Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers

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.

Precision non-invasive brain stimulation: anin silicopipeline for personalized control of brain dynamics

Objective.Non-invasive brain stimulation (NIBS) offers therapeutic benefits for various brain disorders. Personalization may enhance these benefits by optimizing stimulation parameters for individual subjects.Approach.We present a computational pipeline for simulating and assessing the effects of NIBS using personalized, large-scale brain network activity models. Using structural MRI and diffusion-weighted imaging data, the pipeline leverages a convolutional neural network-based segmentation algorithm to generate subject-specific head models with up to 40 tissue types and personalized dielectric properties. We integrate electromagnetic simulations of NIBS exposure with whole-brain network models to predict NIBS-dependent perturbations in brain dynamics, simulate the resulting EEG traces, and quantify metrics of brain dynamics.Main results.The pipeline is implemented on o2S2PARC, an open, cloud-based infrastructure designed for collaborative and reproducible computational life science. Furthermore, a dedicated planning tool provides guidance for optimizing electrode placements for transcranial temporal interference stimulation. In two proof-of-concept applications, we demonstrate that: (i) transcranial alternating current stimulation produces expected shifts in the EEG spectral response, and (ii) simulated baseline network activity exhibits physiologically plausible fluctuations in inter-hemispheric synchronization.Significance.This pipeline facilitates a shift from exposure-based to response-driven optimization of NIBS, supporting new stimulation paradigms that steer brain dynamics towards desired activity patterns in a controlled manner.

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.

The corticospinal tract in multiple sclerosis: correlation between cortical excitability and magnetic resonance imaging measures

Multiple sclerosis (MS) is a central nervous system disease involving gray and white matters. Transcranial magnetic stimulation (TMS) and magnetic resonance imaging (MRI) could help identify potential markers of disease evolution, disability, and treatment response. This work evaluates the relationship between intracortical inhibition and facilitation, motor cortex lesions, and corticospinal tract (CST) integrity. Consecutive adult patients with progressive MS were included. Sociodemographic and clinical data were collected. MRI was acquired to assess primary motor cortex lesions (double inversion and phase-sensitive inversion recovery) and CST integrity (diffusion tensor imaging). TMS outcomes were obtained: motor evoked potentials (MEP) latency, resting motor threshold, short-interval intracortical facilitation (ICF) and inhibition. Correlation analysis was performed. Twenty-five patients completed the study (13 females, age: 55.60 ± 11.49 years, Expanded Disability Status Score: 6.00 ± 1.25). Inverse correlations were found between ICF mean and each of CST radial diffusivity (RD) (ρ =-0.56; p < 0.01), CST apparent diffusion coefficient (ADC) (ρ=-0.44; p = 0.03), and disease duration (ρ=-0.46; p = 0.02). MEP latencies were directly correlated with disability scores (ρ = 0.55; p < 0.01). High ADC/RD and low ICF have been previously reported in patients with MS. While the former could reflect structural damage of the CST, the latter could hint towards an aberrant synaptic transmission as well as a depletion of facilitatory compensatory mechanisms that helps overcoming functional decline. The findings suggest concomitant structural and functional abnormalities at later disease stages that would be accompanied with a heightened disability. The results should be interpreted with caution mainly because of the small sample size that precludes further comparisons (e.g., treated vs. untreated patients, primary vs. secondary progressive MS). The role of these outcomes as potential MS biomarkers merit to be further explored.

Attention-Based Q-Space Deep Learning Generalized for Accelerated Diffusion Magnetic Resonance Imaging

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.

Local abnormal white matter microstructure in the spinothalamic tract in people with chronic neck and shoulder pain

Objective

To investigate differences in the microstructure of the spinothalamic tract (STT) white matter in people with chronic neck and shoulder pain (CNSP) using diffusion tensor imaging, and to assess its correlation with pain intensity and duration of the pain.

Materials and methods

A 3.0T MRI scanner was used to perform diffusion tensor imaging scans on 31 people with CNSP and 24 healthy controls (HCs), employing the Automatic Fiber Segmentation and Quantification (AFQ) method to extract the STT and quantitatively analyze the fractional anisotropy (FA) and mean diffusivity (MD), reflecting the microstructural integrity of nerve fibers. Correlations of these differences with duration of pain and visual analog scale (VAS) scores were analyzed.

Results

No significant differences in the mean FA or MD values of the bilateral STT were observed between people with CNSP and HCs (p > 0.05), as indicated by the two-sample t test. Further point-by-point comparison along 100 equidistant nodes within the STT pathway revealed significant reductions in FA values in the left (segments 12-18, 81-89) and right (segments 9-19, 76-80) STT in the CNSP group compared to HCs; significant increases in MD values were observed in the left (segments 1-13, 26-30, 71-91) and right (segments 8-17, 76-91) STT (p < 0.05, FWE corrected). Partial correlation analysis indicates that in people with CNSP, the FA values of the STT in regions with damaged white matter structure show a negative correlation with VAS scores and duration of pain, whereas MD values show a positive correlation with VAS scores and duration of pain.

Conclusion

This study found that people with CNSP exhibit white matter microstructural abnormalities in the specific segments of STT. These abnormalities are associated with the patient's pain intensity and disease duration. The findings offer a new neuroimaging perspective on the pathophysiological basis of chronic pain in the ascending conduction process and its potential role in developing targeted intervention strategies. However, due to the limited sample size and the lack of statistical significance when analyzing the entire spinothalamic tract, these conclusions should be interpreted with caution. Further research with larger cohorts is necessary to validate these results.

A simulated annealing algorithm for randomizing weighted networks

Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.

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.

