Diffusion-Weighted Imaging, MR Angiography, and Baseline Data in a Systematic Multicenter Analysis of 3,301 MRI Scans of Ischemic Stroke Patients-Neuroradiological Review Within the MRI-GENIE Study.

Background: Magnetic resonance imaging (MRI) serves as a cornerstone in defining stroke phenotype and etiological subtype through examination of ischemic stroke lesion appearance and is therefore an essential tool in linking genetic traits and stroke. Building on baseline MRI examinations from the centralized and structured radiological assessments of ischemic stroke patients in the Stroke Genetics Network, the results of the MRI-Genetics Interface Exploration (MRI-GENIE) study are described in this work. Methods: The MRI-GENIE study included patients with symptoms caused by ischemic stroke (N = 3,301) from 12 international centers. We established and used a structured reporting protocol for all assessments. Two neuroradiologists, using a blinded evaluation protocol, independently reviewed the baseline diffusion-weighted images (DWIs) and magnetic resonance angiography images to determine acute lesion and vascular occlusion characteristics. Results: In this systematic multicenter radiological analysis of clinical MRI from 3,301 acute ischemic stroke patients according to a structured prespecified protocol, we identified that anterior circulation infarcts were most prevalent (67.4%), that infarcts in the middle cerebral artery (MCA) territory were the most common, and that the majority of large artery occlusions 0 to 48 h from ictus were in the MCA territory. Multiple acute lesions in one or several vascular territories were common (11%). Of 2,238 patients with unilateral DWI lesions, 52.6% had left-sided infarct lateralization (P = 0.013 for χ2 test). Conclusions: This large-scale analysis of a multicenter MRI-based cohort of AIS patients presents a unique imaging framework facilitating the relationship between imaging and genetics for advancing the knowledge of genetic traits linked to ischemic stroke.

Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients.

Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics. To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models. For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model. In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice.

Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept.

Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10-20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms ('convert') within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia ('converters'), while 35 had not ('non-converters'). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials.

Multimodal MRI of grey matter, white matter, and functional connectivity in cognitively healthy mutation carriers at risk for frontotemporal dementia and Alzheimer's disease.

BACKGROUND: Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are associated with divergent differences in grey matter volume, white matter diffusion, and functional connectivity. However, it is unknown at what disease stage these differences emerge. Here, we investigate whether divergent differences in grey matter volume, white matter diffusion, and functional connectivity are already apparent between cognitively healthy carriers of pathogenic FTD mutations, and cognitively healthy carriers at increased AD risk. METHODS: We acquired multimodal magnetic resonance imaging (MRI) brain scans in cognitively healthy subjects with (n=39) and without (n=36) microtubule-associated protein Tau (MAPT) or progranulin (GRN) mutations, and with (n=37) and without (n=38) apolipoprotein E ε4 (APOE4) allele. We evaluated grey matter volume using voxel-based morphometry, white matter diffusion using tract-based spatial statistics (TBSS), and region-to-network functional connectivity using dual regression in the default mode network and salience network. We tested for differences between the respective carriers and controls, as well as for divergence of those differences. For the divergence contrast, we additionally performed region-of-interest TBSS analyses in known areas of white matter diffusion differences between FTD and AD (i.e., uncinate fasciculus, forceps minor, and anterior thalamic radiation). RESULTS: MAPT/GRN carriers did not differ from controls in any modality. APOE4 carriers had lower fractional anisotropy than controls in the callosal splenium and right inferior fronto-occipital fasciculus, but did not show grey matter volume or functional connectivity differences. We found no divergent differences between both carrier-control contrasts in any modality, even in region-of-interest analyses. CONCLUSIONS: Concluding, we could not find differences suggestive of divergent pathways of underlying FTD and AD pathology in asymptomatic risk mutation carriers. Future studies should focus on asymptomatic mutation carriers that are closer to symptom onset to capture the first specific signs that may differentiate between FTD and AD.

Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison.

Neuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal clinical studies change head coils halfway. In the present study, we aimed to estimate this possible bias introduced by using different head coils to create awareness and to avoid misinterpretation of results. We acquired, with both an 8 channel and 32 channel head coil, T1-weighted, diffusion tensor imaging and resting state fMRI images at 3T MRI (Philips Achieva) with stable acquisition parameters in a large group of cognitively healthy participants (n = 77). Standard analysis methods, i.e., voxel-based morphometry, tract-based spatial statistics and resting state functional network analyses, were used in a within-subject design to compare 8 and 32 channel head coil data. Signal-to-noise ratios (SNR) for both head coils showed similar ranges, although the 32 channel SNR profile was more homogeneous. Our data demonstrates specific patterns of gray and white matter volume differences between head coils (relative volume change of 6 to 9%), related to altered image contrast and therefore, altered tissue segmentation. White matter connectivity (fractional anisotropy and diffusivity measures) showed hemispherical dependent differences between head coils (relative connectivity change of 4 to 6%), and functional connectivity in resting state networks was higher using the 32 channel head coil in posterior cortical areas (relative change up to 27.5%). This study shows that, even when acquisition protocols are harmonized, the results of standardized analysis models can be severely affected by the use of different head coils. Researchers should be aware of this when combining multiple neuroimaging MRI datasets, to prevent coil-related bias and avoid misinterpretation of their findings.

