The challenge of mapping the human connectome based on diffusion tractography

Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

The Duke Mouse Brain Atlas: MRI and light sheet microscopy stereotaxic atlas of the mouse brain

Atlases of the brain are critical resources that make it possible to share data in a common reference frame. Unexpectedly, there is no three-dimensional (3D) stereotaxic atlas of the mouse brain that provides whole brain coverage at macro to single-cell levels. Diffusion tensor images from five perfusion-fixed (in skull) specimens were acquired at 15 micrometers, the highest resolution ever reported. Diffusion tensor imaging yields multiple 3D volumes, each of which highlights unique cytoarchitecture. The averages were mapped into micro-computed tomography of the mouse skull to create external landmarks (bregma and lambda). Light sheet images of the same brains were coregistered, providing cell maps in the same stereotaxic space. The Allen Reference Atlas was registered to the volume to correct the geometric distortion in that atlas and bring it into the stereotaxic space. The resulting multiscalar (13 terabytes) atlas provides a common spatial framework to anneal data across molecular, structural, and functional studies of mice.

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.

Three-dimensional architecture characteristics and diffusion properties of masticatory muscles assessed with diffusion tensor imaging and diffusion spectrum imaging: a pilot study of differences, reproducibility and sensitivity to microenvironment changes

BACKGROUND

Diffusion spectral imaging (DSI) could overcome the inherent limitation of diffusion tensor imaging (DTI), but its outcomes in masticatory muscle fiber-tracking have not been well-established. Therefore, the objective of this prospective study conducted in China was to evaluate and compare the performance of DTI and DSI in human masticatory muscles.

METHODS

The differences and reproducibility of architecture characteristics and diffusion properties derived from DTI and DSI were evaluated in the masticatory muscles of healthy volunteers (n = 25). The quality of tracked fiber was analyzed based on anatomical information. To assess the sensitivity of DTI and DSI to muscular microenvironment changes, the architecture characteristics and diffusion properties of the masticatory muscles in patients with temporomandibular joint disorders (TMDs) (n = 25) between different subgroups according to the course of diseases were explored. The paired-samples t-test or Wilcoxon signed-rank test, Student's t-test or Mann-Whitney U test, one-way ANOVA or the Kruskal-Wallis test, and the post-hoc multiple comparisons with false discovery rate adjustment were performed. Bland-Altman plots, within-subject coefficient of variation (CV), and relative absolute difference (RAD) were used to evaluate the reproducibility.

RESULTS

In the healthy group, DSI generated significantly more fibers in all masticatory muscles (all P < 0.001) and fewer low-quality fibers in most masticatory muscles (P < 0.050) than DTI did. Moreover, higher values of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were found in DSI (all P < 0.001). Satisfactory coefficient of variation (< 10%), relative absolute difference (< 10%), and agreement exhibited by the Bland-Altman analysis were found between two scans in both DTI and DSI. Compared with DTI, DSI found additional significant changes in the masticatory muscles of TMDs patients.

CONCLUSIONS

Although both DTI and DSI allowed reproducible assessment of masticatory muscles, significant differences existed between them. DSI was more sensitive to the microenvironment changes of the masticatory muscles in TMDs patients.

Differential patterns of axonal loss associated with threat-related adversity in atypical depression and non-atypical depression

BACKGROUND

Major depressive disorder (MDD) encompasses a broad spectrum of heterogeneous symptoms arising from distinct etiological mechanisms. Phenotypic markers of psychopathology are most likely influenced by exposure to childhood maltreatment, yielding distinct subtypes within conventional diagnostic boundaries. However, the biological interactions between MDD subtypes and types of childhood trauma remain unclear.

METHODS

50 atypical depression (AD) patients, 97 non-AD patients and 50 healthy controls were included to complete multi-shell diffusion MRI scans and clinical assessments. Differential tractography was performed to clarify the axonal injury between the AD and non-AD groups. Moreover, correlational tractography was employed to individually assess the relationship between quantitative anisotropy (QA) and all types of childhood trauma in each depressed subgroup.

RESULTS

Our study found that AD and non-AD patients had differential axonal loss primarily involving the bilateral superior longitudinal fasciculus, arcuate fasciculus, inferior longitudinal fasciculus, parietal aslant tract, and corpus callosum. Furthermore, AD patients showed significantly negative associations between QA values, childhood trauma total scores, and threat-related adversity, while significantly positive associations were observed in non-AD patients. However, similar phenomena were not observed for deprivation-related adversities.

DISCUSSION

Our findings indicate differential spatial patterns of axonal alterations associated with threat-related adversity in atypical depression and non-atypical depression. Efforts to attenuate the consequences of childhood maltreatment for MDD should consider the associations between specific patterns of adversity and specific clinical manifestations.

Comparison of structural connectomes for modeling deep brain stimulation pathway activation

INTRODUCTION

Structural connectivity models of the brain are commonly employed to identify pathways that are directly activated during deep brain stimulation (DBS). However, various connectomes differ in the technical parameters, parcellation schemes, and methodological approaches used in their construction.

OBJECTIVE

The goal of this study was to compare and quantify variability in DBS pathway activation predictions when using different structural connectomes, while using identical electrode placements and stimulation volumes in the brain.

APPROACH

We analyzed four example structural connectomes: 1) Horn normative connectome (whole brain), 2) Yeh population-averaged tract-to-region pathway atlas (whole brain), 3) Petersen histology-based pathway atlas (subthalamic focused), and 4) Majtanik histology-based pathway atlas (anterior thalamus focused). DBS simulations were performed with each connectome, at four generalized locations for DBS electrode placement: 1) subthalamic nucleus, 2) anterior nucleus of thalamus, 3) ventral capsule, and 4) ventral intermediate nucleus of thalamus.

RESULTS

The choice of connectome used in the simulations resulted in notably distinct pathway activation predictions, and quantitative analysis indicated little congruence in the predicted patterns of brain network connectivity. The Horn and Yeh tractography-based connectomes provided estimates of DBS connectivity for any stimulation location in the brain, but have limitations in their anatomical validity. The Petersen and Majtanik histology-based connectomes are more anatomically realistic, but are only applicable to specific DBS targets because of their limited representation of pathways.

SIGNIFICANCE

The widely varying and inconsistent inferences of DBS network connectivity raises substantial concern regarding the general reliability of connectomic DBS studies, especially those that lack anatomical and/or electrophysiological validation in their analyses.

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.

Millisecond-Scale White Matter Dynamics Underlying Visuomotor Integration

In the conventional neuropsychological model, nonverbal visuospatial processing is predominantly handled by the right hemisphere, whereas verbal processing occurs in the left, with right-hand responses governed by the left motor cortex. Using intracranial EEG and MRI tractography, we investigated the timing and white matter networks involved in processing nonverbal visuospatial stimuli, forming response decisions, and generating motor outputs. Within 200 ms of stimulus onset, we observed widespread increases in functional connectivity and bidirectional neural flows from visual to association cortices, predominantly in the right hemisphere. Engagement of the right anterior middle frontal gyrus improved response accuracy; however, the accompanying enhancement in intra-hemispheric connectivity delayed response times. In the final 100 ms before right-hand response, functional connectivity and bidirectional communication via the corpus callosum between the right and left motor cortices became prominent. These findings provide millisecond-level support for the established model of hemispheric specialization, while highlighting a trade-off between accuracy and speed governed by the right dorsolateral prefrontal network. They also underscore the critical timing of callosal transmission of response decisions formed in right-hemispheric networks to the left-hemispheric motor system.

