Contralateral Hemispheric Cerebral Blood Flow Measured With Arterial Spin Labeling Can Predict Outcome in Acute Stroke

Association of systolic blood pressure variability with remote ischemic conditioning in acute ischemic stroke

Systolic blood pressure variability (SBPV) is associated with outcome in acute ischemic stroke. Remote ischemic conditioning (RIC) has been demonstrated to be effective in stroke and may affect blood pressure. Relationship between SBPV and RIC treatment after stroke warrants investigation. A total of 1707 patients from per-protocol analysis set of RICAMIS study were included. The SBPV was calculated based on blood pressure measured at admission, Day 7, and Day 12. (I) To investigate the effect of SBPV on efficacy of RIC in stroke, patients were divided into High and Low categories in each SBPV parameter. Primary outcome was excellent functional outcome at 90 days. Compared with Control, efficacy of RIC in each category and interaction between categories were investigated. (II) To investigate the effect of RIC treatment on SBPV, SBPV parameters were compared between RIC and Control groups. Compared with Control, a higher likelihood of primary outcome in RIC was found in high category (max-min: adjusted risk difference [RD] = 7.2, 95% CI 1.2-13.1, P = 0.02; standard deviation: adjusted RD = 11.5, 95% CI 1.6-21.4, P = 0.02; coefficient of variation: adjusted RD = 11.2, 95% CI 1.4-21.0, P = 0.03). Significant interaction of RIC on outcomes were found between High and Low standard deviations (adjusted P < 0.05). No significant difference in SBPV parameters were found between treatment groups. This is the first report that Chinese patients with acute moderate ischemic stroke and presenting with higher SBPV, who were non-cardioemoblic stroke and not candidates for intravenous thrombolysis or endovascular therapy, would benefit more from RIC with respect to functional outcomes at 90 days, but 2-week RIC treatment has no effect on SBPV during hospital.

Effects of bariatric surgery and dietary interventions for obesity on brain neurotransmitter systems and metabolism: A systematic review of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) studies

This systematic review collates studies of dietary or bariatric surgery interventions for obesity using positron emission tomography and single-photon emission computed tomography. Of 604 publications identified, 22 met inclusion criteria. Twelve studies assessed bariatric surgery (seven gastric bypass, five gastric bypass/sleeve gastrectomy), and ten dietary interventions (six low-calorie diet, three very low-calorie diet, one prolonged fasting). Thirteen studies examined neurotransmitter systems (six used tracers for dopamine DRD2/3 receptors: two each for 11 C-raclopride, 18 F-fallypride, 123 I-IBZM; one for dopamine transporter, 123 I-FP-CIT; one used tracer for serotonin 5-HT2A receptor, 18 F-altanserin; two used tracers for serotonin transporter, 11 C-DASB or 123 I-FP-CIT; two used tracer for μ-opioid receptor, 11 C-carfentanil; one used tracer for noradrenaline transporter, 11 C-MRB); seven studies assessed glucose uptake using 18 F-fluorodeoxyglucose; four studies assessed regional cerebral blood flow using 15 O-H2 O (one study also used arterial spin labeling); and two studies measured fatty acid uptake using 18 F-FTHA and one using 11 C-palmitate. The review summarizes findings and correlations with clinical outcomes, eating behavior, and mechanistic mediators. The small number of studies using each tracer and intervention, lack of dietary intervention control groups in any surgical studies, heterogeneity in time since intervention and degree of weight loss, and small sample sizes hindered the drawing of robust conclusions across studies.

Anatomical Modeling and Optimization of Speckle Contrast Optical Tomography

Traditional methods for mapping cerebral blood flow (CBF), such as positron emission tomography and magnetic resonance imaging, offer only isolated snapshots of CBF due to scanner logistics. Speckle contrast optical tomography (SCOT) is a promising optical technique for mapping CBF. However, while SCOT has been established in mice, the method has not yet been demonstrated in humans - partly due to a lack of anatomical reconstruction methods and uncertainty over the optimal design parameters. Herein we develop SCOT reconstruction methods that leverage MRI-based anatomical head models and finite-element modeling of the SCOT forward problem (NIRFASTer). We then simulate SCOT for CBF perturbations to evaluate sensitivity of imaging performance to exposure time and SD-distances. We find image resolution comparable to intensity-based diffuse optical tomography at superficial cortical tissue depth (~1.5 cm). Localization errors can be reduced by including longer SD-measurements. With longer exposure times speckle contrast decreases, however, noise decreases faster, resulting in a net increase in SNR. Specifically, extending exposure time from 10μs to 10ms increased SCOT SNR by 1000X. Overall, our modeling methods provide anatomically-based image reconstructions that can be used to evaluate a broad range of tissue conditions, measurement parameters, and noise sources and inform SCOT system design.

