic conductivity tensors within the intra- and extra-neurite compartments. The multi-compartment spherical mean technique (MC-SMT), utilizing both high and low b-value diffusion-weighted imaging (DWI) data, characterizes the diffusion of water molecules within and across the intra- and extra-neurite compartments by analyzing the microstructural intricacies and the foundational architectural arrangement of the brain's tissues. By analyzing the relationships between the measured DWI data, the microscopic diffusion signal, and the fiber orientation distribution function, we predict the DWI data for the intra- and extra-neurite compartments using spherical harmonic decomposition. Using the predicted DWI data for the intra- and extra-neurite compartments, we develop a conductivity tensor imaging method that operates specifically within the separated compartments. Human brain experiments, involving four healthy volunteers and a brain tumor patient, were performed to assess and confirm the reliability of the proposed method.

Diffusion MRI with Machine Learning

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response

By measuring the molecular diffusion of water molecules in brain tissue, diffusion MRI (dMRI) provides unique insight into the microstructure and structural connections of the brain in living subjects. Since its inception, the application of dMRI in clinical research has expanded our understanding of the possible biological bases of psychiatric disorders and successful responses to different therapeutic interventions. Here, we review the past decade of diffusion imaging-based investigations with a specific focus on studies examining the mechanisms and predictors of therapeutic response in people with mood disorders. We present a brief overview of the general application of dMRI and key methodological developments in the field that afford increasingly detailed information concerning the macro- and micro-structural properties and connectivity patterns of white matter (WM) pathways and their perturbation over time in patients followed prospectively while undergoing treatment. This is followed by a more in-depth summary of particular studies using dMRI approaches to examine mechanisms and predictors of clinical outcomes in patients with unipolar or bipolar depression receiving pharmacological, neurostimulation, or behavioral treatments. Limitations associated with dMRI research in general and with treatment studies in mood disorders specifically are discussed, as are directions for future research. Despite limitations and the associated discrepancies in findings across individual studies, evolving research strongly indicates that the field is on the precipice of identifying and validating dMRI biomarkers that could lead to more successful personalized treatment approaches and could serve as targets for evaluating the neural effects of novel treatments.

Explaining slow seizure propagation with white matter tractography

Epileptic seizures recorded with stereo-EEG can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and stereo-EEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared with regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-Engel IV). The seizure onset zone was more tract-connected to regions of seizure spread than to non-involved regions (P < 0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared with non-involved regions. In seizure-free patients only, regions of seizure spread were more tract-connected to the seizure onset zone than to other regions of spread (P < 0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract-connected to regions of early spread compared with regions of late spread in seizure-free patients only (P < 0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely to be mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone might be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds might be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure-free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship might eventually provide insight into selecting the correct targets for epilepsy surgery.

Developmental differences in canonical cortical networks: Insights from microstructure-informed tractography

In response to a growing interest in refining brain connectivity assessments, this study focuses on integrating white matter fiber-specific microstructural properties into structural connectomes. Spanning ages 8-19 years in a developmental sample, it explores age-related patterns of microstructure-informed network properties at both local and global scales. First, the diffusion-weighted signal fraction associated with each tractography-reconstructed streamline was constructed. Subsequently, the convex optimization modeling for microstructure-informed tractography (COMMIT) approach was employed to generate microstructure-informed connectomes from diffusion MRI data. To complete the investigation, network characteristics within eight functionally defined networks (visual, somatomotor, dorsal attention, ventral attention, limbic, fronto-parietal, default mode, and subcortical networks) were evaluated. The findings underscore a consistent increase in global efficiency across child and adolescent development within the visual, somatomotor, and default mode networks (p < 0.005). Additionally, mean strength exhibits an upward trend in the somatomotor and visual networks (p < 0.001). Notably, nodes within the dorsal and ventral visual pathways manifest substantial age-dependent changes in local efficiency, aligning with existing evidence of extended maturation in these pathways. The outcomes strongly support the notion of a prolonged developmental trajectory for visual association cortices. This study contributes valuable insights into the nuanced dynamics of microstructure-informed brain connectivity throughout different developmental stages.

Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI

The superficial white matter (SWM) consists of numerous short-range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly-curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra-high-resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high-resolution 0.76-mm isotropic acquisition using the generalized slice-dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25-mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP-YA) database. Our results demonstrate significant differences in the cortico-cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract-tracing literature. We find that the high-resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

Associations Between Brain Structural Connectivity and 1-Year Demoralization in Breast Cancer: A Longitudinal Diffusion Tensor Imaging Study

Purposes: This study aims to explore the association between brain structural connectivity and 1-year demoralization in patients with newly diagnosed breast cancer. Methods: Patients were enrolled from a multicenter longitudinal program named as Be Resilient to Breast Cancer (BRBC) between 2017 and 2019. Brain structural connectivity was assessed with diffusion tensor imaging (DTI) at baseline and the demoralization scale II collected self-report data at baseline and 1 year later. A data-driven correlational tractography was performed to recognize significant neural pathways associated with the group membership (increased vs. nonincreased demoralization). The incremental prediction values of Quantitative Anisotropy (QA) extracted from the significant white matter tracts against the group membership were evaluated. Results: 21.2% (N = 31) reported increased 1-year demoralization. Inferior fronto-occipital fasciculus (IFOF) was associated with 1-year demoralization in breast cancer. The incremental prediction values of QAs in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) ranged from 8.11% to 46.89% and 9.12% to 23.95%, respectively, over the conventional tumor-nodal metatasis (TNM) staging model. Conclusion: Anisotropy in IFOF is a potential prediction neuromarker to 1-year demoralization in patients with newly diagnosed breast cancer. Trial Registration: ClinicalTrials.gov identifier: NCT03026374.

Atrophy in the supramarginal gyrus associated with impaired cognitive inhibition in grieving Chinese Shidu parents

Background: The loss of an only child, known as Shidu in China, is a profoundly distressing experience, often leading to Prolonged Grief Disorder (PGD). Despite its impact, the structural brain alterations associated with PGD, potentially influencing cognitive impairments in Shidu parents, remain understudied.Objective: This study aims to identify brain structural abnormalities related to prolonged grief and their relation with cognitive inhibition in Shidu parents.Methods: The study included 40 Shidu parents and 42 non-bereaved participants. Prolonged grief was evaluated using the Prolonged Grief Questionnaire (PG-13). We employed voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) to assess brain structural alterations and their correlation with cognitive inhibition, as measured by Stroop interference scores.Results: Findings suggest that greater prolonged grief intensity correlates with reduced grey matter volume in the right amygdala and the left supramarginal gyrus (SMG). Additionally, enhanced amygdala-to-whole-brain structural connectivity showed a marginal association with prolonged grief, particularly with emotional-related symptoms. Furthermore, a decrease in SMG volume was found to mediate the relation between prolonged grief and Stroop Time Inference (TI) score, indicating an indirect effect of prolonged grief on cognitive inhibition.Conclusions: The study provides insight into the neural correlates of prolonged grief in Shidu parents, highlighting the SMG's role in cognitive inhibition. These findings emphasise the need for comprehensive grief interventions to address the complex cognitive and emotional challenges faced by this unique bereaved population.

Heritability of white matter in twins: A diffusion neuroimaging review

Diffusion neuroimaging has emerged as an essential non-invasive technique to explore in vivo microstructural characteristics of white matter (WM), whose integrity allows complex behaviors and cognitive abilities. Studying the factors contributing to inter-individual variability in WM microstructure can provide valuable insight into structural and functional differences of brain among individuals. Genetic influence on this variation has been largely investigated in twin studies employing different measures derived from diffusion neuroimaging. In this context, we performed a comprehensive literature search across PubMed, Scopus and Web of Science of original twin studies focused on the heritability of WM. Overall, our results highlighted a consistent heritability of diffusion indices (i.e., fractional anisotropy, mean, axial and radial diffusivity), and network topology among twins. The genetic influence resulted prominent in frontal and occipital regions, in the limbic system, and in commissural fibers. To enhance the understanding of genetic influence on WM microstructure further studies in less heterogeneous experimental settings, encompassing all diffusion indices, are warranted.

Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance

Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.

