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Rationale: Thromboembolism is a serious complication of endovascular treatment for ruptured cerebral aneurysms. The administration of antiplatelet agents before endovascular treatment for ruptured cerebral aneurysms may reduce the risk of thromboembolic complications. Aim: This study aimed to assess the safety and efficacy of preoperative aspirin administration in endovascular treatment for ruptured cerebral aneurysms. Sample size estimates: Assuming a 15% incidence rate of both intraoperative thromboembolic morbidity and symptomatic ischemic lesions on magnetic resonance imaging diffusion-weighted imaging scans assessed by an Independent Review Committee, a sample size of 484 will be required to detect a 10% improvement with aspirin administration with 90% power using the Pearson's chi-square test at a two-sided significance level of 2.5% for each primary outcome, after accounting for a 5% dropout rate. Methods and design: ASTOP is a multicenter, randomized, double-blind, placebo-controlled clinical trial. A total of 484 patients with ruptured cerebral aneurysms receiving coil embolization within 72 h of onset will be randomly assigned 1:1 to receive 200 mg of aspirin or placebo before the procedure. Study outcomes: The primary outcomes will be the incidence rates of intraoperative thromboembolic complications and symptomatic ischemic lesions on magnetic resonance imaging diffusion-weighted imaging scans evaluated by the Independent Review Committee. The secondary outcomes will be the incidence rate of cerebral ischemic events and all bleeding events within 14 days of enrollment and functional outcomes defined by the modified Rankin Scale score at 90 days. Discussion: This trial will provide valuable data on the role of antiplatelet agents during endovascular treatment for ruptured cerebral aneurysms. Trial registration: Registration: Japan Registry of Clinical Trials, Identifier: jRCTs031210421. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to investigate the previously reported dependency of intravoxel incoherent motion (IVIM) parameters on simultaneous multislice (SMS) acquisition and repetition time (TR). This includes the influence of slice thickness, slice gaps, and slice order on measured IVIM parameters. Materials and methods: Diffusion-weighted imaging (DWI) of the liver was performed on 10 healthy volunteers (aged 20–30 years) at 3T with a slice thickness of 5 mm, a slice gap of 5 mm, and a linear slice order. Diffusion-weighted images were acquired with 19 b-values (0–800 s/mm2) using both conventional slice excitation with an acceleration factor of one (AF1) and SMS excitation with an acceleration factor of three (AF3). Each of these measurements were carried out with two repetition times (TRs)– 1,300 ms (prefix s) and 4,500 ms (prefix l)–resulting in four different combinations: sAF1, sAF3, lAF1, and lAF3. Five volunteers underwent additional measurements using a 10 mm slice thickness and with AF1. Median signal values in the liver were used to determine the biexponential IVIM parameters. Statistical significances were assessed using the Kruskal-Wallis test, Wilcoxon signed-rank test, and Student's t-test. In-silico investigations were also used to interpret the data. Results: There were no significant differences between the biexponential IVIM parameters acquired from sAF1, sAF3, lAF1, and lAF3. Median values of the perfusion fraction f were as follows: 29.9% (sAF1), 26.9% (sAF3), 28.1% (lAF1), and 27.5% (lAF3). In the 10 mm-thick slices, f decreased from 31.3% (lAF1) to 27.4% (sAF1) (p = 0.141). Conclusion: The slice excitation mode did not appear to have any significant influence on the biexponential IVIM parameters. However, our simulations, as well as values reported from previous publications, show that slice thickness, slice gaps, and slice order are relevant and should thus be reported in IVIM studies. [ABSTRACT FROM AUTHOR]

Objective: With a globally aging population, there is a need to better understand how brain structure relates to function in healthy older and younger adults. Methods: 34 healthy participants divided into older (17; Mean = 70.9, SD = 5.4) and younger adults (17; Mean = 28.1, SD = 2.8) underwent diffusion-weighted imaging and neuropsychological assessment, including the California Verbal Learning Test 2nd Edition and the Trail Making Test (TMT-A and TMT-B). Differences in white matter microstructure for older and younger adults and the association between DTI metrics (fractional anisotropy, FA; mean diffusivity, MD) and cognitive performance were analyzed using tract-based spatial statistics (p < 0.05, corrected). Results: Older adults had significantly lower FA and higher MD than younger adults in widespread brain regions. There was a significant negative correlation between executive function (TMT-B) and MD for older adults in the right superior/anterior corona radiata and the corpus callosum. No significant relationship was detected between DTI metrics and executive function in younger adults or with memory performance in either group. Conclusions: The findings underscore the need to examine brain-behaviour relationships as a function of age. Future studies should include comprehensive assessments in larger lifespan samples to better understand the aging brain. [ABSTRACT FROM AUTHOR]

Purpose: To create and validate an automated pipeline for detection of early signs of irreversible ischemic change from admission CTA in patients with large vessel occlusion (LVO) stroke. Methods: We retrospectively included 368 patients for training and 143 for external validation. All patients had anterior circulation LVO stroke, endovascular therapy with successful reperfusion, and follow-up diffusion-weighted imaging (DWI). We devised a pipeline to automatically segment Alberta Stroke Program Early CT Score (ASPECTS) regions and extracted their relative Hounsfield unit (rHU) values. We determined the optimal rHU cut points for prediction of final infarction in each ASPECT region, performed 10-fold cross-validation in the training set, and measured the performance via external validation in patients from another institute. We compared the model with an expert neuroradiologist for prediction of final infarct volume and poor functional outcome. Results: We achieved a mean area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of 0.69±0.13, 0.69±0.09, 0.61±0.23, and 0.72±0.11 across all regions and folds in cross-validation. In the external validation cohort, we achieved a median [interquartile] AUC, accuracy, sensitivity, and specificity of 0.71 [0.68–0.72], 0.70 [0.68–0.73], 0.55 [0.50–0.63], and 0.74 [0.73–0.77], respectively. The rHU-based ASPECTS showed significant correlation with DWI-based ASPECTS (rS = 0.39, p<0.001) and final infarct volume (rS = -0.36, p<0.001). The AUC for predicting poor functional outcome was 0.66 (95%CI: 0.57–0.75). The predictive capabilities of rHU-based ASPECTS were not significantly different from the neuroradiologist's visual ASPECTS for either final infarct volume or functional outcome. Conclusions: Our study demonstrates the feasibility of an automated pipeline and predictive model based on relative HU attenuation of ASPECTS regions on baseline CTA and its non-inferior performance in predicting final infarction on post-stroke DWI compared to an expert human reader. [ABSTRACT FROM AUTHOR]

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Human cingulate sulcus visual area (CSv) was first identified as an area that responds selectively to visual stimulation indicative of self-motion. It was later shown that the area is also sensitive to vestibular stimulation as well as to bodily motion compatible with locomotion. Understanding the anatomical connections of CSv will shed light on how CSv interacts with other parts of the brain to perform information processing related to self-motion and navigation. A previous neuroimaging study (Smith et al. 2018, Cerebral Cortex, 28, 3685–3596) used diffusion-weighted magnetic resonance imaging (dMRI) to examine the structural connectivity of CSv, and demonstrated connections between CSv and the motor and sensorimotor areas in the anterior and posterior cingulate sulcus. The present study aimed to complement this work by investigating the relationship between CSv and adjacent major white matter tracts, and to map CSv's structural connectivity onto known white matter tracts. By re-analysing the dataset from Smith et al. (2018), we identified bundles of fibres (i.e. streamlines) from the whole-brain tractography that terminate near CSv. We then assessed to which white matter tracts those streamlines may belong based on previously established anatomical prescriptions. We found that a significant number of CSv streamlines can be categorised as part of the dorsalmost branch of the superior longitudinal fasciculus (SLF I) and the cingulum. Given current thinking about the functions of these white matter tracts, our results support the proposition that CSv provides an interface between sensory and motor systems in the context of self-motion. [ABSTRACT FROM AUTHOR]