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model

BACKGROUND

Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study's goal was to predict ordinal 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by fusing a Deep Learning model of diffusion-weighted imaging images and clinical information from the acute period.

METHODS

A total of 640 acute ischemic stroke patients who underwent magnetic resonance imaging within 1 to 7 days poststroke and had 90-day mRS follow-up data were randomly divided into 70% (n=448) for model training, 15% (n=96) for validation, and 15% (n=96) for internal testing. Additionally, external testing on a cohort from Lausanne University Hospital (n=280) was performed to further evaluate model generalization. Accuracy for ordinal mRS, accuracy within ±1 mRS category, mean absolute prediction error, and determination of unfavorable outcome (mRS score >2) were evaluated for clinical only, imaging only, and 2 fused clinical-imaging models.

RESULTS

The fused models demonstrated superior performance in predicting ordinal mRS score and unfavorable outcome in both internal and external test cohorts when compared with the clinical and imaging models. For the internal test cohort, the top fused model had the highest area under the curve of 0.92 for unfavorable outcome prediction and the lowest mean absolute error (0.96 [95% CI, 0.77-1.16]), with the highest proportion of mRS score predictions within ±1 category (79% [95% CI, 71%-88%]). On the external Lausanne University Hospital cohort, the best fused model had an area under the curve of 0.90 for unfavorable outcome prediction and outperformed other models with an mean absolute error of 0.90 (95% CI, 0.79-1.01), and the highest percentage of mRS score predictions within ±1 category (83% [95% CI, 78%-87%]).

CONCLUSIONS

A Deep Learning-based imaging model fused with clinical variables can be used to predict 90-day stroke outcome with reduced subjectivity and user burden.

Different types of percutaneous endovascular interventions for acute ischemic stroke

BACKGROUND

Acute ischemic stroke (AIS) is the abrupt reduction of blood flow to a certain area of the brain which causes neurologic dysfunction. Different types of percutaneous arterial endovascular interventions have been developed, but as yet there is no consensus on the optimal therapy for people with AIS.

OBJECTIVES

To compare the safety and efficacy of different types of percutaneous arterial endovascular interventions for treating people with AIS.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL; Issue 4 of 12, 2022), MEDLINE Ovid (1946 to 13 May 2022), Embase (1947 to 15 May 2022), Science Citation Index Web of Science (1900 to 15 May 2022), Scopus (1960 to 15 May 2022), and China Biological Medicine Database (CBM; 1978 to 16 May 2022). We also searched the ClinicalTrials.gov trials register and the World Health Organization (WHO) International Clinical Trials Registry Platform to 16 May 2022.

SELECTION CRITERIA

Randomized controlled trials (RCTs) comparing one percutaneous arterial endovascular intervention with another in treating adult patients who have a clinical diagnosis of AIS due to large vessel occlusion and confirmed by imaging evidence, including thrombo-aspiration, stent-retrieval thrombectomy, aspiration-retriever combined technique, and thrombus mechanical fragmentation.

DATA COLLECTION AND ANALYSIS

Two review authors independently performed the literature searches, identified eligible trials, and extracted data. A third review author participated in discussions to reach consensus decisions when any disputes occurred. We assessed risk of bias and applied the GRADE approach to evaluate the quality of the evidence. The primary outcome was rate of modified Rankin Scale (mRS) of 0 to 2 at three months. Secondary outcomes included the rate of modified Thrombolysis In Cerebral Infarction (mTICI) of 2b to 3 postprocedure, all-cause mortality within three months, rate of intracranial hemorrhage on imaging at 24 hours, rate of symptomatic intracranial hemorrhage at 24 hours, and rate of procedure-related adverse events within three months.