Highlights

Neural information propagates through fasciculi during a visuomotor task.Non-verbal visuospatial analysis is mediated with right-hemispheric dominance.The right middle frontal gyrus improves response accuracy but delays responses.Interhemispheric information transfer occurs immediately before motor responses.This transfer between motor cortices is mediated by the corpus callosum.

Assessing Visual Pathway White Matter Degeneration in Primary Open-Angle Glaucoma Using Multiple MRI Morphology and Diffusion Metrics

BACKGROUND

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness, is associated with neurodegeneration in the visual pathway, but the underlying pathophysiology remains incompletely resolved.

PURPOSE

To characterize macro- and microstructural white matter abnormalities in optic tract (OT) and optic radiation (OR) of POAG.

STUDY TYPE

Prospective.

POPULATIONS

A total of 34 POAG patients (21 males, 13 females) and 25 healthy controls (HCs) (16 males, nine females).

FIELD STRENGTH/SEQUENCE

3 T; multiband spin-echo echo planar diffusion spectrum imaging (DSI).

ASSESSMENT

We compared multiple morphology metrics, including volume, area, length, and shape metrics, as well as diffusion metrics such as diffusion tensor imaging (fractional anisotropy [FA], mean diffusivity, radial diffusivity, and axial diffusivity), mean apparent propagator (mean squared displacement, q-space inverse variance, return-to-origin probability, return-to-axis probabilities [RTAP] and return-to-plane probabilities, non-Gaussianity, perpendicular non-Gaussianity, parallel non-Gaussianity), and neurite orientation dispersion and density imaging (intracellular volume fraction, orientation dispersion index [ODI], and isotropic volume fraction of the OT and OR).

STATISTICAL TESTS

Statistical comparisons and classifications employed linear mixed model and logistic regression. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC). P-value <0.05 was statistically significant.

RESULTS

Morphology analysis in POAG revealed a lower span in the OR (29.43 ± 2.30 vs. 30.59 ± 2.01, 3.8%) and OT (19.73 ± 2.21 vs. 20.68 ± 1.37, 4.6%), and a higher curl (3.03 ± 0.22 vs. 2.90 ± 0.16, 4.5%) in OT. Diffusion metrics revealed lower mean FA (OR: 0.328 ± 0.03 vs. 0.340 ± 0.018, 3.5%; OT: 0.255 ± 0.022 vs. 0.268 ± 0.018, 4.9%) and lower mean RTAP (OR: 5.919 ± 0.529 vs. 6.216 ± 0.489, 4.8%; OT: 4.089 ± 0.402 vs. 4.280 ± 0.353, 4.5%), with higher mean ODI in the OT (0.448 ± 0.029 vs. 0.433 ± 0.025, 3.5%). Combined models, incorporating these MRI metrics, effectively discriminated POAG from HCs, achieving AUCs of 0.84 for OR and 0.83 for OT.

DATA CONCLUSIONS

DSI-derived morphology and diffusion metrics demonstrated macro- and micro abnormalities in the visual pathway, providing insights into POAG-related neurodegeneration.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 3.

A Riemannian framework for incorporating white matter bundle prior in orientation distribution function based tractography algorithms

Diffusion magnetic resonance imaging (dMRI) tractography is a powerful approach to study brain structural connectivity. However, its reliability in a clinical context is still highly debated. Recent studies have shown that most classical algorithms achieve to recover the majority of existing true bundles. However, the generated tractograms contain many invalid bundles. This is due to the crossing fibers and bottleneck problems which increase the number of false positive fibers. In this work, we proposed to overpass this limitation with a novel method to guide the algorithms in those challenging regions with prior knowledge of the anatomy. We developed a method to create a combination of anatomical prior applicable to any orientation distribution function (ODF)-based tractography algorithms. The proposed method captures the tract orientation distribution (TOD) from an atlas of segmented fiber bundles and incorporates it during the tracking process, using a Riemannian framework. We tested the prior incorporation method on two ODF-based state-of-the-art algorithms, iFOD2 and Trekker PTT, on the diffusion-simulated connectivity (DiSCo) dataset and on the Human Connectome Project (HCP) data. We also compared our method with two bundles priors generated by the bundle specific tractography (BST) method. We showed that our method improves the overall spatial coverage and connectivity of a tractogram on the two datasets, especially in crossing fiber regions. Moreover, the fiber reconstruction may be improved on clinical data, informed by prior extracted on high quality data, and therefore could help in the study of brain anatomy and function.

Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition

The human cerebral cortex exhibits intricate interareal functional synchronization at the macroscale, with substantial individual variability in these functional connections. However, the spatial organization of functional connectivity (FC) variability across the human connectome edges and its significance in cognitive development remain unclear. Here, we identified a connectional axis in the edge-level FC variability. The variability declined continuously along this axis from within-network to between-network connections and from the edges linking association networks to those linking the sensorimotor and association networks. This connectional axis of functional variability is associated with spatial pattern of structural connectivity variability. Moreover, the connectional variability axis evolves in youth with an flatter axis slope. We also observed that the slope of the connectional variability axis was positively related to the performance in the higher-order cognition. Together, our results reveal a connectional axis in functional variability that is linked with structural connectome variability, refines during development, and is relevant to cognition.

Large-Scale High-Resolution Probabilistic Maps of the Human Superior Longitudinal Fasciculus Subdivisions and their Cortical Terminations

The superior longitudinal fasciculus (SLF) is the large white matter association tract connecting the prefrontal and posterior parietal cortices. Past studies in non-human primates have parcellated the SLF into three subdivisions and have outlined the specific cortico-cortical organization and terminations for each subdivision. However, it is difficult to characterize these structural connections in humans to the specificity of tract-tracing studies in animals. This has led to disagreement on how the SLF subdivisions are organized in the human brain, including if the dorsomedial SLF (SLF-I) is part of the cingulum subsystem. Here, we present a novel large-scale, probabilistic map of the SLF subdivisions, using high-resolution diffusion imaging data from the Human Connectome Project (HCP). We used image data from 302 adult males and 405 adult females to model the three SLF subdivisions in each hemisphere, and attempted to characterize the frontal and parietal termination points for each subdivision. SLF subdivisions were successfully modeled in each subject, showing the dorsomedial-to-ventrolateral organization similar to that in nonhuman primate histological studies. We also found minimal differences between SLF-I models with and without the cingulate gyrus excluded, suggesting that the SLF-I may be a separable tract from the cingulum. Lastly, the SLF subdivisions showed differentiable associations with major cognitive domains such as memory and executive functions. While histological confirmation is needed beyond tractography, these probabilistic masks offer a first step in guiding future exploration of frontoparietal organization by providing detailed characterization of the SLF subdivisions and their potential cortical terminations.Significance statement The prefrontal and posterior parietal areas are interconnected via the SLF, which has been characterized in great detail in monkeys. However, it is difficult to map the SLF organization in the human brain, and previous diffusion MRI findings have been inconsistent. Using diffusion MRI data from 707 individuals, our probabilistic tractography revealed dorsomedial-to-ventrolateral organization of the three SLF subdivisions and their cortical terminations. Our tractography also suggests limited shared volume between the SLF-I and the cingulum, a controversy in recent literature. The SLF subdivisions also differ in their cognitive associations. As a result, we created a large-scale, high-resolution probabilistic parcellation of the SLF, representing an advancement toward standardizing the mapping of human frontoparietal structural connections for clinical and scientific research.

