Axial diffusivity changes in the motor pathway above stroke foci and functional recovery after subcortical infarction

Diffusion Tensor Imaging as a Prognostic Tool for Recovery in Acute and Hyperacute Stroke

Stroke represents a major cause of mortality and long-term disability among adult populations, leaving a devastating socioeconomic impact globally. Clinical manifestation of stroke is characterized by great diversity, ranging from minor disability to considerable neurological impairment interfering with activities of daily living and even death. Prognostic ambiguity has stimulated the interest for implementing stroke recovery biomarkers, including those provided by structural neuroimaging techniques, i.e., diffusion tensor imaging (DTI) and tractography for the study of white matter (WM) integrity. Considering the necessity of prompt and accurate prognosis in stroke survivors along with the potential capacity of DTI as a relevant imaging biomarker, the purpose of our study was to review the pertinent literature published within the last decade regarding DTI as a prognostic tool for recovery in acute and hyperacute stroke. We conducted a thorough literature search in two databases (MEDLINE and Science Direct) in order to trace all relevant studies published between 1 January 2012 and 16 March 2022 using predefined terms as key words. Only full-text human studies published in the English language were included. Forty-four studies were identified and are included in this review. We present main findings and by describing several methodological issues, we highlight shortcomings and gaps in the current literature so that research priorities for future research can be outlined. Our review suggests that DTI can track longitudinal changes and identify prognostic correlates in acute and hyperacute stroke patients.

Longitudinal changes in the inferior cerebellar peduncle and lower limb motor recovery following subcortical infarction

Background

The cerebellum receives afferent signals from spinocerebellar pathways regulating lower limb movements. However, the longitudinal changes in the spinocerebellar pathway in the early stage of unilateral supratentorial stroke and their potential clinical significance have received little attention.

Methods

Diffusion tensor imaging and Fugl-Meyer assessment of lower limb were performed 1, 4, and 12 weeks after onset in 33 patients with acute subcortical infarction involving the supratentorial areas, and in 33 healthy subjects. We evaluated group differences in diffusion metrics in the bilateral inferior cerebellar peduncle (ICP) and analyzed the correlation between ICP diffusion metrics and changes to the Fugl-Meyer scores of the affected lower limb within 12 weeks after stroke.

Results

Significantly decreased fractional anisotropy and increased mean diffusivity were found in the contralesional ICP at week 12 after stroke compared to controls (all P < 0.01) and those at week 1 (all P < 0.05). There were significant fractional anisotropy decreases in the ipsilesional ICP at week 4 (P = 0.008) and week 12 (P = 0.004) compared to controls. Both fractional anisotropy (r_s = 0.416, P = 0.025) and mean diffusivity (r_s = -0.507, P = 0.005) changes in the contralesional ICP correlated with changes in Fugl-Meyer scores of the affected lower limb in all patients.

Conclusions

Bilateral ICP degeneration occurs in the early phase of supratentorial stroke, and diffusion metric values of the contralesional ICP are useful indicators of affected lower limb function after supratentorial stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02346-x.

Advanced non-invasive MRI of neuroplasticity in ischemic stroke: Techniques and applications

Ischemic stroke represents a serious medical condition which could cause survivors suffer from long-term and even lifetime disabilities. After a stroke attack, the brain would undergo varying degrees of recovery, in which the central nervous system could be reorganized spontaneously or with the help of appropriate rehabilitation. Magnetic resonance imaging (MRI) is a non-invasive technique which can provide comprehensive information on structural, functional and metabolic features of brain tissue. In the last decade, there has been an increased technical advancement in MR techniques such as voxel-based morphological analysis (VBM), diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI), arterial spin-labeled perfusion imaging (ASL), magnetic sensitivity weighted imaging (SWI), quantitative sensitivity magnetization (QSM) and magnetic resonance spectroscopy (MRS) which have been proven to be a valuable tool to study the brain tissue reorganization. Due to MRI indices of neuroplasticity related to neurological outcome could be translated to the clinic. The ultimate goal of this review is to equip readers with a fundamental understanding of advanced MR techniques and their corresponding clinical application for improving the ability to predict neuroplasticity that are most suitable for stroke management.

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.