Multiple Sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterised by neuroinflammation and neurodegeneration. Fatigue and depression are common, debilitating, and intertwined symptoms in people with relapsing-remitting MS (pwRRMS). An increased understanding of brain changes and mechanisms underlying fatigue and depression in RRMS could lead to more effective interventions and enhancement of quality of life. To elucidate the relationship between depression and fatigue and brain connectivity in pwRRMS we conducted a systematic review. Searched databases were PubMed, Web-of-Science and Scopus. Inclusion criteria were: studied participants with RRMS (n ≥ 20; ≥ 18 years old) and differentiated between MS subtypes; published between 2001-01-01 and 2023-01-18; used fatigue and depression assessments validated for MS; included brain structural, functional magnetic resonance imaging (fMRI) or diffusion MRI (dMRI). Sixty studies met the criteria: 18 dMRI (15 fatigue, 5 depression) and 22 fMRI (20 fatigue, 5 depression) studies. The literature was heterogeneous; half of studies reported no correlation between brain connectivity measures and fatigue or depression. Positive findings showed that abnormal cortico-limbic structural and functional connectivity was associated with depression. Fatigue was linked to connectivity measures in cortico-thalamic-basal-ganglial networks. Additionally, both depression and fatigue were related to altered cingulum structural connectivity, and functional connectivity involving thalamus, cerebellum, frontal lobe, ventral tegmental area, striatum, default mode and attention networks, and supramarginal, precentral, and postcentral gyri. Qualitative analysis suggests structural and functional connectivity changes, possibly due to axonal and/or myelin loss, in the cortico-thalamic-basal-ganglial and cortico-limbic network may underlie fatigue and depression in pwRRMS, respectively, but the overall results were inconclusive, possibly explained by heterogeneity and limited number of studies. This highlights the need for further studies including advanced MRI to detect more subtle brain changes in association with depression and fatigue. Future studies using optimised imaging protocols and validated depression and fatigue measures are required to clarify the substrates underlying these symptoms in pwRRMS. [ABSTRACT FROM AUTHOR]

Purpose: To compare the ability of diffusion parameters obtained by stretched-exponential and kurtosis models of diffusion-weighted imaging (DWI) to distinguish between patients with primary aldosteronism (PA) and healthy controls (HCs) in renal assessment. Materials and methods: A total of 44 participants (22 patients and 22 HCs) underwent renal MRI with an 11 b-value DWI sequence and a 3 b-value diffusion kurtosis imaging (DKI) sequence from June 2021 to April 2022. Binary logistic regression was used to construct regression models combining different diffusion parameters. Receiver-operating characteristic (ROC) curve analysis and comparisons were used to evaluate the ability of single diffusion parameters and combined diffusion models to distinguish between the two groups. Results: A total of six diffusion parameters (including the cortical anomalous exponent term [α_Cortex], medullary fractional anisotropy [FA_Medulla], cortical FA [FA_Cortex], cortical axial diffusivity [Da_Cortex], medullary mean diffusivity [MD_Medulla] and medullary radial diffusivity [Dr_Medulla]) were included, and 10 regression models were studied. The area under the curve (AUC) of Dr_Medulla was 0.855, comparable to that of FA_Cortex and FA_Medulla and significantly higher than that of α_Cortex, Da_Cortex and MD_Medulla. The AUC of the Model_all parameters was 0.967, comparable to that of Model_FA (0.946) and Model_DKI (0.966) and significantly higher than that of the other models. The sensitivity and specificity of Model_all parameters were 87.2% and 95%, respectively. Conclusion: The Model_all parameters, Model_FA and Model_DKI were valid for differentiating between PA patients and HCs with similar differentiation efficacy and were superior to single diffusion parameters and other models. [ABSTRACT FROM AUTHOR]

We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others. [ABSTRACT FROM AUTHOR]

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16–002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma. [ABSTRACT FROM AUTHOR]

Research has shown that maladaptive personality characteristics, such as Neuroticism, are associated with poor outcome after mild traumatic brain injury (mTBI). The current exploratory study investigated the neural underpinnings of this process using dynamic functional network connectivity (dFNC) analyses of resting-state (rs) fMRI, and diffusion MRI (dMRI). Twenty-seven mTBI patients and 21 healthy controls (HC) were included. After measuring the Big Five personality dimensions, principal component analysis (PCA) was used to obtain a superordinate factor representing emotional instability, consisting of high Neuroticism, moderate Openness, and low Extraversion, Agreeableness, and Conscientiousness. Persistent symptoms were measured using the head injury symptom checklist at six months post-injury; symptom severity (i.e., sum of all items) was used for further analyses. For patients, brain MRI was performed in the sub-acute phase (~1 month) post-injury. Following parcellation of rs-fMRI using independent component analysis, leading eigenvector dynamic analysis (LEiDA) was performed to compute dynamic phase-locking brain states. Main patterns of brain diffusion were computed using tract-based spatial statistics followed by PCA. No differences in phase-locking state measures were found between patients and HC. Regarding dMRI, a trend significant decrease in fractional anisotropy was found in patients relative to HC, particularly in the fornix, genu of the corpus callosum, anterior and posterior corona radiata. Visiting one specific phase-locking state was associated with lower symptom severity after mTBI. This state was characterized by two clearly delineated communities (each community consisting of areas with synchronized phases): one representing an executive/saliency system, with a strong contribution of the insulae and basal ganglia; the other representing the canonical default mode network. In patients who scored high on emotional instability, this relationship was even more pronounced. Dynamic phase-locking states were not related to findings on dMRI. Altogether, our results provide preliminary evidence for the coupling between personality and dFNC in the development of long-term symptoms after mTBI. [ABSTRACT FROM AUTHOR]

Background: Germ cell tumors (GCTs) comprise a rare and heterogeneous group of neoplasms presenting different clinical and histological characteristics, leading to a challenging scenario in clinical practice. Diffusion-weighted imaging (DWI) has been suggested as an indirect marker of tumor density and cellularity and could be used to monitor therapeutic response. However, its role in pediatric GCTs needs to be clarified. Purpose: Here, we evaluated the features of DWI in pediatric extracranial GCTs in a reference Brazilian institution. Material and methods: We included 43 pediatric patients with primary GCTs treated between 2008 and 2022 in Hospital de Amor de Barretos. The patients' MRI images included T1-weighted without contrast, T2-weighted, DWI and apparent diffusion coefficient (ADC) maps. DWI was evaluated in the section that exhibited the greatest restricted diffusion in the largest hypersignal area of the image. The lowest ADC value was determined to define the region of interest (ROI). We used a small ROI, avoiding necrotic, adipose tissue, noisy or nonenhancing lesion voxels as recommended. ROI determination was established by visual inspection by two radiologists in accordance. We used two values of b (b = 50 mm2/s or b = 800) for ADC values. Results: The highest mean ADC (mADC) value was observed in pure teratomas (1,403.50 ± 161.76 x10-3 mm2/s; mean ± SD) compared to other histologies (yolk sac, mixed teratoma, dysgerminoma and mixed GCT) of GCT (p<0.001). Furthermore, ROC analysis determined a cutoff mADC value of 1,179.00 x 10−3 mm2/s that differentiated pure teratomas from the other GCT histologies with a sensitivity of 95.8% and a specificity of 92.9% (AUC = 0.979; p<0.01). A significant increase in mADC was observed for malignant GCTs in treatment (1,197.00 ± 372.00 mm2/s; p<0.001) compared to that exhibited at the time of diagnosis (780.00 ± 168.00 mm2/s; mean ± SD. Our findings suggest that mADC assessment could be used as a tool to distinguish pure teratomas from malignant CGT histologies at diagnosis. Additionally, we demonstrated reasonable evidence that it could be used as a complementary tool to monitor treatment response in patients with malignant GCT. [ABSTRACT FROM AUTHOR]

