Diffusion Magnetic Resonance Imaging Research Articles

TDKI-Net: A Joint q-t Space Learning Network for Diffusion-Time-Dependent Kurtosis Imaging.

Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for the non-invasive characterization of tissue microstructure. These models require both densely sampled q-t space data for microstructural fitting, leading to very time-consuming acquisition protocols. To overcome this problem, we present a joint q-t space model-tDKI-Net to estimate diffusion-time dependent kurtosis and the transmembrane exchange, using downsampled q-t space data. The tDKI-Net is composed of several q-Encoders and a t-Encoder, designed based on the extragradient mechanism, each integrated with their respective mapping networks. In the tDKI-Net, two types of encoders along with their mapping networks are employed sequentially to generate kurtosis at individual diffusion times and to fit the transmembrane exchange time ( τm) using the time-dependent kurtosis according the Kärger's model. Meanwhile, we proposed a three-stage training strategy, including physics-informed self-supervised pretraining, DKI warm-up, and joint training, to match the network structure. Our results demonstrated that the proposed tDKI-Net could effectively accelerate tDKI scans, resulting in lower estimation error compared with other methods. Our proposed three-stage training strategy demonstrated superior results than those training from scratch, e.g., the normalized root mean square error (NRMSE) of τm decreased by up to 1.4%. We also investigated the training data size effects and found that although we used one-subject training, the network achieved lower NRMSEs for Kavg, K0 and τm (2.50%, 3.04%, 10.86%) than previous work that used three-subject training (3.8%, 9.5%, 12.1%). tDKI-Net can considerably reduce the scan time by 10.5- fold by joint downsampling the q-t space data without compromising the estimation accuracy.

The connection of motor improvement after deep brain stimulation in Parkinson’s disease and microstructural integrity of the substantia nigra and subthalamic nucleus

BackgroundNigrostriatal microstructural integrity has been suggested as a biomarker for levodopa response in Parkinson’s disease (PD), which is a strong predictor for motor response to deep brain stimulation (DBS) of the subthalamic nucleus (STN). This study aimed to explore the impact of microstructural integrity of the substantia nigra (SN), STN, and putamen on motor response to STN-DBS using diffusion microstructure imaging. MethodsData was collected from 23 PD patients (mean age 63 ± 7, 6 females) who underwent STN-DBS, had preoperative 3 T diffusion magnetic resonance imaging including multishell diffusion-weighted MRI with b-values of 1000 and 2000 s/mm2 and records of motor improvement available. ResultsThe association between a poorer DBS-response and increased free interstitial fluid showed notable effect sizes (rho > |0.4|) in SN and STN, but not in putamen. However, this did not reach significance after Bonferroni correction and controlling for sex and age. ConclusionMicrostructural integrity of SN and STN are potential biomarkers for the prediction of therapy efficacy following STN-DBS, but further studies are required to confirm these associations.

Hybrid model for brain age prediction on MRI images with modified texture features

Machine learning (ML) and diffusion magnetic resonance imaging (dMRI) approaches can be employed to identify individual brain aging conditions in both healthy people and patients with brain diseases. This research establishes a new brain age prediction system, which comprises different phases like pre-processing, feature extraction, feature selection, and prediction. The original magnetic resonance imaging (MRI) picture is first carried out in a pre-processing stage, which enhances image quality as well as visual appearances. Following that, the shape, intensity, as well as texture-based characteristics are retrieved. Furthermore, texture features like modified entropy, as well as modified correlation are retrieved along with the traditional features. Then, a modified principle component analysis (PCA) method is utilized for feature selection stage. In the prediction phase, hybrid classifiers such as multi-layer perception (MLP) as well as long short term memory (LSTM) are used. The weight of LSTM and MLP is optimally tuned with the aid of an implemented self-updated red deer algorithm (SURDA) model to make an accurate prediction. Moreover, the suggested work outperforms other traditional works in terms of MSE, MAPE, MSLE, and MAE, correspondingly.

Dissection of the Temporofrontal Extreme Capsule Fasciculus Using Diffusion MRI Tractography and Association with Lexical Retrieval.

