Characterization of early markers of disease in the mouse model of mucopolysaccharidosis IIIB.

BACKGROUND: Mucopolysaccharidosis (MPS) IIIB, also known as Sanfilippo Syndrome B, is a devastating childhood disease. Unfortunately, there are currently no available treatments for MPS IIIB patients. Yet, animal models of lysosomal storage diseases have been valuable tools in identifying promising avenues of treatment. Enzyme replacement therapy, gene therapy, and bone marrow transplant have all shown efficacy in the MPS IIIB model systems. A ubiquitous finding across rodent models of lysosomal storage diseases is that the best treatment outcomes resulted from intervention prior to symptom onset. Therefore, the aim of the current study was to identify early markers of disease in the MPS IIIB mouse model as well as examine clinically-relevant behavioral domains not yet explored in this model. METHODS: Using the MPS IIIB mouse model, we explored early developmental trajectories of communication and gait, and later social behavior, fear-related startle and conditioning, and visual capabilities. In addition, we examined brain structure and function via magnetic resonance imaging and diffusion tensor imaging. RESULTS: We observed reduced maternal isolation-induced ultrasonic vocalizations in MPS IIIB mice relative to controls, as well as disruption in a number of the spectrotemporal features. MPS IIIB also exhibited disrupted thermoregulation during the first two postnatal weeks without any differences in body weight. The developmental trajectories of gait were largely normal. In early adulthood, we observed intact visual acuity and sociability yet a more submissive phenotype, increased aggressive behavior, and decreased social sniffing relative to controls. MPS IIIB mice showed greater inhibition of startle in response to a pretone with a decrease in overall startle response and reduced cued fear memory. MPS IIIB also weighed significantly more than controls throughout adulthood and showed larger whole brain volumes and normalized regional volumes with intact tissue integrity as measured with magnetic resonance and diffusion tensor imaging, respectively. CONCLUSIONS: Together, these results indicate disease markers are present as early as the first two weeks postnatal in this model. Further, this model recapitulates social, sensory and fear-related clinical features. Our study using a mouse model of MPS IIIB provides essential baseline information that will be useful in future evaluations of potential treatments.

Impact of APOE-ε alleles on brain structure and cognitive function in healthy older adults: A VBM and DTI replication study.

BACKGROUND: The Apolipoprotein E (APOE) gene has been established in the Alzheimer's disease (AD) literature to impact brain structure and function and may also show congruent effects in healthy older adults, although findings in this population are much less consistent. The current study aimed to replicate and expand the multimodal approach employed by Honea et al. Structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and neuropsychological measures were used to investigate the impact of APOE-ε status on grey matter structure, white matter integrity, and cognitive functioning. METHODS: Data were obtained from the Alzheimer's Disease Initiative Phase 3 (ADNI3) database. Baseline MRI, DTI and cognitive composite scores for memory (ADNI-Mem) and executive function (ADNI-EF) were acquired from 116 healthy controls. Participants were grouped according to APOE allele presence (APOE-ε2+ N = 17, APOE-ε3ε3 N = 64, APOE-ε4+ N = 35). Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to compare grey matter volume (GMV) and white matter integrity, respectively, between APOE-ε2+ and APOE-ε3ε3 controls, and again between APOE-ε4+ and APOE-ε3ε3 controls. Multivariate analysis of covariance (MANCOVA) was used to examine the effects of APOE polymorphism on memory and EF across all APOE groups with age, sex and education as regressors of no interest. Cognitive scores were correlated (Pearson r) with imaging metrics within groups. RESULTS: No significant differences were seen across groups, within groups in MRI metrics, or cognitive performance (p>0.05, corrected for multiple comparisons). CONCLUSIONS: The current study partially replicated and extended previous findings from an earlier multimodal study (Honea 2009). Future studies should clarify APOE mechanisms in healthy ageing by adding other imaging, cognitive, and lifestyle metrics and longitudinal design in larger sample sizes.

Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm2) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm3 voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics.

Clinico-radiological factors associated with aphasia outcome in post stroke patients: A prospective follow up study from eastern part of India.