Neonatal brain injury may impact brain development and lead to lifelong functional impairments. Hypoxic-ischemic encephalopathy (HIE) and congenital heart disease (CHD) are two common causes of neonatal brain injury differing in timing and mechanism. Maturation of whole-brain neural networks can be quantified during development using diffusion magnetic resonance imaging (dMRI) in combination with graph theory metrics. DMRI of 35 subjects with CHD and 62 subjects with HIE were compared to understand differences in the effects of HIE and CHD on the development of network topological parameters and functional outcomes. CHD newborns had worse 12-18 month language (P

Purpose: The study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression. Methods: Numerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1–M2-compensated, 67%-M1–M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales. Results: Signal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p < 0.001) with velocity- and/or acceleration-compensation. The partially M1–M2-compensated encoding schemes could restore the black-blood state again. Strongest ADC bias occurred for monopolar encodings (difference between left/right lobe of 0.41 μm2/ms for monopolar vs. < 0.12 μm2/ms for the other encodings). Conclusion: All of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1–M2-compensation does not exist. However, among the examined encodings, the 84%-M1–M2-compensated encodings provided a suitable tradeoff. [ABSTRACT FROM AUTHOR]

Objective: To assess the association between breast cancer tumour stroma and magnetic resonance imaging (MRI) features. Materials and methods: A total of 84 patients with treatment-naïve invasive breast cancer were enrolled into this retrospective study. The tumour stroma ratio (TSR) was estimated from the amount of tumour stroma in the pathology specimen of the breast tumour. The MRI images of the patients were analysed based on Breast Imaging Reporting and Data Systems (ACR-BIRADS) for qualitative features which include T2- weighted, diffusion-weighted images (DWI) and dynamic contrast-enhanced (DCE) for kinetic features. The mean signal intensity (SI) of Short Tau Inversion Recovery (STIR), with the ratio of STIR of the lesion and pectoralis muscle (L/M ratio) and apparent diffusion coefficient (ADC) value, were measured for the quantitative features. Correlation tests were performed to assess the relationship between TSR and MRI features. Results: There was a significant correlation between the margin of mass, enhancement pattern, and STIR signal intensity of breast cancer and TSR. There were 54.76% (n = 46) in the low stromal group and 45.24% (n = 38) in the high stromal group. A significant association were seen between the margin of the mass and TSR (p = 0.034) between the L/M ratio (p <0.001), and between STIR SI of the lesion and TSR (p<0.001). The median L/M ratio was significantly higher in the high TSR group as compared to the lower TSR group (p < 0.001). Conclusion: Breast cancer with high stroma had spiculated margins, lower STIR signal intensity, and a heterogeneous pattern of enhancement. Hence, in this preliminary study, certain MRI features showed a potential to predict TSR. [ABSTRACT FROM AUTHOR]

Methadone-based treatment for pregnant women with opioid use disorder is quite prevalent in the clinical environment. A number of clinical and animal model-based studies have reported cognitive deficits in infants prenatally exposed to methadone-based opioid treatments. However, the long-term impact of prenatal opioid exposure (POE) on pathophysiological mechanisms that govern neurodevelopmental impairment is not well understood. Using a translationally relevant mouse model of prenatal methadone exposure (PME), the aim of this study is to investigate the role of cerebral biochemistry and its possible association with regional microstructural organization in PME offspring. To understand these effects, 8-week-old male offspring with PME (n = 7) and prenatal saline exposure (PSE) (n = 7) were scanned in vivo on 9.4 Tesla small animal scanner. Single voxel proton magnetic resonance spectroscopy (1H-MRS) was performed in the right dorsal striatum (RDS) region using a short echo time (TE) Stimulated Echo Acquisition Method (STEAM) sequence. Neurometabolite spectra from the RDS was first corrected for tissue T1 relaxation and then absolute quantification was performed using the unsuppressed water spectra. High-resolution in vivo diffusion MRI (dMRI) for region of interest (ROI) based microstructural quantification was also performed using a multi-shell dMRI sequence. Cerebral microstructure was characterized using diffusion tensor imaging (DTI) and Bingham-neurite orientation dispersion and density imaging (Bingham-NODDI). MRS results in the RDS showed significant decrease in N-acetyl aspartate (NAA), taurine (tau), glutathione (GSH), total creatine (tCr) and glutamate (Glu) concentration levels in PME, compared to PSE group. In the same RDS region, mean orientation dispersion index (ODI) and intracellular volume fraction (VFIC) demonstrated positive associations with tCr in PME group. ODI also exhibited significant positive association with Glu levels in PME offspring. Significant reduction in major neurotransmitter metabolites and energy metabolism along with strong association between the neurometabolites and perturbed regional microstructural complexity suggest a possible impaired neuroadaptation trajectory in PME offspring which could be persistent even into late adolescence and early adulthood. [ABSTRACT FROM AUTHOR]

Objective: This study compared the dynamic susceptibility contrast (DSC) magnetic resonance imaging parameters and apparent diffusion coefficient (ADC) between pilocytic astrocytoma (PA) and diffuse midline glioma, H3K27-altered (DMG) variants. Methods: The normalized relative cerebral blood volume (nrCBV), normalized relative flow (nrCBF), percentile signal recovery (PSR), and normalized mean ADC (nADCmean) of 23 patients with midline PAs (median age, 13 years [range, 1–71 years]; 13 female patients) and 40 patients with DMG (8.5 years [1–35 years]; 19 female patients), including 35 patients with H3.3- and five patients with H3.1-mutant tumors, treated between January 2016 and May 2022 were statistically compared. Results: DMG had a significantly lower nADCmean (median: 1.48 vs. 1.96; p = 0.00075) and lower PSR (0.97 vs. 1.23, p = 0.13) but higher nrCBV and nrCBF (1.66 vs. 1.17, p = 0.058, respectively, and 1.87 vs. 1.19, p = 0.028, respectively) than PA. The H3.3 variant had a lower nADCmean than the H3.1 variant (1.46 vs. 1.80, p = 0.10). Conclusion: DMG had lower ADC and PSR and higher rCBV and rCBF than PA. The H3.3 variant had a lower ADC than the H3.1 variant. Recognizing the differences and similarities in the DSC parameters and ADC between these tumors may help presurgical diagnosis. [ABSTRACT FROM AUTHOR]

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor-valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different levels of white matter damage in rodent optic nerves. 2) We obtained ex vivo DW-MRI data with b-tensor encoding schemes and calculated quantitative metrics using Q-space trajectory imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI data analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration. [ABSTRACT FROM AUTHOR]

Many previous studies focused on differentiating between benign and malignant soft tissue tumors using radiomics model based on various magnetic resonance imaging (MRI) sequences, but it is still unclear how to set up the input radiomic features from multiple MRI sequences. Here, we evaluated two types of radiomics models generated using different feature incorporation strategies. In order to differentiate between benign and malignant soft tissue tumors (STTs), we compared the diagnostic performance of an ensemble of random forest (R) models with single-sequence MRI inputs to R models with pooled multi-sequence MRI inputs. One-hundred twenty-five STT patients with preoperative MRI were retrospectively included and consisted of training (n = 100) and test (n = 25) sets. MRI included T1-weighted (T1-WI), T2-weighted (T2-WI), contrast-enhanced (CE)-T1-WI, diffusion-weighted images (DWIs, b = 800 sec/mm2) and apparent diffusion coefficient (ADC) maps. After tumor segmentation on each sequence, 100 original radiomic features were extracted from each sequence image and divided into three-feature sets: T features from T1- and T2-WI, CE features from CE-T1-WI, and D features from DWI and ADC maps. Four radiomics models were built using Lasso and R with four combinations of three-feature sets as inputs: T features (R-T), T+CE features (R-C), T+D features (R-D), and T+CE+D features (R-A) (Type-1 model). An ensemble model was built by soft voting of five, single-sequence-based R models (Type-2 model). AUC, sensitivity, specificity, and accuracy of each model was calculated with five-fold cross validation. In Type-1 model, AUC, sensitivity, specificity, and accuracy were 0.752, 71.8%, 61.1%, and 67.2% in R-T; 0.756, 76.1%, 70.4%, and 73.6% in R-C; 0.750, 77.5%, 63.0%, and 71.2% in R-D; and 0.749, 74.6%, 61.1%, and 68.8% R-A models, respectively. AUC, sensitivity, specificity, and accuracy of Type-2 model were 0.774, 76.1%, 68.5%, and 72.8%. In conclusion, an ensemble method is beneficial to incorporate features from multi-sequence MRI and showed diagnostic robustness for differentiating malignant STTs. [ABSTRACT FROM AUTHOR]