The well-known arcuate fasciculus that connects the posterior superior temporal region with the language production region in the ventrolateral frontal cortex constitutes the classic peri-Sylvian dorsal stream of language. A second temporofrontal white matter tract connects ventrally the anterior to intermediate lateral temporal cortex with frontal areas via the extreme capsule. This temporofrontal extreme capsule fasciculus (TFexcF) constitutes the ventral stream of language processing. The precise origin, course, and termination of this pathway has been examined in invasive tract tracing studies in macaque monkeys, but there have been no standard protocols for its reconstruction in the human brain using diffusion imaging tractography. Here we provide a protocol for the dissection of the TFexcF in vivo in the human brain using diffusion magnetic resonance imaging (MRI) tractography which provides a solid basis for exploring its functional role. A key finding of the current dissection protocol is the demonstration that the TFexcF is left hemisphere lateralized. Furthermore, using the present dissection protocol, we demonstrate that the TFexcF is related to lexical retrieval scores measured with the category fluency test, in contrast to the classical arcuate fasciculus (the dorsal language pathway) that was also dissected and was related to sentence repetition.

Deep learning for prediction of mechanism in acute ischemic stroke using brain diffusion magnetic resonance image

Background: Acute ischemic stroke is a disease with multiple etiologies. Therefore, identifying the mechanism of acute ischemic stroke is fundamental to its treatment and secondary prevention. The Trial of Org 10172 in Acute Stroke Treatment classification is currently the most widely used system, but it often has a limitations of classifying unknown causes and inadequate inter-rater reliability. Therefore, we attempted to develop a three-dimensional (3D)-convolutional neural network (CNN)-based algorithm for stroke lesion segmentation and subtype classification using only the diffusion and apparent diffusion coefficient information of patients with acute ischemic stroke. Methods: This study included 2,251 patients with acute ischemic stroke who visited our hospital between February 2013 and July 2019. Results: The segmentation model for lesion segmentation in the training set achieved a Dice score of 0.843±0.009. The subtype classification model achieved an average accuracy of 81.9%, with accuracies of 81.6% for large artery atherosclerosis, 86.8% for cardioembolism, 72.9% for small vessel occlusion, and 86.3% for control.Conclusion: We developed a model to predict the mechanism of cerebral infarction using diffusion magnetic resonance imaging, which has great potential for identifying diffusion lesion segmentation and stroke subtype classification. As deep learning systems are gradually developing, they are becoming useful in clinical practice and applications.

MRI volumetry and diffusion tensor imaging for diagnosis and follow-up of late post-traumatic injuries

BackgroundTraumatic Brain Injury (TBI) is a major cause of acquired disability and can cause devastating and progressive post-traumatic encephalopathy. TBI is a dynamic condition that continues to evolve over time. A better understanding of the pathophysiology of these late lesions is important for the development of new therapeutic strategies. ObjectivesThe primary objective was to compare the ability of fluid-attenuated reversion recovery (FLAIR) and diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) markers to identify participants with a Glasgow outcome scale extended (GOS-E) score of 7–8, up to 10 years after their original TBI. The secondary objective was to study the brain regionalization of DTI markers. Finally, we analyzed the evolution of late-developing brain lesions using repeated MRI images, also taken up to 10 years after the TBI. MethodsIn this retrospective study, participants were included from a cohort of people hospitalized following a severe TBI. Following their discharge, they were followed-up and clinically assessed, including a DTI-MRI scan, between 2012 and 2016. We performed a cross-sectional analysis on 97 participants at a median (IQR) of 5 years (3–6) post-TBI, and a further post-TBI longitudinal analysis over 10 years on a subpopulation (n = 17) of the cohort. ResultsAlthough the area under the curve (AUC) of FLAIR, fractional anisotropy (FA), and mean diffusivity (MD) were not significantly different, only the AUC of FA was statistically greater than 0.5. In addition, only the FA was correlated with clinical outcomes as assessed by GOS-E score (P<10−4). On the cross-sectional analysis, DTI markers allowed study post-TBI white matter lesions by region. In the longitudinal subpopulation analysis, the observed number of brain lesions increased for the first 5 years post-TBI, before stabilizing over the next 5 years. ConclusionsThis study has shown for the first time that post-TBI lesions can present in a two-phase evolution. These results must be confirmed in larger studies. French Data Protection Agency (Commission nationale de l'informatique et des libertés; CNIL) study registration no: 1934708v0.