Role of the central nervous system in cell non-autonomous signaling mechanisms of aging and longevity in mammals

Multiple organs orchestrate the maintenance of proper physiological function in organisms throughout their lifetimes. Recent studies have uncovered that aging and longevity are regulated by cell non-autonomous signaling mechanisms in several organisms. In the brain, particularly in the hypothalamus, aging and longevity are regulated by such cell non-autonomous signaling mechanisms. Several hypothalamic neurons have been identified as regulators of mammalian longevity, and manipulating them promotes lifespan extension or shortens the lifespan in rodent models. The hypothalamic structure and function are evolutionally highly conserved across species. Thus, elucidation of hypothalamic function during the aging process will shed some light on the mechanisms of aging and longevity and, thereby benefiting to human health.

Validation of Tenths Stereotactic Coordinates Method Using Probabilistic Tractography of the Ansa Lenticularis in Parkinson's Disease Patients

OBJECTIVE

To evaluate the accuracy of stereotactic coordinates to target the ansa lenticularis (AL) using 2 surgical planning methods, the conventional millimeter method (MM) and the normalized Tenths method (TM), assessed through individualized probabilistic tractography.

METHODS

Stereotactic targeting of the AL was assessed in 2 groups: 16 patients with Parkinson's disease and 16 healthy controls from Group 1, and 39 Parkinson's disease patients from Group 2. Structural and diffusion magnetic resonance imaging probabilistic tractography identified the AL based on the Schaltenbrand-Wahren Atlas. The MM defined stereotactic coordinates in millimeters, while the TM refined the planning by dividing the intercommissural line (AC-PC) distance into 10 equal parts, normalizing the "X," "Y," and "Z" coordinates for each patient. We subsequently compared the percentage of structural connectivity (%conn) of the AL with predefined regions of interest (ROIs), including the frontopontine-corticothalamic tracts, globus pallidus internus-ventral oral anterior, and ventral oral posterior, and quantified the streamlines in 142 brain hemispheres using the MM and TM coordinates.

RESULTS

Despite anatomical variations in intercommissural (AC-PC) line lengths between both groups (22.5 ± 2.09 mm and 24.4 ± 2.56 mm, respectively; P = 0.002), as well as differences in magnetic resonance imaging acquisition parameters, we found that the TM significantly enhanced streamline identification and %conn compared to the MM. These enhancements were noted across ROIs: frontopontine-corticothalamic and globus pallidus internus-ventral oral anterior in both hemispheres, and globus pallidus internus-ventral oral posterior in the left (P < 0.001) and right hemispheres (P = 0.03).

CONCLUSIONS

TM surpasses MM in identifying the structural connectivity between the AL and predefined ROIs, underscoring the advantages of coordinate normalization. However, variations in AC-PC line lengths and Euclidean distances between methods could lead to inaccuracies in the coordinate settings, potentially affecting the precision of structural connectivity and the efficacy of therapeutic outcomes.

Streamline tractography of the fetal brain in utero with machine learning

Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely heavily on tractography techniques, which reconstruct virtual streamlines representing white matter fibers. Much effort has been devoted to improving tractography methodology for adult brains, while tractography of the fetal brain has been largely neglected. Fetal tractography faces unique difficulties due to low dMRI signal quality, immature and rapidly developing brain structures, and paucity of reference data. To address these challenges, this work presents the first machine learning model, based on a deep neural network, for fetal tractography. The model input consists of five different sources of information: (1) Voxel-wise fiber orientation, inferred from a diffusion tensor fit to the dMRI signal; (2) Directions of recent propagation steps; (3) Global spatial information, encoded as normalized distances to keypoints in the brain cortex; (4) Tissue segmentation information; and (5) Prior information about the expected local fiber orientations supplied with an atlas. In order to mitigate the local tensor estimation error, a large spatial context around the current point in the diffusion tensor image is encoded using convolutional and attention neural network modules. Moreover, the diffusion tensor information at a hypothetical next point is included in the model input. Filtering rules based on anatomically constrained tractography are applied to prune implausible streamlines. We trained the model on manually-refined whole-brain fetal tractograms and validated the trained model on an independent set of 11 test scans with gestational ages between 23 and 36 weeks. Results show that our proposed method achieves superior performance across all evaluated tracts. The new method can significantly advance the capabilities of dMRI for studying normal and abnormal brain development in utero.