There is increasing awareness that self-reported sleep abnormalities are negatively associated with brain structure and function in older adults. Less is known, however, about how objectively measured sleep associates with brain structure. We objectively measured at-home sleep to investigate how sleep architecture and sleep quality related to white matter microstructure in older adults. 43 cognitively normal, older adults underwent diffusion tensor imaging (DTI) and a sleep assessment within a six-month period. Participants completed the PSQI, a subjective measure of sleep quality, and used an at-home sleep recorder (Zeo, Inc.) to measure total sleep time (TST), sleep efficiency (SE), and percent time in light sleep (LS), deep sleep (DS), and REM sleep (RS). Multiple regressions predicted fractional anisotropy (FA) and mean diffusivity (MD) of the corpus callosum as a function of total PSQI score, TST, SE, and percent of time spent in each sleep stage, controlling for age and sex. Greater percent time spent in RS was significantly associated with higher FA (β = 0.41, p = 0.007) and lower MD (β = -0.30, p = 0.03). Total PSQI score, TST, SE, and time spent in LS or DS were not significantly associated with FA or MD (p>0.13). Percent time spent in REM sleep, but not quantity of light and deep sleep or subjective/objective measures of sleep quality, positively predicted white matter microstructure integrity. Our results highlight an important link between REM sleep and brain health that has the potential to improve sleep interventions in the elderly.

BACKGROUND AND PURPOSE:We hypothesized that admission insular infarcts could be associated with early neurological deterioration (END) in acute minor stroke with large vessel occlusion. METHODS:Using acute and follow-up diffusion-weighted imaging (DWI), we assessed insular involvement including the percent insular ribbon infarction (PIRI) scores and follow-up lesion patterns in acute minor stroke (NIHSS ≤5) with MCA/ICA occlusion. Follow-up lesion patterns were classified as swelling, new lesions, or infarct growth. END was defined as any increase in the NIHSS score. RESULTS:Among 166 patients (age: 66±12 y, 60.8% male), 82 (49.4%) had insular lesions on baseline DWI, and 64 (38.6%) had PIRI scores ≥2. On follow-up DWI, infarct growths, new lesions, and swelling were observed in 34.9%, 69.9%, and 29.5% of patients. Infarct growths were significantly more frequent in patients with insular infarcts (43.9%), especially those with a PIRI score of 2 (54.8%), than in patients without insular infarcts (p = 0.02). While END was not significantly different in patients with and without insular lesions, insular lesions were independently associated with infarct growths (OR 2.18, 95% CI 1.12-4.26, p = 0.02) and END due to infarct growth (OR 2.54, 95% CI 1.12-5.76, p = 0.03), particularly in those with PIRI scores ≥2. CONCLUSION:In acute minor stroke with MCA/ICA occlusion, insular lesions on admission DWI, especially in patients with PIRI scores ≥2, were more likely to exhibit infarct growth and END due to infarct growth. This finding may help identify patients with higher risks of clinical worsening following acute minor stroke with large vessel occlusion.

Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients' cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling. [ABSTRACT FROM AUTHOR]

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions—computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems. [ABSTRACT FROM AUTHOR]

Diffusion MRI classically uses gradient fields that vary linearly in space to encode the diffusion of water molecules in the signal magnitude by tempering its intensity. In spin ensembles, a presumably equal number of particles move in positive and negative direction, resulting in approximately zero change in net phase. Hence, in classical diffusion weighted MRI with a linear gradient field, the phase does not carry any information as the incoherent motion of the spins only impacts the magnitude of the signal. Conversely, when the linear gradient field is replaced with one that varies quadratically over space, the diffusion of water molecules in anisotropic media does give rise to a change in net phase and preserves large portion of the signal around the saddle point of the gradient field. In this work, the phase evolution of anisotropic fibre phantoms in the presence of quadratic gradient fields was studied in Monte Carlo simulations and diffusion MRI experiments. The simulations confirm the dependence of the phase change on the degree of anisotropy of the media and the diffusion weighting, as predicted by the derived analytic model. First MR experiments show a phase change depending on the diffusion time in an anisotropic synthetic fibre phantom, and approximately zero phase change for the experiment repeated in an isotropic agar phantom. As predicted by the analytic model, an increase of the diffusion time by approximately a factor of two leads to an increase of approximately a factor of two in the signal phase. [ABSTRACT FROM AUTHOR]

Objective: To provide a straightforward approach to the sequential use of ultrasound (US), magnetic resonance (MR) and serum biomarkers in order to differentiate the origin of pelvic masses, making the most efficient use of these diagnostic resources. Study design: This is a cross-sectional study with 159 patients (133 with ovarian and 26 with non-ovarian tumors) who underwent surgery/biopsy for an adnexal mass. Preoperative CA125 and CEA serum measurements were obtained and a pelvic/abdominal ultrasound was performed. Preoperative pelvic MR studies were performed for all patients. Morphological and advanced MR sequences were obtained. Using a recursive partitioning algorithm to predict tumor origin, we devised a roadmap to determine the probability of non-ovarian origin using only statistically significant US, laboratory and MR parameters. Results: Upfront US classification as ovarian versus non-ovarian and CA125/CEA ratio were significantly associated with non-ovarian tumors. Signal diffusion (absent/low versus high) was the only MR parameter significantly associated with non-ovarian tumors. When upfront US designated a tumor as being of ovarian origin, further MR signal diffusion and CA125/CEA ratio were corrected nearly all US errors: patients with MR signal diffusion low/absent and those with signal high but CA125/CEA ratio ≥25 had an extremely low chance (<1%) of being of non-ovarian origin. However, for women whose ovarian tumors were incorrectly rendered as non-ovarian by upfront US, neither MR nor CA125/CEA ratio were able to determine tumor origin precisely. Conclusion: MR signal diffusion is an extremely useful MR parameter to help determine adnexal mass origin when US and laboratory findings are inconclusive. [ABSTRACT FROM AUTHOR]

The glymphatic system is a low resistance pathway, by which cerebrospinal fluid enters the brain parenchyma along perivascular spaces via AQP4 channels. It is hypothesised that the resulting convective flow of the interstitial fluid provides an efficient mechanism for the removal of waste toxins from the brain. Therefore, enhancing AQP4 function might protect against neurodegenerative diseases such as Alzheimer's disease (AD), in which the accumulation of harmful proteins and solutes is a hallmark feature. Here, we test the effect of a putative AQP4 facilitator, TGN-073, on glymphatic transport in a normal rat brain by employing different MRI techniques. Surgical procedures were undertaken to catheterise the cisterna magna, thereby enabling infusion of the MRI tracer. Followed by the intraperitoneal injection of either TGN-073, or the vehicle. Using a paramagnetic contrast agent (Gd-DTPA) as the MRI tracer, dynamic 3D T1 weighted imaging of the glymphatic system was undertaken over two hours. Further, the apparent diffusion coefficient was measured in different brain regions using diffusion-weighted imaging (DWI). While physiological parameters and arterial blood gas analysis were monitored continuously. We found that rats treated with TGN-073 showed the distribution of Gd-DTPA was more extensive and parenchymal uptake was higher compared with the vehicle group. Water diffusivity was increased in the brain of TGN-073 treated group, which indicates greater water flux. Also, MRI showed the glymphatic transport and distribution in the brain is naturally heterogeneous, which is consistent with previous studies. Our results indicate that compounds such as TGN-073 can improve glymphatic function in the brain. Since glymphatic impairment due to AQP4 dysfunction is potentially associated with several neurological disorders such as AD, dementia and traumatic brain injury, enhancing AQP4 functionality might be a promising therapeutic target. [ABSTRACT FROM AUTHOR]