IMAGING MICROSCOPIC NEUROPATHOLOGY OF ALZHEIMER’S DISEASE USING MRI: FROM MODEL SYSTEMS TO HUMAN IN VIVO

Abstract Microstructural magnetic resonance imaging (MRI) techniques are unique among neuroimaging modalities because they probe tissue features at the micron scale that are invisible using other methods. This class of MRI methods promises to address the unmet needs for neuroimaging in Alzheimer’s disease (AD) and related dementias: (1) early detection of subtle cellular and protein alterations and (2) delineation of comorbid pathologies. In particular, diffusion MRI is sensitive to alterations in cellularity and other microstructural changes, and relaxometery MRI is sensitive to chemical composition and macromolecular content. Benefiting from both modalities, multidimensional MRI is a new and emerging modality that maximizes chemical and microstructural information by probing relaxation and diffusion mechanisms simultaneously. This approach is ideal for detecting AD pathology, namely beta-amyloid plaques and Tau tangles, as well as comorbid pathologies, because of the microstructural and compositional alterations they induce in the affected regions. In order to actualize the promise of this method, a bridge must be built between the pathologic changes that define AD and detectable multidimensional MRI signatures. Our strategy starts with a ``bottom-up’’ approach that first uses radiologic-pathologic correlation in postmortem human tissue to elucidate the associations between specific cellular and macromolecular alterations in AD and their unique multidimensional MRI signatures. Building on that knowledge, we can then adapt this technology towards clinical in vivo application and complete the journey from bench-to-bedside.

Structural neural connectivity correlates with pre-reading abilities in preschool children

Pre-reading abilities are predictive of later reading ability and can be assessed before reading begins. However, the neural correlates of pre-reading abilities in young children are not fully understood. To address this, we examined 246 datasets collected in an accelerated longitudinal design from 81 children aged 2–6 years (age = 4.6 ± 0.98 years, 47 males). Children completed pre-reading assessments (NEPSY-II Phonological Processing and Speeded Naming) and underwent a diffusion magnetic resonance imaging (MRI) scan to assess white matter connectivity. We defined a core neural network of reading and language regions based on prior literature, and structural connections within this network were assessed using graph theory analysis. Linear mixed models accounting for repeated measures were used to test associations between children’s pre-reading performance and graph theory measures for the whole bilateral reading network and each hemisphere separately. Phonological Processing scores were positively associated with global efficiency, local efficiency, and clustering coefficient in the bilateral and right hemisphere networks, as well as local efficiency and clustering coefficient in the left hemisphere network. Our findings provide further evidence that structural neural correlates of Phonological Processing emerge in early childhood, before and during early reading instruction.

Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration

There is extensive synaptic loss from frontotemporal lobar degeneration, in preclinical models and human in vivo and post mortem studies. Understanding the consequences of synaptic loss for network function is important to support translational models and guide future therapeutic strategies. To examine this relationship, we recruited 55 participants with syndromes associated with frontotemporal lobar degeneration and 24 healthy controls. We measured synaptic density with positron emission tomography using the radioligand [11C]UCB-J, which binds to the presynaptic vesicle glycoprotein SV2A, neurite dispersion with diffusion magnetic resonance imaging, and network function with task-free magnetic resonance imaging functional connectivity. Synaptic density and neurite dispersion in patients was associated with reduced connectivity beyond atrophy. Functional connectivity moderated the relationship between synaptic density and clinical severity. Our findings confirm the importance of synaptic loss in frontotemporal lobar degeneration syndromes, and the resulting effect on behaviour as a function of abnormal connectivity.