MAIN RESULTS

Four RCTs were eligible. The current meta-analysis included two trials with 651 participants comparing thrombo-aspiration with stent-retrieval thrombectomy. We judged the quality of evidence to be high in both trials according to Cochrane's risk of bias tool RoB 2. There were no significant differences between thrombo-aspiration and stent-retrieval thrombectomy in rate of mRS of 0 to 2 at three months (risk ratio [RR] 0.97, 95% confidence interval [CI] 0.82 to 1.13; P = 0.68; 633 participants; 2 RCTs); rate of mTICI of 2b to 3 postprocedure (RR 1.01, 95% CI 0.95 to 1.07; P = 0.77; 650 participants; 2 RCTs); all-cause mortality within three months (RR 1.01, 95% CI 0.74 to 1.37; P = 0.95; 633 participants; 2 RCTs); rate of intracranial hemorrhage on imaging at 24 hours (RR 1.03, 95% CI 0.86 to 1.24; P = 0.73; 645 participants; 2 RCTs); rate of symptomatic intracranial hemorrhage at 24 hours (RR 0.90, 95% CI 0.49 to 1.68; P = 0.75; 645 participants; 2 RCTs); and rate of procedure-related adverse events within three months (RR 0.98, 95% CI 0.68 to 1.41; P = 0.90; 651 participants; 2 RCTs). Another two included studies reported no differences for the comparisons of combined therapy versus stent-retrieval thrombectomy or thrombo-aspiration. One RCT is ongoing.

AUTHORS' CONCLUSIONS

This review did not establish any difference in safety and effectiveness between the thrombo-aspiration approach and stent-retrieval thrombectomy for treating people with AIS. Furthermore, the combined group did not show any obvious advantage over either intervention applied alone.

Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning

Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To use a deep learning model to predict the hypoperfusion lesion in stroke and identify patients with a target mismatch profile from diffusion-weighted imaging (DWI) and clinical information alone, using perfusion MRI as the reference standard. Materials and Methods Imaging data sets of patients with acute ischemic stroke with baseline perfusion MRI and DWI were retrospectively reviewed from multicenter data available from 2008 to 2019 (Imaging Collaterals in Acute Stroke, Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2, and University of California, Los Angeles stroke registry). For perfusion MRI, rapid processing of perfusion and diffusion software automatically segmented the hypoperfusion lesion (time to maximum, ≥6 seconds) and ischemic core (apparent diffusion coefficient [ADC], ≤620 × 10-6 mm2/sec). A three-dimensional U-Net deep learning model was trained using baseline DWI, ADC, National Institutes of Health Stroke Scale score, and stroke symptom sidedness as inputs, with the union of hypoperfusion and ischemic core segmentation serving as the ground truth. Model performance was evaluated using the Dice score coefficient (DSC). Target mismatch classification based on the model was compared with that of the clinical-DWI mismatch approach defined by the DAWN trial by using the McNemar test. Results Overall, 413 patients (mean age, 67 years ± 15 [SD]; 207 men) were included for model development and primary analysis using fivefold cross-validation (247, 83, and 83 patients in the training, validation, and test sets, respectively, for each fold). The model predicted the hypoperfusion lesion with a median DSC of 0.61 (IQR, 0.45-0.71). The model identified patients with target mismatch with a sensitivity of 90% (254 of 283; 95% CI: 86, 93) and specificity of 77% (100 of 130; 95% CI: 69, 83) compared with the clinical-DWI mismatch sensitivity of 50% (140 of 281; 95% CI: 44, 56) and specificity of 89% (116 of 130; 95% CI: 83, 94) (P < .001 for all). Conclusion A three-dimensional U-Net deep learning model predicted the hypoperfusion lesion from diffusion-weighted imaging (DWI) and clinical information and identified patients with a target mismatch profile with higher sensitivity than the clinical-DWI mismatch approach. ClinicalTrials.gov registration nos. NCT02225730, NCT01349946, NCT02586415 © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Kallmes and Rabinstein in this issue.

Recent advances in arterial spin labeling perfusion MRI in patients with vascular cognitive impairment

Cognitive impairment (CI) is a major health concern in aging populations. It impairs patients' independent life and may progress to dementia. Vascular cognitive impairment (VCI) encompasses all cerebrovascular pathologies that contribute to cognitive impairment (CI). Moreover, the majority of CI subtypes involve various aspects of vascular dysfunction. Recent research highlights the critical role of reduced cerebral blood flow (CBF) in the progress of VCI, and the detection of altered CBF may help to detect or even predict the onset of VCI. Arterial spin labeling (ASL) is a non-invasive, non-ionizing perfusion MRI technique for assessing CBF qualitatively and quantitatively. Recent methodological advances enabling improved signal-to-noise ratio (SNR) and data acquisition have led to an increase in the use of ASL to assess CBF in VCI patients. Combined with other imaging modalities and biomarkers, ASL has great potential for identifying early VCI and guiding prediction and prevention strategies. This review focuses on recent advances in ASL-based perfusion MRI for identifying patients at high risk of VCI.

Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network

BACKGROUND

For prognosis of stroke, measurement of the diffusion-perfusion mismatch is a common practice for estimating tissue at risk of infarction in the absence of timely reperfusion. However, perfusion-weighted imaging (PWI) adds time and expense to the acute stroke imaging workup. We explored whether a deep convolutional neural network (DCNN) model trained with diffusion-weighted imaging obtained at admission could predict final infarct volume and location in acute stroke patients.

METHODS

In 445 patients, we trained and validated an attention-gated (AG) DCNN to predict final infarcts as delineated on follow-up studies obtained 3 to 7 days after stroke. The input channels consisted of MR diffusion-weighted imaging (DWI), apparent diffusion coefficients (ADC) maps, and thresholded ADC maps with values less than 620 × 10-6 mm2/s, while the output was a voxel-by-voxel probability map of tissue infarction. We evaluated performance of the model using the area under the receiver-operator characteristic curve (AUC), the Dice similarity coefficient (DSC), absolute lesion volume error, and the concordance correlation coefficient (ρc) of the predicted and true infarct volumes.

RESULTS

The model obtained a median AUC of 0.91 (IQR: 0.84-0.96). After thresholding at an infarction probability of 0.5, the median sensitivity and specificity were 0.60 (IQR: 0.16-0.84) and 0.97 (IQR: 0.93-0.99), respectively, while the median DSC and absolute volume error were 0.50 (IQR: 0.17-0.66) and 27 ml (IQR: 7-60 ml), respectively. The model's predicted lesion volumes showed high correlation with ground truth volumes (ρc = 0.73, p < 0.01).

CONCLUSION

An AG-DCNN using diffusion information alone upon admission was able to predict infarct volumes at 3-7 days after stroke onset with comparable accuracy to models that consider both DWI and PWI. This may enable treatment decisions to be made with shorter stroke imaging protocols.

Cerebral neurovascular alterations in stable chronic obstructive pulmonary disease: a preliminary fMRI study

Purpose

Cognitive impairment (CI) is very common in patients with chronic obstructive pulmonary disease (COPD). Cerebral structural and functional abnormalities have been reported in cognitively impaired patients with COPD, and the neurovascular coupling changes are rarely investigated. To address this issue, arterial spin labeling (ASL) and resting-state blood oxygenation level dependent (BOLD) fMRI techniques were used to determine whether any neurovascular changes in COPD patients.

Methods

Forty-five stable COPD patients and forty gender- and age-matched healthy controls were recruited. Furthermore, resting-state BOLD fMRI and ASL were acquired to calculate degree centrality (DC) and cerebral blood flow (CBF) respectively. The CBF-DC coupling and CBF/DC ratio were compared between the two groups.

Results

COPD patients showed abnormal CBF, DC and CBF/DC ratio in several regions. Moreover, lower CBF/DC ratio in the left lingual gyrus negatively correlated with naming scores, lower CBF/DC ratio in medial frontal cortex/temporal gyrus positively correlated with the Montreal Cognitive Assessment (MoCA), visuospatial/executive and delayed recall scores.

Conclusion

These findings may provide new potential insights into neuropathogenesis of cognition decline in stable COPD patients.

Presurgical Magnetic Resonance Imaging Indicators of Revascularization Response in Adults With Moyamoya Vasculopathy

BACKGROUND

Moyamoya is a progressive intracranial vasculopathy, primarily affecting distal segments of the internal carotid and middle cerebral arteries. Treatment may comprise angiogenesis-inducing surgical revascularization; however, lack of randomized trials often results in subjective treatment decisions.

HYPOTHESIS

Compensatory presurgical posterior vertebrobasilar artery (VBA) flow-territory reactivity, including greater cerebrovascular reactivity (CVR) and reduced vascular delay time, portends greater neoangiogenic response verified on digital subtraction angiography (DSA) at 1-year follow-up.

STUDY TYPE

Prospective intervention cohort.

SUBJECTS

Thirty-one patients with moyamoya (26 females; age = 45 ± 13 years; 41 revascularized hemispheres).

METHODS

Anatomical MRI, hypercapnic CVR MRI, and DSA acquired presurgically in adult moyamoya participants scheduled for clinically indicated surgical revascularization. One-year postsurgery, DSA was repeated to evaluate collateralization.

FIELD STRENGTH

3 T.

SEQUENCE

Hypercapnic T 2 * -weighted gradient-echo blood-oxygenation-level-dependent, T2 -weighted turbo-spin-echo fluid-attenuated-inversion-recovery, T1 -weighted magnetization-prepared-rapid-gradient-echo, and T2 -weighted diffusion-weighted-imaging.