BACKGROUND: Aphasia is a language disorder acquired secondary to brain damage. This study aims to evaluate clinical and radiological profile of patients with post stroke aphasia and factors affecting its recovery. METHODS: We conducted a prospective study of patients with first left Middle or Anterior Cerebral Artery infarct or Intracerebral Hemorrhage (ICH) with aphasia admitted within 14 days of stroke onset. Aphasia Quotient (AQ) was assessed at 2 weeks (AQ1) and 3 months (AQ2) using Western Aphasia Battery-Hindi version. Magnetic Resonance Imaging of brain with Diffusion Tensor Imaging (DTI) of bilateral Arcuate Fasciculus (AF) and Corticospinal Tract was done at admission, and stroke volume, Laterality Indices of Fractional Anisotropy (LI-FA), Mean Diffusivity (LI-MD), Radial Diffusivity (LI-RD), Axial Diffusivity (LI-AD) and Apparent Diffusion Coefficient (LI-ADC) were obtained. RESULTS: 36 patients [8 ICH and 28 Acute Ischemic Stroke (AIS)] were included. AQ1 and AQ2 were significantly higher in subcortical stroke than cortical. AQ2 and increase in AQ scores (including its subscores) were significantly higher in ICH than AIS. National Institutes of Health Stroke Scale score at admission and volume of stroke had significant negative correlation with AQ1 and AQ2. Laterality Index of Fractional Anisotropy of Arcuate Fasciculus [LI-FA (AF)] had significant positive correlation with AQ2 and naming score at 3 months. Laterality Index of Mean Diffusivity of Arcuate Fasciculus [LI-MD (AF)] had significant negative correlation with AQ1, AQ2 and all subcomponents of AQ2. Significant positive correlation was seen between improvements in Modified Rankin Scale score and AQ. CONCLUSION: The study shows that DTI can be used to predict severity of aphasia at follow up and recovery in language and motor functions occur in parallel.

Improved inter-subject alignment of the lumbosacral cord for group-level in vivo gray and white matter assessments: A scan-rescan MRI study at 3T.

INTRODUCTION: Magnetic resonance imaging (MRI) enables the investigation of pathological changes in gray and white matter at the lumbosacral enlargement (LSE) and conus medullaris (CM). However, conducting group-level analyses of MRI metrics in the lumbosacral spinal cord is challenging due to variability in CM length, lack of established image-based landmarks, and unknown scan-rescan reliability. This study aimed to improve inter-subject alignment of the lumbosacral cord to facilitate group-level analyses of MRI metrics. Additionally, we evaluated the scan-rescan reliability of MRI-based cross-sectional area (CSA) measurements and diffusion tensor imaging (DTI) metrics. METHODS: Fifteen participants (10 healthy volunteers and 5 patients with spinal cord injury) underwent axial T2*-weighted and diffusion MRI at 3T. We assessed the reliability of spinal cord and gray matter-based landmarks for inter-subject alignment of the lumbosacral cord, the inter-subject variability of MRI metrics before and after adjusting for the CM length, the intra- and inter-rater reliability of CSA measurements, and the scan-rescan reliability of CSA measurements and DTI metrics. RESULTS: The slice with the largest gray matter CSA as an LSE landmark exhibited the highest reliability, both within and across raters. Adjusting for the CM length greatly reduced the inter-subject variability of MRI metrics. The intra-rater, inter-rater, and scan-rescan reliability of MRI metrics were the highest at and around the LSE (scan-rescan coefficient of variation <3% for CSA measurements and <7% for DTI metrics within the white matter) and decreased considerably caudal to it. CONCLUSIONS: To facilitate group-level analyses, we recommend using the slice with the largest gray matter CSA as a reliable LSE landmark, along with an adjustment for the CM length. We also stress the significance of the anatomical location within the lumbosacral cord in relation to the reliability of MRI metrics. The scan-rescan reliability values serve as valuable guides for power and sample size calculations in future longitudinal studies.

Noninvasive- and invasive mapping reveals similar language network centralities - A function-based connectome analysis.

BACKGROUND: Former comparisons between direct cortical stimulation (DCS) and navigated transcranial magnetic stimulation (nTMS) only focused on cortical mapping. While both can be combined with diffusion tensor imaging, their differences in the visualization of subcortical and even network levels remain unclear. Network centrality is an essential parameter in network analysis to measure the importance of nodes identified by mapping. Those include Degree centrality, Eigenvector centrality, Closeness centrality, Betweenness centrality, and PageRank centrality. While DCS and nTMS have repeatedly been compared on the cortical level, the underlying network identified by both has not been investigated yet. METHOD: 27 patients with brain lesions necessitating preoperative nTMS and intraoperative DCS language mapping during awake craniotomy were enrolled. Function-based connectome analysis was performed based on the cortical nodes obtained through the two mapping methods, and language-related network centralities were compared. RESULTS: Compared with DCS language mapping, the positive predictive value of cortical nTMS language mapping is 74.1%, with good consistency of tractography for the arcuate fascicle and superior longitudinal fascicle. Moreover, network centralities did not differ between the two mapping methods. However, ventral stream tracts can be better traced based on nTMS mappings, demonstrating its strengths in acquiring language-related networks. In addition, it showed lower centralities than other brain areas, with decentralization as an indicator of language function loss. CONCLUSION: This study deepens the understanding of language-related functional anatomy and proves that non-invasive mapping-based network analysis is comparable to the language network identified via invasive cortical mapping.

