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Abstract The glymphatic system is functional waste clearance path from the brain parenchyma through dynamic exchange of cerebrospinal fluid (CSF) with interstitial fluid (ISF). Impairment of glymphatic waste clearance is involved in the development of neurodegenerative conditions. Despite many recent studies investigating the glymphatic system, few studies have tried to use a mathematical model to describe this system, quantitatively. In this study, we aim to model the glymphatic system from the kinetics of Gd-DTPA tracer measured using MRI in order to: 1) map the glymphatic system path, 2) derive kinetic parameters of the glymphatic system, and 3) provide quantitative maps of the structure and function of this system. In the proposed model, the brain is clustered to similar regions with respect to the profile of contrast agent (CA) density measured by MRI. Then, each region is described as a two-compartment kinetic model 'derived from' or 'clears to' its neighbors with local input function. We thus fit our model to the local cerebral regions rather than to the averaged time signal curve (TSC) of the whole brain. The estimated parameters showed distinctive differences between diabetes mellitus (DM) and control rats. The results suggest that in a typical DM brain the CSF bulk speed in the para-vasculature network is low. In addition, the resulting maps indicate that there may be increased binding and decreased absorbing of large molecules in a diabetic compared with a non-diabetic brain. The important contribution of this work was to fit the model to the local regions rather than to the averaged time signal curve (TSC) of the whole brain. This enabled us to derive quantitative maps of the glymphatic system from MRI. Highlights • Mathematical modeling of glymphatic system using local input function. • Map the glymphatic system pathways. • Derive kinetic parameters of the glymphatic system. • Provide quantitative maps of the structure and function of this system. • The developed maps are sensitive to diabetic induce brain changes. [ABSTRACT FROM AUTHOR]

Substantial effort is being expended on using micro-structural modeling of the white matter, with the goal of relating diffusion weighted magnetic resonance imaging (DWMRI) to the underlying structure of the tissue, such as axonal density. However, one of the important parameters affecting diffusion is the water exchange rate between the intra- and extra-axonal space, which has not been fully investigated and is a crucial marker of brain injury such as multiple sclerosis (MS), stroke, and traumatic brain injury (TBI). To our knowledge, there is no diffusion analytical model which includes the Water eXchange Rate (WXR) without the requirement of short gradient pulse (SGP) approximation. We therefore propose a new analytical model by deriving the diffusion signal for a permeable cylinder, assuming a clinically feasible pulse gradient spin echo (PGSE) sequence. Simulations based on Markov Random Walk confirm that the exchange parameter included in our model has a linear correlation (R2>0.88) with the actual WXR. Moreover, increasing WXR causes the estimated values of diameter and volume fraction of the cylinders to increase and decrease, respectively, which is consistent with our findings from histology measurements in tissues near TBI regions. This model was also applied to the diffusion signal acquired from ex vivo brains of 14 male (10 TBI and 4 normal) rats using hybrid diffusion imaging. The estimated values of axon diameter and axonal volume fraction are in agreement with their corresponding histological measurements in normal brains, with 0.96 intra-class correlation coefficient value resulting from consistency analysis. Moreover, a significant increase (p = 0.001) in WXR and diameter and decrease in axonal volume fraction in the TBI boundary were detected in the TBI rats compared with the normal rats. [ABSTRACT FROM AUTHOR]

Abstract: Modeling of water diffusion in white matter is useful for revealing microstructure of the brain tissue and hence diagnosis and evaluation of white matter diseases. Researchers have modeled diffusion in white matter using mathematical and mechanical analysis at the cellular level. However, less work has been devoted to evaluate these models using macroscopic real data such as diffusion tensor magnetic resonance imaging (DTMRI) data. DTMRI is a noninvasive tool for evaluating white matter microstructure by measuring random motion of water molecules referred to as diffusion. It reflects directional information of microscopic structures such as fibers. Thus, it is applicable for evaluation and modification of mathematical models of white matter. Nevertheless, a realistic relation between a fiber model and imaging data does not exist. This work opens a promising avenue for relating DTMRI data to microstructural parameters of white matter. First, we propose a strategy for relating DTMRI and fiber model parameters to evaluate mathematical models in light of real data. The proposed strategy is then applied to evaluate and extend an existing model of white matter based on clinically available DTMRI data. Next, the proposed strategy is used to estimate microstructural characteristics of fiber tracts. We illustrate this approach through its application to approximation of myelin sheath thickness and fraction of volume occupied by fibers. Using sufficiently small imaging voxels, the proposed approach is capable of estimating model parameters with desirable precision. [Copyright &y& Elsevier]