Impulsivity in ADHD and Borderline Personality Disorder: A Systematic Review of Gray and White Matter Variations

Introduction: Impulsivity is one of the overlapping symptoms common to borderline personality disorder (BPD) and attention deficit hyperactivity disorder (ADHD), but the neurobiological basis of these disorders remains uncertain. This systematic review aims to identify abnormalities in the gray and white matter associated with impulsivity in BPD and ADHD. Methods: We conducted a systematic search of the PubMed, Embase, and SCOPUS databases, adhering to PRISMA guidelines. Studies that investigated gray and white matter alterations in BPD or ADHD populations and their relationship with impulsivity were included. We reviewed information from 23 studies involving 992 participants, which included findings from structural MRI and DTI. Results: The review identified various nonhomogeneous changes associated with impulsivity in BPD and ADHD. BPD was mainly associated with abnormalities in the prefrontal cortex (PFC) and limbic areas, which correlated negatively with impulsivity. In contrast, impulsivity associated with ADHD was associated with structural changes in the caudate nucleus and frontal-striatal pathways. Despite the overlapping symptoms of impulsivity, the neurobiological mechanisms appeared to differ between the two disorders. Conclusions: These findings emphasize the distinct neurostructural correlates of impulsivity in BPD and ADHD. While both disorders show impulsivity as one of their main symptoms, the fundamental brain structures associated with this trait are different. BPD is primarily associated with abnormalities in the prefrontal cortex and limbic system, whereas the alterations seen in ADHD tend to focus on the caudate nucleus and frontostriatal pathways. Further research is needed to clarify these differences and their implications for treatment.

Denoising complex-valued diffusion MR images using a two-step non-local principal component analysis approach

Purpose

to propose a two-step non-local principal component analysis (PCA) method and demonstrate its utility for denoising diffusion tensor MRI (DTI) with a few diffusion directions.

Methods

A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data preprocessing for g-factor normalization and phase stabilization before data denoising with a non-local PCA algorithm. At the heart of our proposed pipeline was the use of a data-driven optimal shrinkage algorithm to manipulate the singular values in a way that would optimally estimate the noise-free signal. Our approach's denoising performances were evaluated using simulation and in-vivo human data experiments. The results were compared to those obtained with existing local-PCA-based methods.

Results

In both simulation and human data experiments, our approach substantially enhanced image quality relative to the noisy counterpart, yielding improved performances for estimation of relevant DTI metrics. It also outperformed existing local-PCA-based methods in reducing noise while preserving anatomic details. It also led to improved whole-brain tractography relative to the noisy counterpart.

Conclusion

The proposed denoising method has the utility for improving image quality for DTI with reduced diffusion directions and is believed to benefit many applications especially those aiming to achieve quality parametric mapping using only a few image volumes.

A theoretical framework for predicting the heterogeneous stiffness map of brain white matter tissue

Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element (FE) model of the fibrous tissue was subjected to six loading cases, and their corresponding stress-strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale FE simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The proposed method, leveraging brain fiber tractography, was applied to a localized volume of white matter, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments.

Mapping stroke outcomes: A review of brain connectivity atlases

The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.

Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

Clinical characteristics of post-stroke basal ganglia aphasia and the study of language-related white matter tracts based on diffusion spectrum imaging

BACKGROUND

Stroke often damages the basal ganglia, leading to atypical and transient aphasia, indicating that post-stroke basal ganglia aphasia (PSBGA) may be related to different anatomical structural damage and functional remodeling rehabilitation mechanisms. The basal ganglia contain dense white matter tracts (WMTs). Hence, damage to the functional tract may be an essential anatomical structural basis for the development of PSBGA.

METHODS

We first analyzed the clinical characteristics of PSBGA in 28 patients and 15 healthy controls (HCs) using the Western Aphasia Battery and neuropsychological test batteries. Moreover, we investigated white matter injury during the acute stage using diffusion magnetic resonance imaging scans for differential tractography. Finally, we used multiple regression models in correlation tractography to analyze the relationship between various language functions and quantitative anisotropy (QA) of WMTs.

RESULTS

Compared with HCs, patients with PSBGA showed lower scores for fluency, comprehension (auditory word recognition and sequential commands), naming (object naming and word fluency), reading comprehension of sentences, Mini-Mental State Examination, and Montreal Cognitive Assessment, along with increased scores in Hamilton Anxiety Scale-17 and Hamilton Depression Scale-17 within 7 days after stroke onset (P < 0.05). Differential tractography revealed that patients with PSBGA had damaged fibers, including in the body fibers of the corpus callosum, left cingulum bundles, left parietal aslant tracts, bilateral superior longitudinal fasciculus II, bilateral thalamic radiation tracts, left fornix, corpus callosum tapetum, and forceps major, compared with HCs (FDR < 0.02). Correlation tractography highlighted that better comprehension was correlated with a higher QA of the left inferior fronto-occipital fasciculus (IFOF), corpus callosum forceps minor, and left extreme capsule (FDR < 0.0083). Naming was positively associated with the QA of the left IFOF, forceps minor, left arcuate fasciculus, and uncinate fasciculus (UF) (FDR < 0.0083). Word fluency of naming was also positively associated with the QA of the forceps minor, left IFOF, and thalamic radiation tracts (FDR < 0.0083). Furthermore, reading was positively correlated with the QA of the forceps minor, left IFOF, and UF (FDR < 0.0083).

CONCLUSION

PSBGA is primarily characterized by significantly impaired word fluency of naming and preserved repetition abilities, as well as emotional and cognitive dysfunction. Damaged limbic pathways, dorsally located tracts in the left hemisphere, and left basal ganglia pathways are involved in PSBGA pathogenesis. The results of connectometry analysis further refine the current functional localization model of higher-order neural networks associated with language functions.