A practical guide for combining functional regions of interest and white matter bundles

Diffusion-weighted imaging (DWI) is the primary method to investigate macro- and microstructure of neural white matter in vivo. DWI can be used to identify and characterize individual-specific white matter bundles, enabling precise analyses on hypothesis-driven connections in the brain and bridging the relationships between brain structure, function, and behavior. However, cortical endpoints of bundles may span larger areas than what a researcher is interested in, challenging presumptions that bundles are specifically tied to certain brain functions. Functional MRI (fMRI) can be integrated to further refine bundles such that they are restricted to functionally-defined cortical regions. Analyzing properties of these Functional Sub-Bundles (FSuB) increases precision and interpretability of results when studying neural connections supporting specific tasks. Several parameters of DWI and fMRI analyses, ranging from data acquisition to processing, can impact the efficacy of integrating functional and diffusion MRI. Here, we discuss the applications of the FSuB approach, suggest best practices for acquiring and processing neuroimaging data towards this end, and introduce the FSuB-Extractor, a flexible open-source software for creating FSuBs. We demonstrate our processing code and the FSuB-Extractor on an openly-available dataset, the Natural Scenes Dataset.

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.

Multiparametric Characterization and Spatial Distribution of Different MS Lesion Phenotypes

BACKGROUND AND PURPOSE

MS lesions exhibit varying degrees of axonal and myelin damage. A comprehensive description of lesion phenotypes could contribute to an improved radiologic evaluation of smoldering inflammation and remyelination processes. This study aimed to identify in vivo distinct MS lesion types using quantitative susceptibility mapping and susceptibility mapping-weighted imaging and to characterize them through T1-relaxometry, myelin mapping, and diffusion MR imaging. The spatial distribution of lesion phenotypes in relation to ventricular CSF was investigated.

MATERIALS AND METHODS

MS lesions of 53 individuals were categorized into iso- or hypointense lesions, hyperintense lesions, and paramagnetic rim lesions, on the basis of their appearance on quantitative susceptibility mapping alone, according to published criteria, and with the additional support of susceptibility mapping-weighted imaging. Susceptibility values, T1-relaxation times, myelin and free water fractions, intracellular volume fraction, and the orientation dispersion index were compared among lesion phenotypes. The distance of the geometric center of each lesion from the ventricular CSF was calculated.

RESULTS

Eight hundred ninety-six MS lesions underwent the categorization process using quantitative susceptibility mapping and susceptibility mapping-weighted imaging. The novel use of susceptibility mapping-weighted images, which revealed additional microvasculature details, led us to re-allocate several lesions to different categories, resulting in a 35.6% decrease in the number of paramagnetic rim lesions, a 22.5% decrease in hyperintense lesions, and a 17.2% increase in iso- or hypointense lesions, with respect to the categorization based on quantitative susceptibility mapping only. The outcome of the categorization based on the joint use of quantitative susceptibility mapping and susceptibility mapping-weighted imaging was that 44.4% of lesions were iso- or hypointense lesions, 47.9% were hyperintense lesions, and 7.7% were paramagnetic rim lesions. A worsening gradient was observed from iso- or hypointense lesions to hyperintense lesions to paramagnetic rim lesions in T1-relaxation times, myelin water fraction, free water fraction, and intracellular volume fraction. Paramagnetic rim lesions were located closer to ventricular CSF than iso- or hypointense lesions. The volume of hyperintense lesions was associated with a more severe disease course.

CONCLUSIONS

Quantitative susceptibility mapping and susceptibility mapping-weighted imaging allow in vivo classification of MS lesions into different phenotypes, characterized by different levels of axonal and myelin loss and spatial distribution. Hyperintense lesions and paramagnetic rim lesions, which have the most severe microstructural damage, were more often observed in the periventricular WM and were associated with a more severe disease course.

Exploring white matter microstructural alterations in mild cognitive impairment: a multimodal diffusion MRI investigation utilizing diffusion kurtosis and free-water imaging

Background

Mild Cognitive Impairment (MCI) is a transitional stage from normal aging to dementia, characterized by noticeable changes in cognitive function that do not significantly impact daily life. Diffusion MRI (dMRI) plays a crucial role in understanding MCI by assessing white matter integrity and revealing early signs of axonal degeneration and myelin breakdown before cognitive symptoms appear.

Methods

This study utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to compare white matter microstructure in individuals with MCI to cognitively normal (CN) individuals, employing advanced dMRI techniques such as diffusion kurtosis imaging (DKI), mean signal diffusion kurtosis imaging (MSDKI), and free water imaging (FWI).