Age- and disease-related cerebral white matter changes in patients with Parkinson's disease.

As age and Parkinson's disease (PD) both play a role in the degeneration of brain white matter, we aimed to investigate a possible interaction effect of age and disease presence on white matter integrity in patients with PD. We studied white matter hyperintensity volume, global fractional anisotropy, mean diffusivity and mean magnetization transfer ratio of normal appearing white matter in 163 patients with PD and 218 age- and gender-matched healthy control subjects. We investigated the relationship between age and these parameters in both groups, and interaction between age and disease presence. Patients with PD had a higher load of white matter hyperintensities with a preferential periventricular and anterior distribution as compared with healthy control subjects. Visuospatial functioning was related to total and postural instability and gait difficulty was related to periventricular white matter hyperintensity volume in patients with PD. The age-related decline of white matter integrity was similar for both groups. Global microstructural integrity of the normal appearing white matter did not differ between patients and healthy control subjects, suggesting that PD-specific changes do not exceed normal age-associated change in white matter without lesions.

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.

Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers.

BACKGROUND: Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up ('converters') and non-converting carriers ('non-converters'). METHODS: We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time. RESULTS: Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001). CONCLUSIONS: Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis.

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI.

BACKGROUND: Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. METHODS: Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). RESULTS: The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004). CONCLUSIONS: FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.

Multiple Approaches to Diffusion Magnetic Resonance Imaging in Hereditary Cerebral Amyloid Angiopathy Mutation Carriers.

Background Cerebral amyloid angiopathy ( CAA ) is a major cause of lobar intracerebral hemorrhage in elderly adults; however, presymptomatic diagnosis of CAA is difficult. Hereditary cerebral hemorrhage with amyloidosis-Dutch type ( HCHWA -D) is a rare autosomal-dominant disease that leads to pathology similar to sporadic CAA . Presymptomatic HCHWA -D mutation carriers provide a unique opportunity to study CAA -related changes before any symptoms have occurred. In this study we investigated early CAA -related alterations in the white matter. Methods and Results We investigated diffusion magnetic resonance imaging ( dMRI ) data for 15 symptomatic and 11 presymptomatic HCHWA -D mutation carriers and 30 noncarrier control participants using 4 different approaches. We looked at (1) the relation between age and global dMRI measures for mutation carriers versus controls, (2) voxel-wise d MRI , (3) independent component-clustered dMRI measures, and (4) structural connectomics between presymptomatic or symptomatic carriers and controls. Fractional anisotropy decreased, and mean diffusivity and peak width of the skeletonized mean diffusivity increased significantly over age for mutation carriers compared with controls. In addition, voxel-wise and independent component-wise fractional anisotropy, and mean diffusivity, and structural connectomics were significantly different between HCHWA -D patients and control participants, mainly in the periventricular frontal and occipital regions and in the occipital lobe. We found no significant differences between presymptomatic carriers and control participants. Conclusions The d MRI technique is sensitive in detecting alterations in symptomatic HCHWA -d carriers but did not show alterations in presymptomatic carriers. This result indicates that d MRI may be less suitable for identifying early white matter changes in CAA .

Gray and white matter changes in presymptomatic genetic frontotemporal dementia: a longitudinal MRI study.

In genetic frontotemporal dementia, cross-sectional studies have identified profiles of presymptomatic neuroanatomical loss for C9orf72 repeat expansion, MAPT, and GRN mutations. In this study, we characterize longitudinal gray matter (GM) and white matter (WM) brain changes in presymptomatic frontotemporal dementia. We included healthy carriers of C9orf72 repeat expansion (n = 12), MAPT (n = 15), GRN (n = 33) mutations, and related noncarriers (n = 53), that underwent magnetic resonance imaging at baseline and 2-year follow-up. We analyzed cross-sectional baseline, follow-up, and longitudinal GM and WM changes using voxel-based morphometry and cortical thickness analysis in SPM and tract-based spatial statistics in FSL. Compared with noncarriers, C9orf72 repeat expansion carriers showed lower GM volume in the cerebellum and insula, and WM differences in the anterior thalamic radiation, at baseline and follow-up. MAPT mutation carriers showed emerging GM temporal lobe changes and longitudinal WM degeneration of the uncinate fasciculus. GRN mutation carriers did not show presymptomatic neurodegeneration. This study shows distinct presymptomatic cross-sectional and longitudinal patterns of GM and WM changes across C9orf72 repeat expansion, MAPT, and GRN mutation carriers compared with noncarriers.