ASSESSMENT

Presurgical maximum CVR and response times were evaluated in VBA flow-territories. Revascularization success was determined using an ordinal scoring system of neoangiogenic collateralization from postsurgical DSA by two cerebrovascular neurosurgeons (R.V.C. with 8 years of experience; M.R.F. with 9 years of experience) and one neuroradiologist (L.T.D. with 8 years of experience). Stroke risk factors (age, sex, race, vasculopathy, and diabetes) were recorded.

STATISTICAL TESTS

Fisher's exact and Wilcoxon rank-sum tests were applied to compare presurgical variables between cohorts with angiographically confirmed good (>1/3 middle cerebral artery [MCA] territory revascularized) vs. poor (<1/3 MCA territory revascularized) outcomes.

SIGNIFICANCE

two-sided P < 0.05. Normalized odds ratios (ORs) were calculated.

RESULTS

Criteria for good collateralization were met in 25 of the 41 revascularized hemispheres. Presurgical normalized VBA flow-territory CVR was significantly higher in those with good (1.12 ± 0.13 unitless) vs. poor (1.04 ± 0.05 unitless) outcomes. Younger (OR = -0.60 ± 0.67) and White (OR = -1.81 ± 1.40) participants had highest revascularization success (good outcomes: age = 42 ± 14 years, race = 84% White; poor outcomes: age = 49 ± 11 years, race = 44% White).

DATA CONCLUSION

Presurgical MRI-measures of VBA flow-territory CVR are highest in moyamoya participants with better angiographic responses to surgical revascularization.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 4.

Evaluation of collateral status and outcome in patients with middle cerebral artery stenosis in late time window by CT perfusion imaging

Objectives

Collateral status (CS) is a crucial determinant of outcome in patients with ischemic stroke. We aimed to test whether the cerebral blood volume (CBV) and cerebral blood flow (CBF) based on computed tomography perfusion (CTP) measurements can quantitatively evaluate CS and explore the predictive ability of CTP parameters in determining clinical outcomes in patients with MCA severe stenosis or occlusion presenting beyond 24 h.

Materials and methods

In this retrospective study, data obtained from September 2018 to March 2022 in consecutive stroke patients caused by isolated middle cerebral artery severe stenosis or occlusion were reviewed within 24–72 h after onset. Correlation between the collateral score systems assessed with CT angiography (CTA) and CTP parameters was calculated using the Spearman correlation. The optimal threshold of the CBV ratio for predicting a good outcome was determined using receiver operating characteristic curve (ROC) analysis.

Results

A total of 69 patients met inclusion criteria. Both the CBV ratio and the CBF ratio had significant correlation with collateral score systems assessed with CTA [CBV ratio and Tan score: rs = 0.702, P &lt; 0.0001; CBV ratio and regional leptomeningeal collateral (rLMC) score: rs = 0.705, P &lt; 0.0001; CBV ratio and Miteff score: rs = 0.625, P &lt; 0.0001. CBF ratio and Tan score: rs= 0.671, P &lt; 0.0001; CBF ratio and rLMC score: rs = 0.715, P &lt; 0.0001; CBF ratio and Miteff score: rs = 0.535, P &lt; 0.0001]. ROC analysis revealed the CBV ratio performed better than the qualitative collateral assessments and the CBF ratio in the prediction of a favorable 90-day modified Rankin scale score. The CBV ratio was a useful parameter that predicted a good functional outcome [area under the curve (AUC), 0.922; 95% CI, 0.862 ± 0.982].

Conclusions

In late time window stroke patients, the CBV and CBF ratio on CTP may be valuable parameters for quantitatively revealing the collateral status after stroke. In addition, the CBV ratio was the predictor of clinical outcomes in patients with MCA severe stenosis or occlusion.

Clinical Significance and Value of Serum Homocysteine and Urine 11 Dehydrothromboxane B2 Combined with Transferrin-Specific Peptide in the Diagnosis of Cerebral Apoplexy

Objective

To explore the clinical significance and value of serum homocysteine (Hcy) and urine 11 dehydrothromboxane B2 (urine 11-DH-TXB2) combined with transferrin-specific peptide (TF-UP) in the diagnosis of stroke.

