Diffusion-weighted Magnetic Resonance Imaging Data Research Articles

Anatomy-based diffusion-weighted MRI quality metric: a proof-of-concept for deriving accurate muscle fiber orientation.

Diffusion-weighted MRI (DW-MRI) is used to quantitatively characterize the microscopic structure of muscle through anisotropic water diffusion in soft tissue. Applications such as tumor propagation modeling require precise detection of muscle fiber orientation. That is, the direction along the fibers that coincides with the direction of the principal eigenvector of the diffusion tensor reconstructed from DW-MRI data. For clinical applications, the quality of image data is determined by the signal-to-noise ratio (SNR) that must be achieved within the appropriate scan time. The acquisition protocol must therefore be optimized. This implies the need for SNR criteria that match the data quality of the application. 
Approach. Muscles with known structural heterogeneity, e.g. bipennate muscles such as the rectus femoris in the thigh, provide a natural quality benchmark to determine accuracy of inferred fiber orientation at different scan parameters. In this study, we analyze DW-MR images of the thigh of a healthy volunteer at different SNRs and use PCA to identify subsets of voxels with different directions of diffusion tensor eigenvectors corresponding to different pennate angles. We propose to use the separation index of spatial co-localization of the clustered eigenvectors as a quality metric for fiber orientation detection.
Main results. The clustering in the PCA component coordinates can be translated to the separation of the two compartments of the bipennate muscle on either side of the central tendon according to the pennate angle. The separation index reflects the degree of the separation and is a function of SNR. 
Significance. Because the separation index allows joint estimation of spatial and directional noise in DW-MRI as a single parameter, it will allow future quantitative optimization of DW-MRI soft tissue protocols.

Deciphering white matter microstructural alterations in catatonia according to ICD-11: replication and machine learning analysis

AbstractCatatonia is a severe psychomotor disorder characterized by motor, affective and cognitive-behavioral abnormalities. Although previous magnetic resonance imaging (MRI) studies suggested white matter (WM) dysconnectivity in the pathogenesis of catatonia, it is unclear whether microstructural alterations of WM tracts connecting psychomotor regions might contribute to a better classification of catatonia patients. Here, diffusion-weighted MRI data were collected from two independent cohorts (whiteCAT/replication cohort) of patients with (n = 45/n = 13) and without (n = 56/n = 26) catatonia according to ICD-11 criteria. Catatonia severity was examined using the Northoff (NCRS) and Bush-Francis (BFCRS) Catatonia Rating Scales. We used tract-based spatial statistics (TBSS), tractometry (TractSeg) and machine-learning (ML) to classify catatonia patients from tractometry values as well as tractomics features generated by the newly developed tool RadTract. Catatonia patients showed fractional anisotropy (FA) alterations measured via TractSeg in different corpus callosum segments (CC_1, CC_3, CC_4, CC_5 and CC_6) compared to non-catatonia patients across both cohorts. Our classification results indicated a higher level of performance when trained on tractomics as opposed to traditional tractometry values. Moreover, in the CC_6, we successfully trained two classifiers using the tractomics features identified in the whiteCAT data. These classifiers were applied separately to the whiteCAT and replication cohorts, demonstrating comparable performance with Area Under the Receiver Operating Characteristics (AUROC) values of 0.79 for the whiteCAT cohort and 0.76 for the replication cohort. In contrast, training on FA tractometry resulted in lower AUROC values of 0.66 for the whiteCAT cohort and 0.51 for the replication cohort. In conclusion, these findings underscore the significance of CC WM microstructural alterations in the pathophysiology of catatonia. The successful use of an ML based classification model to identify catatonia patients has the potential to improve diagnostic precision.

