Dynamics of Water Diffusion Changes in Different Tissue Compartments From Acute to Chronic Stroke-A Serial Diffusion Tensor Imaging Study

White matter network underlying semantic processing: evidence from stroke patients

The hub-and-spoke theory of semantic representation fractionates the neural underpinning of semantic knowledge into two essential components: the sensorimotor modality-specific regions and a crucially important semantic hub region. Our previous study in patients with semantic dementia has found that the hub region is located in the left fusiform gyrus. However, because this region is located within the brain damage in patients with semantic dementia, it is not clear whether the semantic deficit is caused by structural damage to the hub region itself or by its disconnection from other brain regions. Stroke patients do not have any damage to the left fusiform gyrus, but exhibit amodal and modality-specific deficits in semantic processing. Therefore, in this study, we validated the semantic hub region from a brain network perspective in 79 stroke patients and explored the white matter connections associated with it. First, we collected data of diffusion-weighted imaging and behavioural performance on general semantic tasks and modality-specific semantic tasks (assessing object knowledge on form, colour, motion, sound, manipulation and function). We then used correlation and regression analyses to examine the association between the nodal degree values of brain regions in the whole-brain structural network and general semantic performance in the stroke patients. The results revealed that the connectivity of the left fusiform gyrus significantly predicted general semantic performance, indicating that this region is the semantic hub. To identify the semantic-relevant connections of the semantic hub, we then correlated the white matter integrity values of each tract connected to the left fusiform gyrus separately with performance on general and modality-specific semantic processing. We found that the hub region accomplished general semantic processing through white matter connections with the left superior temporal pole, middle temporal gyrus, inferior temporal gyrus and hippocampus. The connectivity between the hub region and the left hippocampus, superior temporal pole, middle temporal gyrus, inferior temporal gyrus and parahippocampal gyrus was differentially involved in object form, colour, motion, sound, manipulation and function processing. After statistically removing the effects of potential confounding variables (i.e. whole-brain lesion volume, lesion volume of regions of interest and performance on non-semantic control tasks), the observed effects remained significant. Together, our findings support the role of the left fusiform gyrus as a semantic hub region in stroke patients and reveal its crucial connectivity in the network. This study provides new insights and evidence for the neuroanatomical organization of semantic memory in the human brain.

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

Purpose

This study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).

Methods

The MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.

Results

Twenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.

Conclusion

The ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model.

Investigation of white matter and grey matter alteration in the monkey brain following ischemic stroke by using diffusion tensor imaging

Background

Investigation of stroke lesion has mostly focused on grey matter (GM) in previous studies and white matter (WM) degeneration during acute stroke is understudied. In the present study, monkeys were utilized to investigate the alterations of GM and WM in the brain following ischemic occlusion using diffusion tensor imaging (DTI).

Methods

Permanent middle cerebral artery occlusion (pMCAO) was induced in rhesus monkeys (n=6) with an interventional approach. Serial DTI was conducted on a clinical 3T in the hyperacute phase (2-6 hours), 48, and 96 hours post occlusion. Regions of interest in GM and WM of lesion areas were selected for data analysis.

Results

Mean diffusivity (MD), radial diffusivity (RD), and axial Diffusivity (AD) in WM decreased substantially during hyperacute stroke, as similar as those seen in GM. No obvious fractional anasotropy (FA) changes were seen in GM and WM during hyper acute phase. until 48 hours post stroke when significant fiber losses were oberved also. Pseudo-normalization of MD, AD, and RD was seen at 96 hours. Pathological changes of WM and GM were observed in ischemic areas at 8, 48, and 96 hours post stroke. Relative changes of MD, AD and RD of WM were correlated negatively with infarction volumes at 6 hours post stroke.

Conclusion

The present study revealed the microstructural changes in gray matter and white matter of monkey brains during acute stroke by using DTI. The preliminary results suggest axial and radial diffusivity (AD and RD) may be sensitive surrogate markers to assess specific microstructural changes in white matter during hyper-acute stroke.