Methods

One hundred stroke patients treated from January 2019 to June 2021 were enrolled in our hospital as the study group. All the patients in the study group met the diagnostic criteria of stroke. The focus of stroke was confirmed by CT or MRI, and the first onset was less than 48 hours. One hundred healthy persons who went through physical examination in our hospital were enrolled as the control group. The comparison was taken to explore the clinical significance and value of Hcy and urine 11-DH-TXB2 combined with TF-UP in the diagnosis of stroke.

Results

There exhibited no significant difference in the history of smoking, drinking, and atrial fibrillation (P > 0.05). There were significant differences in systolic blood pressure, diastolic blood pressure, eGFR, history of hypertension, diabetes, and coronary heart disease (P < 0.05). In terms of the levels of Hcy, urine 11-DH-TXB2, and TF-UP, the levels of Hcy and urine 11-DH-TXB2 in the study group were higher compared to the control group, while the level of TF-UP in the study group was lower compared to the control group (P < 0.05). The results of logistic regression analysis indicated that there was a significant correlation between Hcy, urine 11-DH-TXB2, TF-UP, and stroke, and Hcy and urine 11-DH-TXB2 indicated positive correlation with stroke disease, while TF-UP level was negatively correlated with stroke disease (P < 0.05). The levels of Hcy, urine 11-DH-TXB2, and TF-UP were adopted as evaluation indexes to draw ROC curve. The results show that the area under the curve (AUC) of Hcy is 0.760 (95% CI 0.670~0.850). The best critical point was 3342.5 pg/mg Ucr, the sensitivity was 65.6%, and the specificity was 77.1%. The AUC of urine 11-DH-TXB2 was 0.773 (95% CI 0.685~0.861). The best critical point was 3354.44 pg/mg Ucr, the sensitivity was 71.2%, and the specificity was 78.3%. The AUC of TF-UP was 0.735 (95% CI 0.641~0.829). The best critical point was 3365.43 pg/mg Ucr, the sensitivity was 68.4%, and the specificity was 80.5%. If Hcy was detected in combination with other indexes, AUC increased to 0.749 when combined with urine 11-DH-TXB2, and AUC increased to 0.797 when combined with TF-UP. When the three are combined, the AUC can reach 0.836, the sensitivity is 79.1%, and the specificity is 80%. It shows that the combined detection of Hcy, urine 11-DH-TXB2, and TF-UP is of higher diagnostic value. The difference of data exhibited statistically significant (P < 0.05).

Conclusion

There is imbalance between Hcy, urine 11-DH-TXB2, and TF-UP in patients with acute stroke. High Hcy, urine 11-DH-TXB2, and low TF-UP are closely related to the occurrence of cerebral infarction. Hcy, urine 11-DH-TXB2, and TF-UP may be the risk factors of stroke and positively correlated with the degree of neurological impairment. Effective monitoring of Hcy and urine 11-DH-TXB2 combined with TF-UP levels and positive intervention measures may effectively prevent the occurrence and development of cerebral infarction, reduce Hcy and urine 11-DH-TXB2, or increase the level of TF-UP, which may provide new ideas for the treatment of cerebrovascular diseases.

Repetitive Transcranial Magnetic Stimulation Induces Quantified Functional and Structural Changes in Subcortical Stroke: A Combined Arterial Spin Labeling Perfusion and Diffusion Tensor Imaging Study

Purpose

To explore the changes of cerebral blood flow (CBF) and fractional anisotropy (FA) in stroke patients with motor dysfunction after repetitive transcranial magnetic stimulation (rTMS) treatment, and to better understand the role of rTMS on motor rehabilitation of subcortical stroke patients from the perfusion and structural level.

Materials and Methods

In total, 23 first-episode acute ischemic stroke patients and sixteen healthy controls (HCs) were included. The patients were divided into the rTMS and sham group. The rehabilitation assessments and examination of perfusion and structural MRI were performed before and after rTMS therapy for each patient. Voxel-based analysis was used to detect the difference in CBF and FA among all three groups. The Pearson correlation analysis was conducted to evaluate the relationship between the CBF/FA value and the motor scales.

Results

After rTMS, significantly increased CBF was found in the ipsilesional supplementary motor area, postcentral gyrus, precentral gyrus, pons, medulla oblongata, contralesional midbrain, superior cerebellar peduncle, and middle cerebellar peduncle compared to that during the prestimulation and in the sham group, these fasciculi comprise the cortex-pontine-cerebellum-cortex (CPC) loop. Besides, altered CBF in the ipsilesional precentral gyrus, postcentral gyrus, and pons was positively associated with the improved Fugl-Meyer assessment (FMA) scores. Significantly decreased FA was found in the contralesional precentral gyrus, increased FA was found in the ipsilesional postcentral gyrus, precentral gyrus, contralesional supplementary motor area, and bilateral cerebellum, these fasciculi comprise the corticospinal tract (CST). The change of FMA score was positively correlated with altered FA value in the ipsilesional postcentral gyrus and negatively correlated with altered FA value in the contralesional precentral gyrus.