A number of restricted diffusion (RD) imaging techniques, such as diffusion kurtosis (DK) imaging and Q space imaging, have been developed and proven to be useful for the diagnosis of diseases, including cerebral gliomas and cerebrovascular infarction. In particular, apparent diffusion coefficient (ADC) subtraction method (ASM) imaging has become available recently as a novel RD imaging technique. ASM is based on the difference between the ADC values in an image pair of two ADC maps, ADC basic (ADCb) and ADC modify (ADCm), which are created from diffusion-weighted images taken using short and long effective diffusion times, respectively. The present study aimed to assess the potential of different types of ASM imaging by comparing them with DK imaging which is the gold-standard RD imaging technique. In the present basic study using both polyethylene glycol phantom and cell-containing bio-phantom, three different types of ASM images were created using different calculation processes. ASM/A is an image calculated by dividing the absolute difference between ADCb and ADCm by ADCb several times. By contrast, ASM/S is an image created by dividing the absolute difference between ADCb and ADCm by the standard deviation of ADCb several times. As for positive ASM/A image (PASM/A), the positive image, which was resultant after subtracting ADCb from ADCm, was divided by ADCb several times. A comparison was made between the types of ASM and DK images. The results showed the same tendency between ASM/A in addition to both ASM/S and PASM/A. By increasing the number of divisions by ADCb from three to five times, ASM/A images transformed from DK-mimicking to more RD-sensitive images compared with DK images. These observations suggest that ASM/A images may prove useful for future clinical applications in RD imaging protocols for the diagnosis of diseases. [ABSTRACT FROM AUTHOR]

Purpose: Bevacizumab-related imaging abnormality (BRIA), appearing as areas of restricted diffusion on magnetic resonance imaging (MRI) and representing atypical coagulative necrosis pathologically, has been observed in patients with brain tumors receiving radiotherapy and bevacizumab. We investigated the role of cumulative radiation dose in BRIA development in a voxel-wise analysis. Methods: Patients (n = 18) with BRIA were identified. All had high-grade gliomas or brain metastases treated with radiotherapy and bevacizumab. Areas of BRIA were segmented semi-automatically on diffusion-weighted MRI with apparent diffusion coefficient (ADC) images. To avoid confounding by possible tumor, hypoperfusion was confirmed with perfusion imaging. ADC images and radiation dose maps were co-registered to a high-resolution T1-weighted MRI and registration accuracy was verified. Voxel-wise normal tissue complication probability analyses were performed using a logistic model analyzing the relationship between cumulative voxel equivalent total dose in 2 Gy fractions (EQD2) and BRIA development at each voxel. Confidence intervals for regression model predictions were estimated with bootstrapping. Results: Among 18 patients, 39 brain tumors were treated. Patients received a median of 4.5 cycles of bevacizumab and 1–4 radiation courses prior to BRIA appearance. Most (64%) treated tumors overlapped with areas of BRIA. The median proportion of each BRIA region of interest volume overlapping with tumor was 98%. We found a dose-dependent association between cumulative voxel EQD2 and the relative probability of BRIA (β0 = -5.1, β1 = 0.03 Gy-1, γ = 1.3). Conclusions: BRIA is likely a radiation dose-dependent phenomenon in patients with brain tumors receiving bevacizumab and radiotherapy. The combination of radiation effects and tumor microenvironmental factors in potentiating BRIA in this population should be further investigated. [ABSTRACT FROM AUTHOR]

Age-related changes in the human brain can be investigated from either structural or functional perspectives. Analysis of structural and functional age-related changes throughout the lifespan may help to understand the normal brain development process and monitor the structural and functional pathology of the brain. This study, combining dedicated electroencephalography (EEG) and magnetic resonance imaging (MRI) approaches in adults (20–78 years), highlights the complex relationship between micro/macrostructural properties and the functional responses to visual stimuli. Here, we aimed to relate age-related changes of the latency of visual evoked potentials (VEPs) to micro/macrostructural indexes and find any correlation between micro/macrostructural features, as well. We studied age-related structural changes in the brain, by using the MRI and diffusion-weighted imaging (DWI) as preferred imaging methods for extracting brain macrostructural parameters such as the cortical thickness, surface area, folding and curvature index, gray matter volume, and microstructural parameters such as mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). All the mentioned features were significantly correlated with age in V1 and V2 regions of the visual cortex. Furthermore, we highlighted, negative correlations between structural features extracted from T1-weighted images and DWI. The latency and amplitude of the three dominants peaks (C1, P1, N1) of the VEP were considered as the brain functional features to be examined for correlation with age and structural features of the corresponding age. We observed significant correlations between mean C1 latency and GM volume averaged in V1 and V2. In hierarchical regression analysis, the structural index did not contribute to significant variance in the C1 latency after regressing out the effect of age. However, the age explained significant variance in the model after regressing out the effect of structural feature. [ABSTRACT FROM AUTHOR]

Purpose: To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. Material and methods: This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall. Results: Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p<0.001) and (AUC = 0.903 95% CI: 0.854, 0.952; p<0.001) respectively), outperformed that using mean tumor ADC (AUC = 0.850 (95% CI: 0.764, 0.936; p<0.001)). The AUC using all MRI data was 0.933 (95% CI: 0.836, 1.03; p<0.001). The AUC using non-MRI data was 0.919 (95% CI: 0.848, 0.99; p<0.001). The highest AUC of 0.951 (95% CI: 0.909, 0.993; p<0.001) was achieved with all MRI and all non-MRI data at all time points as inputs. Conclusion: Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes. [ABSTRACT FROM AUTHOR]

Diffusion-weighted magnetic resonance imaging of the human optic nerve and tract is technically difficult because of its small size, the inherent strong signal generated by the surrounding fat and the cerebrospinal fluid, and due to eddy current-induced distortions and subject movement artifacts. The effects of the bone canal through which the optic nerve passes, and the proximity of blood vessels, muscles and tendons are generally unknown. Also, the limited technical capabilities of the scanners and the minimization of acquisition times result in poor quality diffusion-weighted images. It is challenging for current tractography methods to accurately track optic pathway fibers that correspond to known anatomy. Despite these technical limitations and low image resolution, here we show how to visualize the optic nerve and tract and quantify nerve atrophy. Our visualization method based on the analysis of the diffusion tensor shows marked differences between a healthy male subject and a male subject with progressive optic nerve neuropathy. These differences coincide with diffusion scalar metrics and are not visible on standard morphological images. A quantification of the degree of optic nerve atrophy in a systematic way is provided and it is tested on 9 subjects from the Human Connectome Project. [ABSTRACT FROM AUTHOR]

Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinical application in the identification and diagnosis of WM abnormalities. Further recommendation is given regarding the tracts used with SSDS. [ABSTRACT FROM AUTHOR]