Abstract: Classifying brain white matter fibers into bundles is of growing interest in neuroscience. Quantification of diffusion characteristics inside a fiber bundle provides new insights for disease evolutions, therapy effects, and surgical interventions. In this paper, we present a novel method for segmenting fiber bundles using spherical harmonic coefficients (SHC) that describe diffusion signal obtained from High Angular Resolution Diffusion Imaging (HARDI) protocols. Based on SHC, we define a similarity measure and use it as a speed function term in level set framework. We show advantages of the proposed measure over similarity measures based on Diffusion Tensor Imaging (DTI) indices. Without any assumptions about diffusion model, we deal with diffusion signal instead of orientation distribution function (ODF) calculated using complicated mathematics, inaccurate simplifications, and time-consuming implementation. By applying the proposed algorithm on synthetic data, its superior accuracy and robustness in low SNR conditions are shown. Application of the proposed method on real HARDI MRI data also illustrates its superior performance, especially in heterogeneous diffusion areas with low traditional diffusion anisotropies. [Copyright &y& Elsevier]

Background Objective Methods Results Conclusion The cardinal motor symptoms of Parkinson's disease (PD) include rigidity, bradykinesia, and rest tremor. Rigidity and bradykinesia correlate with contralateral nigrostriatal degeneration and striatal dopamine deficit, but association between striatal dopamine function and rest tremor has remained unclear.The aim of this study was to investigate the possible link between dopamine function and rest tremor using Parkinson's Progression Markers Initiative dataset, the largest prospective neuroimaging cohort of patients with PD.Clinical, [123I]N‐ω‐fluoropropyl‐2β‐carbomethoxy‐3β‐(4‐iodophenyl)nortropane ([123I]FP‐CIT) single photon emission computed tomography (SPECT), and structural magnetic resonance imaging data from 354 early PD patients and 166 healthy controls were included in this study. We employed a novel approach allowing nonlinear registration of individual scans accurately to a standard space and voxelwise analyses of the association between motor symptoms and striatal dopamine transporter (DAT) binding.Severity of both rigidity and bradykinesia was negatively associated with contralateral striatal DAT binding (PFWE < 0.05 [FWE, family‐wise error corrected]). However, rest tremor amplitude was positively associated with increased ipsilateral DAT binding (PFWE < 0.05). The association between rest tremor and binding remained the same controlling for Hoehn & Yahr stage, Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) part III score, bradykinesia–rigidity score, or motor phenotype. The association between rest tremor and binding was independent of bradykinesia‐rigidity and replicated using 2‐year follow‐up data (PFWE < 0.05).In agreement with the existing literature, we did not find a consistent association between rest tremor and contralateral dopamine defect. However, our results demonstrate a link between rest tremor and increased or less decreased ipsilateral DAT binding. Our findings provide novel information about the association between dopaminergic function and parkinsonian rest tremor. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