Conclusion

Our results suggested that rTMS could facilitate the motor recovery of stroke patients. High frequency could promote the improvement of functional activity of ipsilesional CPC loop and the recovery of the microstructure of CST.

Multiphase arterial spin labeling imaging to predict early recurrent ischemic lesion in acute ischemic stroke

In acute ischemic stroke (AIS), the hemodynamics around the lesion are important because they determine the recurrence or prognosis of the disease. This study evaluated the effects of perfusion deficits in multiphase arterial spin labeling (ASL) and related radiological parameters on the occurrence of early recurrent ischemic lesions (ERILs) in AIS. We assessed AIS patients who underwent multiphase ASL within 24 h of symptom onset and follow-up diffusion-weighted imaging within 7 days. ASL perfusion deficit, arterial transit artifact (ATA), and intra-arterial high-intensity signal (IAS) were manually rated as ASL parameters. A total of 134 patients were evaluated. In the multivariable analyses, ASL perfusion deficit [adjusted odds ratio (aOR) = 2.82, 95% confidence interval = 1.27–6.27] was positively associated with ERIL. Furthermore, when ATA was accompanied, the ASL perfusion deficit was not associated with ERIL occurrence. Meanwhile, IAS showed a synergistic effect with ASL perfusion deficit on the occurrence of ERIL. In conclusion, we demonstrated the association between perfusion deficits in multiphase ASL with ERIL in patients with AIS. This close association was attenuated by ATA and was enhanced by IAS. ASL parameters may help identify high-risk patients of ERIL occurrence during the acute period.

Cerebral perfusion mediated by thalamo-cortical functional connectivity in non-dominant thalamus affects naming ability in aphasia

Naming is a commonly impaired language domain in various types of aphasia. Emerging evidence supports the cortico‐subcortical circuitry subserving naming processing, although neurovascular regulation of the non‐dominant thalamic and basal ganglia subregions underlying post‐stroke naming difficulty remains unclear. Data from 25 subacute stroke patients and 26 age‐, sex‐, and education‐matched healthy volunteers were analyzed. Region‐of‐interest‐wise functional connectivity (FC) was calculated to measure the strength of cortico‐subcortical connections. Cerebral blood flow (CBF) was determined to reflect perfusion levels. Correlation and mediation analyses were performed to identify the relationship between cortico‐subcortical connectivity, regional cerebral perfusion, and naming performance. We observed increased right‐hemispheric subcortical connectivity in patients. FC between the right posterior superior temporal sulcus (pSTS) and lateral/medial prefrontal thalamus (lPFtha/mPFtha) exhibited significantly negative correlations with total naming score. Trend‐level increased CBF in subcortical nuclei, including that in the right lPFtha, and significant negative correlations between naming and regional perfusion of the right lPFtha were observed. The relationship between CBF in the right lPFtha and naming was fully mediated by the lPFtha‐pSTS connectivity in the non‐dominant hemisphere. Our findings suggest that perfusion changes in the right thalamic subregions affect naming performance through thalamo‐cortical circuits in post‐stroke aphasia. This study highlights the neurovascular pathophysiology of the non‐dominant hemisphere and demonstrates thalamic involvement in naming after stroke.

Association of cerebral microvascular perfusion and diffusion dynamics detected by intravoxel incoherent motion-diffusion weighted imaging with initial neurological function and clinical outcome in acute ischemic stroke

Background

This work aimed to explore the association of cerebral microvascular perfusion and diffusion dynamics measured by intravoxel incoherent motion (IVIM) imaging with initial neurological function and clinical outcome in acute stroke.

Methods

In total, 39 patients were assessed with admission National Institutes of Health Stroke Scale (NIHSS) and day-90 modified Rankin Scale (mRS). The parametrical maps of IVIM were obtained, including apparent diffusion coefficient (ADC), pseudo-diffusion coefficient (D*), true diffusion coefficient (D) and perfusion fraction (f). The fD* was the product of f and D*. Moreover, the ratios of lesioned/contralateral parameters (rADC, rD, rD*, rf and rfD*) were also obtained. The differences of these parameters between the poor outcome group and good outcome group were evaluated. Partial correlation analysis was used to evaluate the correlations between the admission NIHSS/day-90 mRS and each parameter ratio, with lesion volumes controlled.