Sex-specific differences in white matter microvascular integrity after ischaemic stroke.

Background and purpose: Functional outcomes after ischaemic stroke are worse in women, despite adjusting for differences in comorbidities and treatment approaches. White matter microvascular integrity represents one risk factor for poor long-term functional outcomes after ischaemic stroke. The aim of the study is to characterise sex-specific differences in microvascular integrity in individuals with acute ischaemic stroke. Methods: A retrospective analysis of subjects with acute ischaemic stroke and brain MRI with diffusion-weighted (DWI) and dynamic-susceptibility contrast-enhanced (DSC) perfusion-weighted imaging obtained within 9 hours of last known well was performed. In the hemisphere contralateral to the acute infarct, normal-appearing white matter (NAWM) microvascular integrity was measured using the K2 coefficient and apparent diffusion coefficient (ADC) values. Regression analyses for predictors of K2 coefficient, DWI volume and good outcome (90-day modified Rankin scale (mRS) score <2) were performed. Results: 105 men and 79 women met inclusion criteria for analysis. Despite no difference in age, women had increased NAWM K2 coefficient (1027.4 vs 692.7×10-6/s; p=0.006). In women, atrial fibrillation (ß=583.6; p=0.04) and increasing NAWM ADC (ß=4.4; p=0.02) were associated with increased NAWM K2 coefficient. In multivariable regression analysis, the K2 coefficient was an independent predictor of DWI volume in women (ß=0.007; p=0.01) but not men. Conclusions: In women with acute ischaemic stroke, increased NAWM K2 coefficient is associated with increased infarct volume and chronic white matter structural integrity. Prospective studies investigating sex-specific differences in white matter microvascular integrity are needed.

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI.

Background: Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods: Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results: The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005). Conclusions: FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

BACKGROUND/OBJECTIVE: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

Design and rationale for examining neuroimaging genetics in ischemic stroke: The MRI-GENIE study.

OBJECTIVE: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study. METHODS: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease. CONCLUSIONS: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates.

BACKGROUND: Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T2 and diffusion MRI indices into MRI 'tissue signatures', (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. RESULTS: An improved iterative self-organizing data analysis algorithm was used to combine T2, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P < 0.001). The distribution of signatures between brains with permanent and transient occlusions varied significantly between groups (P < 0.001). Qualitative comparisons with histopathology revealed that these signatures represented regions with different histopathology. Two signatures identified areas of progressive injury marked by severe necrosis and the presence of gitter cells. Another signature identified less severe but pronounced neuronal and axonal degeneration, while the other signatures depicted tissue remodeling with vascular proliferation and astrogliosis. CONCLUSION: These exploratory results demonstrate the potential of temporally and spatially combined voxel-based methods to generate tissue signatures that may correlate with distinct histopathological features. The identification of distinct ischemic MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting.

Role of Acute Lesion Topography in Initial Ischemic Stroke Severity and Long-Term Functional Outcomes.

BACKGROUND AND PURPOSE: Acute infarct volume, often proposed as a biomarker for evaluating novel interventions for acute ischemic stroke, correlates only moderately with traditional clinical end points, such as the modified Rankin Scale. We hypothesized that the topography of acute stroke lesions on diffusion-weighted magnetic resonance imaging may provide further information with regard to presenting stroke severity and long-term functional outcomes. METHODS: Data from a prospective stroke repository were limited to acute ischemic stroke subjects with magnetic resonance imaging completed within 48 hours from last known well, admission NIH Stroke Scale (NIHSS), and 3-to-6 months modified Rankin Scale scores. Using voxel-based lesion symptom mapping techniques, including age, sex, and diffusion-weighted magnetic resonance imaging lesion volume as covariates, statistical maps were calculated to determine the significance of lesion location for clinical outcome and admission stroke severity. RESULTS: Four hundred ninety subjects were analyzed. Acute stroke lesions in the left hemisphere were associated with more severe NIHSS at admission and poor modified Rankin Scale at 3 to 6 months. Specifically, injury to white matter (corona radiata, internal and external capsules, superior longitudinal fasciculus, and uncinate fasciculus), postcentral gyrus, putamen, and operculum were implicated in poor modified Rankin Scale. More severe NIHSS involved these regions, as well as the amygdala, caudate, pallidum, inferior frontal gyrus, insula, and precentral gyrus. CONCLUSIONS: Acute lesion topography provides important insights into anatomic correlates of admission stroke severity and poststroke outcomes. Future models that account for infarct location in addition to diffusion-weighted magnetic resonance imaging volume may improve stroke outcome prediction and identify patients likely to benefit from aggressive acute intervention and personalized rehabilitation strategies.