The role of language-related functional brain regions and white matter tracts in network plasticity of post-stroke aphasia

The neural mechanisms underlying language recovery after a stroke remain controversial. This review aimed to summarize the plasticity and reorganization mechanisms of the language network through neuroimaging studies. Initially, we discussed the involvement of right language homologues, perilesional tissue, and domain-general networks. Subsequently, we summarized the white matter functional mapping and remodeling mechanisms associated with language subskills. Finally, we explored how non-invasive brain stimulation (NIBS) promoted language recovery by inducing neural network plasticity. It was observed that the recruitment of right hemisphere language area homologues played a pivotal role in the early stages of frontal post-stroke aphasia (PSA), particularly in patients with larger lesions. Perilesional plasticity correlated with improved speech performance and prognosis. The domain-general networks could respond to increased "effort" in a task-dependent manner from the top-down when the downstream language network was impaired. Fluency, repetition, comprehension, naming, and reading skills exhibited overlapping and unique dual-pathway functional mapping models. In the acute phase, the structural remodeling of white matter tracts became challenging, with recovery predominantly dependent on cortical activation. Similar to the pattern of cortical activation, during the subacute and chronic phases, improvements in language functions depended, respectively, on the remodeling of right white matter tracts and the restoration of left-lateralized language structural network patterns. Moreover, the midline superior frontal gyrus/dorsal anterior cingulate cortex emerged as a promising target for NIBS. These findings offered theoretical insights for the early personalized treatment of aphasia after stroke.

Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation

OBJECTIVES

Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth.

METHODS

Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes.

RESULTS

Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05).

CONCLUSIONS

CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

Decreased uncinate fasciculus integrity in functional seizures following traumatic brain injury

OBJECTIVE

The uncinate fasciculus (UF) has been implicated previously in contributing to the pathophysiology of functional (nonepileptic) seizures (FS). FS are frequently preceded by adverse life events (ALEs) and present with comorbid psychiatric symptoms, yet neurobiological correlates of these factors remain unclear. To address this gap, using advanced diffusion magnetic resonance imaging (dMRI), UF tracts in a large cohort of patients with FS and pre-existing traumatic brain injury (TBI + FS) were compared to those in patients with TBI without FS (TBI-only). We hypothesized that dMRI measures in UF structural connectivity would reveal UF differences when controlling for TBI status. Partial correlation tests assessed the potential relationships with psychiatric symptom severity measures.

METHODS

Participants with TBI-only (N = 46) and TBI + FS (N = 55) completed a series of symptom questionnaires and MRI scanning. Deterministic tractography via diffusion spectrum imaging (DSI) was implemented in DSI studio (https://dsi-studio.labsolver.org) with q-space diffeomorphic reconstruction (QSDR), streamline production, and manual segmentation to assess bilateral UF integrity. Fractional anisotropy (FA), radial diffusivity (RD), streamline counts, and their respective asymmetry indices (AIs) served as estimates of white matter integrity.

RESULTS

Compared to TBI-only, TBI + FS participants demonstrated decreased left hemisphere FA and RD asymmetry index (AI) for UF tracts (both p < .05, false discovery rate [FDR] corrected). Additionally, TBI + FS reported higher symptom severity in depression, anxiety, and PTSD measures (all p < .01). Correlation tests comparing UF white matter integrity differences to psychiatric symptom severity failed to reach criteria for significance (all p > .05, FDR corrected).

SIGNIFICANCE

In a large, well-characterized sample, participants with FS had decreased white matter health after controlling for the history of TBI. Planned follow-up analysis found no evidence to suggest that UF connectivity measures are a feature of group differences in mood or anxiety comorbidities for FS. These findings suggest that frontolimbic structural connectivity may play a role in FS symptomology, after accounting for prior ALEs and comorbid psychopathology severity.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Elossaily G, Vellapandian C, Prajapati B. Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection

Pollution is ubiquitous, and much of it is anthropogenic in nature, which is a severe risk factor not only for respiratory infections or asthma sufferers but also for Alzheimer's disease, which has received a lot of attention recently. This Review aims to investigate the primary environmental risk factors and their profound impact on Alzheimer's disease. It underscores the pivotal role of multidimensional imaging in early disease identification and prevention. Conducting a comprehensive review, we delved into a plethora of literature sources available through esteemed databases, including Science Direct, Google Scholar, Scopus, Cochrane, and PubMed. Our search strategy incorporated keywords such as "Alzheimer Disease", "Alzheimer's", "Dementia", "Oxidative Stress", and "Phytotherapy" in conjunction with "Criteria Pollutants", "Imaging", "Pathology", and "Particulate Matter". Alzheimer's disease is not only a result of complex biological factors but is exacerbated by the infiltration of airborne particles and gases that surreptitiously breach the nasal defenses to traverse the brain, akin to a Trojan horse. Various imaging modalities and noninvasive techniques have been harnessed to identify disease progression in its incipient stages. However, each imaging approach possesses inherent limitations, prompting exploration of a unified technique under a single umbrella. Multidimensional imaging stands as the linchpin for detecting and forestalling the relentless march of Alzheimer's disease. Given the intricate etiology of the condition, identifying a prospective candidate for Alzheimer's disease may take decades, rendering the development of a multimodal imaging technique an imperative. This research underscores the pressing need to recognize the chronic ramifications of invisible particulate matter and to advance our understanding of the insidious environmental factors that contribute to Alzheimer's disease.