Network-based Molecular Constraints on in vivo Synaptic Density Alterations in Schizophrenia

Converging neuroimaging, genetic, and post-mortem evidence show a fundamental role of synaptic deficits in schizophrenia pathogenesis. However, the underlying molecular and cellular mechanisms that drive the onset and progression of synaptic pathology remain to be established. Here, we used synaptic density positron emission tomography (PET) imaging using the [11C]UCB-J radiotracer to reveal a prominent widespread pattern (p FWE < 0.05) of lower synaptic density in individuals with schizophrenia (n=29), compared to a large sample of healthy controls (n=93). We found that the spatial pattern of lower synaptic density in schizophrenia is spatially aligned (r cca = 0.67; p < 0.001) with higher normative distributions of GABAA/BZ, 5HT1B, 5HT2A, and 5HT6, and lower levels of CB1 and 5HT1A. Competing neighborhood deformation network models revealed that regional synaptic pathology strongly correlated with estimates predicted using a model constrained by both interregional structural connectivity and molecular similarity (.42 < r < .61; p FWE < 0.05). These data suggest that synaptic pathology in schizophrenia is jointly constrained by both global axonal connectivity and local molecular vulnerability. Simulation-based network diffusion models were used to identify regions that may represent the initial sources of pathology, nominating left prefrontal areas (p FWE < 0.05) as potential foci from which synaptic pathology initiates and propagates to molecularly similar areas. Overall, our findings provide in vivo evidence for widespread deficit in synaptic density in schizophrenia that is jointly constrained by axonal connectivity and molecular similarity between regions, and that synaptic deficits spread from initial source regions to axonally connected and molecularly similar territories.

Assessing measurement consistency of a diffusion tensor imaging (DTI) quality control (QC) anisotropy phantom

OBJECTIVES

We evaluated a quality control (QC) phantom designed to mimic diffusion characteristics and white matter fiber tracts in the brain. We hypothesized that acquisition of diffusion tensor imaging (DTI) data on different vendors and over multiple repeated measures would not contribute to significant variability in calculated diffusion tensor scalar metrics such as fractional anisotropy (FA) and mean diffusivity (MD).

MATERIALS AND METHODS

The DTI QC phantom was scanned using a 32-direction DTI sequence on General Electric (GE), Siemens, and Philips 3 Tesla scanners. Motion probing gradients (MPGs) were investigated as a source of variance in our statistical design, and data were acquired on GE and Siemens scanners using GE, Siemens, and Philips vendor MPGs for 32 directions. In total, 8 repeated scans were made for each GE/Siemens combination of vendor and MPGs with 8 repeated scans on a Philips machine using its stock DTI sequence. Data were analyzed using 2-way ANOVAs to investigate repeat scan and vendor variances and 3-way ANOVAs with repeat, MPG, and vendor as factors.

RESULTS

No statistical differences (i.e., P > 0.05) were found in any DTI scalar metrics (FA, MD) or for any factor, suggesting system constancy across imaging platforms and the specified phantom's reliability and reproducibility across vendors and conditions.

DISCUSSION

A DTI QC phantom demonstrates that DTI measurements maintain their consistency across different MRI systems and can contribute to a standard that is more reliable for quantitative MRI analyses.

From Circuits to Lifespan: Translating Mouse and Human Timelines with Neuroimaging-Based Tractography

Animal models are commonly used to investigate developmental processes and disease risk, but humans and model systems (e.g., mice) differ substantially in the pace of development and aging. The timeline of human developmental circuits is well known, but it is unclear how such timelines compare with those in mice. We lack age alignments across the lifespan of mice and humans. Here, we build upon our Translating Time resource, which is a tool that equates corresponding ages during development. We collected 1,125 observations from age-related changes in body, bone, dental, and brain processes to equate corresponding ages across humans, mice, and rats to boost power for comparison across humans and mice. We acquired high-resolution diffusion MR scans of mouse brains (n = 16) of either sex at sequential stages of postnatal development [postnatal day (P)3, 4, 12, 21, 60] to track brain circuit maturation (e.g., olfactory association, transcallosal pathways). We found heterogeneity in white matter pathway growth. Corpus callosum growth largely ceases days after birth, while the olfactory association pathway grows through P60. We found that a P3-4, mouse equates to a human at roughly GW24 and a P60 mouse equates to a human in teenage years. Therefore, white matter pathway maturation is extended in mice as it is in humans, but there are species-specific adaptations. For example, olfactory-related wiring is protracted in mice, which is linked to their reliance on olfaction. Our findings underscore the importance of translational tools to map common and species-specific biological processes from model systems to humans.

Functional connectivity-based compensation in the brains of non-demented older adults and the influence of lifestyle: A longitudinal 7-year study

INTRODUCTION

The aging brain is characterized by structural decline and functional connectivity changes towards dedifferentiation, leading to cognitive decline. To some degree, the brain can compensate for structural deterioration. In this study, we aim to answer two questions: Where can we detect longitudinal functional connectivity-based compensation in the brains of cognitively healthy older adults? Can lifestyle predict the strength of this functional compensation?

METHODS

Using longitudinal data from 228 cognitively healthy older adults, we analyzed five measurement points over 7 years. Network-based statistics and latent growth modeling were employed to examine changes in structural and functional connectivity, as well as potential functional compensation for declines in processing speed and memory. Random forest and linear regression were used to predict the amplitude of compensation based on demographic, biological, and lifestyle factors.

RESULTS

Both functional and structural connectivity showed increases and decreases over time, depending on the specific connection and measure. Increased functional connectivity of 27 connections was linked to smaller declines in cognition. Five of those connections showed simultaneous decreases in fractional anisotropy, indicating direct compensation. The degree of compensation depended on the type of compensation and the cognitive ability, with demographic, biological, and lifestyle factors explaining 3.4-8.9% of the variance.

CONCLUSIONS

There are widespread changes in structural and functional connectivity in older adults. Despite the trend of dedifferentiation in functional connectivity, we detected both direct and indirect compensatory subnetworks that mitigated the decline in cognitive performance. The degree of compensation was influenced by demographic, biological, and lifestyle factors.

Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of diffusion MRI

Diffusion magnetic resonance imaging (dMRI) provides insight into the micro and macro-structure of the brain. Multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) models the underlying local fiber orientation distributions (FODs) using the dMRI signal. While generally producing high-quality FODs, MSMT-CSD is a voxel-wise method that can be impacted by noise and produce erroneous FODs. Local models also do not use the spatial correlation between neighboring voxels to increase parameter estimating power. Additionally, voxel-wise methods require interpolation at arbitrary locations outside of voxel centers. These interpolations can be computationally costly or inaccurate, depending on the method of choice. Expanding upon previous work, we apply the implicit neural representation (INR) methodology to the MSMT-CSD model. This results in an unsupervised machine-learning framework that generates a continuous representation of a given dMRI dataset. The input of the INR consists of coordinates in the volume, which produce the spherical harmonics coefficients parameterizing an FOD at any desired location. A key characteristic of our model is its ability to leverage spatial correlations in the volume, which acts as a form of regularization. We evaluate the output FODs quantitatively and qualitatively in synthetic and real dMRI datasets and compare them to existing methods.

