Comparison of spatial normalization strategies of diffusion MRI data for studying motor outcome in subacute-chronic and acute stroke

A prospective reappraisal of motor outcome prediction in patients with acute stroke by using atlas-based diffusion tensor imaging biomarkers

BACKGROUND

Diffusion tensor imaging (DTI) biomarkers can be used to quantify microstructural changes in the cerebral white matter (WM) following injury.

OBJECTIVES

This prospective single-center study aimed to evaluate whether atlas-based DTI-derived metrics obtained within 1 week after stroke can predict the motor outcome at 3 months.

METHODS

Forty patients with small acute stroke (2-7 days after onset) involving the corticospinal tract were included. Each patient underwent magnetic resonance imaging (MRI) within 1 week and at 3 months after stroke, and the changes based on DTI-derived metrics were compared by performing WM tract atlas-based quantitative analysis.

RESULTS

A total of 40 patients were included, with median age 63.5 years and a majority of males (72.5%). Patients were classified into good-prognosis group (mRS 0-2, n = 27) and poor-prognosis group (mRS 3-5, n = 13) by outcome. The median (25th-75th percentile) of MD (0.7 (0.6-0.7) vs. 0.7 (0.7-0.8); p = 0.049) and AD (0.6 (0.5, 0.7) vs. 0.7 (0.6, 0.8); p = 0.023) ratios within 1 week were significantly lower in the poor-prognosis group compared to the good-prognosis group. The ROC curve of the combined DTI-derived metrics model showed comparable Youden index (65.5% vs. 58.4%-65.4%) and higher specificity (96.3% vs. 69.2%-88.5%) compared to clinical indexes. The area under the ROC curve of the combined DTI-derived metrics model is comparable to those of the clinical indexes (all p > 0.1) and higher than those of the individual DTI-derived metrics parameters.

CONCLUSIONS

Atlas-based DTI-derived metrics at acute stage provide objective information for prognosis prediction of patients with ischemic or lacunar stroke.

Association between superior longitudinal fasciculus, motor recovery, and motor outcome after stroke: a cohort study

Introduction

Parieto-frontal interactions are mediated by the superior longitudinal fasciculus (SLF) and are crucial to integrate visuomotor information and mediate fine motor control. In this study, we aimed to characterize the relation of white matter integrity of both parts of the SLF (SLF I and SLF II) to both motor outcome and recovery and its evolution over time in stroke patients with upper limb motor deficits.

Materials and methods

Fractional anisotropy (FA) values over the SLF I, SLF II, and corticospinal tract (CST) and upper limb motor performance evaluated by both the upper limb Fugl-Meyer Assessment score and maximum grip strength were measured for 16 patients at 3 weeks, 6 weeks, and 12 weeks poststroke. FA changes were assessed over time using repeated-measures Friedman ANOVA, and correlations between motor recovery, motor outcome at 12 weeks, and FA values in the CST, SLF I, and SLF II at 3 weeks were performed using Spearman's rank-order correlation.

Results

FA values in the affected hemisphere's SLF I and SLF II at 3 weeks correlated with motor recovery at 12 weeks when assessed by the Fugl-Meyer Assessment for upper limb extremity (rho: 0.502, p: 0.04 and rho: 0.510, p: 0.04, respectively) but not when assessed by grip strength. FA values in the SLF I and SLF II were not correlated with motor outcomes. FA values in the SLF II in the affected hemisphere changed significantly over time (p: 0.016).

Conclusion

Both SLF I and SLF II appeared to participate in poststroke motor recovery of complex movements but not in the motor outcome. These results argue that visually/spatially oriented motor tasks as well as more complex motor tasks using parietal associative areas should be used for poststroke rehabilitation strategies.

The elusive metric of lesion load

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

In this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners.

Quantifying Differences Between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images

Spatial normalization helps us to compare quantitatively two or more input brain scans. Although using an affine normalization approach preserves the anatomical structures, the neuroimaging field is more common to find works that make use of nonlinear transformations. The main reason is that they facilitate a voxel-wise comparison, not only when studying functional images but also when comparing MRI scans given that they fit better to a reference template. However, the amount of bias introduced by the nonlinear transformations can potentially alter the final outcome of a diagnosis especially when studying functional scans for neurological disorders like Parkinson's Disease. In this context, we have tried to quantify the bias introduced by the affine and the nonlinear spatial registration of FP-CIT SPECT volumes of healthy control subjects and patients with PD. For that purpose, we calculated the deformation fields of each participant and applied these deformation fields to a 3D-grid. As the space between the edges of small cubes comprising the grid change, we can quantify which parts from the brain have been enlarged, compressed or just remain the same. When the nonlinear approach is applied, scans from PD patients show a region near their striatum very similar in shape to that of healthy subjects. This artificially increases the interclass separation between patients with PD and healthy subjects as the local intensity is decreased in the latter region, and leads machine learning systems to biased results due to the artificial information introduced by these deformations.

