Musical Sophistication and Multilingualism: Effects on Arcuate Fasciculus Characteristics.

The processing of auditory stimuli which are structured in time is thought to involve the arcuate fasciculus, the white matter tract which connects the temporal cortex and the inferior frontal gyrus. Research has indicated effects of both musical and language experience on the structural characteristics of the arcuate fasciculus. Here, we investigated in a sample of n = 84 young adults whether continuous conceptualizations of musical and multilingual experience related to structural characteristics of the arcuate fasciculus, measured using diffusion tensor imaging. Probabilistic tractography was used to identify the dorsal and ventral parts of the white matter tract. Linear regressions indicated that different aspects of musical sophistication related to the arcuate fasciculus' volume (emotional engagement with music), volumetric asymmetry (musical training and music perceptual abilities), and fractional anisotropy (music perceptual abilities). Our conceptualization of multilingual experience, accounting for participants' proficiency in reading, writing, understanding, and speaking different languages, was not related to the structural characteristics of the arcuate fasciculus. We discuss our results in the context of other research on hemispheric specializations and a dual-stream model of auditory processing.

Comparing the imaging characteristics of middle-aged patients with multiple sclerosis and CADASIL: A retrospective cross-sectional study.

BACKGROUND: Few studies have quantitatively analyzed the imaging disparities between multiple sclerosis (MS) and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). We aimed to compare the imaging characteristics of MS and CADASIL in middle-aged patients. MATERIALS AND METHODS: This retrospective study used a single-center database and included patients aged 40-60 years with MS and CADASIL who underwent the designated imaging protocol including 3D T1-weighted imaging and fluid attenuated inversion recovery (FLAIR), diffusion tensor imaging and susceptibility-weighted imaging between January 2018 and March 2023. Patients with MRI-detected macrobleeds were excluded. RESULTS: A total of 27 patients with MS (mean age, 46.7 years ± 4.4, 8 men) and 30 patients with CADASIL (mean age, 51.6 years ± 5.8, 14 men) were included. No significant differences were observed in the Fazekas grades of white matter lesions (WMLs). Patients with CADASIL exhibited greater external capsule involvement (56.7% vs.18.5 %; p = 0.006), whereas the MS group had more lesions in the corpus callosum (81.5% vs. 53.3 %, p = 0.02) and brainstem (74.1% vs. 46.7 %, p = 0.04). The CADASIL group exhibited a higher incidence of microbleeds (12.07 vs. 0.11, p = 0.001). The WMLs in the MS group exhibited a lower T1 lesion/cerebrospinal fluid signal index (2.206 vs. 2.882, p < 0.001). A value of ≤2.57 demonstrated a sensitivity of 92.6 % and a specificity of 90.0 % in differentiating MS. Patients with MS had a thinner corpus callosum (7.18 mm vs 7.86 mm, p = 0.04), while patients with CADASIL showed significantly higher mean diffusivity (0.8776 × 10-3 vs. 0.7637 × 10-3 mm2/s, p = 0.03) and lower fractional anisotropy (0.7581 vs. 0.8389, p = 0.04) in the splenium of the corpus callosum. CONCLUSION: Middle-aged patients with MS and CADASIL showed comparable Fazekas grades for WMLs. However, lesion distribution, T1 signal characteristics, and splenic diffusivity changes can help differentiate between MS and CADASIL.

Anatomical and subcortical invasiveness in diffuse low-grade astrocytomas differ between IDH status and provide prognostic information.