Reliability of the freehand region-of-interest method in quantitative cerebral diffusion tensor imaging

Background

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique used for evaluating changes in the white matter in brain parenchyma. The reliability of quantitative DTI analysis is influenced by several factors, such as the imaging protocol, pre-processing and post-processing methods, and selected diffusion parameters. The region-of-interest (ROI) method is most widely used of the post-processing methods because it is found in commercial software. The focus of our research was to study the reliability of the freehand ROI method using various intra- and inter-observer analyses.

Methods

This study included 40 neurologically healthy participants who underwent diffusion MRI of the brain with a 3 T scanner. The measurements were performed at nine different anatomical locations using a freehand ROI method. The data extracted from the ROIs included the regional mean values, intra- and inter-observer variability and reliability. The used DTI parameters were fractional anisotropy (FA), the apparent diffusion coefficient (ADC), and axial (AD) and radial (RD) diffusivity.

Results

The average intra-class correlation coefficient (ICC) of the intra-observer was found to be 0.9 (excellent). The single ICC results were excellent (> 0.8) or adequate (> 0.69) in eight out of the nine regions in terms of FA and ADC. The most reliable results were found in the frontobasal regions. Significant differences between age groups were also found in the frontobasal regions. Specifically, the FA and AD values were significantly higher and the RD values lower in the youngest age group (18–30 years) compared to the other age groups.

Conclusions

The quantitative freehand ROI method can be considered highly reliable for the average ICC and mostly adequate for the single ICC. The freehand method is suitable for research work with a well-experienced observer. Measurements should be performed at least twice in the same region to ensure that the results are sufficiently reliable. In our study, reliability was slightly undermined by artifacts in some regions such as the cerebral peduncle and centrum semiovale. From a clinical point of view, the results are most reliable in adults under the age of 30, when age-related changes in brain white matter have not yet occurred.

A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke

INTRODUCTION

Stroke remains a leading cause of death and disability worldwide. Effective and prompt prognostic evaluation is vital for determining the appropriate management strategy. Radiomics is an emerging noninvasive method used to identify the quantitative imaging indicators for predicting important clinical outcomes. This study was conducted to investigate and validate a radiomics nomogram for predicting ischemic stroke prognosis using the modified Rankin scale (mRS).

METHODS

A total of 598 consecutive patients with subacute infarction confirmed by diffusion-weighted imaging (DWI), from January 2018 to December 2019, were retrospectively assessed. They were assigned to the good (mRS ≤ 2) and poor (mRS > 2) functional outcome groups, respectively. Then, 399 patients examined by MR scanner 1 and 199 patients scanned by MR scanner 2 were assigned to the training and validation cohorts, respectively. Infarction lesions underwent manual segmentation on DWI, extracting 402 radiomic features. A radiomics nomogram encompassing patient characteristics and the radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the training and validation cohorts. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram. The performance of infarction lesion volume was also evaluated using univariate analysis.

RESULTS

Stroke lesion volume showed moderate performance, with an area under the curve (AUC) of 0.678. The radiomics signature, including 11 radiomics features, exhibited good prediction performance. The radiomics nomogram, encompassing clinical characteristics (age, hemorrhage, and 24 h National Institutes of Health Stroke Scale score) and the radiomics signature, presented good discriminatory potential in the training cohort [AUC = 0.80; 95% confidence interval (CI) 0.75-0.86], which was validated in the validation cohort (AUC = 0.73; 95% CI 0.63-0.82). In addition, it demonstrated good calibration in the training (p = 0.55) and validation (p = 0.21) cohorts. Decision curve analysis confirmed the clinical value of this nomogram.

CONCLUSION

This novel noninvasive clinical-radiomics nomogram shows good performance in predicting ischemic stroke prognosis.