Efficient PCA denoising of spatially correlated redundant MRI data

Abstract Marčenko-Pastur PCA (MPPCA) denoising is emerging as an effective means for noise suppression in MR imaging (MRI) acquisitions with redundant dimensions. However, MPPCA performance can be severely compromised by spatially correlated noise—an issue typically affecting most modern MRI acquisitions—almost to the point of returning the original images with little or no noise removal. In this study, we explore different threshold criteria for principal component analysis (PCA) component classification that enable efficient and robust denoising of MRI data even when noise exhibits high spatial correlations, especially in cases where data are acquired with Partial Fourier and when only magnitude data are available. We show that efficient denoising can be achieved by incorporating a-priori information about the noise variance into PCA denoising thresholding. Based on this, two denoising strategies developed here are: 1) General PCA (GPCA) denoising that uses a-priori noise variance estimates without assuming specific noise distributions; and 2) Threshold PCA (TPCA) denoising which removes noise components with a threshold computed from a-priori estimated noise variance to determine the upper bound of the Marčenko-Pastur (MP) distribution. These strategies were tested in simulations with known ground truth and applied for denoising diffusion MRI data acquired using pre-clinical (16.4T) and clinical (3T) MRI scanners. In synthetic phantoms, MPPCA denoising failed to denoise spatially correlated data, while GPCA and TPCA better classified components as dominated by signal/noise. In cases where the noise variance was not accurately estimated (as can be the case in many practical scenarios), TPCA still provides excellent denoising performance. Our experiments in pre-clinical diffusion data with highly corrupted by spatial correlated noise revealed that both GPCA and TPCA robustly denoised the data while MPPCA denoising failed. In in vivo diffusion MRI data acquired on a clinical scanner in healthy subjects, MPPCA weakly removed noised, while TPCA was found to have the best performance, likely due to misestimations of the noise variance. Thus, our work shows that these novel denoising approaches can strongly benefit future pre-clinical and clinical MRI applications.

The neural substrates of transdiagnostic cognitive-linguistic heterogeneity in primary progressive aphasia

BackgroundClinical variants of primary progressive aphasia (PPA) are diagnosed based on characteristic patterns of language deficits, supported by corresponding neural changes on brain imaging. However, there is (i) considerable phenotypic variability within and between each diagnostic category with partially overlapping profiles of language performance between variants and (ii) accompanying non-linguistic cognitive impairments that may be independent of aphasia magnitude and disease severity. The neurobiological basis of this cognitive-linguistic heterogeneity remains unclear. Understanding the relationship between these variables would improve PPA clinical/research characterisation and strengthen clinical trial and symptomatic treatment design. We address these knowledge gaps using a data-driven transdiagnostic approach to chart cognitive-linguistic differences and their associations with grey/white matter degeneration across multiple PPA variants.MethodsForty-seven patients (13 semantic, 15 non-fluent, and 19 logopenic variant PPA) underwent assessment of general cognition, errors on language performance, and structural and diffusion magnetic resonance imaging to index whole-brain grey and white matter changes. Behavioural data were entered into varimax-rotated principal component analyses to derive orthogonal dimensions explaining the majority of cognitive variance. To uncover neural correlates of cognitive heterogeneity, derived components were used as covariates in neuroimaging analyses of grey matter (voxel-based morphometry) and white matter (network-based statistics of structural connectomes).ResultsFour behavioural components emerged: general cognition, semantic memory, working memory, and motor speech/phonology. Performance patterns on the latter three principal components were in keeping with each variant’s characteristic profile, but with a spectrum rather than categorical distribution across the cohort. General cognitive changes were most marked in logopenic variant PPA. Regardless of clinical diagnosis, general cognitive impairment was associated with inferior/posterior parietal grey/white matter involvement, semantic memory deficits with bilateral anterior temporal grey/white matter changes, working memory impairment with temporoparietal and frontostriatal grey/white matter involvement, and motor speech/phonology deficits with inferior/middle frontal grey matter alterations.ConclusionsCognitive-linguistic heterogeneity in PPA closely relates to individual-level variations on multiple behavioural dimensions and grey/white matter degeneration of regions within and beyond the language network. We further show that employment of transdiagnostic approaches may help to understand clinical symptom boundaries and reveal clinical and neural profiles that are shared across categorically defined variants of PPA.