Brain connectivity disruptions in PTSD related to early adversity: a multimodal neuroimaging study

ABSTRACT Background: Post-traumatic stress disorder (PTSD) is increasingly prevalent in individuals with adverse childhood experiences (ACE). However, the underlying neurobiology of ACE-related PTSD remains unclear. Objective: The present study investigated the brain connectivity in ACE-related PTSD using multimodal neuroimaging data. Methods: Using a total of 119 participants with ACE (70 with ACE-related PTSD and 49 ACE-exposed controls), this study acquired T1-weighted MRI, diffusion-weighted MRI, and resting-state fMRI data to examine structural and functional connectivity between groups. Joint connectivity matrix independent component analysis (Jcm-ICA) was employed to allow shared information from all modalities to be examined and assess structural and functional connectivity differences between groups. Results: Jcm-ICA revealed distinct connectivity alterations in key brain regions involved in cognitive control, self-referential processing, and social behaviour. Compared to controls, the PTSD group exhibited functional hyperconnectivity of the right medial prefrontal cortex (PFC) of the default mode network and right inferior temporal cortex, and functional hypoconnectivity in the lateral-PFC of the central executive network and structural hypoconnectivity in white matter pathways including the right orbitofrontal region (OFC) linked to social behaviour. Post-hoc analyses using the joint brain-based information revealed that the severity of ACE, the number of traumas, and PTSD symptoms later in life significantly predicted the effects of ACE-related PTSD on the brain. Notably, no direct association between brain connectivity alterations and PTSD symptoms or the number of traumas within the PTSD group was observed. Conclusion: This study offers novel insights into the neurobiology of ACE-related PTSD using multimodal data fusion. We identified alterations in key brain networks (DMN, CEN) and OFC, suggesting potential deficits in cognitive control and social behaviour alongside heightened emotional processing in individuals with PTSD. Furthermore, our findings highlight the combined influence of ACE exposure, number of traumas experienced, and PTSD severity on brain connectivity disruptions, potentially informing future interventions.

Subcortical Alterations in Newly Diagnosed Epilepsy and Associated Changes in Brain Connectivity and Cognition.

Patients with chronic focal epilepsy commonly exhibit subcortical atrophy, particularly of the thalamus. The timing of these alterations remains uncertain, though preliminary evidence suggests that observable changes may already be present at diagnosis. It is also not yet known how these morphological changes are linked to the coherence of white matter pathways throughout the brain, or to neuropsychological function often compromised before antiseizure medication treatment. This study investigates localized atrophy in subcortical regions using surface shape analysis in individuals with newly diagnosed focal epilepsy (NDfE) and assesses their implications on brain connectivity and cognitive function. We collected structural (T1w) and diffusion-weighted MRI and neuropsychological data from 104 patients with NDfE and 45 healthy controls (HCs) matched for age, sex, and education. A vertex-based shape analysis was performed on subcortical structures to compare patients with NDfE and HC, adjusting for age, sex, and intracranial volume. The mean deformation of significance areas (pcor < 0.05) was used to identify white matter pathways associated with overall shape alterations in patients relative to controls using correlational tractography. Additionally, the relationship between significant subcortical shape values and neuropsychological outcomes was evaluated using a generalized canonical correlation approach. Shape analysis revealed bilateral focal inward deformation (a proxy for localized atrophy) in anterior areas of the right and left thalamus and right pallidum in patients with NDfE compared to HC (FWE corrected). No structures showed areas of outward deformation in patients. The connectometry analysis revealed that fractional anisotropy (FA) was positively correlated with thalamic and pallidal shape deformation, that is, reduced FA was associated with inward deformation in tracts proximal to and or connecting with the thalamus including the fornix, frontal, parahippocampal, and corticothalamic pathways. Thalamic and pallidal shape changes were also related to increased depression and anxiety and reduced memory and cognitive function. These findings suggest that atrophy of the thalamus, which has previously been associated with the generation and maintenance of focal seizures, may present at epilepsy diagnosis and relate to alterations in both white matter connectivity and cognitive performance. We suggest that at least some alterations in brain structure and consequent impact on cognitive and affective processes are the result of early epileptogenic processes rather than exclusively due to the chronicity of longstanding epilepsy, recurrent seizures, and treatment with antiseizure medication.

Robust, fast and accurate mapping of diffusional mean kurtosis

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Robust, fast and accurate mapping of diffusional mean kurtosis.

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Objective Determination of Site-of-Lesion in Auditory Neuropathy.