Introduction: Drug development for neurodegenerative diseases such as Friedreich's ataxia (FRDA) is limited by a lack of validated, sensitive biomarkers of pharmacodynamic response in affected tissue and disease progression. Studies employing neuroimaging measures to track FRDA have thus far been limited by their small sample sizes and limited follow up. TRACK-FA, a longitudinal, multi-site, and multi-modal neuroimaging natural history study, aims to address these shortcomings by enabling better understanding of underlying pathology and identifying sensitive, clinical trial ready, neuroimaging biomarkers for FRDA. Methods: 200 individuals with FRDA and 104 control participants will be recruited across seven international study sites. Inclusion criteria for participants with genetically confirmed FRDA involves, age of disease onset ≤ 25 years, Friedreich's Ataxia Rating Scale (FARS) functional staging score of ≤ 5, and a total modified FARS (mFARS) score of ≤ 65 upon enrolment. The control cohort is matched to the FRDA cohort for age, sex, handedness, and years of education. Participants will be evaluated at three study visits over two years. Each visit comprises of a harmonized multimodal Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) scan of the brain and spinal cord; clinical, cognitive, mood and speech assessments and collection of a blood sample. Primary outcome measures, informed by previous neuroimaging studies, include measures of: spinal cord and brain morphometry, spinal cord and brain microstructure (measured using diffusion MRI), brain iron accumulation (using Quantitative Susceptibility Mapping) and spinal cord biochemistry (using MRS). Secondary and exploratory outcome measures include clinical, cognitive assessments and blood biomarkers. Discussion: Prioritising immediate areas of need, TRACK-FA aims to deliver a set of sensitive, clinical trial-ready neuroimaging biomarkers to accelerate drug discovery efforts and better understand disease trajectory. Once validated, these potential pharmacodynamic biomarkers can be used to measure the efficacy of new therapeutics in forestalling disease progression. Clinical trial registration: ClinicalTrails.gov Identifier: NCT04349514. [ABSTRACT FROM AUTHOR]

Introduction: Magnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV-2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. Here we describe the development of such a protocol, based upon the UK Biobank, and its validation with a travelling heads study. A multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI), and arterial spin labelling (ASL), was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N = 8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (King's College London). Over 2,000 Imaging Derived Phenotypes (IDPs), measuring both data quality and regional image properties of interest, were automatically estimated by customised UKB image processing pipelines (S2 File). Components of variance and intra-class correlations (ICCs) were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. Intra-class correlations for many IDPs indicated good-to-excellent between-site reliability. Considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, although there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data. Conclusion: These results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonisation of data collected from sites using scanners supplied by different manufacturers. These acquisition and analysis protocols are now in use for MRI assessments of post-COVID patients (N = 700) as part of the ongoing COVID-CNS study. [ABSTRACT FROM AUTHOR]

Objective: There is a paucity of large cohort-based evidence regarding the need and added value of diffusion-weighted imaging (DWI) in patients attending outpatient clinic for cognitive impairment. We aimed to evaluate the diagnostic yield of DWI in patients attending outpatient clinic for cognitive impairment. Materials and methods: This retrospective, observational, single-institution study included 3,298 consecutive patients (mean age ± SD, 71 years ± 10; 1,976 women) attending outpatient clinic for cognitive impairment with clinical dementia rating ≥ 0.5 who underwent brain MRI with DWI from January 2010 to February 2020. Diagnostic yield was defined as the proportion of patients in whom DWI supported the diagnosis that underlies cognitive impairment among all patients. Subgroup analyses were performed by age group and sex, and the Chi-square test was performed to compare the diagnostic yields between groups. Results: The overall diagnostic yield of DWI in patients with cognitive impairment was 3.2% (106/3,298; 95% CI, 2.6–3.9%). The diagnostic yield was 2.5% (83/3,298) for acute or subacute infarct, which included recent small subcortical infarct for which the diagnostic yield was 1.6% (54/3,298). The diagnostic yield was 0.33% (11/3,298) for Creutzfeldt-Jakob disease (CJD), 0.15% (5/3,298) for transient global amnesia (TGA), 0.12% (4/3,298) for encephalitis and 0.09% (3/3,298) for lymphoma. There was a trend towards a higher diagnostic yield in the older age group with age ≥ 70 years old (3.6% vs 2.6%, P =.12). There was an incremental increase in the diagnostic yield from the age group 60–69 years (2.6%; 20/773) to 90–99 years (8.0%; 2/25). Conclusion: Despite its low overall diagnostic yield, DWI supported the diagnosis of acute or subacute infarct, CJD, TGA, encephalitis and lymphoma that underlie cognitive impairment, and there was a trend towards a higher diagnostic yield in the older age group. [ABSTRACT FROM AUTHOR]

Background and objective: Computed tomography perfusion (CTP) is widely used in the evaluation of acute ischemic stroke patients for endovascular thrombectomy (EVT). The stability of CTP core estimation is suboptimal and varies between software packages. We aimed to quantify the volumetric and spatial agreement between the CTP ischemic core and follow-up infarct for four ischemic core estimation approaches using syngo.via. Methods: We included successfully reperfused, EVT-treated patients with baseline CTP and 24h follow-up diffusion weighted magnetic resonance imaging (DWI) (November 2017–September 2020). Data were processed with syngo.via VB40 using four core estimation approaches based on: cerebral blood volume (CBV)<1.2mL/100mL with and without smoothing filter, relative cerebral blood flow (rCBF)<30%, and rCBF<20%. The follow-up infarct was segmented on DWI. Results: In 59 patients, median estimated CTP core volumes for four core estimation approaches ranged from 12–39 mL. Median 24h follow-up DWI infarct volume was 11 mL. The intraclass correlation coefficient (ICC) showed moderate–good volumetric agreement for all approaches (range 0.61–0.76). Median Dice was low for all approaches (range 0.16–0.21). CTP core overestimation >10mL occurred least frequent (14/59 [24%] patients) using the CBV-based core estimation approach with smoothing filter. Conclusions: In successfully reperfused patients who underwent EVT, syngo.via CTP ischemic core estimation showed moderate volumetric and spatial agreement with the follow-up infarct on DWI. In patients with complete reperfusion after EVT, the volumetric agreement was excellent. A CTP core estimation approach based on CBV<1.2 mL/100mL with smoothing filter least often overestimated the follow-up infarct volume and is therefore preferred for clinical decision making using syngo.via. [ABSTRACT FROM AUTHOR]