Background: Diffuse astrocytomas preferentially infiltrate eloquent areas affecting the outcome. A preoperative understanding of isocitrate dehydrogenase (IDH) status may offer opportunities for specific targeted therapies impacting treatment management. The aim of this study was to analyze clinical, topographical, radiological in WHO 2 astrocytomas with different IDH status and the long-term patient's outcome. Methods: A series of confirmed WHO 2 astrocytoma patients (between 2005 and 2015) were retrospectively analyzed. MRI sequences (FLAIR) were used for tumor volume segmentation and to create a frequency map of their locations into the Montreal Neurological Institute (MNI) space. The Brain-Grid (BG) system (standardized radiological tool of intersected lines according to anatomical landmarks) was used as an overlay for infiltration analysis of each tumor. Long-term follow-up was used to perform a survival analysis. Results: Forty patients with confirmed IDH status (26 IDH-mutant, IDHm/14 IDH-wild type, IDHwt) according to WHO 2021 classification were included with a mean follow-up of 7.8 years. IDHm astrocytomas displayed a lower number of BG-voxels (P < 0.05) and were preferentially located in the anterior insular region. IDHwt group displayed a posterior insular and peritrigonal location. IDHwt group displayed a shorter OS compared with IDHm (P < 0.05), with the infiltration of 7 or more BG-voxels as an independent factor predicting a shorter OS. Conclusions: IDHm and IDHwt astrocytomas differed in preferential location, number of BG-voxels and OS at long follow-up time. The number of BG-voxels affected the OS in IDHwt was possibly reflecting higher tumor invasiveness. We encourage the systematic use of alternative observational tools, such as gradient maps and the Brain-Grid analysis, to better detect differences of tumor invasiveness in diffuse low-grade gliomas subtypes.

Microstructural Changes in the Corpus Callosum in Neurodegenerative Diseases.

The corpus callosum, the largest white matter structure in the brain, plays a crucial role in interhemispheric communication and cognitive function. This review examines the microstructural changes observed in the corpus callosum across various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis (ALS). New neuroimaging studies, mainly those that use diffusion tensor imaging (DTI) and advanced tractography methods, were put together to show how changes have happened in the organization of white matter and the connections between them. Some of the most common ways the corpus callosum breaks down are discussed, including less fractional anisotropy, higher mean diffusivity, and atrophy in certain regions. The relationship between these microstructural changes and cognitive decline, motor dysfunction, and disease progression is explored. Additionally, we consider the potential of corpus callosum imaging as a biomarker for early disease detection and monitoring. Studies show that people with these disorders have lower fractional anisotropy and higher mean diffusivity in the corpus callosum, often in ways that are specific to the disease. These changes often happen before gray matter atrophy and are linked to symptoms, which suggests that the corpus callosum could be used as an early sign of neurodegeneration. The review also highlights the implications of these findings for understanding disease mechanisms and developing therapeutic strategies. Future directions, including the application of advanced imaging techniques and longitudinal studies, are discussed to elucidate the role of corpus callosum degeneration in neurodegenerative processes. This review underscores the importance of the corpus callosum in understanding the pathophysiology of neurodegenerative diseases and its potential as a target for therapeutic interventions.

In vivo cardiac diffusion tensor imaging on an MR system featuring ultrahigh performance gradients with 200 mT/m maximum gradient strength.

PURPOSE: Our aim is to assess the potential of an MR system with ultrahigh performance gradients (200 mT/m maximum gradient strength) to address two interrelated challenges in cardiac DTI: low SNR and sensitivity to bulk motion. METHODS: Imaging was performed in 20 healthy volunteers, two patients, and one swine post-myocardial infarction. The impact of maximum gradient strength was assessed with spin echo cardiac DTI featuring second-order motion compensation and varying maximum system gradient strengths (40, 80, 200 mT/m). Motion compensation requirements at 200 mT/m were assessed with sequences featuring zeroth-, first-, and second-order motion compensation. SNR, mean diffusivity, fractional anisotropy, helix angle transmurality, and secondary eigenvector angle in the left ventricle were compared. RESULTS: Increasing maximum system gradient strength from 40 and 80 mT/m to 200 mT/m increased SNR of b = 500 s/mm2 images by 150% and 40% due to reductions in TE. Observed improvements in DTI metrics included reduction in variance in mean diffusivity and helix angle transmurality across healthy volunteers, improved visualization of myocardial borders and delineation of suspected scar. Whereas second-order motion compensation acquisitions were robust to motion-induced signal dropout, zeroth- and first-order motion compensation acquisitions suffered from severe signal loss and localized signal voids, respectively. CONCLUSION: Ultrahigh performance gradients (200 mT/m) enable high SNR DWIs of the heart and resultant improvements in diffusion tensor metrics. Despite reduced diffusion-encoding duration, second-order motion compensation is required to overcome sensitivity to cardiac motion.