Orbitofrontal and striatal metabolism, volume, thickness and structural connectivity in relation to social anhedonia in depression: A multimodal study

BackgroundSocial anhedonia is common within major depressive disorder (MDD) and associated with worse treatment outcomes. The orbitofrontal cortex (OFC) is implicated in both reward (medial OFC) and punishment (lateral OFC) in social decision making. Therefore, to understand the biology of social anhedonia in MDD, medial/lateral OFC metabolism, volume, and thickness, as well as structural connectivity to the striatum, amygdala, and ventral tegmental area/nucleus accumbens were examined. A positive relationship between social anhedonia and these neurobiological outcomes in the lateral OFC was hypothesized, whereas an inverse relationship was hypothesized for the medial OFC. The association between treatment-induced changes in OFC neurobiology and depression improvement were also examined. Methods85 medication-free participants diagnosed with MDD were assessed with Wisconsin Schizotypy Scales to assess social anhedonia and received pretreatment simultaneous fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI), including structural and diffusion. Participants were then treated in an 8-week randomized placebo-controlled double-blind course of escitalopram. PET/MRI were repeated following treatment. Metabolic rate of glucose uptake was quantified from dynamic FDG-PET frames using Patlak graphical analysis. Structure (volume and cortical thickness) was quantified from structural MRI using Freesurfer. To assess structural connectivity, probabilistic tractography was performed on diffusion MRI and average FA was calculated within the derived tracts. Linear mixed models with Bonferroni correction were used to examine the relationships between variables. ResultsA significantly negative linear relationship between pretreatment social anhedonia score and structural connectivity between the medial OFC and the amygdala (estimated coefficient: −0.006, 95 % CI: −0.0108 – −0.0012, p-value = 0.0154) was observed. However, this finding would not survive multiple comparisons correction. No strong evidence existed to show a significant linear relationship between pretreatment social anhedonia score and metabolism, volume, thickness, or structural connectivity to any of the regions examined. There was also no strong evidence to suggest significant linear relationships between improvement in depression and percent change in these variables. ConclusionsBased on these multimodal findings, the OFC likely does not underlie social anhedonia in isolation and therefore should not be the sole target of treatment for social anhedonia. This is consistent with previous reports that other areas of the brain such as the amygdala and the striatum are highly involved in this behavior. Relatedly, amygdala-medial OFC structural connectivity could be a future target. The results of this study are crucial as, to our knowledge, they are the first to relate structure/function of the OFC with social anhedonia severity in MDD. Future work may need to involve a whole brain approach in order to develop therapeutics for social anhedonia.

Maternal Diet Quality during Pregnancy Is Associated with Neonatal Brain White Matter Development.

Maternal diet and nutrient intake are important for fetal growth and development. In this study, we aim to evaluate whether there are associations between maternal diet quality and the offspring's brain white matter development. Healthy pregnant women's (N = 44) nutrition intake was assessed by the Healthy Eating Index-2015 (HEI-2015) during the first, second, and third trimesters, respectively. Correlations between MRI diffusion tensor imaging measured fractional anisotropy (FA) of the neonatal brain and the HEI-2015 scores were evaluated using voxel-wise analysis with appropriate multiple comparisons correction and post hoc analysis based on regions of interest. Significant correlations were found between sodium scores at the first trimester of pregnancy and mean neonatal FA values in parietal white matter (R = 0.39, p = 0.01), anterior corona radiata (R = 0.43, p = 0.006), posterior limb of internal capsule (R = 0.53, p < 0.001), external capsule (R = 0.44, p = 0.004), and temporal white matter (R = 0.50, p = 0.001) of the left hemisphere. No other correlations were identified. In conclusion, the relationships between the maternal sodium intake score and the neonatal white matter microstructural development indicate sodium intake patterns better aligned with the Dietary Guidelines for Americans during early pregnancy are associated with greater white matter development in the offspring's brain.

Glymphatic System Dysfunction in Myelin Oligodendrocyte Glycoprotein Immunoglobulin G Antibody-Associated Disorders: Association with Clinical Disability.