The glymphatic system is a newly discovered waste drainage pathway in the brain; it plays an important role in many neurological diseases. Ongoing research utilizing various cerebrospinal fluid tracer infusions, either directly or indirectly into the brain parenchyma, is investigating clearance pathways by using distinct imaging techniques. In the present review, we discuss the role of the glymphatic system in various neurological diseases and efflux pathways of brain waste clearance based on current evidence and controversies. We mainly focus on new magnetic resonance imaging (MRI) modeling techniques, along with traditional computational modeling, for a better understanding of the glymphatic system function. Future sophisticated modeling techniques hold the potential to generate quantitative maps for glymphatic system parameters that could contribute to the diagnosis, monitoring, and prognosis of neurological diseases. The non-invasive nature of MRI may provide a safe and effective way to translate glymphatic system measurements from bench-to-bedside. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: To develop an automatic atlas-based method for segmentation of fiber bundles using High Angular Resolution Diffusion Imaging (HARDI) data. Hypothesis: Quantitative evaluation of diffusion characteristics inside specific fiber bundles provides new insights into disease developments, evolutions, therapy effects, and surgical interventions. Background: Most of previous segmentation methods use similarity measures and strategies that do not lead to accurate segmentation results. They also suffer from subjectivity of initial seeds and regions of interest (ROI) defined by operator. Materials and methods: We propose a novel method that uses Spherical Harmonic Coefficients (SHC) of HARDI diffusion signals to compute Orientation Distribution Function (ODF) and to extract Principal Diffusion Directions (PDDs). The proposed method selects most collinear PDD of neighbors of each voxel. Then, based on SHC and selected PDD, a similarity measure is proposed and used as a speed function in the level set framework that segments fiber bundles. To automate the process, an atlas-based method is used to select initial seeds for fiber bundles. To generate data for evaluation of the proposed method, an artificial pattern consisting of three crossing bundles intersected by a circular bundle is created. Also, two normal controls are imaged by two different HARDI protocols. Results: Segmentation results for different fiber bundles in simulated data and normal control data are presented. Influence of threshold selection on the proposed segmentation method is evaluated using Dice coefficient. Also, effect of increasing the number of gradient directions on accuracy of extracted PDDs is evaluated. Conclusion: The proposed segmentation method has advantages over previous methods especially those that use similarity measures based on diffusion tensor imaging (DTI) data. These advantages are achieved by proper propagation of a hyper-surface in fiber crossing areas without making assumptions about diffusivity profile and selection of initial seeds or ROI. [Copyright &y& Elsevier]

Ongoing neurovascular dysfunction contributes to type 2 diabetes mellitus (T2DM)-induced cognitive deficits. However, it is not known whether early post onset of T2DM interventions may reduce evolving neurovascular dysfunction and thereby lead to diminution of T2DM-induced cognitive deficits. Using multiple MRI metrics, we evaluated neurovascular changes in T2DM rats treated with exosomes derived from cerebral endothelial cells (CEC-Exos). Two months after induction of T2DM in middle-aged male rats by administration of streptozotocin nicotinamide, rats were randomly treated with CEC-Exos twice weekly or saline for 4 consecutive weeks (n = 10/group). MRI measurements were performed at the end of the treatment, which included cerebral blood flow (CBF), contrast-enhanced T1-weighted imaging, and relaxation time constants T1 and T2. MRI analysis showed that compared with controls, the CEC-Exo-treated T2DM rats exhibited significant elevation of T2 and CBF in white matter and significant augmentation of T1 and reduction of blood-brain barrier permeability in gray matter. In the hippocampus, CEC-Exo treatment significantly increased T1 and CBF. Furthermore, CEC-Exo treatment significantly reduced T2DM-induced cognitive deficits measured by the Morris water maze and odor recognition tests. Collectively, our corresponding MRI data demonstrate that treatment of T2DM rats with CEC-Exos robustly reduced neurovascular dysfunction in gray and white matter and the hippocampus. [ABSTRACT FROM AUTHOR]

Abstract: Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive tool for the investigation of white matter structure within the brain. However, the traditional tensor model is unable to characterize anisotropies of orders higher than two in heterogeneous areas containing more than one fiber population. To resolve this issue, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding gradients is used along with reconstruction methods such as Q-ball. Using HARDI data, the fiber orientation distribution function (ODF) on the unit sphere is calculated and used to extract the principal diffusion directions (PDDs). Fast and accurate estimation of PDDs is a prerequisite for tracking algorithms that deal with fiber crossings. In this paper, the PDDs are defined as the directions around which the ODF data is concentrated. Estimates of the PDDs based on this definition are less sensitive to noise in comparison with the previous approaches. A clustering approach to estimate the PDDs is proposed which is an extension of fuzzy c-means clustering developed for orientation of points on a sphere. MDL (Minimum description length) principle is proposed to estimate the number of PDDs. Using both simulated and real diffusion data, the proposed method has been evaluated and compared with some previous protocols. Experimental results show that the proposed clustering algorithm is more accurate, more resistant to noise, and faster than some of techniques currently being utilized. [Copyright &y& Elsevier]