The use of DTI in the differentiation and surgical planning of suprasellar hypothalamic-opticochiasmatic glioma and craniopharyngioma in children.

BACKGROUND AND OBJECTIVES: Suprasellar hypothalamic-opticochiasmatic glioma (HOCG) and craniopharyngioma (CP) have similar appearances on conventional MRI and are difficult to distinguish. Moreover, these tumors are situated near vital structures like the optic chiasm and hypothalamus, rendering conventional surgery susceptible to significant complications. We mainly discussed the surgical application value and diagnostic value of diffusion tensor imaging (DTI) in HOCG and CP. METHODS: The retrospective analysis of 13 cases of HOCG and 16 cases of CP was conducted. All patients underwent conventional MRI and DTI prior to surgery, and were pathologically diagnosed postoperatively. RESULTS: Both CP and HOCG appeared as heterogeneous mixed signal masses on conventional MRI. For HOCGs, fiber tractography revealed two different growth patterns of the tumor: infiltrative type and inflated type. The surgical approach and risk levels differ between these growth patterns. Additionally, fiber tractography demonstrates significant differences compared to CPs. The surgical approach and extent of resection for all cases of these two tumors were guided by DTI. CONCLUSION: DTI enhances the accuracy of HOCG and CP differentiation. Furthermore, patterns of tractography described in this study assist neurosurgeons in delineating the surgical pathway and tumor resection range without damaging important fiber bundles, thereby avoiding permanent neurological deficits and improving survival quality for patients.

Predicting Age from White Matter Diffusivity with Residual Learning.

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

A diffusion tensor imaging white matter atlas of the domestic canine brain.

There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion-weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts-including association, projection, commissural, brainstem, olfactory, and cerebellar tracts-were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.

Similarity and characterization of structural and functional neural connections within species under isoflurane anesthesia in the common marmoset.

The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.

Age-related brain-region-specific changes in diffusion anisotropy in subarachnoid space of young adults.

The glymphatic system theory postulates that brain waste is removed through the cerebrospinal fluid (CSF) flow. According to this theory, CSF in the subarachnoid space (SAS) moves to the perivascular space around the penetrating arteries, flows into parenchyma to mix with interstitial fluid and brain waste, and then moves to the perivenous space to be flushed out of the brain. Despite the controversies about the glymphatic theory, it is clear that SAS plays a key role in waste clearance. For instance, the SAS around the middle cerebral artery is known to be highly involved in glymphatic influx. While diffusion tensor imaging has been used for studying the glymphatic system, there has been limited exploration of age-related changes in diffusion anisotropy within SAS and their regional variations. Given the narrow and heterogeneous morphology of SAS, the fractional anisotropy (FA) in diverse brain regions may be more relevant to glymphatic transport than mean diffusivity (MD). The goal of this study was to investigate FA in SAS to observe age-related changes across different brain regions and to interpret the results based on the glymphatic transport. We segmented SAS in the whole brain of 83 young adults and divided SAS into four cortical lobes. We demonstrated regional variations in FA and MD within SAS and an age-related decline in FA among young adults, indicating that diffusion within SAS becomes more isotropic with aging. These findings raise new questions about the factors influencing diffusion anisotropy within SAS, which are relevant to glymphatic transport.

Relationships between neuroimaging biomarkers and glymphatic-system activity in dementia with Lewy bodies.