Auditory neuropathy (AN), a complex hearing disorder, presents challenges in diagnosis and management due to limitations of current diagnostic assessment. This study aims to determine whether diffusion-weighted magnetic resonance imaging (MRI) can be used to identify the site and severity of lesions in individuals with AN. This case-control study included 10 individuals with AN of different etiologies, 7 individuals with neurofibromatosis type 1 (NF1), 5 individuals with cochlear hearing loss, and 37 control participants. Participants were recruited through the University of Melbourne's Neuroaudiology Clinic and the Murdoch Children's Research Institute specialist outpatient clinics. Diffusion-weighted MRI data were collected for all participants and the auditory pathways were evaluated using the fixel-based analysis metric of apparent fiber density. Data on each participant's auditory function were also collected including hearing thresholds, otoacoustic emissions, auditory evoked potentials, and speech-in-noise perceptual ability. Analysis of diffusion-weighted MRI showed abnormal white matter fiber density in distinct locations within the auditory system depending on etiology. Compared with controls, individuals with AN due to perinatal oxygen deprivation showed no white matter abnormalities ( p > 0.05), those with a neurodegenerative conditions known/predicted to cause VIII cranial nerve axonopathy showed significantly lower white matter fiber density in the vestibulocochlear nerve ( p < 0.001), while participants with NF1 showed lower white matter fiber density in the auditory brainstem tracts ( p = 0.003). In addition, auditory behavioral measures of speech perception in noise and gap detection were correlated with fiber density results of the VIII nerve. Diffusion-weighted MRI reveals different patterns of anatomical abnormality within the auditory system depending on etiology. This technique has the potential to guide management recommendations for individuals with peripheral and central auditory pathway abnormality.

Discrimination Between Benign and Malignant Lesions With Restriction Spectrum Imaging MRI in an Enriched Breast Cancer Screening Cohort.

Breast cancer screening with dynamic contrast-enhanced MRI (DCE-MRI) is recommended for high-risk women but has limitations, including variable specificity and difficulty in distinguishing cancerous (CL) and high-risk benign lesions (HRBL) from average-risk benign lesions (ARBL). Complementary non-invasive imaging techniques would be useful to improve specificity. To evaluate the performance of a previously-developed breast-specific diffusion-weighted MRI (DW-MRI) model (BS-RSI3C) to improve discrimination between CL, HRBL, and ARBL in an enriched screening population. Prospective. Exactly 187 women, either with mammography screening recommending additional imaging (N = 49) or high-risk individuals undergoing routine breast MRI (N = 138), before the biopsy. Multishell DW-MRI echo planar imaging sequence with a reduced field of view at 3.0 T. A total of 72 women had at least one biopsied lesion, with 89 lesions categorized into ARBL, HRBL, CL, and combined CLs and HRBLs (CHRLs). DW-MRI data were processed to produce apparent diffusion coefficient (ADC) maps, and estimate signal contributions (C1, C2, and C3-restricted, hindered, and free diffusion, respectively) from the BS-RSI3C model. Lesion regions of interest (ROIs) were delineated on DW images based on suspicious DCE-MRI findings by two radiologists; control ROIs were drawn in the contralateral breast. One-way ANOVA and two-sided t-tests were used to assess differences in signal contributions and ADC values among groups. P-values were adjusted using the Bonferroni method for multiple testing, P = 0.05 was used for the significance level. Receiver operating characteristics (ROC) curves and intra-class correlations (ICC) were also evaluated. C1, √C1C2, and were significantly different in HRBLs compared with ARBLs (P-values < 0.05). The had the highest AUC (0.821) in differentiating CHRLs from ARBLs, performing better than ADC (0.696), especially in non-mass enhancement (0.776 vs. 0.517). This study demonstrated the BS-RSI3C could differentiate HRBLs from ARBLs in a screening population, and separate CHRLs from ARBLs better than ADC. 2.

Relevance of lesion size in navigator-triggered and free-breathing diffusion-weighted liver MRI.