Diffusion phantom study of fiber crossings at varied angles reconstructed with ODF-Fingerprinting

White matter fiber reconstructions based on seeking local maxima of Orientation Distribution Functions (ODFs) typically fail to identify fibers crossing at narrow angles below 45°. ODF-Fingerprinting (ODF-FP) replaces the ODF maxima localization mechanism with pattern matching, allowing the use of all information stored in ODFs. In this work, we study the ability of ODF-FP to reconstruct fibers crossing at varied angles spanning 10°-90° in physical diffusion phantoms composed of textile tubes with 0.8μm diameter, approaching the anatomical scale of axons. Our results show that ODF-FP is able to correctly identify 80 ± 8% of the crossing fibers regardless of the crossing angle and provide the highest average reconstruction accuracy.

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains

Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

The Brain Connectome for Clinical Neuroscience

In this chapter, we introduce the topic of the brain connectome, consisting of the complete set of both the structural and functional connections of the brain. Connectomic information and the large-scale network architecture of the brain provide an improved understanding of the organization and functional relevance of human cortical and subcortical anatomy. We discuss various analytical methods to both identify and interpret structural and functional connectivity data. In turn, we discuss how these data provide significant clinical promise for neurosurgery, neurology, and psychiatry in that more informed decisions can be made based on connectomic information. These data can provide safer and more informed network-based neurosurgery for brain tumor patients and even offer the possibility to modulate the brain connectome.

Age-associated alterations in thalamocortical structural connectivity in youths with a psychosis-spectrum disorder

Psychotic symptoms typically emerge in adolescence. Age-associated thalamocortical connectivity differences in psychosis remain unclear. We analyzed diffusion-weighted imaging data from 1254 participants 8-23 years old (typically developing (TD):N = 626, psychosis-spectrum (PS): N = 329, other psychopathology (OP): N = 299) from the Philadelphia Neurodevelopmental Cohort. We modeled thalamocortical tracts using deterministic fiber tractography, extracted Q-Space Diffeomorphic Reconstruction (QSDR) and diffusion tensor imaging (DTI) measures, and then used generalized additive models to determine group and age-associated thalamocortical connectivity differences. Compared to other groups, PS exhibited thalamocortical reductions in QSDR global fractional anisotropy (GFA, p-values range = 3.0 × 10-6-0.05) and DTI fractional anisotropy (FA, p-values range = 4.2 × 10-4-0.03). Compared to TD, PS exhibited shallower thalamus-prefrontal age-associated increases in GFA and FA during mid-childhood, but steeper age-associated increases during adolescence. TD and OP exhibited decreases in thalamus-frontal mean and radial diffusivities during adolescence; PS did not. Altered developmental trajectories of thalamocortical connectivity may contribute to the disruptions observed in adults with psychosis.

Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging-Tractography in Resective Brain Surgery: Lesion Coverage Strategies and Patient Outcomes

Diffusion tensor imaging (DTI)-tractography and functional magnetic resonance imaging (fMRI) have dynamically entered the presurgical evaluation context of brain surgery during the past decades, providing novel perspectives in surgical planning and lesion access approaches. However, their application in the presurgical setting requires significant time and effort and increased costs, thereby raising questions regarding efficiency and best use. In this work, we set out to evaluate DTI-tractography and combined fMRI/DTI-tractography during intra-operative neuronavigation in resective brain surgery using lesion-related preoperative neurological deficit (PND) outcomes as metrics. We retrospectively reviewed medical records of 252 consecutive patients admitted for brain surgery. Standard anatomical neuroimaging protocols were performed in 127 patients, 69 patients had additional DTI-tractography, and 56 had combined DTI-tractography/fMRI. fMRI procedures involved language, motor, somatic sensory, sensorimotor and visual mapping. DTI-tractography involved fiber tracking of the motor, sensory, language and visual pathways. At 1 month postoperatively, DTI-tractography patients were more likely to present either improvement or preservation of PNDs (p = 0.004 and p = 0.007, respectively). At 6 months, combined DTI-tractography/fMRI patients were more likely to experience complete PND resolution (p < 0.001). Low-grade lesion patients (N = 102) with combined DTI-tractography/fMRI were more likely to experience complete resolution of PNDs at 1 and 6 months (p = 0.001 and p < 0.001, respectively). High-grade lesion patients (N = 140) with combined DTI-tractography/fMRI were more likely to have PNDs resolved at 6 months (p = 0.005). Patients with motor symptoms (N = 80) were more likely to experience complete remission of PNDs at 6 months with DTI-tractography or combined DTI-tractography/fMRI (p = 0.008 and p = 0.004, respectively), without significant difference between the two imaging protocols (p = 1). Patients with sensory symptoms (N = 44) were more likely to experience complete PND remission at 6 months with combined DTI-tractography/fMRI (p = 0.004). The intraoperative neuroimaging modality did not have a significant effect in patients with preoperative seizures (N = 47). Lack of PND worsening was observed at 6 month follow-up in patients with combined DTI-tractography/fMRI. Our results strongly support the combined use of DTI-tractography and fMRI in patients undergoing resective brain surgery for improving their postoperative clinical profile.