Cerebello-Motor Paired Associative Stimulation and Motor Recovery in Stroke: a Randomized, Sham-Controlled, Double-Blind Pilot Trial

Cerebellum is a key structure for functional motor recovery after stroke. Enhancing the cerebello-motor pathway by paired associative stimulation (PAS) might improve upper limb function. Here, we conducted a randomized, double-blind, sham-controlled pilot trial investigating the efficacy of a 5-day treatment of cerebello-motor PAS coupled with physiotherapy for promoting upper limb motor function compared to sham stimulation. The secondary objectives were to determine in the active treated group (i) whether improvement of upper limb motor function was associated with changes in corticospinal excitability or changes in functional activity in the primary motor cortex and (ii) whether improvements were correlated to the structural integrity of the input and output pathways. To that purpose, hand dexterity and maximal grip strength were assessed along with TMS recordings and multimodal magnetic resonance imaging, before the first treatment, immediately after the last one and a month later. Twenty-seven patients were analyzed. Cerebello-motor PAS was effective compared to sham in improving hand dexterity (p: 0.04) but not grip strength. This improvement was associated with increased activation in the ipsilesional primary motor cortex (p: 0.04). Moreover, the inter-individual variability in clinical improvement was partly explained by the structural integrity of the afferent (p: 0.06) and efferent pathways (p: 0.02) engaged in this paired associative stimulation (i.e., cortico-spinal and dentato-thalamo-cortical tracts). In conclusion, cerebello-motor-paired associative stimulation combined with physiotherapy might be a promising approach to enhance upper limb motor function after stroke.Clinical Trial Registration URL: http://www.clinicaltrials.gov . Unique identifier: NCT02284087.

Chronic pontine strokes: Diffusion tensor imaging of corticospinal tract indicates the prognosis in terms of motor outcome

OBJECTIVE

To investigate relationship between the diffusion indexes of corticospinal tract (CST) and the neurological motor outcomes in chronic pontine stroke patients.

METHODS

Diffusion tensor imaging (DTI) is performed in 27 patients with chronic pontine stroke. Fractional anisotropy (FA) values along the CST area, the track number, and the CST length are measured. Neurological and motor outcomes are evaluated based on Fugl-meyer (FM), National Institutes of Health Stroke Scale (NIHSS), Barthel index (BI), and modified Rankin scale (mRS) scores. The relationships between FA ratios (rFAs) in the CST of stroke subjects and their clinical motor scores are analyzed through Spearman's correlation analysis. Then, diffusion tensor tractography (DTT) is performed to show the injury degree of CST.

RESULTS

First, FA values are decreased in the infarct area, cerebral peduncle, posterior limb of the internal capsule, and precentral gyrus compared with those in the contralateral side. The number of CST is decreased in the ipsilateral side of the infarct. Second, rFAs in the cerebral peduncle, posterior limb of the internal capsule, and CST rnum correlate positively with FM scores (r = 0.824, 0.672, 0.651, p < 0.001) and negatively with mRS scores (r = -0.835, -0.604, -0.645, p≤0.001). Third, the injury degree of CST correlates negatively with FM scores (r = -0.627, p < 0.001).

CONCLUSIONS

The study demonstrates that rFAs in the cerebral peduncle, posterior limb of the internal capsule, and CST rnum associate with motor outcome, suggesting that DTI may be applicable for outcome evaluation.

Comparison of Fractional Anisotropy from Tract-Based Spatial Statistics with and without Lesion Masking in Patients with Intracerebral Hemorrhage: A Technical Note

BACKGROUND

Diffusion-tensor fractional anisotropy (FA) is an index of neural-fiber damage in patients following stroke. To better characterize FA, tract-based spatial statistics (TBSS) is frequently used, which involves spatial transformation into the standard brain space. Despite its utility, this technique is susceptible to space-occupying hematoma in patients with intracerebral hemorrhage. To correct this, "lesion making" has been proposed. Here, FA values from TBSS without lesion masking and TBSS with lesion masking were compared, and the clinical utility was evaluated.