Purpose: Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson's disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques.Procedures: We designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson's Progressive Marker Initiative (PPMI) database. The task was to predict the unified PD rating scale (UPDRS) part III motor score in year 4 from 92 imaging features extracted on 12 different regions as well as 6 non-imaging measures at baseline (year 0). We first performed univariate screening (including the adjustment for false discovery) to select 4 regions each having 10 features with significant performance in classifying year 4 motor outcome into two classes of patients (divided by the UPDRS III threshold of 30). The leave-one-out strategy was then applied to train and test the ANNs for individual and combinations of features. The prediction statistics were calculated from 100 rounds of experiments, and the accuracy in appropriate prediction (classification of year 4 outcome) was quantified.Results: Out of the baseline non-imaging features, only the UPDRS III (at year 0) was predictive, while multiple imaging features depicted significance. The different selected features reached a predictive accuracy of 70 % if used individually. Combining the top imaging features from the selected regions significantly improved the prediction accuracy to 75 % (p < 0.01). The combination of imaging features with the year 0 UPDRS III score also improved the prediction accuracy to 75 %.Conclusion: This study demonstrated the added predictive value of radiomic features extracted from DAT SPECT images in serving as a biomarker for PD progression tracking. [ABSTRACT FROM AUTHOR]

• TBI results in a diffuse WM damage, as revealed by DKI-derived metrics. • Changes of FA, AWF and RK in normal-appearing WM region are monitored up to 3-months post-TBI. • Transplantation of hMSCs after TBI promotes neural tissue remodeling. • Transplantation of hMSCs after TBI induces an earlier presence of augmented recovery phase. • DKI can detect microstructural alteration, arising from both TBI and cell therapy. Diffuse white matter (WM) response to traumatic brain injury (TBI) and transplantation of human bone marrow stromal cells (hMSCs) after the injury were non-invasively and dynamically investigated. Male Wistar rats (300–350 g) subjected to TBI were intravenously injected with 1 ml of saline (n = 10) or with hMSCs in suspension (∼3 × 106 hMSCs, n = 10) 1-week post-TBI. MRI measurements of T2-weighted imaging and diffusional kurtosis imaging (DKI) were acquired on all animals at multiple time points up to 3-months post-injury. Functional outcome was assessed using the Morris water maze test. DKI-derived metrics of fractional anisotropy (FA), axonal water fraction (AWF) and radial kurtosis (RK) longitudinally reveal an evolving pattern of structural alteration post-TBI occurring in the brain region remote from primary impact site. The progressive structural change is characterized by gradual disruption of WM integrity at an early stage (weeks post-TBI), followed by spontaneous recovery at a later stage (months post-TBI). Transplantation of hMSCs post-TBI promotes this structural plasticity as indicated by significantly increased FA and AWF in conjunction with substantially elevated RK at the later stage. Our long-term imaging data demonstrate that hMSC therapy leads to modified temporal profiles of these metrics, inducing an earlier presence of enhanced structural remodeling, which may contribute to improved functional recovery. [ABSTRACT FROM AUTHOR]

Background: Medial temporal lobe epilepsy (mTLE) has been associated with widespread white mater (WM) alternations in addition to mesial temporal sclerosis (MTS). Herein, we aimed to investigate the correlation between disease duration and WM structural abnormalities in mTLE using diffusion MRI (DMRI) connectometry approach.Method: DMRI connectometry was conducted on 24 patients with mTLE. A multiple regression model was used to investigate white matter tracts with microstructural correlates to disease duration, controlling for age and sex. DMRI data were processed in the MNI space using q-space diffeomorphic reconstruction to obtain the spin distribution function (SDF). The SDF values were converted to quantitative anisotropy (QA) and used in further analyses.Results: Connectometry analysis identified impaired white matter QA of the following fibers to be correlated with disease duration: bilateral retrosplenial cingulum, bilateral fornix, right inferior longitudinal fasciculus (ILF), and genu of corpus callosum (CC) (FDR = 0.009).Conclusion: Our results were obtained from DMRI connectometry, which indicates the connectivity and the level of diffusion in nerve fibers rather just the direction of diffusion. Compared to previous studies investigating the correlation between duration of epilepsy and white matter integrity in mTLE patients, we detected broader and somewhat different associations in midline structures and component of limbic system. However, further studies with larger sample sizes are required to elucidate previous and current results. [ABSTRACT FROM AUTHOR]