Predictability of intelligence and age from structural connectomes.

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

Diffusion tensor imaging in anisotropic tissues: application of reduced gradient vector schemes in peripheral nerves.

BACKGROUND: In contrast to the brain, fibers within peripheral nerves have distinct monodirectional structure questioning the necessity of complex multidirectional gradient vector schemes for DTI. This proof-of-concept study investigated the diagnostic utility of reduced gradient vector schemes in peripheral nerve DTI. METHODS: Three-Tesla magnetic resonance neurography of the tibial nerve using 20-vector DTI (DTI20) was performed in 10 healthy volunteers, 12 patients with type 2 diabetes, and 12 age-matched healthy controls. From the full DTI20 dataset, three reduced datasets including only two or three vectors along the x- and/or y- and z-axes were built to calculate major parameters. The influence of nerve angulation and intraneural connective tissue was assessed. The area under the receiver operating characteristics curve (ROC-AUC) was used for analysis. RESULTS: Simplified datasets achieved excellent diagnostic accuracy equal to DTI20 (ROC-AUC 0.847-0.868, p ≤ 0.005), but compared to DTI20, the reduced models yielded mostly lower absolute values of DTI scalars: median fractional anisotropy (FA) ≤ 0.12; apparent diffusion coefficient (ADC) ≤ 0.25; axial diffusivity ≤ 0.96, radial diffusivity ≤ 0.07). The precision of FA and ADC with the three-vector model was closest to DTI20. Intraneural connective tissue was negatively correlated with FA and ADC (r ≥ -0.49, p < 0.001). Small deviations of nerve angulation had little effect on FA accuracy. CONCLUSIONS: In peripheral nerves, bulk tissue DTI metrics can be approximated with only three predefined gradient vectors along the scanner's main axes, yielding similar diagnostic accuracy as a 20-vector DTI, resulting in substantial scan time reduction. RELEVANCE STATEMENT: DTI bulk tissue parameters of peripheral nerves can be calculated with only three predefined gradient vectors at similar diagnostic performance as a standard DTI but providing a substantial scan time reduction. KEY POINTS: • In peripheral nerves, DTI parameters can be approximated using only three gradient vectors. • The simplified model achieves a similar diagnostic performance as a standard DTI. • The simplified model allows for a significant acceleration of image acquisition. • This can help to introduce multi-b-value DTI techniques into clinical practice.

Renal interstitial fibrotic assessment using non-Gaussian diffusion kurtosis imaging in a rat model of hyperuricemia.

BACKGROUND: To investigate the feasibility of Diffusion Kurtosis Imaging (DKI) in assessing renal interstitial fibrosis induced by hyperuricemia. METHODS: A hyperuricemia rat model was established, and the rats were randomly split into the hyperuricemia (HUA), allopurinol (AP), and AP + empagliflozin (AP + EM) groups (n = 19 per group). Also, the normal rats were selected as controls (CON, n = 19). DKI was performed before treatment (baseline) and on days 1, 3, 5, 7, and 9 days after treatment. The DKI indicators, including mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) of the cortex (CO), outer stripe of the outer medulla (OS), and inner stripe of the outer medulla (IS) were acquired. Additionally, hematoxylin and eosin (H&E) staining, Masson trichrome staining, and nuclear factor kappa B (NF-κB) immunostaining were used to reveal renal histopathological changes at baseline, 1, 5, and 9 days after treatment. RESULTS: The HUA, AP, and AP + EM group MKOS and MKIS values gradually increased during this study. The HUA group exhibited the highest MK value in outer medulla. Except for the CON group, all the groups showed a decreasing trend in the FA and MD values of outer medulla. The HUA group exhibited the lowest FA and MD values. The MKOS and MKIS values were positively correlated with Masson's trichrome staining results (r = 0.687, P < 0.001 and r = 0.604, P = 0.001, respectively). The MDOS and FAIS were negatively correlated with Masson's trichrome staining (r = -626, P < 0.0014 and r = -0.468, P = 0.01, respectively). CONCLUSION: DKI may be a non-invasive method for monitoring renal interstitial fibrosis induced by hyperuricemia.