The purpose of this study was to investigate the relevance of focal liver lesions (FLL) size for lesion detection comparing navigator triggering (TRIG) to free breathing (FB) liver Diffusion-weighted magnetic resonance imaging (DWI). Patients with known or suspected FLL were prospectively (registry number 276_19 B) included from October to December 2019 in this study, out of which 32 had liver lesions. Echo planar spin-echo DWI data both with TRIG and FB were with approximately constant acquisition times acquired at 1.5 T. Lesions were segmented in the b = 800 s/mm² images in both the TRIG and FB images. The lesion size, location (liver segment), liver lesion visibility, as well as contrast-to-noise ratio (CNR) were recorded. The CNR was assessed with the Wilcoxon-Mann-Whitney test and the number of visible lesions with the Fisher test. Data from 43 patients (22 female) were analyzed. The mean patient age was 58 ± 14 years. A total of 885 FLL (Ntotal) were segmented. Among these, 811 lesions (Nboth) were detected with TRIG and FB, 65 lesions exclusively with TRIG (NTRIG_Only), and nine exclusively in FB (NFB_Only). The largest additional lesion in TRIG/FB had a diameter of 10.4 mm/7.6 mm. The number of additional lesions detected with TRIG decreased with size. Among all lesions ≤ 4.7 mm, the relative number of additional lesions was 15.6%. Additional lesions were found in all liver segments with TRIG. In the left liver lobe, the relative proportion was 9.2%, and in the right liver lobe 5.4%. CNR and visibility were significantly higher in TRIG than in FB (p < 0.001). In relation to size, the difference is significant in terms of visibility and CNR for lesion diameters ≤ 8 mm. Respiration triggering can improve the detection of small liver lesions with diameters up to approx. 1 cm in the whole liver. Question Can respiration triggering (TRIG) improve the detection of small FLL compared to FB diffusion-weighted imaging? Findings Among 885 segmented FLL, TRIG was superior to FB for lesions smaller than 8 mm and had improved CNR and visibility. Clinical relevance Diffusion-weighted magnetic resonance imaging is used for the detection of focal liver lesions and image quality is influenced by breathing motion. Navigator triggering becomes more important for smaller lesions, and seems recommendable for the detection of small focal liver lesions.

Multi-modality multi-task model for mRS prediction using diffusion-weighted resonance imaging

This study focuses on predicting the prognosis of acute ischemic stroke patients with focal neurologic symptoms using a combination of diffusion-weighted magnetic resonance imaging (DWI) and clinical information. The primary outcome is a poor functional outcome defined by a modified Rankin Scale (mRS) score of 3–6 after 3 months of stroke. Employing nnUnet for DWI lesion segmentation, the study utilizes both multi-task and multi-modality methodologies, integrating DWI and clinical data for prognosis prediction. Integrating the two modalities was shown to improve performance by 0.04 compared to using DWI only. The model achieves notable performance metrics, with a dice score of 0.7375 for lesion segmentation and an area under the curve of 0.8080 for mRS prediction. These results surpass existing scoring systems, showing a 0.16 improvement over the Totaled Health Risks in Vascular Events score. The study further employs grad-class activation maps to identify critical brain regions influencing mRS scores. Analysis of the feature map reveals the efficacy of the multi-tasking nnUnet in predicting poor outcomes, providing insights into the interplay between DWI and clinical data. In conclusion, the integrated approach demonstrates significant advancements in prognosis prediction for cerebral infarction patients, offering a superior alternative to current scoring systems.

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundlewise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxelwise cross-acquisition relationships was the most effective at harmonizing connectomic, microstructure, and macrostructure features, but requires the same subject be scanned at each site co-registered. NeSH, a spatial and angular resampling method, was also effective and has generalizable framework not reliant co-registration. Our code is available at &lt;a href='https://github.com/nancynewlin-masi/QuantConn/'&gt;https://github.com/nancynewlin-masi/QuantConn/&lt;/a&gt;

A worldwide study of white matter microstructural alterations in people living with Parkinson’s disease

The progression of Parkinson’s disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20–89 years; 33% female) and 885 controls (age: 19–84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen’s d effect sizes reached d = −1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder.

Structure-function coupling reveals the brain hierarchical structure dysfunction in Alzheimer's disease: A multicenter study.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline. To date, the specific dysfunction in the brain's hierarchical structure in AD remains unclear. We introduced the structural decoupling index (SDI), based on a multi-site data set comprising functional and diffusion-weighted magnetic resonance imaging data from 793 subjects, to assess their brain hierarchy. Compared to normal controls (NCs), individuals with AD exhibited increased SDI within the posterior superior temporal sulcus, insular gyrus, precuneus, hippocampus, amygdala, postcentral gyrus, and cingulate gyrus; meanwhile, the patients with AD demonstrated decreased SDI in the frontal lobe. The SDI in those regions also showed a significant correlation with cognitive ability. Moreover, the SDI was a robust AD neuroimaging biomarker capable of accurately distinguishing diagnostic status (area under the curve [AUC]=0.86). Our findings revealed the dysfunction of the brain's hierarchical structure in AD. Furthermore, the SDI could serve as a promising neuroimaging biomarker for AD. This study utilized multi-center, multi-modal data from East Asian populations. We found an increased spatial gradient of the structure decoupling index (SDI) from sensory-motor to higher-order cognitive regions. Changes in SDI are associated with energy metabolism and mitochondria. SDI can identify Alzheimer's disease (AD) and further uncover the disease mechanisms of AD.