Alpha-synuclein deposits in the brain have been suspected to cause Parkinson's disease and dementia with Lewy bodies (DLB). It was recently revealed that the glymphatic system is largely responsible for the removal of alpha-synuclein. We investigated changes in the glymphatic system's activity by determining the DTI­ALPS (diffusion tensor image analysis along the perivascular space) index in DLB patients. Twenty-six patients with DLB and 43 healthy subjects underwent diffusion tensor imaging (DTI) scanning at our hospital during the period April 2013 to March 2023. We retrospectively computed each subject's DTI­ALPS index to evaluate his/her glymphatic-system activity and then analyzed the relationships between the subjects' DTI­ALPS index data and their DLB neuroimaging biomarker values. A significant reduction of the DTI­ALPS index was observed in the patients with DLB compared to the healthy subjects. Significant positive correlations were also detected in the DLB group between the DTI­ALPS index and the regional gray matter volume in the left insula and between the index and the specific binding ratio of 123I-N-ω-fluoropropyl-2ß-carboxymethoxy-3ß-(4-iodophenyl)nortropane ([123I]-FP-CIT). These results indicate that (i) the DTI­ALPS index is a good biomarker of the progression of DLB, and (ii) this index might be effective to distinguish DLB from other neurocognitive disorders.

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.

Multi-modal risk factors differentiate suicide attempters from ideators in military veterans with major depressive disorder.

BACKGROUND: The suicide rate for United States military veterans is 1.5× higher than that of non-veterans. To meaningfully advance suicide prevention efforts, research is needed to delineate factors that differentiate veterans with suicide attempt/s, particularly in high-risk groups, e.g., major depressive disorder (MDD), from those with suicidal ideation (no history of attempt/s). The current study aimed to identify clinical, neurocognitive, and neuroimaging variables that differentiate suicide-severity groups in veterans with MDD. METHODS: Sixty-eight veterans with a DSM-5 diagnosis of MDD, including those with no ideation or suicide attempt (N = 21; MDD-SI/SA), ideation-only (N = 17; MDD + SI), and one-or-more suicide attempts (N = 30; MDD + SA; aborted, interrupted, actual attempts), participated in this study. Participants underwent a structured diagnostic interview, neurocognitive assessment, and 3 T-structural/diffusion tensor magnetic-resonance-imaging (MRI). Multinomial logistic regression models were conducted to identify variables that differentiated groups with respect to the severity of suicidal behavior. RESULTS: Relative to veterans with MDD-SI/SA, those with MDD + SA had significantly higher left cingulum fractional anisotropy, decreased attentional control on emotional-Stroop, and faster response time with intact accuracy on Go/No-Go. Relative to MDD + SI, MDD + SA had higher left cingulum fractional anisotropy and faster response time with intact accuracy on Go/No-Go. LIMITATIONS: Findings are based on retrospective, cross-sectional data and cannot identify causal relationships. Also, a healthy control group was not included given the study's focus on differentiating suicide profiles in MDD. CONCLUSIONS: This study suggests that MRI and neurocognition differentiate veterans with MDD along the suicide-risk spectrum and could inform suicide-risk stratification and prevention efforts in veterans and other vulnerable populations.

From Brownian motion to virtual biopsy: a historical perspective from 40 years of diffusion MRI.

Diffusion MRI was introduced in 1985, showing how the diffusive motion of molecules, especially water, could be spatially encoded with MRI to produce images revealing the underlying structure of biologic tissues at a microscopic scale. Diffusion is one of several Intravoxel Incoherent Motions (IVIM) accessible to MRI together with blood microcirculation. Diffusion imaging first revolutionized the management of acute cerebral ischemia by allowing diagnosis at an acute stage when therapies can still work, saving the outcomes of many patients. Since then, the field of diffusion imaging has expanded to the whole body, with broad applications in both clinical and research settings, providing insights into tissue integrity, structural and functional abnormalities from the hindered diffusive movement of water molecules in tissues. Diffusion imaging is particularly used to manage many neurologic disorders and in oncology for detecting and classifying cancer lesions, as well as monitoring treatment response at an early stage. The second major impact of diffusion imaging concerns the wiring of the brain (Diffusion Tensor Imaging, DTI), allowing to obtain from the anisotropic movement of water molecules in the brain white-matter images in 3 dimensions of the brain connections making up the Connectome. DTI has opened up new avenues of clinical diagnosis and research to investigate brain diseases, neurogenesis and aging, with a rapidly extending field of application in psychiatry, revealing how mental illnesses could be seen as Connectome spacetime disorders. Adding that water diffusion is closely associated to neuronal activity, as shown from diffusion fMRI, one may consider that diffusion MRI is ideally suited to investigate both brain structure and function. This article retraces the early days and milestones of diffusion MRI which spawned over 40 years, showing how diffusion MRI emerged and expanded in the research and clinical fields, up to become a pillar of modern clinical imaging.