Lesion development and reperfusion benefit in relation to vascular occlusion patterns after embolic stroke in rats.

Vascular occlusion sites largely determine the pattern of cerebral tissue damage and likelihood of subsequent reperfusion after acute ischemic stroke. We aimed to elucidate relationships between flow obstruction in segments of the internal carotid artery (ICA) and middle cerebral artery (MCA), and (1) profiles of acute ischemic lesions and (2) probability of subsequent beneficial reperfusion. Embolic stroke was induced by unilateral intracarotid blood clot injection in normotensive (n=53) or spontaneously hypertensive (n=20) rats, followed within 2 hours by magnetic resonance (MR) angiography (MRA), diffusion- (DWI) and perfusion-weighted magnetic resonance imaging (MRI) (PWI). In a subset of animals (n=9), MRI was repeated after 24 and 168 hours to determine the predictive value of the occlusion pattern on benefit of reperfusion. The extent of cerebral perfusion and diffusion abnormality was related to the pattern of flow obstruction in ICA and MCA segments. Hypertensive animals displayed significantly larger cortical perfusion lesions. Acute perfusion-diffusion lesion mismatches were detected in all animals that subsequently benefitted from reperfusion. Yet, the presence of an angiography-diffusion mismatch was more specific in predicting reperfusion benefit. Combination of DWI, PWI, and MRA exclusively informs on the impact of arterial occlusion profiles after acute ischemic stroke, which may improve prognostication and subsequent treatment decisions.

Progression of brain lesions in relation to hyperperfusion from subacute to chronic stages after experimental subarachnoid hemorrhage: a multiparametric MRI study.

BACKGROUND: The pathogenesis of delayed cerebral injury after aneurysmal subarachnoid hemorrhage (SAH) is largely unresolved. In particular, the progression and interplay of tissue and perfusion changes, which can significantly affect the outcome, remain unclear. Only a few studies have assessed pathophysiological developments between subacute and chronic time points after SAH, which may be ideally studied with noninvasive methods in standardized animal models. Therefore, our objective was to characterize the pattern and correlation of brain perfusion and lesion status with serial multiparametric magnetic resonance imaging (MRI) from subacute to chronical after experimental SAH in rats. METHODS: SAH was induced by endovascular puncture of the intracranial bifurcation of the right internal carotid artery in adult male Wistar rats (n = 30). Diffusion-, T2-, perfusion- and contrast-enhanced T1-weighted MRI were performed on a 4.7-tesla animal MR system to measure cytotoxic and vasogenic edema, hemodynamic parameters and blood-brain barrier permeability, respectively, at days 2 and 7 after SAH. The neurological status was repeatedly monitored with different behavioral tests between days -1 and 7 after SAH. Lesioned tissue - identified by edema-associated T2 prolongation - and unaffected tissue were outlined on multislice images and further characterized based on tissue and perfusion indices. Correlation analyses were performed to evaluate relationships between different MRI-based parameters and between MRI-based parameters and neurological scores. RESULTS: Similar to clinical SAH and previous studies in this experimental SAH model, mortality up to day 2 was high (43%). In surviving animals, neurological function was significantly impaired subacutely, and tissue damage (characterized by T2 prolongation and diffusion reduction) and blood-brain barrier leakage (characterized by contrast agent extravasation) were apparent in ipsilateral cortical and subcortical tissue as well as in contralateral cortical tissue. Notably, ipsilateral cortical areas revealed increased cerebral blood flow and volume. Animals that subsequently died between days 2 and 7 after SAH had markedly elevated ipsilateral perfusion levels at day 2. After a week, neurological function had improved in surviving animals, and brain edema was partially resolved, while blood-brain barrier permeability and hyperperfusion persisted. The degree of brain damage correlated significantly with the level of perfusion elevation (r = 0.78 and 0.85 at days 2 and 7, respectively; p < 0.05). Furthermore, chronic (day 7 after SAH) blood-brain barrier permeability and vasogenic edema formation were associated with subacute (day 2 after SAH) hyperperfusion (r = 0.53 and 0.66, respectively; p < 0.05). CONCLUSION: Our imaging findings indicate that SAH-induced brain injury at later stages is associated with progressive changes in tissue perfusion and that chronic hyperperfusion may contribute or point to delayed cerebral damage. Furthermore, multiparametric MRI may significantly aid in diagnosing the brain's status after SAH.