Eigenmode-based approach reveals a decline in brain structure-function liberality across the human lifespan

While brain function is supported and constrained by the underlying structure, the connectome-based link estimated by current approaches is either relatively moderate or accompanied by high model complexity, with the essential principles underlying structure-function coupling remaining elusive. Here, by proposing a mapping method based on network eigendecomposition, we present a concise and strong correspondence between structure and function. We show that the explanation of functional connectivity can be significantly improved by incorporating interactions between different structural eigenmodes. We also demonstrate the pronounced advantage of the present mapping in capturing individual-specific information with simple implementation. Applying our methodology to the human lifespan, we find that functional diversity decreases with age, with functional interactions increasingly dominated by the leading functional mode. We also find that structure-function liberality weakens with age, which is driven by the decreases in functional components that are less constrained by anatomy, while the magnitude of structure-aligned components is preserved. Overall, our work enhances the understanding of structure-function coupling from a collective, connectome-oriented perspective and promotes a more refined identification of functional portions relevant to human aging, holding great potential for mechanistic insights into individual differences associated with cognition, development, and neurological disorders.

Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes

While the link between brain structure and function remains an ongoing challenge, the prevailing hypothesis is that the structure-function relationship may itself be gradually decoupling from unimodal to transmodal cortex. However, this hypothesis is constrained by the underlying models which may neglect requisite information. Here we relate structural and functional connectivity derived from diffusion and functional MRI through orthogonal eigenmodes governing frequency-specific diffusion patterns. We find that low-frequency eigenmodes contribute little to functional interactions in transmodal cortex, resulting in divergent structure-function relationships. Conversely, high-frequency eigenmodes predominantly support neuronal coactivation patterns in these areas, inducing structure-function convergence along a unimodal-transmodal hierarchy. High-frequency information, although weak and scattered, could enhance the structure-function tethering, especially in transmodal association cortices. Our findings suggest that the structure-function decoupling may not be an intrinsic property of brain organization, but can be narrowed through multiplexed and regionally specialized spatiotemporal propagation regimes.

The coupling between the spatial and temporal scales of neural processes revealed by a joint time-vertex connectome spectral analysis

Brain oscillations are produced by the coordinated activity of large groups of neurons and different rhythms are thought to reflect different modes of information processing. These modes, in turn, are known to occur at different spatial scales. Nevertheless, how these rhythms support different spatial modes of information processing at the brain scale is not yet fully understood. Here we use "Joint Time-Vertex Spectral Analysis" to characterize the joint spectral content of brain activity both in time (temporal frequencies) and in space over the connectivity graph (spatial connectome harmonics). This method allows us to characterize the relationship between spatially localized or distributed neural processes on one side and their respective temporal frequency bands in source-reconstructed M/EEG signals. We explore this approach on two different datasets, an auditory steady-state response (ASSR) and a visual grating task. Our results suggest that different information processing mechanisms are carried out at different frequency bands: while spatially distributed activity (which may also be interpreted as integration) specifically occurs at low temporal frequencies (alpha and theta) and low graph spatial frequencies, localized electrical activity (i.e., segregation) is observed at high temporal frequencies (high and low gamma) over restricted high spatial graph frequencies. Crucially, the estimated contribution of the distributed and localized neural activity predicts performance in a behavioral task, demonstrating the neurophysiological relevance of the joint time-vertex spectral representation.

Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson's disease

Parkinson's disease pathology is hypothesized to spread through the brain via axonal connections between regions and is further modulated by local vulnerabilities within those regions. The resulting changes to brain morphology have previously been demonstrated in both prodromal and de novo Parkinson's disease patients. However, it remains unclear whether the pattern of atrophy progression in Parkinson's disease over time is similarly explained by network-based spreading and local vulnerability. We address this gap by mapping the trajectory of cortical atrophy rates in a large, multi-centre cohort of Parkinson's disease patients and relate this atrophy progression pattern to network architecture and gene expression profiles. Across 4-year follow-up visits, increased atrophy rates were observed in posterior, temporal, and superior frontal cortices. We demonstrated that this progression pattern was shaped by network connectivity. Regional atrophy rates were strongly related to atrophy rates across structurally and functionally connected regions. We also found that atrophy progression was associated with specific gene expression profiles. The genes whose spatial distribution in the brain was most related to atrophy rate were those enriched for mitochondrial and metabolic function. Taken together, our findings demonstrate that both global and local brain features influence vulnerability to neurodegeneration in Parkinson's disease.

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

Adaptive compensation of arcuate fasciculus lateralization in developmental dyslexia

Previous studies have reported anomalies in the arcuate fasciculus (AF) lateralization in developmental dyslexia (DD). Still, the relationship between AF lateralization and literacy skills in DD remains largely unknown. The purpose of our study is to investigate the relationship between lateralization of three segments of AF (AF anterior segment (AFAS), AF long segment (AFLS), and AF posterior segment (AFPS)) and literacy skills in DD. A total of 26 children with dyslexia and 31 age-matched control children were included in this study. High angular diffusion imaging, combined with spherical deconvolution tractography, was used to reconstruct the AF. Connectivity measures of hindrance-modulated orientational anisotropy (HMOA) were computed for each of the three segments of the AF. The lateralization index (LI) of each AF segment was calculated by (right HMOA - left HMOA)/(right HMOA + left HMOA). Results showed that the LIs of AFAS and AFLS were positively correlated with reading accuracy in children with dyslexia. Specifically, the LI of AFAS was positively correlated with nonword and meaningless text reading accuracy, while the LI of AFLS accounted for word reading accuracy. The results suggest adaptive compensation of arcuate fasciculus lateralization in developmental dyslexia and functional dissociation of the anterior segment and long segment in the compensation.

Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: A graph signal processing study

OBJECTIVE

Structure-function coupling remains largely unknown in brain disorders. We studied this coupling during interictal epileptic discharges (IEDs), using graph signal processing in temporal lobe epilepsy (TLE).