Bridging the 3D geometrical organisation of white matter pathways across anatomical length scales and species

We used diffusion MRI and x-ray synchrotron imaging on monkey and mice brains to examine the organisation of fibre pathways in white matter across anatomical scales. We compared the structure in the corpus callosum and crossing fibre regions and investigated the differences in cuprizone-induced demyelination in mouse brains versus healthy controls. Our findings revealed common principles of fibre organisation that apply despite the varying patterns observed across species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways. Fasciculi exhibited non-straight paths around obstacles like blood vessels, comparable across the samples of varying fibre complexity and demyelination. Quantifications of fibre orientation distributions were consistent across anatomical length scales and modalities, whereas tissue anisotropy had a more complex relationship, both dependent on the field-of-view. Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across length scales.

A global effort to benchmark predictive models and reveal mechanistic diversity in long-term stroke outcomes

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover 'optimal recipes' for predicting each domain. The differences in these 'optimal recipes' reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com.

Structural connectome construction using constrained spherical deconvolution in multi-shell diffusion-weighted magnetic resonance imaging

Connectional neuroanatomical maps can be generated in vivo by using diffusion-weighted magnetic resonance imaging (dMRI) data, and their representation as structural connectome (SC) atlases adopts network-based brain analysis methods. We explain the generation of high-quality SCs of brain connectivity by using recent advances for reconstructing long-range white matter connections such as local fiber orientation estimation on multi-shell dMRI data with constrained spherical deconvolution, which yields both increased sensitivity to detecting crossing fibers compared with competing methods and the ability to separate signal contributions from different macroscopic tissues, and improvements to streamline tractography such as anatomically constrained tractography and spherical-deconvolution informed filtering of tractograms, which have increased the biological accuracy of SC creation. Here, we provide step-by-step instructions to creating SCs by using these methods. In addition, intermediate steps of our procedure can be adapted for related analyses, including region of interest-based tractography and quantification of local white matter properties. The associated software MRtrix3 implements the relevant tools for easy application of the protocol, with specific processing tasks deferred to components of the FSL software. The protocol is suitable for users with expertise in dMRI and neuroscience and requires between 2 h and 13 h to complete, depending on the available computational system.

Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks

Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, techniques for estimating connectivity are paramount when attempting to understand the network structure of neural systems from their recorded activity patterns. To date, no universally accepted method exists for the inference of effective connectivity, which describes how the activity of a neural node mechanistically affects the activity of other nodes. Here, focussing on purely excitatory networks of small to intermediate size and continuous node dynamics, we provide a systematic comparison of different approaches for estimating effective connectivity. Starting with the Hopf neuron model in conjunction with known ground truth structural connectivity, we reconstruct the system's connectivity matrix using a variety of algorithms. We show that, in sparse non-linear networks with delays, combining a lagged-cross-correlation (LCC) approach with a recently published derivative-based covariance analysis method provides the most reliable estimation of the known ground truth connectivity matrix. We outline how the parameters of the Hopf model, including those controlling the bifurcation, noise, and delay distribution, affect this result. We also show that in linear networks, LCC has comparable performance to a method based on transfer entropy, at a drastically lower computational cost. We highlight that LCC works best for small sparse networks, and show how performance decreases in larger and less sparse networks. Applying the method to linear dynamics without time delays, we find that it does not outperform derivative-based methods. We comment on this finding in light of recent theoretical results for such systems. Employing the Hopf model, we then use the estimated structural connectivity matrix as the basis for a forward simulation of the system dynamics, in order to recreate the observed node activity patterns. We show that, under certain conditions, the best method, LCC, results in higher trace-to-trace correlations than derivative-based methods for sparse noise-driven systems. Finally, we apply the LCC method to empirical biological data. Choosing a suitable threshold for binarization, we reconstruct the structural connectivity of a subset of the nervous system of the nematode C. elegans. We show that the computationally simple LCC method performs better than another recently published, computationally more expensive reservoir computing-based method. We apply different methods to this dataset and find that they all lead to similar performances. Our results show that a comparatively simple method can be used to reliably estimate directed effective connectivity in sparse neural systems in the presence of spatio-temporal delays and noise. We provide concrete suggestions for the estimation of effective connectivity in a scenario common in biological research, where only neuronal activity of a small set of neurons, but not connectivity or single-neuron and synapse dynamics, are known.

Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout

Genetic information is involved in the gradual emergence of cortical areas since the neural tube begins to form, shaping the heterogeneous functions of neural circuits in the human brain. Informed by invasive tract-tracing measurements, the cortex exhibits marked interareal variation in connectivity profiles, revealing the heterogeneity across cortical areas. However, it remains unclear about the organizing principles possibly shared by genetics and cortical wiring to manifest the spatial heterogeneity across the cortex. Instead of considering a complex one-to-one mapping between genetic coding and interareal connectivity, we hypothesized the existence of a more efficient way that the organizing principles are embedded in genetic profiles to underpin the cortical wiring space. Leveraging vertex-wise tractography in diffusion-weighted MRI, we derived the global connectopies (GCs) in both female and male to reliably index the organizing principles of interareal connectivity variation in a low-dimensional space, which captured three dominant topographic patterns along the dorsoventral, rostrocaudal, and mediolateral axes of the cortex. More importantly, we demonstrated that the GCs converge with the gradients of a vertex-by-vertex genetic correlation matrix on the phenotype of cortical morphology and the cortex-wide spatiomolecular gradients. By diving into the genetic profiles, we found that the critical role of genes scaffolding the GCs was related to brain morphogenesis and enriched in radial glial cells before birth and excitatory neurons after birth. Taken together, our findings demonstrated the existence of a genetically determined space that encodes the interareal connectivity variation, which may give new insights into the links between cortical connections and arealization.

The inferior fronto-occipital fasciculus: bridging phylogeny, ontogeny and functional anatomy

The inferior-fronto-occipital fasciculus is a long-range white matter tract that connects the prefrontal cortex with parietal, posterior temporal and occipital cortices. First identified in the nineteenth century through the pioneering studies of Mayo and Meynert using blunt dissection, its anatomy and function remain contentious topics. Structurally, its projections are well-documented in human blunt dissection and tractography literature, yet its existence has been questioned by tract-tracing studies in macaques. Functionally, while traditional results from direct white matter stimulation during awake surgery suggested a contribution to language, recent evidence from stimulation and lesion data may indicate a broader role in executive control, extending to attention, motor cognition, memory, reading, emotion recognition, and theory of mind. This review begins by examining anatomical evidence suggesting that the inferior fronto-occipital fasciculus evolved in non-human primates to connect temporal and occipital cortices to prefrontal regions involved in context-dependent selection of visual features for action. We then integrate developmental, electrophysiological, functional and anatomical evidence for the human inferior fronto-occipital fasciculus to propose it has a similar role in manipulation of visual features in our species-particularly when inhibition of overriding but task-irrelevant stimuli is required to prioritize a second, task-relevant stimulus. Next, we introduce a graded model in which dorsal (orbitofrontal, superior and middle frontal to precuneal, angular and supero-occipital projections) and ventral (inferior frontal to posterotemporal, basal temporal and infero-occipital) projections of the inferior fronto-occipital fasciculus support perceptual or conceptual control of visual representations for action, respectively. Leveraging this model, we address controversies in the current literature regarding language, motor cognition, attention and emotion under the unifying view of cognitive control. Finally, we discuss surgical implications for this model and its impact on predicting and preventing neurological deficits in neurosurgery.