Results

Analyzing data from 55 CN subjects and 46 individuals with MCI, this study found significant differences in white matter integrity, particularly in free water levels and kurtosis values, suggesting neuroinflammatory responses and microstructural integrity disruption in MCI. Moreover, negative correlations between Mini-Mental State Examination (MMSE) scores and free water levels in the brain within the MCI group point to the potential of these measures as early biomarkers for cognitive impairment.

Conclusion

In conclusion, this study demonstrates how a multimodal advanced diffusion imaging approach can uncover early microstructural changes in MCI, offering insights into the neurobiological mechanisms behind cognitive decline.

Advanced Brain Imaging in Central Nervous System Demyelinating Diseases

In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.

Characterizing normal perinatal development of the human brain structural connectivity

Early brain development is characterized by the formation of a highly organized structural connectome, which underlies brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development, inherently low signal quality, imaging difficulties, and high inter-subject variability. These factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational method based on spatio-temporal averaging in the image space for determining such baselines. We used this method to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in the perinatal stage. We observed increases in measures of network integration and segregation, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. Our proposed method also showed considerable agreement with an alternative technique based on connectome averaging. The new computational method and results of this study can be useful for assessing normal and abnormal development of the structural connectome early in life.

Structural Brain Connectivity Guided Optimal Contact Selection for Deep Brain Stimulation of the Subthalamic Nucleus

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy in ameliorating the motor symptoms of Parkinson disease. However, postoperative optimal contact selection is crucial for achieving the best outcome of deep brain stimulation of the subthalamic nucleus surgery, but the process is currently a trial-and-error and time-consuming procedure that relies heavily on surgeons' clinical experience.

METHODS

In this study, we propose a structural brain connectivity guided optimal contact selection method for deep brain stimulation of the subthalamic nucleus. Firstly, we reconstruct the DBS electrode location and estimate the stimulation range using volume of tissue activated from each DBS contact. Then, we extract the structural connectivity features by concatenating fractional anisotropy and the number of streamlines features of activated regions and the whole brain regions. Finally, we use a convolutional neural network with convolutional block attention module to identify the structural connectivity features for the optimal contact selection.

RESULTS

We review the data of 800 contacts from 100 patients with Parkinson disease for the experiment. The proposed method achieves promising results, with the average accuracy of 97.63%, average precision of 94.50%, average recall of 94.46%, and average specificity of 98.18%, respectively. Our method can provide the suggestion for optimal contact selection.

CONCLUSIONS

Our proposed method can improve the efficiency and accuracy of DBS optimal contact selection, reduce the dependence on surgeons' experience, and has the potential to facilitate the development of advanced DBS technology.

Progressive remodeling of structural networks following surgery for operculo-insular epilepsy

Introduction

Operculo-insular epilepsy (OIE) is a rare condition amenable to surgery in well-selected cases. Despite the high rate of neurological complications associated with OIE surgery, most postoperative deficits recover fully and rapidly. We provide insights into this peculiar pattern of functional recovery by investigating the longitudinal reorganization of structural networks after surgery for OIE in 10 patients.

Methods

Structural T1 and diffusion-weighted MRIs were performed before surgery (t0) and at 6 months (t1) and 12 months (t2) postoperatively. These images were processed with an original, comprehensive structural connectivity pipeline. Using our method, we performed comparisons between the t0 and t1 timepoints and between the t1 and t2 timepoints to characterize the progressive structural remodeling.

Results

We found a widespread pattern of postoperative changes primarily in the surgical hemisphere, most of which consisted of reductions in connectivity strength (CS) and regional graph theoretic measures (rGTM) that reflect local connectivity. We also observed increases in CS and rGTMs predominantly in regions located near the resection cavity and in the contralateral healthy hemisphere. Finally, most structural changes arose in the first six months following surgery (i.e., between t0 and t1).

Discussion

To our knowledge, this study provides the first description of postoperative structural connectivity changes following surgery for OIE. The ipsilateral reductions in connectivity unveiled by our analysis may result from the reversal of seizure-related structural alterations following postoperative seizure control. Moreover, the strengthening of connections in peri-resection areas and in the contralateral hemisphere may be compatible with compensatory structural plasticity, a process that could contribute to the recovery of functions seen following operculo-insular resections for focal epilepsy.

White matter tract crossing and bottleneck regions in the fetal brain

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.