A novel biomarker selection method using multimodal neuroimaging data.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

MRI Data Analysis in Malformations of Cortical Development.

Brain magnetic resonance imaging (MRI) is a noninvasive imaging modality that utilizes powerful magnets and radio waves to generate detailed images of the brain, making it a valuable tool for investigating malformations of cortical development (MCD). Various MRI techniques, including 3D T1-weighted, multiplanar thin-sliced T2-weighted, and 3D fluid-attenuated inversion recovery (FLAIR) sequences, can provide high-resolution images with excellent spatial and contrast resolution, allowing for a detailed visualization of cortical anatomy and abnormalities. Almost all MCD can be detected and characterized using MRI. Advanced techniques, such as arterial spin labeling MR perfusion, diffusion tensor imaging (DTI), and functional MRI (fMRI), may be used to improve the detection rate of these malformations and to plan surgery in case of drug-resistant epilepsy. However, there are also limitations related to high cost, relatively low availability, need for sedation or anesthesia, and limited sensitivity for detecting subtle focal cortical malformations. Despite these limitations, brain MRI plays a crucial role in the investigation of MCD, providing valuable information for diagnosis, treatment planning, and patient management.

Effect of transcranial direct current stimulation combined with transcutaneous auricular vagus nerve stimulation on poststroke cognitive impairment: a study protocol for a randomised controlled trial.

INTRODUCTION: Poststroke cognitive impairment is a common complication in stroke survivors, seriously affecting their quality of life. Therefore, it is crucial to improve cognitive function of patients who had a stroke. Transcranial direct current stimulation (tDCS) and transcutaneous auricular vagus nerve stimulation (taVNS) are non-invasive, safe treatments with great potential to improve cognitive function in poststroke patients. However, further improvements are needed in the effectiveness of a single non-invasive brain stimulation technique for cognitive rehabilitation. This study protocol aims to investigate the effect and neural mechanism of the combination of tDCS and taVNS on cognitive function in patients who had a stroke. METHODS AND ANALYSIS: In this single-centre, prospective, parallel, randomised controlled trial, a total of 66 patients with poststroke cognitive impairment will be recruited and randomly assigned (1:1:1) to the tDCS group, the taVNS group and the combination of tDCS and taVNS group. Each group will receive 30 min of treatment daily, five times weekly for 3 weeks. Primary clinical outcome is the Montreal Cognitive Assessment. Secondary clinical outcomes include the Mini-Mental State Examination, Stroop Colour Word Test, Trail Marking Test, Symbol Digit Modalities Test and Modified Barthel Index. All clinical outcomes, functional MRI and diffusion tensor imaging will be measured at preintervention and postintervention. ETHICS AND DISSEMINATION: The trial has been approved by the Ethics Committee of the First Affiliated Hospital of Yangtze University (approval no: KY202390). The results will be submitted for publication in peer-reviewed journals or at scientific conferences. TRIAL REGISTRATION NUMBER: ChiCTR2300076632.

Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected.

HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to HIV uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.

Atypical brain structural connectivity and social cognition in childhood maltreatment and peer victimisation.

BACKGROUND: Childhood maltreatment (CM) is associated with neurobiological aberrations and atypical social cognition. Few studies have examined the neural effects of another common early-life interpersonal stressor, namely peer victimisation (PV). This study examines the associations between tract aberrations and childhood interpersonal stress from caregivers (CM) and peers (PV), and explores how the observed tract alterations are in turn related to affective theory of mind (ToM). METHODS: Data from 107 age-and gender-matched youths (34 CM [age = 19.9 ± 1.68; 36%male], 35 PV [age = 19.9 ± 1.65; 43%male], 38 comparison subjects [age = 20.0 ± 1.66; 42%male] were analysed using tractography and whole-brain tract-based spatial statistics (TBSS). RESULTS: At the whole-brain level using TBSS, the CM group had higher fractional anisotropy (FA) than the PV and comparison groups in a cluster of predominantly limbic and corpus callosal pathways. Segmented tractography indicated the CM group had higher FA in right uncinate fasciculus compared to both groups. They also had smaller right anterior thalamic radiation (ATR) tract volume than the comparison group and higher left ATR FA than the PV group, with these metrics associated with higher emotional abuse and enhanced affective ToM within the CM group, respectively. The PV group had lower inferior fronto-occipital fasciculus FA than the other two groups, which was related to lower affective ToM within the PV group. CONCLUSION: Findings suggest that exposure to early-life stress from caregivers and peers are differentially associated with alterations of neural pathways connecting the frontal, temporal and occipital cortices involved in cognitive and affective control, with possible links to their atypical social cognition.