The Chimpanzee Brainnetome Atlas reveals distinct connectivity and gene expression profiles relative to humans

Chimpanzees (Pan troglodytes) are one of humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occurred along the human lineage. However, such comparisons are hindered by the absence of cross-species brain atlases established within the same framework. To address this gap, we developed the Chimpanzee Brainnetome Atlas (ChimpBNA) using a connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns between the two species across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex. These differences deviate sharply from the pattern of cortical expansion observed when comparing humans to chimpanzees, highlighting more complex and nuanced connectivity changes in brain evolution than previously recognized. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes highly expressed in regions of divergent connectivities were enriched in cell types crucial for cortical projection circuits and synapse formation, whose pronounced differences in expression patterns hint at genetic influences on neural circuit development, function, and evolution. Our study provides a fine-scale chimpanzee brain atlas and highlights the chimpanzee-human connectivity divergence in a rigorous and comparative manner. In addition, these results suggest potential gene expression correlates for species-specific differences by linking neuroimaging and genetic data, offering insights into the evolution of human-unique cognitive capabilities.

Tutorial: a guide to diffusion MRI and structural connectomics

Diffusion magnetic resonance imaging (dMRI) is a versatile imaging technique that has gained popularity thanks to its sensitive ability to measure displacement of water molecules within a living tissue on a micrometer scale. Although dMRI has been around since the early 1990s, its applications are constantly evolving, primarily regarding the inference of structural connectomics from nerve fiber trajectories. However, these applications require expertise in image processing and statistics, and it can be difficult for a newcomer to choose an appropriate pipeline to fit their research needs, not least because dMRI is such a flexible methodology that dozens of acquisition and analysis pipelines have been developed over the years. This introductory guide is designed for graduate students and researchers in the neuroscience community who are interested in integrating this new methodology regardless of their background in neuroimaging and computational tools. The guide provides a brief overview of the basic dMRI methodologies but focuses on its applications in neuroplasticity and connectomics. The guide starts with dMRI experimental designs and a complete step-by-step pipeline for structural connectomics. The following section covers the basics of dMRI, including parameters and clinical applications (apparent diffusion coefficient, mean diffusivity, fractional anisotropy and microscopic fractional anisotropy), as well as different approaches and models. The final section focuses on structural connectomics, covering subjects from fiber tracking (techniques, evaluation and limitations) to structural networks (constructing, analyzing and visualizing a network).

Intermodal Consistency of Whole-Brain Connectivity and Signal Propagation Delays

Measuring propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires advancement. Here, we compare results from a recently introduced, noninvasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of patients suffering from pharmaco-resistant epilepsies. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays, in both cases characterized with Pearson correlations greater than 0.5 (when grouping by stimulated parcel is applied for delays). This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e., neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of the estimate of functional delays obtained noninvasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally induced perturbations in patients capture physiological activities in healthy subjects. In conclusion, this manuscript constitutes a mutual validation between two modalities, broadening their scope of applicability and interpretation. Importantly, the capability to measure delays noninvasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy

OBJECTIVE

To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC).

METHODS

We employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of true-positive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN.

RESULTS

The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5 of F-statistics across different LMNs. The prediction accuracy increased by up to 40 across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96/94/96 to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort.

CONCLUSION

These domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery.

SIGNIFICANCE

DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery.

Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer's Disease Classification

As the leading cause of dementia worldwide, Alzheimer's Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18). Unlike most studies primarily focusing on performance, our work places explainability at the forefront. Specifically, we define a novel Explainable Artificial Intelligence (XAI) metric, based on gradient-weighted class activation mapping. Its aim is quantitatively measuring how effectively these models fare against established AD biomarkers in their decision-making. The XAI assessment was conducted across 132 brain parcels. Results were compared to AD-relevant regions to measure adherence to domain knowledge. Then, differences in explainability patterns between the two models were assessed to explore the insights offered by each piece of data (i.e., MRI vs. connectivity). Classification performance was satisfactory in terms of both the median true positive (ResNet18: 0.817, BC-GCN-SE: 0.703) and true negative rates (ResNet18: 0.816; BC-GCN-SE: 0.738). Statistical tests (p < 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE. Additionally, our findings suggest that different imaging modalities provide complementary information to DL models. This lays the foundation for bioengineering advancements in developing more comprehensive and trustworthy DL models, potentially enhancing their applicability as diagnostic support tools for neurodegenerative diseases.

ASSOCIATIONS BETWEEN EPILEPSY-RELATED POLYGENIC RISK AND BRAIN MORPHOLOGY IN CHILDHOOD

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is associated with a complex genetic architecture, but the translation from genetic risk factors to brain vulnerability remains unclear. Here, we examined associations between epilepsy-related polygenic risk scores for HS (PRS-HS) and brain structure in a large sample of neurotypical children, and correlated these signatures with case-control findings in in multicentric cohorts of patients with TLE-HS. Imaging-genetic analyses revealed PRS-related cortical thinning in temporo-parietal and fronto-central regions, strongly anchored to distinct functional and structural network epicentres. Compared to disease-related effects derived from epilepsy case-control cohorts, structural correlates of PRS-HS mirrored atrophy and epicentre patterns in patients with TLE-HS. By identifying a potential pathway between genetic vulnerability and disease mechanisms, our findings provide new insights into the genetic underpinnings of structural alterations in TLE-HS and highlight potential imaging-genetic biomarkers for early risk stratification and personalized interventions.

Validation of Structure Tensor Analysis for Orientation Estimation in Brain Tissue Microscopy

Accurate localization of white matter pathways using diffusion MRI is critical to investigating brain connectivity, but the accuracy of current methods is not thoroughly understood. A fruitful approach to validating accuracy is to consider microscopy data that have been co-registered with MRI of post mortem samples. In this setting, structure tensor analysis is a standard approach to computing local orientations for validation. However, structure tensor analysis itself has not been well-validated and is subject to uncertainty in its angular resolution, and selectivity to specific spatial scales. In this work, we conducted a simulation study to investigate the accuracy of using structure tensors to estimate the orientations of fibers arranged in configurations with and without crossings. We examined a range of simulated conditions, with a focus on investigating the method's behavior on images with anisotropic resolution, which is particularly common in microscopy data acquisition. We also analyzed 2D and 3D optical microscopy data. Our results show that parameter choice in structure tensor analysis has relatively little effect on accuracy for estimating single orientations, although accuracy decreases with anisotropy. On the other hand, when estimating the orientations of crossing fibers, the choice of parameters becomes critical, and poor choices result in orientation estimates that are essentially random. This work provides a set of recommendations for researchers seeking to apply structure tensor analysis effectively in the study of axonal orientations in brain imaging data and quantifies the method's limitations, particularly in the case of anisotropic data.