Purpose: This study systematically investigates the predictive power of volumetric imaging feature sets extracted from select neuroanatomical sites in lateralizing the epileptogenic focus in mesial temporal lobe epilepsy (mTLE) patients. Methods: A cohort of 68 unilateral mTLE patients who had achieved an Engel class I outcome postsurgically was studied retrospectively. The volumes of multiple brain structures were extracted from preoperative magnetic resonance (MR) images in each. The MR image data set consisted of 54 patients with imaging evidence for hippocampal sclerosis (HS-P) and 14 patients without (HS-N). Data mining techniques (i.e., feature extraction, feature selection, machine learning classifiers) were applied to provide measures of the relative contributions of structures and their correlations with one another. After removing redundant correlated structures, a minimum set of structures was determined as a marker for mTLE lateralization. Results: Using a logistic regression classifier, the volumes of both hippocampus and amygdala showed correct lateralization rates of 94.1%. This reflected about 11.7% improvement in accuracy relative to using hippocampal volume alone. The addition of thalamic volume increased the lateralization rate to 98.5%. This ternary-structural marker provided a 100% and 92.9% mTLE lateralization accuracy, respectively, for the HS-P and HS-N groups. Conclusions: The proposed tristructural MR imaging biomarker provides greater lateralization accuracy relative to single- and double-structural biomarkers and thus, may play a more effective role in the surgical decision-making process. Also, lateralization of the patients with insignificant atrophy of hippocampus by the proposed method supports the notion of associated structural changes involving the amygdala and thalamus. [ABSTRACT FROM AUTHOR]

Background. Dystonia is a syndrome with varied phenomenology but our understanding of its mechanisms is deficient. With neuroimaging techniques, such as fiber tractography (FT) and magnetoencephalography (MEG), pathway connectivity can be studied to that end. We present a hemidystonia patient treated with deep brain stimulation (DBS). Methods. After 10 years of left axial hemidystonia, a 45-year-old male underwent unilateral right globus pallidus internus (GPi) DBS. Whole brain MEG before and after anticholinergic medication was performed prior to surgery. 26-direction diffusion tensor imaging (DTI) was obtained in a 3 T MRI machine along with FT. The patient was assessed before and one year after surgery by using the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS). Results. In the eyes-closed MEG study there was an increase in brain coherence in the gamma band after medication in the middle and inferior frontal region. FT demonstrated over 50% more intense ipsilateral connectivity in the right hemisphere compared to the left. After DBS, BFMDRS motor and disability scores both dropped by 71%. Conclusion. Multimodal neuroimaging techniques can offer insights into the pathophysiology of dystonia and can direct choices for developing therapeutics. Unilateral pallidal DBS can provide significant symptom control in axial hemidystonia poorly responsive to medication. [ABSTRACT FROM AUTHOR]

The glymphatic system has recently been shown to clear brain extracellular solutes and abnormalities in glymphatic clearance system may contribute to both initiation and progression of neurological diseases. Despite that diabetes is known as a risk factor for vascular diseases, little is known how diabetes affects the glymphatic system. The current study is the first investigation of the effect of diabetes on the glymphatic system and the link between alteration of glymphatic clearance and cognitive impairment in Type-2 diabetes mellitus rats. MRI analysis revealed that clearance of cerebrospinal fluid contrast agent Gd-DTPA from the interstitial space was slowed by a factor of three in the hippocampus of Type-2 diabetes mellitus rats compared to the non-DM rats and confirmed by florescence imaging analysis. Cognitive deficits detected by behavioral tests were highly and inversely correlated to the retention of Gd-DTPA contrast and fluorescent tracer in the hippocampus of Type-2 diabetes mellitus rats. Type-2 diabetes mellitus suppresses clearance of interstitial fluid in the hippocampus and hypothalamus, suggesting that an impairment of the glymphatic system contributes to Type-2 diabetes mellitus-induced cognitive deficits. Whole brain MRI provides a sensitive, non-invasive tool to quantitatively evaluate cerebrospinal fluid and interstitial fluid exchange in Type-2 diabetes mellitus and possibly in other neurological disorders, with potential clinical application. [ABSTRACT FROM AUTHOR]