METHODS

We decomposed IEDs of 17 patients on spatial maps, i.e. network harmonics, extracted from a structural connectome. Harmonics were split in smooth maps (long-range interactions reflecting integration) and coarse maps (short-range interactions reflecting segregation) and were used to reconstruct the part of the signal coupled (Xc) and decoupled (Xd) from the structure, respectively. We analysed how Xc and Xd embed the IED energy over time, at global and regional level.

RESULTS

For Xc, the energy was smaller than for Xd before the IED onset (p < .001), but became larger around the first IED peak (p < .05, cluster 2, C2). Locally, the ipsilateral mesial regions were significantly coupled to the structure over the whole epoch. The ipsilateral hippocampus increased its coupling during C2 (p < .01).

CONCLUSIONS

At whole-brain level, segregation gives way to integrative processes during the IED. Locally, brain regions commonly involved in the TLE epileptogenic network increase their reliance on long-range couplings during IED (C2).

SIGNIFICANCE

In TLE, integration mechanisms prevail during the IED and are localized in the ipsilateral mesial temporal regions.

Negative versus withdrawn maternal behavior: Differential associations with infant gray and white matter during the first 2 years of life

Distinct neural effects of threat versus deprivation emerge by childhood, but little data are available in infancy. Withdrawn versus negative parenting may represent dimensionalized indices of early deprivation versus early threat, but no studies have assessed neural correlates of withdrawn versus negative parenting in infancy. The objective of this study was to separately assess the links of maternal withdrawal and maternal negative/inappropriate interaction with infant gray matter volume (GMV), white matter volume (WMV), amygdala, and hippocampal volume. Participants included 57 mother-infant dyads. Withdrawn and negative/inappropriate aspects of maternal behavior were coded from the Still-Face Paradigm at four months infant age. Between 4 and 24 months (M age = 12.28 months, SD = 5.99), during natural sleep, infants completed an MRI using a 3.0 T Siemens scanner. GMV, WMV, amygdala, and hippocampal volumes were extracted via automated segmentation. Diffusion weighted imaging volumetric data were also generated for major white matter tracts. Maternal withdrawal was associated with lower infant GMV. Negative/inappropriate interaction was associated with lower overall WMV. Age did not moderate these effects. Maternal withdrawal was further associated with reduced right hippocampal volume at older ages. Exploratory analyses of white matter tracts found that negative/inappropriate maternal behavior was specifically associated with reduced volume in the ventral language network. Results suggest that quality of day-to-day parenting is related to infant brain volumes during the first two years of life, with distinct aspects of interaction associated with distinct neural effects.

versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database

The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.

The structural connectivity mapping of the intralaminar thalamic nuclei

The intralaminar nuclei of the thalamus play a pivotal role in awareness, conscious experience, arousal, sleep, vigilance, as well as in cognitive, sensory, and sexual processing. Nonetheless, in humans, little is known about the direct involvement of these nuclei in such multifaceted functions and their structural connections in the brain. Thus, examining the versatility of structural connectivity of the intralaminar nuclei with the rest of the brain seems reasonable. Herein, we attempt to show the direct structural connectivity of the intralaminar nuclei to diencephalic, mesencephalic, and cortical areas using probabilistic tracking of the diffusion data from the human connectome project. The intralaminar nuclei fiber distributions span a wide range of subcortical and cortical areas. Moreover, the central medial and parafascicular nucleus reveal similar connectivity to the temporal, visual, and frontal cortices with only slight variability. The central lateral nucleus displays a refined projection to the superior colliculus and fornix. The centromedian nucleus seems to be an essential component of the subcortical somatosensory system, as it mainly displays connectivity via the medial and superior cerebellar peduncle to the brainstem and the cerebellar lobules. The subparafascicular nucleus projects to the somatosensory processing areas. It is interesting to note that all intralaminar nuclei have connections to the brainstem. In brief, the structural connectivity of the intralaminar nuclei aligns with the structural core of various functional demands for arousal, emotion, cognition, sensory, vision, and motor processing. This study sheds light on our understanding of the structural connectivity of the intralaminar nuclei with cortical and subcortical structures, which is of great interest to a broader audience in clinical and neuroscience research.

White matter microstructural abnormalities in individuals with attenuated positive symptom syndromes

White matter (WM) microstructural alterations have been extensively studied in patients with psychosis, but research on the microstructure of WM in individuals with attenuated positive symptom syndrome (APSS) is currently limited. To improve the understanding of the neuropathology in APSS, this study investigated the WM of individuals with APSS using diffusion tensor and T1-weighted imaging. Automated fiber quantification was used to calculate the diffusion index values along the trajectories of 20 major fiber tracts in 42 individuals with APSS and 51 age-and sex-matched healthy control (HC) individuals. The diffusion index values in each of fiber tracts were compared node-by-node between the 2 groups. Compared with the HC group, the APSS group showed differences in the diffusion index values in partial segments of the callosum forceps minor, left and right cingulum cingulate, inferior fronto-occipital fasciculus, right corticospinal tract, left superior longitudinal fasciculus, and arcuate fasciculus. Notably, in the APSS group positive associations were found between the axial diffusivity values of the partial nodes of the left and right cingulum cingulate and the current Global Assessment of Functioning scores, as well as between the axial diffusivity values of the partial nodes of the right corticospinal tract and negative symptoms scores and reasoning and problem-solving scores. These findings suggest that individuals with APSS exhibit reduced WM integrity or possible impaired myelin in certain segments of WM tracts involved in the frontal- and limbic-cortical connections. Additionally, abnormal WM tracts appear to be associated with impaired general function and neurocognitive function. This study provides important new insights into the neurobiology of APSS and highlights potential targets for future intervention and treatment.