Impaired glymphatic function has been suggested to be implicated in the pathophysiology of MS and aquaporin-4 immunoglobulin G-positive neuromyelitis optica spectrum disorder. This study aimed to investigate the interstitial fluid dynamics in the brain in patients with myelin oligodendrocyte glycoprotein antibody disorders (MOGAD), another demyelinating disorder, using a noninvasive imaging technique called the diffusivity along the perivascular space (ALPS) index. A prospective study was conducted on 16 patients with MOGAD in remission and 22 age- and sex-matched healthy control subjects. MR imaging was performed using a 3T scanner, and the ALPS index was calculated using diffusion MR imaging data with a b-value of 1000 s/mm2. The ALPS index and gray matter volumes were compared between the 2 groups, and these parameters were correlated with the Expanded Disability Status Scale. The mean ALPS index of patients with MOGAD was significantly lower than that of healthy controls (Cohen d = 0.93, false discovery rate-corrected P = .02). The lower mean ALPS index was significantly associated with a worse Expanded Disability Status Scale score (Spearman ρ = -0.51; 95% CI, -0.85 to -0.02; P = .03). However, cortical volume and deep gray matter volume were not significantly different between the 2 groups, and they were not correlated with the Expanded Disability Status Scale. This study suggests that patients with MOGAD may have impaired glymphatic function, as measured by the ALPS index, which is associated with patient disability. Further study is warranted with a larger sample size.

Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer.

Radiomics has been extensively applied in predicting Ki-67 in breast cancer (BC). However, this is often confined to the exploration of a single sequence, without considering the varying sensitivity and specificity among different sequences. To develop a nomogram based on dual-sequence MRI derived radiomic features combined with clinical characteristics for assessing Ki-67 expression in BC. Retrospective. 227 females (average age, 51 years) with 233 lesions and pathologically confirmed BC, which were divided into the training set (n = 163) and test set (n = 70). 3.0-T, T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (EPI sequence). The regions of interest were manually delineated on ADC and DCE-MRI sequences. Three radiomics models of ADC, DCE-MRI, and dsMRI (combined ADC and DCE-MRI sequences) were constructed by logistic regression and the radiomics score (Radscore) of the best model was calculated. The correlation between Ki-67 expression and clinical characteristics such as receptor status, axillary lymph node (ALN) metastasis status, ADC value, and time signal intensity curve was analyzed, and the clinical model was established. The Radscore was combined with clinical predictors to construct a nomogram. The independent sample t-test, Mann-Whitney U test, Chi-squared test, Interclass correlation coefficients (ICCs), single factor analysis, least absolute shrinkage and selection operator (LASSO), logistic regression, receiver operating characteristics, Delong test, Hosmer_Lemeshow test, calibration curve, decision curve. A P-value <0.05 was considered statistically significant. In the test set, the prediction efficiency of the dsMRI model (AUC = 0.862) was higher than ADC model (AUC = 0.797) and DCE-MRI model (AUC = 0.755). With the inclusion of estrogen receptor (ER) and ALN metastasis, the nomogram displayed quality improvement (AUC = 0.876), which was superior to the clinical model (AUC = 0.787) and radiomics model. The nomogram based on dsMRI radiomic features and clinical characteristics may be able to assess Ki-67 expression in BC. 3 TECHNICAL EFFICACY: Stage 3.

Characterization of Vasogenic and Cytotoxic Brain Edema Formation After Experimental Traumatic Brain Injury by Free Water Diffusion Magnetic Resonance Imaging.