Posttraumatic survivor guilt is associated with white matter microstructure alterations

BackgroundMilitary veterans with posttraumatic stress disorder (PTSD) commonly experience posttraumatic guilt. Guilt over commission or omission evolves when responsibility is assumed for an unfortunate outcome (e.g., the death of a fellow combatant). Survivor guilt is a state of intense emotional distress experienced by the weight of knowing that one survived while others did not. MethodsThis study of the Translational Research Center for TBI and Stress Disorders (TRACTS) analyzed structural and diffusion-weighted magnetic resonance imaging data from 132 male Iraq/Afghanistan veterans with PTSD. The Clinician-Administered PTSD Scale for DSM-IV (CAPS-IV) was employed to classify guilt. Thirty (22.7 %) veterans experienced guilt over acts of commission or omission, 34 (25.8 %) experienced survivor guilt, and 68 (51.5 %) had no posttraumatic guilt. White matter microstructure (fractional anisotropy, FA), cortical thickness, and cortical volume were compared between veterans with guilt over acts of commission or omission, veterans with survivor guilt, and veterans without guilt. ResultsVeterans with survivor guilt had significantly lower white matter FA compared to veterans who did not experience guilt (p < .001), affecting several regions of major white matter fiber bundles. There were no significant differences in white matter FA, cortical thickness, or volumes between veterans with guilt over acts of commission or omission and veterans without guilt (p > .050). LimitationsThis cross-sectional study with exclusively male veterans precludes inferences of causality between the studied variables and generalizability to the larger veteran population that includes women. ConclusionSurvivor guilt may be a particularly impactful form of posttraumatic guilt that requires specific treatment efforts targeting brain health.

Tractometry of the Human Connectome Project: resources and insights.

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

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

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

Insomnia Subtypes Have Differentiating Deviations in Brain Structural Connectivity

BackgroundInsomnia disorder is the most common sleep disorder. A better understanding of insomnia-related deviations in the brain could inspire better treatment. Insufficiently recognized heterogeneity within the insomnia population could obscure detection of involved brain circuits. In the current study, we investigated whether structural brain connectivity deviations differed between recently discovered and validated insomnia subtypes. MethodsStructural and diffusion-weighted 3T magnetic resonance imaging data from 4 independent studies were harmonized. The sample consisted of 73 control participants without sleep complaints and 204 participants with insomnia who were grouped into 5 insomnia subtypes based on their fingerprint of mood and personality traits assessed with the Insomnia Type Questionnaire. Linear regression correcting for age and sex was used to evaluate group differences in structural connectivity strength, indicated by fractional anisotropy, streamline volume density, and mean diffusivity and evaluated within 3 different atlases. ResultsInsomnia subtypes showed differentiating profiles of deviating structural connectivity that were concentrated in different functional networks. Permutation testing against randomly drawn heterogeneous subsamples indicated significant specificity of deviation profiles in 4 of the 5 subtypes: highly distressed, moderately distressed reward sensitive, slightly distressed low reactive, and slightly distressed high reactive. Connectivity deviation profile significance ranged from p = .001 to p = .049 for different resolutions of brain parcellation and connectivity weight. ConclusionsOur results provide an initial indication that different insomnia subtypes exhibit distinct profiles of deviations in structural brain connectivity. Subtyping insomnia may be essential for a better understanding of brain mechanisms that contribute to insomnia vulnerability.