Diffusion weighted imaging in acute ischemic stroke: A review of its interpretation pitfalls and advanced diffusion imaging application

Diffusion weighted imaging (DWI) is a widely used imaging technique to evaluate patients with stroke. It can detect brain ischemia within minutes of stroke onset. However, DWI has few potential pitfalls that should be recognized during interpretation. DWI lesion could be reversible in the early hours of stroke and the entire lesion may not represent ischemic core. False negative DWI could lead to diagnosis of DWI negative stroke or to a missed stroke diagnosis. Ischemic stroke mimics can occur on DWI with non-cerebrovascular neurological conditions. In this article, the history of DWI, its clinical applications, and potential pitfalls for use in acute ischemic stroke are reviewed. Advanced diffusion imaging techniques with reference to Diffusion Kurtosis Imaging and Diffusion Tensor Imaging that has been studied to evaluate ischemic core are discussed.

Cervical Cordotomy for Intractable Pain: Do Postoperative Imaging Features Correlate with Pain Outcomes and Mirror Pain?

BACKGROUND AND PURPOSE

Percutaneous cervical cordotomy offers relief of unilateral intractable oncologic pain. We aimed to find anatomic and postoperative imaging features that may correlate with clinical outcomes, including pain relief and postoperative contralateral pain.

MATERIALS AND METHODS

We prospectively followed 15 patients with cancer who underwent cervical cordotomy for intractable pain during 2018 and 2019 and underwent preoperative and up to 1-month postoperative cervical MR imaging. Lesion volume and diameter were measured on T2-weighted imaging and diffusion tensor imaging (DTI). Lesion mean diffusivity and fractional anisotropy values were extracted. Pain improvement up to 1 month after surgery was assessed by the Numeric Rating Scale and Brief Pain Inventory.

RESULTS

All patients reported pain relief from 8 (7-10) to 0 (0-4) immediately after surgery (P = .001), and 5 patients (33%) developed contralateral pain. The minimal percentages of the cord lesion volume required for pain relief were 10.0% on T2-weighted imaging and 6.2% on DTI. Smaller lesions on DWI correlated with pain improvement on the Brief Pain Inventory scale (r = 0.705, P = .023). Mean diffusivity and fractional anisotropy were significantly lower in the ablated tissue than contralateral nonlesioned tissue (P = .003 and P = .001, respectively), compatible with acute-phase tissue changes after injury. Minimal postoperative mean diffusivity values correlated with an improvement of Brief Pain Inventory severity scores (r = -0.821, P = .004). The average lesion mean diffusivity was lower among patients with postoperative contralateral pain (P = .037).

CONCLUSIONS

Although a minimal ablation size is required during cordotomy, larger lesions do not indicate better outcomes. DWI metrics changes represent tissue damage after ablation and may correlate with pain outcomes.

Pathophysiologic mechanisms, neuroimaging and treatment in wake-up stroke

Wake-up stroke (WUS) or ischemic stroke occurring during sleep accounts for 14%-29.6% of all ischemic strokes. Management of WUS is complicated by its narrow therapeutic time window and attributable risk factors, which can affect the safety and efficacy of administering intravenous (IV) tissue plasminogen activator (t-PA). This manuscript will review risk factors of WUS, with a focus on obstructive sleep apnea, potential mechanisms of WUS, and evaluate studies assessing safety and efficacy of IV t-PA treatment in WUS patients guided by neuroimaging to estimate time of symptom onset. The authors used PubMed (1966 to March 2018) to search for the term "Wake-Up Stroke" cross-referenced with "pathophysiology," ''pathogenesis," "pathology," "magnetic resonance imaging," "obstructive sleep apnea," or "treatment." English language Papers were reviewed. Also reviewed were pertinent papers from the reference list of the above-matched manuscripts. Studies that focused only on acute Strokes with known-onset of symptoms were not reviewed. Literature showed several potential risk factors associated with increased risk of WUS. Although the onset of WUS is unknown, a few studies investigated the potential benefit of magnetic resonance imaging (MRI) in estimating the age of onset which encouraged conducting clinical trials assessing the efficacy of MRI-guided thrombolytic therapy in WUS.