Background: Knowledge of the normal myocardial–myocyte orientation could theoretically allow the definition of relevant quantitative biomarkers in clinical routine to diagnose heart pathologies. A whole heart diffusion tensor template representative of the global myofiber organization over species is therefore crucial for comparisons across populations. In this study, we developed a groupwise registration and tractography framework to resolve the global myofiber arrangement of large mammalian sheep hearts. To demonstrate the potential application of the proposed method, a novel description of sub-regions in the intraventricular septum is presented. Methods: Three explanted sheep (ovine) hearts (size ~12×8×6 cm3, heart weight ~ 150 g) were perfused with contrast agent and fixative and imaged in a 9.4T magnet. A group-wise registration of high-resolution anatomical and diffusion-weighted images were performed to generate anatomical and diffusion tensor templates. Diffusion tensor metrics (eigenvalues, eigenvectors, fractional anisotropy ...) were computed to provide a quantitative and spatially-resolved analysis of cardiac microstructure. Then tractography was performed using deterministic and probabilistic algorithms and used for different purposes: i) Visualization of myofiber architecture, ii) Segmentation of sub-area depicting the same fiber organization, iii) Seeding and Tract Editing. Finally, dissection was performed to confirm the existence of macroscopic structures identified in the diffusion tensor template. Results: The template creation takes advantage of high-resolution anatomical and diffusion-weighted images obtained at an isotropic resolution of 150 μm and 600 μm respectively, covering ventricles and atria and providing information on the normal myocardial architecture. The diffusion metric distributions from the template were found close to the one of the individual samples validating the registration procedure. Small new sub-regions exhibiting spatially sharp variations in fiber orientation close to the junctions of the septum and ventricles were identified. Each substructure was defined and represented using streamlines. The existence of a fiber-bundles in the posterior junction was validated by anatomical dissection. A complex structural organization of the anterior junction in comparison to the posterior junction was evidenced by the high-resolution acquisition. Conclusions: A new framework combining cardiac template generation and tractography was applied on the whole sheep heart. The framework can be used for anatomical investigation, characterization of microstructure and visualization of myofiber orientation across samples. Finally, a novel description of the ventricular junction in large mammalian sheep hearts was proposed. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) diffusion-weighted imaging (DWI) is often used to detect focal liver lesions (FLLs), though DWI image quality can be limited in the left liver lobe owing to the pulsatile motion of the nearby heart. Flow-compensated (FloCo) diffusion encoding has been shown to reduce this pulsation artifact. The purpose of this prospective study was to intra-individually compare DWI of the liver acquired with conventional monopolar and FloCo diffusion encoding for assessing metastatic FLLs in non-cirrhotic patients. Forty patients with known or suspected multiple metastatic FLLs were included and measured at 1.5 T field strength with a conventional (monopolar) and a FloCo diffusion encoding EPI sequence (single refocused; b-values, 50 and 800 s/mm2). Two board-certified radiologists analyzed the DWI images independently. They issued Likert-scale ratings (1 = worst, 5 = best) for pulsation artifact severity and counted the difference of lesions visible at b = 800 s/mm² separately for small and large FLLs (i.e., < 1 cm or > 1 cm) and separately for left and right liver lobe. Differences between the two diffusion encodings were assessed with the Wilcoxon signed-rank test. Both readers found a reduction in pulsation artifact in the liver with FloCo encoding (p < 0.001 for both liver lobes). More small lesions were detected with FloCo diffusion encoding in both liver lobes (left lobe: six and seven additional lesions by readers 1 and 2, respectively; right lobe: five and seven additional lesions for readers 1 and 2, respectively). Both readers found one additional large lesion in the left liver lobe. Thus, flow-compensated diffusion encoding appears more effective than monopolar diffusion encoding for the detection of liver metastases. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion-weighted imaging (DWI) is an essential element of almost every brain MRI examination. The most widely applied DWI technique, a single-shot echo-planar imaging DWI (EPI-DWI) sequence, suffers from a high sensitivity to magnetic field inhomogeneities. As an alternative, a single-shot stimulated echo acquisition mode diffusion-weighted MRI (STEAM-DWI) has recently been re-introduced after it became significantly faster. The aim of the study was to investigate the applicability of STEAM-DWI as a substitute to EPI-DWI in a daily routine of pediatric radiology. Methods: Retrospectively, brain MRI examinations of 208 children with both EPI-DWI and STEAM-DWI were assessed. Visual resolution and diagnostic confidence were evaluated, the extent of susceptibility artifacts was quantified, and contrast-to-noise ratio was calculated in case of diffusion restriction. Furthermore, the correlation of apparent diffusion coefficient values between STEAM-DWI and EPI-DWI was tested. Results: STEAM-DWI was inferior to EPI-DWI in visual resolution but with higher diagnostic confidence and lower artifact size. The apparent diffusion coefficient values of both sequences demonstrated excellent correlation. The contrast-to-noise ratio of STEAM-DWI was only half of that of EPI-DWI (58% resp. 112%). Conclusion: STEAM-DWI is a robust alternative to EPI-DWI when increased susceptibility artifacts are to be expected. Drawbacks are a lower contrast-to-noise ratio and poorer visual resolution. [ABSTRACT FROM AUTHOR]

This is one of the first studies to examine the structural brain anatomy and connectivity associated with an ADHD diagnosis and child as well as adult ADHD symptoms in young adults. It was hypothesized that an adult ADHD diagnosis and in particular childhood symptoms, are associated with widespread changes in the brain macro- and microstructure, which can be used to develop a morphometric biomarker for ADHD.Voxel-wise linear regression models were used to examine structural and diffusion-weighted MRI data in 72 participants (31 young adults with ADHD and 41 controls without ADHD) in relation to diagnosis and the number of self-reported child and adult symptoms.Findings revealed significant associations between ADHD diagnosis and widespread changes to the maturation of white matter fiber bundles and gray matter density in the brain, such as structural shape changes (incomplete maturation) of the middle and superior temporal gyrus, and fronto-basal portions of both frontal lobes. ADHD symptoms in childhood showed the strongest association with brain macro- and microstructural abnormalities. At the brain circuitry level, the superior longitudinal fasciculus (SLF) and cortico-limbic areas are dysfunctional in individuals with ADHD. The morphometric findings predicted an ADHD diagnosis correctly up to 83% of all cases.An adult ADHD diagnosis and in particular childhood symptoms are associated with widespread micro- and macrostructural changes. The SLF and cortico-limbic findings suggest complex audio-visual, motivational, and emotional dysfunctions associated with ADHD in young adults. The sensitivity of the morphometric findings in predicting an ADHD diagnosis was sufficient, which indicates that MRI-based assessments are a promising strategy for the development of a biomarker.

Background: The correct estimation of fibre orientations is a crucial step for reconstructing human brain tracts. Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques (bedpostx) is able to estimate several fibre orientations and their diffusion parameters per voxel using Markov Chain Monte Carlo (MCMC) in a whole brain diffusion MRI data, and it is capable of running on GPUs, achieving speed-up of over 100 times compared to CPUs. However, few studies have looked at whether the results from the CPU and GPU algorithms differ. In this study, we compared CPU and GPU bedpostx outputs by running multiple trials of both algorithms on the same whole brain diffusion data and compared each distribution of output using Kolmogorov-Smirnov tests. Results: We show that distributions of fibre fraction parameters and principal diffusion direction angles from bedpostx and bedpostx_gpu display few statistically significant differences in shape and are localized sparsely throughout the whole brain. Average output differences are small in magnitude compared to underlying uncertainty. Conclusions: Despite small amount of differences in output between CPU and GPU bedpostx algorithms, results are comparable given the difference in operation order and library usage between CPU and GPU bedpostx. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare parameter estimates for the 2-compartment and diffusion kurtosis imaging models obtained from diffusion-weighted imaging (DWI) of aquaporin-4 (AQP4) expression-controlled cells, and to look for biomarkers that indicate differences in the cell membrane water permeability. DWI was performed on AQP4-expressing and non-expressing cells and the signal was analyzed with the 2-compartment and diffusion kurtosis imaging models. For the 2-compartment model, the diffusion coefficients (Df, Ds) and volume fractions (Ff, Fs, Ff = 1-Fs) of the fast and slow compartments were estimated. For the diffusion kurtosis imaging model, estimates of the diffusion kurtosis (K) and corrected diffusion coefficient (D) were obtained. For the 2-compartment model, Ds and Fs showed clear differences between AQP4-expressing and non-expressing cells. Fs was also sensitive to cell density. There was no clear relationship with the cell type for the diffusion kurtosis imaging model parameters. Changes to cell membrane water permeability due to AQP4 expression affected DWI of cell suspensions. For the 2-compartment and diffusion kurtosis imaging models, Ds was the parameter most sensitive to differences in AQP4 expression. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) motion artefacts frequently complicate structural and diffusion MRI analyses. While diffusion imaging is easily 'scrubbed' of motion affected volumes, the same is not true for T1w or T2w 'structural' images. Structural images are critical to most diffusion-imaging pipelines thus their corruption can lead to disproportionate data loss. To enable diffusion-image processing when structural images are missing or have been corrupted, we propose a means by which synthetic structural images can be generated from diffusion MRI. This technique combines multi-tissue constrained spherical deconvolution, which is central to many existing diffusion analyses, with the Bloch equations that allow simulation of MRI intensities for given scanner parameters and magnetic resonance (MR) tissue properties. We applied this technique to 32 scans, including those acquired on different scanners, with different protocols and with pathology present. The resulting synthetic T1w and T2w images were visually convincing and exhibited similar tissue contrast to acquired structural images. These were also of sufficient quality to drive a Freesurfer-based tractographic analysis. In this analysis, probabilistic tractography connecting the thalamus to the primary sensorimotor cortex was delineated with Freesurfer, using either real or synthetic structural images. Tractography for real and synthetic conditions was largely identical in terms of both voxels encountered (Dice 0.88–0.95) and mean fractional anisotropy (intrasubject absolute difference 0.00–0.02). We provide executables for the proposed technique in the hope that these may aid the community in analysing datasets where structural image corruption is common, such as studies of children or cognitively impaired persons. [ABSTRACT FROM AUTHOR]