Diffusion-related magnetic resonance imaging parametric maps may be employed to characterize white matter of brain. We hypothesize that entropy of diffusion anisotropy may be most effective for detecting therapeutic effects of bone marrow stromal cell treatment of ischemia in type 2 diabetes mellitus rats. Type 2 diabetes mellitus was induced in adult male Wistar rats. These rats were then subjected to 2 h of middle cerebral artery occlusion, and received bone marrow stromal cell (5 x 106, n = 8) or an equal volume of saline (n = 8) via tail vein injection at three days after middle cerebral artery occlusion. Magnetic resonance imaging was performed on day one and then weekly for five weeks post middle cerebral artery occlusion. The diffusion metrics complementarily permitted characterization of axons and axonal myelination. All six magnetic resonance imaging diffusion metrics, confirmed by histological measures, demonstrated that bone marrow stromal cell treatment significantly (p < 0.05) improved magnetic resonance imaging diffusion indices of white matter in type 2 diabetes mellitus rats after middle cerebral artery occlusion compared with the saline-treated rats. Superior to the fractional anisotropy metric that provided measures related to organization of neuronal fiber bundles, the entropy metric can also identify microstructures and low-density axonal fibers of cerebral tissue after stroke in type 2 diabetes mellitus rats. [ABSTRACT FROM AUTHOR]

Using magnetic resonance imaging (MRI) and an animal model of traumatic brain injury (TBI), we investigated the capacity and sensitivity of diffusion-derived measures, fractional anisotropy (FA), and diffusion entropy, to longitudinally identify structural plasticity in the injured brain in response to the transplantation of human bone marrow stromal cells (hMSCs). Male Wistar rats (300-350g, n = 30) were subjected to controlled cortical impact TBI. At 6 h or 1 week post-injury, these rats were intravenously injected with 1 mL of saline (at 6 h or 1 week, n = 5/group) or with hMSCs in suspension (∼3 × 106 hMSCs, at 6 h or 1 week, n = 10/group). In vivo MRI measurements and sensorimotor function estimates were performed on all animals pre-injury, 1 day post-injury, and weekly for 3 weeks post-injury. Bielschowsky's silver and Luxol fast blue staining were used to reveal the axon and myelin status, respectively, with and without cell treatment after TBI. Based on image data and histological observation, regions of interest encompassing the structural alterations were made and the values of FA and entropy were monitored in these specific brain regions. Our data demonstrate that administration of hMSCs after TBI leads to enhanced white matter reorganization particularly along the boundary of contusional lesion, which can be identified by both FA and entropy. Compared with the therapy performed at 1 week post-TBI, cell intervention executed at 6 h expedites the brain remodeling process and results in an earlier functional recovery. Although FA and entropy present a similar capacity to dynamically detect the microstructural changes in the tissue regions with predominant orientation of fiber tracts, entropy exhibits a sensitivity superior to that of FA, in probing the structural alterations in the tissue areas with complex fiber patterns. [ABSTRACT FROM AUTHOR]

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into the connectivity domain by generating connectivity weights for each subject. Comparison of the two domains using a data-driven method suggests several advantages in analyzing data using data-driven methods in the connectivity domain over the time domain. We also demonstrate the feasibility of applying model-based methods in the connectivity domain, which offers a new pathway for the use of first-level model-based methods on rsfMRI data. The connectivity domain, furthermore, demonstrates a unique opportunity to perform first-level feature-based data-driven and model-based analyses. The connectivity domain can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling us to perform a wide range of model-based and data-driven approaches on rsfMRI data, decreasing susceptibility of analysis techniques to parameters that are not related to brain connectivity information, and evaluating both static and dynamic functional connectivity of the brain from a new perspective. [ABSTRACT FROM AUTHOR]