Glutamate chemical exchange saturation transfer (GluCEST) MRI to evaluate the relationship between demyelination and glutamate content in depressed mice.

Glutamatergic alteration is one of the potential mechanisms of depression. However, there is no consensus on whether glutamate metabolism changes affect the myelin structure of depression in mouse models. Glutamate chemical exchange saturation transfer (GluCEST) is a novel and powerful molecular imaging technique that can visualize glutamate distribution. In this study, we used the GluCEST imaging technique to look at glutamate levels in mice under chronic unpredictable mild stress (CUMS) and how they relate to demyelination. The CUMS mice were exposed to different stress factors for 6 weeks. Evaluated of depression in CUMS mice by behavioral tests. MRI scans were then performed, including T2-mapping, GluCEST, and diffusion tensor imaging (DTI) sequences. Brain tissues were collected for Luxol Fast Blue staining and immunofluorescence staining to analyze the changes in the myelin sheath. Artificially sketched regions of interest (ROI) (corpus callosum, hippocampus, and thalamus) were used to calculate the GluCEST value, fractional anisotropy (FA), and T2 value. Compared with the control group, the GluCEST value in the ROIs of CUMS mice significantly decreased. Similarly, the FA value in ROIs was lower in the CUMS group than in the CTRL group, but the T2 value did not differ significantly between the two groups. The histological results showed that ROIs in the CUMS group had demyelination compared with the CTRL group, indicating that DTI was more sensitive than T2 mapping in detecting myelin abnormalities. Furthermore, the GluCEST value in the ROIs correlates positively with the FA value. These findings suggest that altered glutamate metabolism may be one of the important factors leading to demyelination in depression, and GluCEST is expected to serve as an imaging biological marker for the diagnosis of demyelination in depression.

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks.

Multimodal neuroimaging modeling has become a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from various modalities-functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI-within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function. This study not only advances multi-modal neuroimaging analytics by offering a novel method for the integrated analysis of diverse imaging modalities but also improves the understanding of intricate relationship between the brain's structural and functional networks and cognitive development.

A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods.

The internal arrangement of a muscle's fibers with respect to its mechanical line of action (muscle architecture) is a major determinant of muscle function. Muscle architecture can be quantified using diffusion tensor magnetic resonance imaging-based tractography, which propagates streamlines from a set of seed points by integrating vectors that represent the direction of greatest water diffusion (and by inference, the local fiber orientation). Previous work has demonstrated that tractography outcomes are sensitive to the method for defining seed points, but this sensitivity has not been fully examined. To do so, we developed a realistic simulated muscle architecture and implemented four novel methods for tract seeding: seeding along the muscle-aponeurosis boundary with an updated procedure for rounding seed points prior to lookup in the muscle boundary mask and diffusion tensor matrix (APO-3); voxel-based seeding throughout the muscle volume at a user-specified spatial frequency (VXL-1); voxel-based seeding throughout the muscle volume at a variable spatial frequency (VXL-2), and seeding near external and internal muscle boundaries (VXL-3). We then implemented these methods in an example human dataset. The updated aponeurosis seeding procedures allow more accurate and robust tract propagation from seed points. The voxel-based seeding methods had quantification outcomes that closely matched the updated aponeurosis seeding method. Further, the voxel-based methods can accelerate the overall workflow and may be beneficial in high throughput analysis of multi-muscle datasets. Continued evaluation of these methods in a wider range of muscle architectures is warranted.

The brain extracellular space in chronic kidney disease.