Tentative results from feasibility analyses are critical for planning future randomized control trials (RCTs) in the emerging field of neural biomarkers of behavioral interventions. The current feasibility study used MRI-derived diffusion imaging data to investigate whether it would be possible to identify neural biomarkers of a behavioral intervention among people diagnosed with autism spectrum disorder (ASD). The corpus callosum has been linked to cognitive processing and callosal abnormalities have been previously found in people diagnosed with ASD. We used a case-control design to evaluate the association between the type of intervention people diagnosed with ASD had previously received and their current white matter integrity in the corpus callosum. Twenty-six children and adolescents with ASD, with and without a history of parent-managed behavioral intervention, underwent an MRI scan with a diffusion data acquisition sequence. We conducted tract-based spatial statistics and a region of interest analysis. The fractional anisotropy values (believed to indicate white matter integrity) in the posterior corpus callosum was significantly different across cases (exposed to parent-managed behavioral intervention) and controls (not exposed to parent-managed behavioral intervention). The effect was modulated by the intensity of the behavioral intervention according to a dose-response relationship. The current feasibility case-control study provides the basis for estimating the statistical power required for future RCTs in this field. In addition, the study demonstrated the effectiveness of purposely-developed motion control protocols and helped to identify regions of interest candidates. Potential clinical applications of diffusion tensor imaging in the evaluation of treatment outcomes in ASD are discussed. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Intravoxel incoherent motion (IVIM) is an MRI technique with potential applications in measuring brain tumor perfusion, but its clinical impact remains to be determined. We assessed the usefulness of IVIM-metrics in predicting survival in newly diagnosed glioblastoma. METHODS: Fifteen patients with glioblastoma underwent MRI including spin-echo echo-planar DWI using 13 b-values ranging from 0 to 1000 s/mm2. Parametric maps for diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were generated for contrast-enhancing regions (CER) and non-enhancing regions (NCER). Regions of interest were manually drawn in regions of maximum f and on the corresponding dynamic susceptibility contrast images. Prognostic factors were evaluated by Kaplan-Meier survival and Cox proportional hazards analyses. RESULTS: We found that fCER and D*CER correlated with rCBFCER. The best cutoffs for 6-month survival were fCER>9.86% and D*CER>21.712 x10-3mm2/s (100% sensitivity, 71.4% specificity, 100% and 80% positive predictive values, and 80% and 100% negative predictive values; AUC:0.893 and 0.857, respectively). Treatment yielded the highest hazard ratio (5.484; 95% CI: 1.162-25.88; AUC: 0.723; P = 0.031); fCER combined with treatment predicted survival with 100% accuracy. CONCLUSIONS: The IVIM-metrics fCER and D*CER are promising biomarkers of 6-month survival in newly diagnosed glioblastoma.

IntroductionDiffusion Weighted Imaging (DWI), which is based on Echo Planar Imaging (EPI) protocols, is becoming increasingly important for neurosurgical applications. However, its use in this context is limited in part by significant spatial distortion inherent to EPI.MethodWe evaluated an efficient algorithm for EPI distortion correction (EPIC) across 814 DWI scans from 250 brain tumor patients and quantified the magnitude of geometric distortion for whole brain and multiple brain regions.ResultsEvaluation of the algorithm's performance revealed significantly higher mutual information between T1-weighted pre-contrast images and corrected b = 0 images than the uncorrected b = 0 images (p < 0.001). The distortion magnitude across all voxels revealed a median EPI distortion effect of 2.1 mm, ranging from 1.2 mm to 5.9 mm, the 5th and 95th percentile, respectively. Regions adjacent to bone-air interfaces, such as the orbitofrontal cortex, temporal poles, and brain stem, were the regions most severely affected by DWI distortion.ConclusionUsing EPIC to estimate the degree of distortion in 814 DWI brain tumor images enabled the creation of a topographic atlas of DWI distortion across the brain. The degree of displacement of tumors boundaries in uncorrected images is severe but can be corrected for using EPIC. Our results support the use of distortion correction to ensure accurate and careful application of DWI to neurosurgical practice.

Background and purpose: Microstructure imaging with advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin echo (OGSE) dMRI, implemented by varying the oscillating gradient frequency, and microscopic anisotropy (μA) dMRI, implemented via tensor valued diffusion encoding, may provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The aims of this study were to characterize the test-retest reproducibility of in vivo OGSE and μA dMRI metrics in the mouse brain at 9.4 Tesla and provide estimates of required sample sizes for future investigations. Methods: Twelve adult C57Bl/6 mice were scanned twice (5 days apart). Each imaging session consisted of multifrequency OGSE and μA dMRI protocols. Metrics investigated included μA, linear diffusion kurtosis, isotropic diffusion kurtosis, and the diffusion dispersion rate (Λ), which explores the power-law frequency dependence of mean diffusivity. The dMRI metric maps were analyzed with mean region-of-interest (ROI) and whole brain voxel-wise analysis. Bland-Altman plots and coefficients of variation (CV) were used to assess the reproducibility of OGSE and μA metrics. Furthermore, we estimated sample sizes required to detect a variety of effect sizes. Results: Bland-Altman plots showed negligible biases between test and retest sessions. ROI-based CVs revealed high reproducibility for most metrics (CVs < 15%). Voxel-wise CV maps revealed high reproducibility for μA (CVs ~ 10%), but low reproducibility for OGSE metrics (CVs ~ 50%). Conclusion: Most of the μA dMRI metrics are reproducible in both ROI-based and voxel-wise analysis, while the OGSE dMRI metrics are only reproducible in ROI-based analysis. Given feasible sample sizes (10–15), μA metrics and OGSE metrics may provide sensitivity to subtle microstructural changes (4–8%) and moderate changes (> 6%), respectively. [ABSTRACT FROM AUTHOR]

Introduction: Over the last decade, there have been significant advances in treatments for anterior ischemic stroke, most notably endovascular thrombectomy (EVT). Despite the success of EVT on overall outcomes, intracerebral hemorrhage (ICH) is an important post-procedure complication, often associated with mortality and disability. Hence, predicting the risk of ICH can inform EVT decision making. The ASPECT score is used globally to predict patients' prognosis post-reperfusion therapy. Our objective is to perform a systematic review to collect and synthesize data on the association between ASPECT scores on CT, CTP and DWI-MRI (CT-ASPECT, CTP-ASPECT, and DWI-ASPECT) and the risk of symptomatic ICH after EVT for anterior circulation strokes., Methods and Analysis: We will conduct a broad search of various electronic databases (MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, and the Cochrane Database of Systematic Reviews) to identify studies published after January 1st, 2012 (commonly accepted as the beginning of the modern EVT era based on availability of stent-retrievers). Two independent reviewers will screen and include studies evaluating associations between symptomatic ICH after thrombectomy and baseline CT-ASPECT, CTP-ASPECT and DWI-ASPECT scores. Data will be extracted to quantify the risk of sICH after EVT based on the ASPECT scoring., Trial Registration: PROSPERO registration number: CRD42023459860., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Brissette et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Conte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)