Brain edema formation is a key factor for secondary tissue damage after traumatic brain injury (TBI), however, the type of brain edema and the temporal profile of edema formation are still unclear. We performed free water imaging, a bi-tensor model based diffusion MRI analysis, to characterize vasogenic brain edema (VBE) and cytotoxic edema (CBE) formation up to 7 days after experimental TBI. Male C57/Bl6 mice were subjected to controlled cortical impact (CCI) or sham surgery and investigated by MRI 4h, 1, 2, 3, 5, and 7 days thereafter (n = 8/group). We determined mean diffusivity (MD) and free water (FW) in contusion, pericontusional area, ipsi- and contralateral brain tissue. Free (i.e., non-restricted) water was interpreted as VBE, restricted water as CBE. To verify the results, VBE formation was investigated by in-vivo 2-Photon Microscopy (2-PM) 48h after surgery. We found that MD and FW values decreased for 48h within the contusion, indicating the occurrence of CBE. In pericontusional tissue, MD and FW indices were increased at all time points, suggesting the formation of VBE. This was consistent with our results obtained by 2-PM. Taken together, CBE formation occurs for 48h after trauma and is restricted to the contusion, while VBE forms in pericontusional tissue up to 7 days after TBI. Our results indicate that free water magnetic resonance imaging may represent a promising tool to investigate vasogenic and cytotoxic brain edema in the laboratory and in patients.

Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data.

This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.

Rehabilitation facilitates functional improvement following intravenous infusion of mesenchymal stem cells in the chronic phase of cerebral ischemia in rats

The primary objective of this study was to investigate the potential facilitating effects of daily rehabilitation for chronic cerebral ischemia following the intravenous infusion of mesenchymal stem cells (MSC) in rats. The middle cerebral artery (MCA) was occluded by intraluminal occlusion using a microfilament (MCAO). Eight weeks after MCAO induction, the rats were used as a chronic cerebral ischemia model. Four experimental groups were studied: Vehicle group (medium only, no cells); Rehab group (vehicle + rehabilitation), MSC group (MSC only); and Combined group (MSC + rehabilitation). Rat MSCs were intravenously infused eight weeks after MCAO induction, and the rats received daily rehabilitation through treadmill exercise for 20 min. Behavioral testing, lesion volume assessment using magnetic resonance imaging (MRI), and histological analysis were performed during the observation period until 16 weeks after MCAO induction. All treated animals showed functional improvement compared with the Vehicle group; however, the therapeutic efficacy was greatest in the Combined group. The combination therapy is associated with enhanced neural plasticity shown with histological analysis and MRI diffusion tensor imaging. These findings provide behavioral evidence for enhanced recovery by combined therapy with rehabilitation and intravenous infusion of MSCs, and may form the basis for the development of clinical protocols in the future.

Quantitative perfusion and water transport time model from multi b-value diffusion magnetic resonance imaging validated against neutron capture microspheres.

Quantification of perfusion in ml/100 g/min, rather than comparing relative values side-to-side, is critical at the clinical and research levels for large longitudinal and multi-center trials. Intravoxel incoherent motion (IVIM) is a non-contrast magnetic resonance imaging diffusion-based scan that uses a multitude of -values to measure various speeds of molecular perfusion and diffusion, sidestepping inaccuracy of arterial input functions or bolus kinetics. Questions remain as to the original of the signal and whether IVIM returns quantitative and accurate perfusion in a pathology setting. This study tests a novel method of IVIM perfusion quantification compared with neutron capture microspheres. We derive an expression for the quantification of capillary blood flow in ml/100 g/min by solving the three-dimensional Gaussian probability distribution and defining water transport time (WTT) as when 50% of the original water remains in the tissue of interest. Calculations were verified in a six-subject pre-clinical canine model of normocapnia, induced hypercapnia, and middle cerebral artery occlusion (ischemic stroke) and compared with quantitative microsphere perfusion. Linear regression analysis of IVIM and microsphere perfusion showed agreement (slope = 0.55, intercept = 52.5, ) with a Bland-Altman mean difference of . Linear regression between dynamic susceptibility contrast mean transit time and IVIM WTT asymmetry in infarcted tissue was excellent (, intercept = 0.3, ). Strong linear agreement was found between IVIM and reference standard infarct volume (slope = 1.01, ). The simulation of cerebrospinal fluid (CSF) suppression via inversion recovery returned a blood signal reduced by 82% from combined T1 and T2 effects. The accuracy and sensitivity of IVIM provides evidence that observed signal changes reflect cytotoxic edema and tissue perfusion and can be quantified with WTT. Partial volume contamination of CSF may be better removed during post-processing rather than with inversion recovery.

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

BackgroundThe consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery.MethodsForty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student’s t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs.ResultsDDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03).ConclusionHistogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.