Revealing excitation-inhibition imbalance in Alzheimer's disease using multiscale neural model inversion of resting-state functional MRI

BACKGROUND

Alzheimer's disease (AD) is a serious neurodegenerative disorder without a clear understanding of pathophysiology. Recent experimental data have suggested neuronal excitation-inhibition (E-I) imbalance as an essential element of AD pathology, but E-I imbalance has not been systematically mapped out for either local or large-scale neuronal circuits in AD, precluding precise targeting of E-I imbalance in AD treatment.

METHOD

In this work, we apply a Multiscale Neural Model Inversion (MNMI) framework to the resting-state functional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify brain regions with disrupted E-I balance in a large network during AD progression.

RESULTS

We observe that both intra-regional and inter-regional E-I balance is progressively disrupted from cognitively normal individuals, to mild cognitive impairment (MCI) and to AD. Also, we find that local inhibitory connections are more significantly impaired than excitatory ones and the strengths of most connections are reduced in MCI and AD, leading to gradual decoupling of neural populations. Moreover, we reveal a core AD network comprised mainly of limbic and cingulate regions. These brain regions exhibit consistent E-I alterations across MCI and AD, and thus may represent important AD biomarkers and therapeutic targets. Lastly, the E-I balance of multiple brain regions in the core AD network is found to be significantly correlated with the cognitive test score.

CONCLUSIONS

Our study constitutes an important attempt to delineate E-I imbalance in large-scale neuronal circuits during AD progression, which may facilitate the development of new treatment paradigms to restore physiological E-I balance in AD.

Brainwide Anatomical Connectivity and Prediction of Longitudinal Outcomes in Antipsychotic-Naïve First-Episode Psychosis

BACKGROUND

Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown.

METHODS

We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15-25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning.

RESULTS

Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error-corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error-corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes.

CONCLUSIONS

Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services.

Differential tractography identifies a distinct pattern of white matter alterations in essential tremor with or without resting tremor

Essential Tremor (ET) is characterized by action tremor often associated with resting tremor (rET). Although previous studies have identified widespread brain white matter (WM) alterations in ET patients, differences between ET and rET have been less explored. In this study we employed differential tractography to investigate WM microstructural alterations in these tremor disorders. We conducted a Diffusion Tensor Imaging (DTI) study on age- and sex-matched cohorts: 25 healthy controls (HC), 30 ET, and 30 rET patients. Differential tractography using DSI Studio was employed to pairwise compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) among cohorts. ET and rET patients compared to HC exhibited similar widespread MD increase especially in basal ganglia and brainstem projections. WM changes were more pronounced in the left cerebral hemisphere and cerebellum (crus I and II) in ET, while in rET patients WM alterations were prevalent in right cerebral hemisphere and cerebellum crus I. Small FA decrease was found in rET but not in ET patients. ET patients showed changes in the left non-decussating dentato-rubro-thalamic tract (ndDRTT), whereas rET patients showed changes in both left ndDRTT and right decussating DRTT. In conclusion, our findings confirmed the DRTT involvement in essential tremor and demonstrated that ET and rET exhibited similar microstructural WM changes in the brain, with different hemispheric involvement-greater on the left side in ET and on the right side in rET-suggesting that these tremor disorders may be distinct subtypes of the same disease.

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.

Microstructural mapping of neural pathways in Alzheimer's disease using macrostructure-informed normative tractometry

INTRODUCTION

Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry.

METHODS

We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compared MINT-derived metrics with univariate diffusion tensor imaging (DTI) metrics to examine how fiber geometry may impact the interpretation of microstructure.

RESULTS

In two multisite cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia.

DISCUSSION

We show that MINT, by jointly modeling tract shape and microstructure, has the potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

HIGHLIGHTS

Changes in diffusion tensor imaging metrics may be due to macroscopic changes. Normative models encode normal variability of diffusion metrics in healthy controls. Variational autoencoder applied on tractography can learn patterns of fiber geometry. WM microstructure and macrostructure are modeled with multivariate methods. Transfer learning uses pretraining and fine-tuning for increased efficiency.

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.

Structural MRI of brain similarity networks

Recent advances in structural MRI analytics now allow the network organization of individual brains to be comprehensively mapped through the use of the biologically principled metric of anatomical similarity. In this Review, we offer an overview of the measurement and meaning of structural MRI similarity, especially in relation to two key assumptions that often underlie its interpretation: (i) that MRI similarity can be representative of architectonic similarity between cortical areas and (ii) that similar areas are more likely to be axonally connected, as predicted by the homophily principle. We first introduce the historical roots and technical foundations of MRI similarity analysis and compare it with the distinct MRI techniques of structural covariance and tractography analysis. We contextualize this empirical work with two generative models of homophilic networks: an economic model of cost-constrained connectional homophily and a heterochronic model of ontogenetically phased cortical maturation. We then review (i) studies of the genetic and transcriptional architecture of MRI similarity in population-averaged and disorder-specific contexts and (ii) developmental studies of normative cohorts and clinical studies of neurodevelopmental and neurodegenerative disorders. Finally, we prioritize knowledge gaps that must be addressed to consolidate structural MRI similarity as an accessible, valid marker of the architecture and connectivity of an individual brain network.

Complex harmonics reveal low-dimensional manifolds of critical brain dynamics

The brain needs to perform time-critical computations to ensure survival. A potential solution lies in the nonlocal, distributed computation at the whole-brain level made possible by criticality and amplified by the rare long-range connections found in the brain's unique anatomical structure. This nonlocality can be captured by the mathematical structure of Schrödinger's wave equation, which is at the heart of the complex harmonics decomposition (CHARM) framework that performs the necessary dimensional manifold reduction able to extract nonlocality in critical spacetime brain dynamics. Using a large neuroimaging dataset of over 1000 people, CHARM captured the critical, nonlocal and long-range nature of brain dynamics and the underlying mechanisms were established using a precise whole-brain model. Equally, CHARM revealed the significantly different critical dynamics of wakefulness and sleep. Overall, CHARM is a promising theoretical framework for capturing the low-dimensionality of the complex network dynamics observed in neuroscience and provides evidence that networks of brain regions rather than individual brain regions are the key computational engines of critical brain dynamics.

Asymmetries in the human brain

The human brain is an intricate network of cortical regions interconnected by white matter pathways, dynamically supporting cognitive functions. While cortical asymmetries have been consistently reported, the asymmetry of white matter connections remains less explored. This chapter provides a brief overview of asymmetries observed at the cortical, subcortical, cytoarchitectural, and receptor levels before exploring the detailed connectional anatomy of the human brain. It thoroughly examines the lateralization and interindividual variability of 56 distinct white matter tracts, offering a comprehensive review of their structural characteristics and interindividual variability. Additionally, we provide an extensive update on the asymmetry of a wide range of white matter tracts using high-resolution data from the Human Connectome Project (7T HCP www.humanconnectome.org). Future research and advanced quantitative analyses are crucial to understanding fully how asymmetry contributes to interindividual variability. This comprehensive exploration enhances our understanding of white matter organization and its potential implications for brain function.