The brain extracellular space (ECS) is a highly complex structure between the innumerable and intermingled processes of brain cells (neurons and glial cells). This space represents up to 20 % of total brain volume (excluding the ventricles) and hosts an extracellular matrix of proteoglycans. The regulation of this space is unclear, though it may differ from other organs due to the presence of the blood brain barrier. Changes in the ECS may modify the diffusion timing of volume-dependent neurotransmitters such as dopamine, thus potentially altering most brain activities. Indeed, recently it has been shown that mild cognitive impairment is correlated to a reduction of ECS. Because water and electrolyte homeostasis are tightly regulated by the kidney, it is possible that a reduced kidney filtration may change the brain extracellular space and therefore explain the reduced cognitive functions exhibited during kidney diseases. The present communication explores the regulation of ECS in the presence of kidney diseases, discussing how reduced kidney function might impact on brain structure and function in both mice and humans, and suggests potential mechanisms for this link.

The impact of indirect structure on functional connectivity in schizophrenia using a multiplex brain network.

It is known that abnormal functional connectivity (FC) in schizophrenia (SZ) is closely related to structural connectivity (SC). We speculate that indirect SC also have an impact on FC in SZ patients. Conventional single-layer network has limitations for studying the relationship between indirect SC and FC. Thus, this study constructed a multiplex network based on structural connectivity and functional connectivity (SC-FC). The SC-FC bandwidth and SC-FC cost are used to analyze the impact of indirect SC on FC. Moreover, this paper proposed mediation ability, mediation cost, mediated strength and mediated cost to quantify the effects of mediator nodes and mediated nodes on indirect SC. The results show that SZ patients exhibit lower SC-FC bandwidth and SC-FC cost compared to healthy controls (HC), which could be caused by the limbic and subcortical network (LSN), default mode network (DMN) and visual network (VN). The mediator and mediated nodes in indirect SC of SZ patients also showed diminished effects. These findings suggest that functional communication ability and cost in SZ patients are influenced by indirect SC. This study provides new perspectives for understanding the relationship between indirect SC and FC, and provides strong evidence for interpreting the physiological mechanisms of SZ patients.

Diffusion tensor imaging and gray matter volumetry to evaluate cerebral remodeling processes after a pure motor stroke: a longitudinal study.

BACKGROUND AND OBJECTIVES: Clinical factors are not sufficient to fix a prognosis of recovery after stroke. Pyramidal tract or alternate motor fiber (aMF: reticulo-, rubrospinal pathways and transcallosal fibers) integrity and remodeling processes assessable by diffusion tensor MRI (DTI) and voxel-based morphometry (VBM) may be of interest. The primary objective was to study longitudinal cortical brain changes using VBM and longitudinal corticospinal tract changes using DTI during the first 4 months after lacunar cerebral infarction. The second objective was to determine which changes were correlated to clinical improvement. METHODS: Twenty-one patients with deep brain ischemic infarct with pure motor deficit (NIHSS score ≥ 2) were recruited at Purpan Hospital and included. Motor deficit was measured [Nine peg hole test (NPHT), dynamometer (DYN), Hand-Tapping Test (HTT)], and a 3T MRI scan (VBM and DTI) was performed during the acute and subacute phases. RESULTS: White matter changes: corticospinal fractional anisotropy (FACST) was significantly reduced at follow-up (approximately 4 months) on the lesion side. FAr (FA ratio in affected/unaffected hemispheres) in the corona radiata was correlated to the motor performance at the NPHT, DYN, and HTT at follow-up. The presence of aMFs was not associated with the extent of recovery. Grey matter changes: VBM showed significant increased cortical thickness in the ipsilesional premotor cortex at follow-up. VBM changes in the anterior cingulum positively correlated with improvement in motor measures between baseline and follow-up. DISCUSSION: To our knowledge, this study is original because is a longitudinal study combining VBM and DTI during the first 4 months after stroke in a series of patients selected on pure motor deficit. Our data would suggest that good recovery relies on spared CST fibers, probably from the premotor cortex, rather than on the aMF in this group with mild motor deficit. The present study suggests that VBM and FACST could provide reliable biomarkers of post-stroke atrophy, reorganization, plasticity and recovery. GOV IDENTIFIER: NCT01862172, registered May 24, 2013.