METHODS

Forty patients from our previously published work were entered into the study. Diffusion-tensor imagings were acquired 14-21 days after onset and FA maps were generated. Lesion masks were produced in reference with nondiffusion (b = 0) brain images. Two types (with or without lesion masking) of TBSS were then performed. For both types, using individual data we extracted mean FA values within for the corticospinal tract (CST) and the superior longitudinal fasciculus (SLF). FA ratio (rFA) between the lesioned hemisphere and the unaffected hemisphere was then calculated. The two sets of the data were then compared by assessing residuals of mean root sum square error (RMSE).

RESULTS

Although rFA obtained from TBSS with lesion masking tended to be slightly smaller, the estimated RMSE was .025 for both the CST and the SLF.

CONCLUSIONS

The estimated FA differences between the two sets of TBSS were very small. Considering the time for manual labor for producing lesion masks, regular TBSS without lesion masking may be sufficient in terms of clinical utility.

Acute Diffusivity Biomarkers for Prediction of Motor and Language Outcome in Mild-to-Severe Stroke Patients

Background and Purpose—

Early severity of stroke symptoms—especially in mild-to-severe stroke patients—are imperfect predictors of long-term motor and aphasia outcome. Motor function and language processing heavily rely on the preservation of important white matter fasciculi in the brain. Axial diffusivity (AD) from the diffusion tensor imaging model has repeatedly shown to accurately reflect acute axonal damage and is thus optimal to probe the integrity of important white matter bundles and their relationship with long-term outcome. Our aim was to investigate the independent prognostic value of the AD of white matter tracts in the motor and language network evaluated at 24 hours poststroke for motor and aphasia outcome at 3 months poststroke.

Methods—

Seventeen (motor cohort) and 28 (aphasia cohort) thrombolyzed patients with initial mild-to-severe stroke underwent a diffusion tensor imaging sequence at 24 hours poststroke. Motor and language outcome were evaluated at 3 months poststroke with a composite motor score and the aphasia handicap scale. We first used stepwise regression to determine which classic (age, initial motor or aphasia severity, and lesion volume) and imaging (ratio of affected/unaffected AD of motor and language fasciculi) factors were related to outcome. Second, to determine the specificity of our a priori choices of fasciculi, we performed voxel-based analyses to determine if the same, additional, or altogether new regions were associated with long-term outcome.

Results—

The ratio of AD in the corticospinal tract was the sole predictor of long-term motor outcome, and the ratio of AD in the arcuate fasciculus—along with age and initial aphasia severity—was an independent predictor of 3-month aphasia outcome. White matter regions overlapping with these fasciculi naturally emerged in the corresponding voxel-based analyses.

Conclusions—

AD of the corticospinal tract and arcuate fasciculus are effective biomarkers of long-term motor and aphasia outcome, respectively.

Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review

Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2-7 days (acute), and >7-90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.

Multivariate prediction of functional outcome using lesion topography characterized by acute diffusion tensor imaging

The relationship between stroke topography and functional outcome has largely been studied with binary manual lesion segmentations. However, stroke topography may be better characterized by continuous variables capable of reflecting the severity of ischemia, which may be more pertinent for long-term outcome. Diffusion Tensor Imaging (DTI) constitutes a powerful means of quantifying the degree of acute ischemia and its potential relation to functional outcome. Our aim was to investigate whether using more clinically pertinent imaging parameters with powerful machine learning techniques could improve prediction models and thus provide valuable insight on critical brain areas important for long-term outcome. Eighty-seven thrombolyzed patients underwent a DTI sequence at 24 h post-stroke. Functional outcome was evaluated at 3 months post-stroke with the modified Rankin Score and was dichotomized into good (mRS ≤ 2) and poor (mRS > 2) outcome. We used support vector machines (SVM) to classify patients into good vs. poor outcome and evaluate the accuracy of different models built with fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity asymmetry maps, and lesion segmentations in combination with lesion volume, age, recanalization status, and thrombectomy treatment. SVM classifiers built with axial diffusivity maps yielded the best accuracy of all imaging parameters (median [IQR] accuracy = 82.8 [79.3-86.2]%), compared to that of lesion segmentations (76.7 [73.3-82.8]%) when predicting 3-month functional outcome. The analysis revealed a strong contribution of clinical variables, notably - in descending order - lesion volume, thrombectomy treatment, and recanalization status, in addition to the deep white matter at the crossroads of major white matter tracts, represented by brain regions where model weights were highest. Axial diffusivity is a more appropriate imaging marker to characterize stroke topography for predicting long-term outcome than binary lesion segmentations.