Background: Functional recovery after brain injury in animals is improved by marrow stromal cells (MSC) which stimulate neurite reorganization. However, MRI measurement of neurite density changes after injury has not been performed. In this study, we investigate the feasibility of MRI measurement of neurite density in an animal model of traumatic brain injury (TBI) with and without MSC treatment. Methods: Fifteen male Wistar rats, were treated with saline (n = 6) or MSCs (n = 9) and were sacrificed at 6 weeks after controlled cortical impact (CCI). Healthy non-CCI rats (n = 5), were also employed. Ex-vivo MRI scans were performed two days after the rats were sacrificed. Multiple-shell hybrid diffusion imaging encoding scheme and spherical harmonic expansion of a two-compartment water diffusion displacement model were used to extract neurite related parameters. Bielshowski and Luxol Fast blue was used for staining axons and myelin, respectively. Modified Morris water maze and neurological severity score (mNSS) test were performed for functional evaluation. The treatment effects, the correlations between neurite densities measured by MRI and histology, and the correlations between MRI and functional variables were calculated by repeated measures analysis of variance, the regression correlation analysis tests, and spearman correlation coefficients. Results: Neurite densities exhibited a significant correlation (R2>0.80, p<1E−20) between MRI and immuno-histochemistry measurements with 95% lower bound of the intra-correlation coefficient (ICC) as 0.86. The conventional fractional anisotropy (FA) correlated moderately with histological neurite density (R2 = 0.59, P<1E−5) with 95% lower bound of ICC as 0.76. MRI data revealed increased neurite reorganization with MSC treatment compared with saline treatment, confirmed by histological data from the same animals. mNSS were significantly correlated with MRI neurite density in the hippocampus region. Conclusions: The present studies demonstrated that neurite density can be estimated by MRI after TBI and MRI measurement of neurite density is a sensitive marker to MSC treatment response. [ABSTRACT FROM AUTHOR]

• CE-MRI is sensitive to the chronic changes of glymphatic function after mTBI. • mTBI affects both influx and efflux of contrast agent along the glymphatic pathway. • mTBI induces an overall suppression of glymphatic function at the chronic stage. • mTBI leads to the heterogeneous glymphatic effects on different anatomical regions. • CE-MRI and kinetic analysis provide a means for an objective assessment of mTBI. We investigated the effect of mild traumatic brain injury (mTBI) on the glymphatic pathway using contrast-enhanced magnetic resonance imaging (CE-MRI) and quantified with kinetic parameters obtained from an advanced two-compartment model. mTBI was induced in male Wistar rats using a closed head impact. Animals with and without mTBI (n = 7/group) underwent the identical MRI protocol 10-weeks post-injury, including T2-weighted imaging and 3D T1-weighted imaging with intra-cisterna magna injection of contrast agent (Gd-DTPA). The parameters of infusion rate, clearance rate and clearance time constant, characterizing the kinetic features of glymphatic tracer transport in a living brain, were quantified in multiple brain tissue regions. In the majority of examined regions, our quantification demonstrated significantly reduced infusion and clearance rates, and significantly increased clearance time constant in the mTBI animals compared to the healthy controls. These data indicate that mTBI induces chronic changes in influx and efflux of contrast agent and glymphatic pathway dysfunction. While the reduced efficiency of glymphatic function after mTBI was apparent in brain, regional evaluation revealed heterogeneous glymphatic effects of the mTBI in different anatomical regions. The suppression of glymphatic function, rather than the presence of focal lesions, indicates a persistent injury of the brain after mTBI. Thus, dynamic CE-MRI in conjunction with advanced kinetic analysis may offer a useful methodology for an objective assessment and confirmatory diagnosis of mTBI. [ABSTRACT FROM AUTHOR]

• Multiple MRI measurements of brain for young, mid-aged and diabetic rats. • Aging only reduces T 1 value, diabetes reduces T 1 and T 2 values, of gray matter. • Aging only decreases gray matter FA, besides diabetes elevates white matter FA. • These findings may be readily applied in the clinic trial and in human research. Normal aging is a risk factor for metabolic disorders such as diabetes, and diabetes is also a recognized cause of accelerated aging. Being able to distinguish changes caused by normal aging from those caused by diabetes, would provide insight into how the aging brain interacts with diabetes. Eight types of MRI metric maps (magnetization relaxation time constants of T 1 and T 2 , cerebral blood flow, cerebrovascular permeability, mean diffusivity, diffusion fractional anisotropy, mean diffusion kurtosis and diffusion directional entropy) were generated for all rats from the three groups of normal young, healthy and 1.5-month diabetic middle-aged rats under investigation. Measurements of multiple MRI indices of cerebral white and gray matter from animals of the three groups provide complementary results and insight into differences between healthy and diabetic white / gray matter in the mid-aged rats. Our data indicate that MRI may distinguish between the normal and diabetes in mid-aged rat brains by measuring either T 1 and T 2 of gray matter, or fractional anisotropy of white matter and gray matter. Therefore, MRI can distinguish changes of cerebral tissue due to the normal aging from diabetic aging, which may lead to be able to better understand how diabetes accelerates aging in normal brain. [ABSTRACT FROM AUTHOR]