The role of neuronal plasticity in cervical spondylotic myelopathy surgery: functional assessment and prognostic implication

Cervical spondylotic myelopathy (CSM) is a degenerative disease representing the most common spinal cord disorder in the adult population. It is characterized by chronic compression leading to neurological dysfunction due to static and dynamic injury of the spinal cord in cervical spine. These insidious damage mechanisms can result in the reorganization of cortical and subcortical areas. The cerebral cortex can reorganize due to spinal cord injury and may play a role in preserving neurological function. To date, the gold standard treatment of cervical myelopathy is surgery, comprising anterior, posterior, and combined approaches. However, the complex physiologic recovery processes involving cortical and subcortical neural reorganization following surgery are still inadequately understood. It has been demonstrated that diffusion MRI and functional imaging and techniques, such as transcranial magnetic stimulation (TMS) or functional magnetic resonance imaging (fMRI), can provide new insights into the diagnosis and prognosis of CSM. This review aims to shed light on the state-of-the-art regarding the pattern of cortical and subcortical areas reorganization and recovery before and after surgery in CSM patients, underlighting the critical role of neuroplasticity.

Language function of the superior longitudinal fasciculus in patients with arteriovenous malformation as evidenced by automatic fiber quantification

The superior longitudinal fasciculus (SLF) is a major fiber tract involved in language processing and has been used to investigate language impairments and plasticity in many neurological diseases. The SLF is divided into four main branches that connect with different cortex regions, with two branches (SLF II, SLF III) being directly related to language. However, most white matter analyses consider the SLF as a single bundle, which may underestimate the relationship between these fiber bundles and language function. In this study, we investigated the differences between branches of the SLF in patients with arteriovenous malformation (AVM), which is a unique model to investigate language reorganization. We analyzed diffusion tensor imaging data of AVM patients and healthy controls to generate whole-brain fiber tractography, and then segmented the SLF into SLF II and III based on their distinctive waypoint regions. The SLF, SLF II, and III were further quantified, and four diffusion parameters of three branches were compared between the AVMs and controls. No significant diffusivity differences of the whole SLF were observed between two groups, however, the right SLF II and III in AVMs showed significant reorganization or impairment patterns as compared to the controls. Results demonstrating the need to subtracting SLF branches when studying structure-function relationship in neurological diseases that have SLF damage.

A joint subspace mapping between structural and functional brain connectomes

Understanding the connection between the brain's structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject's functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.

Pseudo multishot echo-planar imaging for geometric distortion improvement

Conventional echo-planar imaging (EPI) uses a radiofrequency pulse for excitation and a prolonged echo train to sample k space, while off-resonance and T₂ * decay effects caused by magnetic susceptibility variation accumulate within each echo, leading to geometric distortion. Multishot EPI methods, which divide k space into segments, can shorten the effective echo spacing and reduce the distortion on EPI images. But multiple shots cost longer scan time and render susceptibility to motion. In this study, we propose a new "multishot" EPI method termed pseudo multishot EPI (pmsEPI), in which phase-encoding lines are segmented as in multishot EPI but are collected within a single shot. With the magnetization divided into different pathways via interleaved excitation instead of refocusing in a single long echo train, the total phase error accumulation is reduced in each segmented acquisition, thereby improving distortion of the resultant EPI image. The performance of the pmsEPI method is demonstrated by phantom and in vivo brain experiments on a 3-T scanner. The experimental results show that the distortion displacements of pmsEPI acquisition compared with conventional EPI decrease by 50% with two pseudo shots and 66% with three pseudo shots, validating the ability of the method to obtain images with reduced distortion in a single shot, although magnetization splitting may induce more than 40% SNR loss and minor artifacts. Specifically, the ability of pmsEPI in diffusion-weighted imaging with different trajectory options is highlighted, and the flexibility is demonstrated in a single-shot blip up and down acquisition.

Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI

Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.

Column-based cortical depth analysis of the diffusion anisotropy and radiality in submillimeter whole-brain diffusion tensor imaging of the human cortical gray matter in vivo

High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders.

A narrative review of the anatomy and function of the white matter tracts in language production and comprehension

Much is known about the role of cortical areas in language processing. The shift towards network approaches in recent years has highlighted the importance of uncovering the role of white matter in connecting these areas. However, despite a large body of research, many of these tracts' functions are not well-understood. We present a comprehensive review of the empirical evidence on the role of eight major tracts that are hypothesized to be involved in language processing (inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, extreme capsule, middle longitudinal fasciculus, superior longitudinal fasciculus, arcuate fasciculus, and frontal aslant tract). For each tract, we hypothesize its role based on the function of the cortical regions it connects. We then evaluate these hypotheses with data from three sources: studies in neurotypical individuals, neuropsychological data, and intraoperative stimulation studies. Finally, we summarize the conclusions supported by the data and highlight the areas needing further investigation.

Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales

The human brain is composed of a complex web of pathways. Diffusion magnetic resonance (MR) tractography is a neuroimaging technique that relies on the principle of diffusion to reconstruct brain pathways. Its tractography is broadly applicable to a range of problems as it is amenable for study in individuals of any age and from any species. However, it is well known that this technique can generate biologically implausible pathways, especially in regions of the brain where multiple fibers cross. This review highlights potential misconnections in two cortico-cortical association pathways with a focus on the aslant tract and inferior frontal occipital fasciculus. The lack of alternative methods to validate observations from diffusion MR tractography means there is a need to develop new integrative approaches to trace human brain pathways. This review discusses integrative approaches in neuroimaging, anatomical, and transcriptional variation as having much potential to trace the evolution of human brain pathways.

Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies

Diffusion tensor imaging (DTI) is gaining traction in neuroscience research as a tool for evaluating neural fibers. The technique can be used to assess white matter (WM) microstructure in neurodegenerative disorders, including Parkinson disease (PD). There is evidence that the uncinate fasciculus and the cingulum bundle are involved in the pathogenesis of PD. These fasciculus and bundle alterations correlate with the symptoms and stages of PD. PRISMA 2022 was used to search PubMed and Scopus for relevant articles. Our search revealed 759 articles. Following screening of titles and abstracts, a full-text review, and implementing the inclusion criteria, 62 papers were selected for synthesis. According to the review of selected studies, WM integrity in the uncinate fasciculus and cingulum bundles can vary according to symptoms and stages of Parkinson disease. This article provides structural insight into the heterogeneous PD subtypes according to their cingulate bundle and uncinate fasciculus changes. It also examines if there is any correlation between these brain structures' structural changes with cognitive impairment or depression scales like Geriatric Depression Scale-Short (GDS). The results showed significantly lower fractional anisotropy values in the cingulum bundle compared to healthy controls as well as significant correlations between FA and GDS scores for both left and right uncinate fasciculus regions suggesting that structural damage from disease progression may be linked to cognitive impairments seen in advanced PD patients. This review help in developing more targeted treatments for different types of Parkinson's disease, as well as providing a better understanding of how cognitive impairments may be related to these structural changes. Additionally, using DTI scans can provide clinicians with valuable information about white matter tracts which is useful for diagnosing and monitoring disease progression over time.

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Integrated diffusion image operator (iDIO): A pipeline for automated configuration and processing of diffusion MRI data

The preprocessing of diffusion magnetic resonance imaging (dMRI) data involve numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for nonexpert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated diffusion image operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or nonspecialist users to adopt the state-of-art dMRI processing steps.

Structural-functional connectivity mapping of the insular cortex: a combined data-driven and meta-analytic topic mapping

In this study, we examined structural and functional profiles of the insular cortex and mapped associations with well-described functional networks throughout the brain using diffusion tensor imaging (DTI) and resting-state functional connectivity (RSFC) data. We used a data-driven method to independently estimate the structural-functional connectivity of the insular cortex. Data were obtained from the Human Connectome Project comprising 108 adult participants. Overall, we observed moderate to high associations between the structural and functional mapping scores of 3 different insular subregions: the posterior insula (associated with the sensorimotor network: RSFC, DTI = 50% and 72%, respectively), dorsal anterior insula (associated with ventral attention: RSFC, DTI = 83% and 83%, respectively), and ventral anterior insula (associated with the frontoparietal: RSFC, DTI = 42% and 89%, respectively). Further analyses utilized meta-analytic decoding maps to demonstrate specific cognitive and affective as well as gene expression profiles of the 3 subregions reflecting the core properties of the insular cortex. In summary, given the central role of the insular in the human brain, our results revealing correspondence between DTI and RSFC mappings provide a complementary approach and insight for clinical researchers to identify dysfunctional brain organization in various neurological disorders associated with insular pathology.

Simultaneous superresolution reconstruction and distortion correction for single-shot EPI DWI using deep learning

PURPOSE

Single-shot (SS) EPI is widely used for clinical DWI. This study aims to develop an end-to-end deep learning-based method with a novel loss function in an improved network structure to simultaneously increase the resolution and correct distortions for SS-EPI DWI.

THEORY AND METHODS

Point-spread-function (PSF)-encoded EPI can provide high-resolution, distortion-free DWI images. A distorted image from SS-EPI can be described as the convolution between a PSF function with a distortion-free image. The deconvolution process to recover the distortion-free image can be achieved with a convolution neural network, which also learns the mapping function between low-resolution SS-EPI and high-resolution reference PSF-EPI to achieve superresolution. To suppress the oversmoothing effect, we proposed a modified generative adversarial network structure, in which a dense net with gradient map guidance and a multilevel fusion block was used as the generator. A fractional anisotropy loss was proposed to utilize the diffusion anisotropy information among diffusion directions. In vivo brain DWI data were used to test the proposed method.

RESULTS

The results show that distortion-corrected high-resolution DWI images with restored structural details can be obtained from low-resolution SS-EPI images by taking advantage of the high-resolution anatomical images. Additionally, the proposed network can improve the quantitative accuracy of diffusion metrics compared with previously reported networks.

CONCLUSION

Using high-resolution, distortion-free EPI-DWI images as references, a deep learning-based method to simultaneously increase the perceived resolution and correct distortions for low-resolution SS-EPI was proposed. The results show that DWI image quality and diffusion metrics can be improved.

Altered white matter connectivity in children with congenital heart disease with single ventricle physiology

Children born with congenital heart disease (CHD) have seen a dramatic decrease in mortality thanks to surgical innovations. However, there are numerous risk factors associated with CHD that can disrupt neurodevelopment. Recent studies have found that psychological deficits and structural brain abnormalities persist into adulthood. The goal of the current study was to investigate white matter connectivity in early school-age children (6-11 years), born with complex cyanotic CHD (single ventricle physiology), who have undergone Fontan palliation, compared to a group of heart-healthy, typically developing controls (TPC). Additionally, we investigated associations between white matter tract connectivity and measures on a comprehensive neuropsychological battery within each group. Our results suggest CHD patients exhibit widespread decreases in white matter connectivity, and the extent of these decreases is related to performance in several cognitive domains. Analysis of network topology showed that hub distribution was more extensive and bilateral in the TPC group. Our results are consistent with previous studies suggesting perinatal ischemia leads to white matter lesions and delayed maturation.