MR Imaging-based Biomarker Development in Hemorrhagic Stroke Patients Including Brain Iron Quantification, Diffusion Tensor Imaging, and Phenomenon of Ultra-early Erythrolysis.

This review article discusses the role of MR imaging-based biomarkers in understanding and managing hemorrhagic strokes, focusing on intracerebral hemorrhage (ICH) and aneurysmal subarachnoid hemorrhage. ICH is a severe type of stroke with high mortality and morbidity rates, primarily caused by the rupture of small blood vessels in the brain, resulting in hematoma formation. MR imaging-based biomarkers, including brain iron quantification, ultra-early erythrolysis detection, and diffusion tensor imaging, offer valuable insights for hemorrhagic stroke management. These biomarkers could improve early diagnosis, risk stratification, treatment monitoring, and patient outcomes in the future, revolutionizing our approach to hemorrhagic strokes.

Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method.

Objective.Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation, overlooking the dynamic nature of the diffusion process and providing an incomplete representation of the predicted FC.Approach.In this study, we propose an alternative approach based on the HDM. First, we introduced a multiple-timescale fusion method to capture the dynamic features of the diffusion process. Additionally, to enhance the smoothness of the predicted FC values, we employed the Wavelet reconstruction method to maintain local consistency and remove noise. Moreover, to provide a more accurate representation of the relationship between SC and FC, we calculated the linear transformation between the smoothed FC and the empirical FC.Main results.We conducted extensive experiments in two independent datasets. By fusing different time scales in the diffusion process for predicting FC, the proposed method demonstrated higher predictive correlation compared with method considering only critical time points (Singlescale). Furthermore, compared with other existing methods, the proposed method achieved the highest predictive correlations of 0.6939±0.0079 and 0.7302±0.0117 on the two datasets respectively. We observed that the visual network at the network level and the parietal lobe at the lobe level exhibited the highest predictive correlations, indicating that the functional activity in these regions may be closely related to the direct diffusion of information between brain regions.Significance.The multiple-timescale fusion method proposed in this study provides insights into the dynamic aspects of the diffusion process, contributing to a deeper understanding of how brain structure gives rise to brain function.

White Matter Alterations in Military Service Members With Remote Mild Traumatic Brain Injury.

Importance: Mild traumatic brain injury (mTBI) is the signature injury experienced by military service members and is associated with poor neuropsychiatric outcomes. Yet, there is a lack of reliable clinical tools for mTBI diagnosis and prognosis. Objective: To examine the white matter microstructure and neuropsychiatric outcomes of service members with a remote history of mTBI (ie, mTBI that occurred over 2 years ago) using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Design, Setting, and Participants: This case-control study examined 98 male service members enrolled in a study at the National Intrepid Center of Excellence. Eligible participants were active duty status or able to enroll in the Defense Enrollment Eligibility Reporting system, ages 18 to 60 years, and had a remote history of mTBI; controls were matched by age. Exposures: Remote history of mTBI. Main Outcomes and Measures: White matter microstructure was assessed using a region-of-interest approach of skeletonized diffusion images, including DTI (fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity) and NODDI (orientation dispersion index [ODI], isotropic volume fraction, intra-cellular volume fraction). Neuropsychiatric outcomes associated with posttraumatic stress disorder (PTSD) and postconcussion syndrome were assessed. Results: A total of 65 male patients with a remote history of mTBI (mean [SD] age, 40.5 [5.0] years) and 33 age-matched male controls (mean [SD] age, 38.9 [5.6] years) were included in analysis. Compared with the control cohort, the 65 service members with mTBI presented with significantly more severe PTSD-like symptoms (mean [SD] PTSD CheckList-Civilian [PCL-C] version scores: control, 19.0 [3.8] vs mTBI, 41.2 [11.6]; P < .001). DTI and NODDI metrics were altered in the mTBI group compared with the control, including intra-cellular volume fraction of the right cortico-spinal tract (ß = -0.029, Cohen d = 0.66; P < .001), ODI of the left posterior thalamic radiation (ß = -0.006, Cohen d = 0.55; P < .001), and ODI of the left uncinate fasciculus (ß = 0.013, Cohen d = 0.61; P < .001). In service members with mTBI, fractional anisotropy of the left uncinate fasciculus was associated with postconcussion syndrome (ß = 5.4 × 10-3; P = .003), isotropic volume fraction of the genu of the corpus callosum with PCL-C (ß = 4.3 × 10-4; P = .01), and ODI of the left fornix and stria terminalis with PCL-C avoidance scores (ß = 1.2 × 10-3; P = .02). Conclusions and Relevance: In this case-control study of military-related mTBI, the results suggest that advanced magnetic resonance imaging techniques using NODDI can reveal white matter microstructural alterations associated with neuropsychiatric symptoms in the chronic phase of mTBI. Diffusion trends observed throughout widespread white matter regions-of-interest may reflect mechanisms of neurodegeneration as well as postinjury tissue scarring and reorganization.