Structural connectivity changes in focal epilepsy: Beyond the epileptogenic zone

OBJECTIVE

Epilepsy is recognized increasingly as a network disease, with changes extending beyond the epileptogenic zone (EZ). However, more studies of structural connectivity are needed to better understand the behavior and nature of this condition.

METHODS

In this study, we applied differential tractography, a novel technique that measures changes in anisotropic diffusion, to assess widespread structural connectivity alterations in a total of 42 patients diagnosed with medically refractory epilepsy (MRE), including 27 patients with focal epilepsy and 15 patients with multifocal epilepsy that were included to validate our hypothesis. All patients were compared individually to an averaged database constructed from 19 normal controls regressed by age and sex.

RESULTS

Statistical analyses revealed specific distribution patterns of tracts with increased connectivity that were located in multiple subcortical structures across all patients including the arcuate fasciculus, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, uncinate fasciculus, fornix, and short U fibers. Conversely, pathways with a significant decrease in connectivity (p < .05) exhibited a more central distribution near mesial structures across all patients (corpus callosum, cingulum, corticospinal tract, and sensory fibers).

SIGNIFICANCE

Our findings add to the growing evidence that focal epilepsy is not solely anatomically confined, but is rather a network disorder that extends beyond the EZ, and differential tractography shows strong potential as a clinical biomarker for assessing structural connectivity alterations in patients with epilepsy.

Sex-Specific White Matter Abnormalities Across the Dynamic Pain Connectome in Neuropathic Pain: A Fixel-Based Analysis Study

A fundamental issue in neuroscience is a lack of understanding regarding the relationship between brain function and the white matter architecture that supports it. Individuals with chronic neuropathic pain (NP) exhibit functional abnormalities throughout brain networks collectively termed the "dynamic pain connectome" (DPC), including the default mode network (DMN), salience network, and ascending nociceptive and descending pain modulation systems. These functional abnormalities are often observed in a sex-dependent fashion. However, the enigmatic white matter structural features underpinning these functional networks and the relationship between structure and function/dysfunction in NP remain poorly understood. Here we used fixel-based analysis of diffusion weighted imaging data in 80 individuals (40 with NP [21 female, 19 male] and 40 sex- and age-matched healthy controls [HCs]) to evaluate white matter microstructure (fiber density [FD]), macrostructure (fiber bundle cross section) and combined microstructure and macrostructure (fiber density and cross section) within anatomical connections that support the DPC. We additionally examined whether there are sex-specific abnormalities in NP white matter structure. We performed fixel-wise and connection-specific mean analyses and found three main ways in which individuals with NP differed from HCs: (1) people with NP exhibited abnormally low FD and FDC within the corona radiata consistent with the ascending nociceptive pathway between the sensory thalamus and primary somatosensory cortex (S1). Furthermore, the entire sensory thalamus-S1 pathway exhibited abnormally low FD and FDC in people with NP, and this effect was driven by the females with NP; (2) females, but not males, with NP had abnormally low FD within the cingulum consistent with the right medial prefrontal cortex-posterior cingulate cortex DMN pathway; and (3) individuals with NP had higher connection-specific mean FDC than HCs in the anterior insula-temporoparietal junction and sensory thalamus-posterior insula pathways. However, sex-specific analyses did not corroborate these connection-specific findings in either females or males with NP. Our findings suggest that females with NP exhibit microstructural and macrostructural white matter abnormalities within the DPC networks including the ascending nociceptive system and DMN. We propose that aberrant white matter structure contributes to or is driven by functional abnormalities associated with NP. Our sex-specific findings highlight the utility and importance of using sex-disaggregated analyses to identify white matter abnormalities in clinical conditions such as chronic pain.

A multisite validation of brain white matter pathways of resilience to chronic back pain

Chronic back pain (CBP) is a global health concern with significant societal and economic burden. While various predictors of back pain chronicity have been proposed, including demographic and psychosocial factors, neuroimaging studies have pointed to brain characteristics as predictors of CBP. However, large-scale, multisite validation of these predictors is currently lacking. In two independent longitudinal studies, we examined white matter diffusion imaging data and pain characteristics in patients with subacute back pain (SBP) over 6- and 12-month periods. Diffusion data from individuals with CBP and healthy controls (HC) were analyzed for comparison. Whole-brain tract-based spatial statistics analyses revealed that a cluster in the right superior longitudinal fasciculus (SLF) tract had larger fractional anisotropy (FA) values in patients who recovered (SBPr) compared to those with persistent pain (SBPp), and predicted changes in pain severity. The SLF FA values accurately classified patients at baseline and follow-up in a third publicly available dataset (Area under the Receiver Operating Curve ~0.70). Notably, patients who recovered had FA values larger than those of HC suggesting a potential role of SLF integrity in resilience to CBP. Structural connectivity-based models also classified SBPp and SBPr patients from the three data sets (validation accuracy 67%). Our results validate the right SLF as a robust predictor of CBP development, with potential for clinical translation. Cognitive and behavioral processes dependent on the right SLF, such as proprioception and visuospatial attention, should be analyzed in subacute stages as they could prove important for back pain chronicity.

Microstructural Characteristics of Cervical Spinal Cord Using High Angular Resolution Diffusion Imaging (HARDI) and Tractography in Healthy Subjects

PURPOSE

This study aimed to characterize spinal cord microstructure in healthy subjects using high angular resolution diffusion imaging (HARDI) and tractography.

METHODS

Forty-nine healthy subjects (18-50 years, divided into 2 age groups) were included in a prospective study. HARDI of the cervical spinal cord were acquired using a 3T MRI scanner with: 64 directions, b‑value: 1000s/mm2, reduced field-of-view (zonally magnified oblique multi-slice), and opposed phase-encoding directions. Distortions were corrected using the FSL software package. Fiber tracking was performed using a deterministic approach with DSI-Studio software. Tensor metrics-fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD)-and tractography statistics were then extracted, at each spine level, and after grey-white matter segmentation.

RESULTS

The microstructural organization of the spinal cord differed between upper and lower cervical spine levels: FA, and AD significantly decreased (p < 0.001); and RD significantly increased (p < 0.05) in lower levels, demonstrating changes in axonal density and myelinated fibers according to a cranio-caudal axis. FA, MD, AD, and RD values were significantly higher in spinal cord white matter (p < 0.0001), compared to grey matter. Age was not associated with a significant change in FA, while there is for MD, AD and RD (p < 0.05). Spinal cord tractography may provide information on the architectural organization of fibers and spinal tracts.

CONCLUSION

This study proposes a database in cervical spinal cord HARDI, allowing to study the microstructural organization of the spinal cord in healthy subjects, and providing a foundation for comparison with patients presenting spinal cord pathologies.

Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis

Traditional Diffusion Tensor Imaging (DTI) metrics are affected by crossing fibers and lesions. Most of the previous tractometry works use the single diffusion tensor, which leads to limited sensitivity and challenging interpretation of the results in crossing fiber regions. In this work, we propose a tractometry pipeline that combines white matter tractography with multi-tensor fixel-based metrics. These multi-tensors are estimated using the stable, accurate and robust to noise Multi-Resolution Discrete Search method (MRDS). The spatial coherence of the multi-tensor field estimated with MRDS, which includes up to three anisotropic and one isotropic tensors, is tractography-regularized using the Track Orientation Density Imaging method. Our end-to-end tractometry pipeline goes from raw data to track-specific multi-tensor-metrics tract profiles that are robust to noise and crossing fibers. A comprehensive evaluation conducted in a phantom simulating healthy and damaged tissue with the standard model, as well as in a healthy cohort of 20 individuals scanned along 5 time points, demonstrates the advantages of using multi-tensor metrics over traditional single-tensor metrics in tractometry. Qualitative assessment in a cohort of patients with relapsing-remitting multiple sclerosis reveals that the pipeline effectively detects white matter anomalies in the presence of crossing fibers and lesions.

Predicting an individual's functional connectivity from their structural connectome: Evaluation of evidence, recommendations, and future prospects

Several recent studies have optimized deep neural networks to learn high-dimensional relationships linking structural and functional connectivity across the human connectome. However, the extent to which these models recapitulate individual-specific characteristics of resting-state functional brain networks remains unclear. A core concern relates to whether current individual predictions outperform simple benchmarks such as group averages and null conditions. Here, we consider two measures to statistically evaluate whether functional connectivity predictions capture individual effects. We revisit our previously published functional connectivity predictions for 1,000 healthy adults and provide multiple lines of evidence supporting that our predictions successfully capture subtle individual-specific variation in connectivity. While predicted individual effects are statistically significant and outperform several benchmarks, we find that effect sizes are small (i.e., 8%-11% improvement relative to group-average benchmarks). As such, initial expectations about individual prediction performance expressed by us and others may require moderation. We conclude that individual predictions can significantly outperform appropriate benchmark conditions and we provide several recommendations for future studies in this area. Future studies should statistically assess the individual prediction performance of their models using one of the measures and benchmarks provided here.

CoCoNest: A continuous structural connectivity-based nested family of parcellations of the human cerebral cortex

Despite the widespread exploration and availability of parcellations for the functional connectome, parcellations designed for the structural connectome are comparatively limited. Current research suggests that there may be no single "correct" parcellation and that the human brain is intrinsically a multiresolution entity. In this work, we propose the Continuous Structural Connectivitity-based, Nested (CoCoNest) family of parcellations-a fully data-driven, multiresolution family of parcellations derived from structural connectome data. The CoCoNest family is created using agglomerative (bottom-up) clustering and error-complexity pruning, which strikes a balance between the complexity of each parcellation and how well it preserves patterns in vertex-level, high-resolution connectivity data. We draw on a comprehensive battery of internal and external evaluation metrics to show that the CoCoNest family is competitive with or outperforms widely used parcellations in the literature. Additionally, we show how the CoCoNest family can serve as an exploratory tool for researchers to investigate the multiresolution organization of the structural connectome.

Structure of subcortico-cortical tracts in middle-aged and older adults with autism spectrum disorder

Middle-aged and older adults with autism spectrum disorder may be susceptible to accelerated neurobiological changes in striato- and thalamo-cortical tracts due to combined effects of typical aging and existing disparities present from early neurodevelopment. Using magnetic resonance imaging, we employed diffusion-weighted imaging and automated tract-segmentation to explore striato- and thalamo-cortical tract microstructure and volume differences between autistic (n = 29) and typical comparison (n = 33) adults (40 to 70 years old). Fractional anisotropy, mean diffusivity, and tract volumes were measured for 14 striato-cortical and 12 thalamo-cortical tract bundles. Data were examined using linear regressions for group by age effects and group plus age effects, and false discovery rate correction was applied. Following false discovery rate correction, volumes of thalamocortical tracts to premotor, pericentral, and parietal regions were significantly reduced in autism spectrum disorder compared to thalamo-cortical groups, but no group by age interactions were found. Uncorrected results suggested additional main effects of group and age might be present for both tract volume and mean diffusivity across multiple subcortico-cortical tracts. Results indicate parallel rather than accelerated changes during adulthood in striato-cortical and thalamo-cortical tract volume and microstructure in those with autism spectrum disorder relative to thalamo-cortical peers though thalamo-cortical tract volume effects are the most reliable.

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.

A Reproducible Pipeline for Parcellation of the Anterior Limb of the Internal Capsule

BACKGROUND

The anterior limb of the internal capsule (ALIC) is a white matter structure that connects the prefrontal cortex (PFC) to the brainstem, thalamus, and subthalamic nucleus. It is a target for deep brain stimulation for obsessive-compulsive disorder. There is strong interest in improving deep brain stimulation targeting by using diffusion tractography to reconstruct and target specific ALIC fiber pathways, but this methodology is susceptible to errors and lacks validation. To address these limitations, we developed a novel diffusion tractography pipeline that generates reliable and biologically validated ALIC white matter reconstructions.

METHODS

Following algorithm development and refinement, we analyzed 43 control participants, each with 2 sets of 3T magnetic resonance imaging data and a subset of 5 control participants with 7T data from the Human Connectome Project. We generated 22 segmented ALIC fiber bundles (11 per hemisphere) based on PFC regions of interest, and we analyzed the relationships among bundles.

RESULTS

We successfully reproduced the topographies established by previous anatomical work using images acquired at both 3T and 7T. Quantitative assessment demonstrated significantly smaller intraparticipant variability than interparticipant variability for both test and retest groups across all but one PFC region. We examined the overlap between fibers from different PFC regions and a response tract for obsessive-compulsive disorder deep brain stimulation, and we reconstructed the PFC hyperdirect pathway using a modified version of our pipeline.

CONCLUSIONS

Our diffusion magnetic resonance imaging algorithm reliably generates biologically validated ALIC white matter reconstructions, thereby allowing for more precise modeling of fibers for neuromodulation therapies.

Structural connectivity of dopaminergic pathways in major depressive disorder: An ultra-high resolution 7-Tesla diffusion MRI study

Accumulating evidence points to imbalanced dopamine (DA) signaling and circulating levels in the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented a thorough examination of DA neural pathways in MDD. We uniquely employed ultra-high field diffusion MRI at 7.0 Tesla to map the white matter architecture and integrity of several DA pathways in MDD patients. Fifty-three MDD patients and 12 healthy controls (HCs) were enrolled in the final analysis. Images were acquired using a 7.0 Tesla MRI scanner. FreeSurfer was used to segment components of DA pathways, and MRtrix was used to perform preprocessing and tractography of mesolimbic, mesocortical, nigrostriatal, and unconventional DA pathways. Bayesian analyses assessed the impact of MDD and clinical features on DA tracts. MDD was associated with perturbed white matter microstructural properties of the nigrostriatal pathway, while several MDD features (severity of depression/age of onset/insomnia) related to connectivity changes within mesocortical, nigrostriatal, and unconventional pathways. MDD is associated with microstructural differences in the nigrostriatal pathway. The findings provide insight into the structural architecture and integrity of several DA pathways in MDD, and implicate their involvement in the clinical manifestation of MDD.