Association of cortical gyrification, white matter microstructure, and phenotypic profile in medication-naïve obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is thought to arise from dysconnectivity among interlinked brain regions resulting in a wide spectrum of clinical manifestations. Cortical gyrification, a key morphological feature of human cerebral cortex, has been considered associated with developmental connectivity in early life. Monitoring cortical gyrification alterations may provide new insights into the developmental pathogenesis of OCD. Sixty-two medication-naive patients with OCD and 59 healthy controls (HCs) were included in this study. Local gyrification index (LGI) was extracted from T1-weighted MRI data to identify the gyrification changes in OCD. Total distortion (splay, bend, or twist of fibers) was calculated using diffusion-weighted MRI data to examine the changes in white matter microstructure in patients with OCD. Compared with HCs, patients with OCD showed significantly increased LGI in bilateral medial frontal gyrus and the right precuneus, where the mean LGI was positively correlated with anxiety score. Patients with OCD also showed significantly decreased total distortion in the body, genu, and splenium of the corpus callosum (CC), where the average distortion was negatively correlated with anxiety scores. Intriguingly, the mean LGI of the affected cortical regions was significantly correlated with the mean distortion of the affected white matter tracts in patients with OCD. We demonstrated associations among increased LGI, aberrant white matter geometry, and higher anxiety in patients with OCD. Our findings indicate that developmental dysconnectivity-driven alterations in cortical folding are one of the neural substrates underlying the clinical manifestations of OCD.

Structural Neuroplasticity Effects of Singing in Chronic Aphasia.

Singing-based treatments of aphasia can improve language outcomes, but the neural benefits of group-based singing in aphasia are unknown. Here, we set out to determine the structural neuroplasticity changes underpinning group-based singing-induced treatment effects in chronic aphasia. Twenty-eight patients with at least mild nonfluent poststroke aphasia were randomized into two groups that received a 4-month multicomponent singing intervention (singing group) or standard care (control group). High-resolution T1 images and multishell diffusion-weighted MRI data were collected in two time points (baseline/5 months). Structural gray matter (GM) and white matter (WM) neuroplasticity changes were assessed using language network region of interest-based voxel-based morphometry (VBM) and quantitative anisotropy-based connectometry, and their associations to improved language outcomes (Western Aphasia Battery Naming and Repetition) were evaluated. Connectometry analyses showed that the singing group enhanced structural WM connectivity in the left arcuate fasciculus (AF) and corpus callosum as well as in the frontal aslant tract (FAT), superior longitudinal fasciculus, and corticostriatal tract bilaterally compared with the control group. Moreover, in VBM, the singing group showed GM volume increase in the left inferior frontal cortex (Brodmann area 44) compared with the control group. The neuroplasticity effects in the left BA44, AF, and FAT correlated with improved naming abilities after the intervention. These findings suggest that in the poststroke aphasia group, singing can bring about structural neuroplasticity changes in left frontal language areas and in bilateral language pathways, which underpin treatment-induced improvement in speech production.

A novel imaging marker of cortical “cellularity” in multiple sclerosis patients

Pathological data showed focal inflammation and regions of diffuse neuronal loss in the cortex of people with multiple sclerosis (MS). In this work, we applied a novel model (“soma and neurite density imaging (SANDI)”) to multishell diffusion-weighted MRI data acquired in healthy subjects and people with multiple sclerosis (pwMS), in order to investigate inflammation and degeneration-related changes in the cortical tissue of pwMS. We aimed to (i) establish whether SANDI is applicable in vivo clinical data; (ii) investigate inflammatory and degenerative changes using SANDI soma fraction (fsoma)—a marker of cellularity—in both cortical lesions and in the normal-appearing-cortex and (iii) correlate SANDI fsoma with clinical and biological measures in pwMS. We applied a simplified version of SANDI to a clinical scanners. We then provided evidence that pwMS exhibited an overall decrease in cortical SANDI fsoma compared to healthy subjects, suggesting global degenerative processes compatible with neuronal loss. On the other hand, we have found that progressive pwMS showed a higher SANDI fsoma in the outer part of the cortex compared to relapsing–remitting pwMS, possibly supporting current pathological knowledge of increased innate inflammatory cells in these regions. A similar finding was obtained in subpial lesions in relapsing–remitting patients, reflecting existing pathological data in these lesion types. A significant correlation was found between SANDI fsoma and serum neurofilament light chain—a biomarker of inflammatory axonal damage—suggesting a relationship between SANDI soma fraction and inflammatory processes in pwMS again. Overall, our data show that SANDI fsoma is a promising biomarker to monitor changes in cellularity compatible with neurodegeneration and neuroinflammation in the cortex of MS patients.