Comparative analysis of glymphatic system alterations in multiple sclerosis and neuromyelitis optica spectrum disorder using MRI indices from diffusion tensor imaging.

OBJECTIVE: The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Glymphatic system dysfunction has been observed in both multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), indicating the role of neuroinflammation in the glymphatic system. However, little is known about how the two diseases differently affect the human glymphatic system. The present study aims to evaluate the diffusion MRI-based measures of the glymphatic system by contrasting MS and NMOSD. METHODS: This prospective study included 63 patients with NMOSD (n = 21) and MS (n = 42) who underwent DTI. The fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. Age and EDSS scores were adjusted. RESULTS: Using Bayesian hypothesis testing, we show that the present data substantially favor the null model of no differences between MS and NMOSD for the diffusion MRI-based measures of the glymphatic system. The inclusion Bayes factor (BF10) of model-averaged probabilities of the group (MS, NMOSD) was 0.280 for FW and 0.236 for the ALPS index. CONCLUSION: Together, these findings suggest that glymphatic alteration associated with MS and NMOSD might be similar and common as an eventual result, albeit the disease etiologies differ. PRACTITIONER POINTS: Previous literature indicates important glymphatic system alteration in MS and NMOSD. We explore the difference between MS and NMOSD using diffusion MRI-based measures of the glymphatic system. We show support for the null hypothesis of no difference between MS and NMOSD. This suggests that glymphatic alteration associated with MS and NMOSD might be similar and common etiology.

Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models.

BACKGROUND: Diffusion-weighted imaging (DWI) has been developed to stage liver fibrosis. However, its diagnostic performance is inconsistent among studies. Therefore, it is worth studying the diagnostic value of various diffusion models for liver fibrosis in one cohort. AIM: To evaluate the clinical potential of six diffusion-weighted models in liver fibrosis staging and compare their diagnostic performances. METHODS: This prospective study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants. All participants underwent multi-b value DWI. The main DWI-derived parameters included Mono-apparent diffusion coefficient (ADC) from mono-exponential DWI, intravoxel incoherent motion model-derived true diffusion coefficient (IVIM-D), diffusion kurtosis imaging-derived apparent diffusivity (DKI-MD), stretched exponential model-derived distributed diffusion coefficient (SEM-DDC), fractional order calculus (FROC) model-derived diffusion coefficient (FROC-D) and FROC model-derived microstructural quantity (FROC-µ), and continuous-time random-walk (CTRW) model-derived anomalous diffusion coefficient (CTRW-D) and CTRW model-derived temporal diffusion heterogeneity index (CTRW-α). The correlations between DWI-derived parameters and fibrosis stages and the parameters' diagnostic efficacy in detecting significant fibrosis (SF) were assessed and compared. RESULTS: CTRW-D (r = -0.356), CTRW-α (r = -0.297), DKI-MD (r = -0.297), FROC-D (r = -0.350), FROC-µ (r = -0.321), IVIM-D (r = -0.251), Mono-ADC (r = -0.362), and SEM-DDC (r = -0.263) were significantly correlated with fibrosis stages. The areas under the ROC curves (AUCs) of the combined index of the six models for distinguishing SF (0.697-0.747) were higher than each of the parameters alone (0.524-0.719). The DWI models' ability to detect SF was similar. The combined index of CTRW model parameters had the highest AUC (0.747). CONCLUSION: The DWI models were similarly valuable in distinguishing SF in patients with liver disease. The combined index of CTRW parameters had the highest AUC.

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations.

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.