Results

The ADC, D, D*, f and fD* values of lesions were significantly reduced than those of the contralateral regions. The rADC and rD were significantly decreased in the poor outcome group than good outcome group (all p < 0.01). With lesion volume controlled, rADC showed a weak negative correlation (r = −0.340, p = 0.037) and a notable negative correlation (r = −0.688, p < 0.001) with admission NIHSS score and day-90 mRS score, respectively. In addition, rD showed a strong negative correlation (r = −0.731, p < 0.001) with day-90 mRS score.

Conclusion

Significant negative correlations were revealed between IVIM derived diffusion dynamics parameters and initial neurological function as well as clinical outcome for patients with acute ischemic stroke. IVIM can be therefore suggested as an effective non-invasive method for evaluating the acute ischemic stroke.

Hemispheric cerebral blood flow predicts outcome in acute small subcortical infarcts

The association between baseline perfusion measures and clinical outcomes in patients with acute small subcortical infarcts (SSIs) has not been studied in detail. Post-processed acute perfusion CT and follow-up diffusion-weighted imaging of 71 patients with SSIs were accurately co-registered. Relative perfusion values were calculated from the perfusion values of the infarct lesion divided by those of the mirrored contralateral area. The association between perfusion measures with clinical outcomes and the interaction with intravenous thrombolysis were studied. Additionally, the perfusion measures for patients having perfusion CT before and after thrombolysis were compared. Higher contralateral hemispheric cerebral blood flow (CBF) was the only independent predictor of an excellent clinical outcome (modified Rankin Scale of 0-1) at 3 months (OR = 1.3, 95% CI 1.1-1.4, P = 0.001) amongst all the perfusion parameters, and had a significant interaction with thrombolysis (P = 0.04). Patients who had perfusion CT after thrombolysis demonstrated a better perfusion profile (relative CBF ≥1) than those who had perfusion CT before thrombolysis (After:45.5%, Before:21.1%, P = 0.03). This study implies that for patients with SSIs, hemispheric CBF is a predictor of clinical outcome and has an influence on the effect of intravenous thrombolysis.

Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke

BACKGROUND AND PURPOSE

In acute stroke patients with large vessel occlusions, it would be helpful to be able to predict the difference in the size and location of the final infarct based on the outcome of reperfusion therapy. Our aim was to demonstrate the value of deep learning-based tissue at risk and ischemic core estimation. We trained deep learning models using a baseline MR image in 3 multicenter trials.

MATERIALS AND METHODS

Patients with acute ischemic stroke from 3 multicenter trials were identified and grouped into minimal (≤20%), partial (20%-80%), and major (≥80%) reperfusion status based on 4- to 24-hour follow-up MR imaging if available or into unknown status if not. Attention-gated convolutional neural networks were trained with admission imaging as input and the final infarct as ground truth. We explored 3 approaches: 1) separate: train 2 independent models with patients with minimal and major reperfusion; 2) pretraining: develop a single model using patients with partial and unknown reperfusion, then fine-tune it to create 2 separate models for minimal and major reperfusion; and 3) thresholding: use the current clinical method relying on apparent diffusion coefficient and time-to-maximum of the residue function maps. Models were evaluated using area under the curve, the Dice score coefficient, and lesion volume difference.

RESULTS

Two hundred thirty-seven patients were included (minimal, major, partial, and unknown reperfusion: n = 52, 80, 57, and 48, respectively). The pretraining approach achieved the highest median Dice score coefficient (tissue at risk = 0.60, interquartile range, 0.43-0.70; core = 0.57, interquartile range, 0.30-0.69). This was higher than the separate approach (tissue at risk = 0.55; interquartile range, 0.41-0.69; P = .01; core = 0.49; interquartile range, 0.35-0.66; P = .04) or thresholding (tissue at risk = 0.56; interquartile range, 0.42-0.65; P = .008; core = 0.46; interquartile range, 0.16-0.54; P < .001).

CONCLUSIONS

Deep learning models with fine-tuning lead to better performance for predicting tissue at risk and ischemic core, outperforming conventional thresholding methods.

Creation and Validation of a Stroke Scale to Increase Utility of National Inpatient Sample Administrative Data for Clinical Stroke Research

INTRODUCTION

The National Inpatient Sample (NIS) has led to several breakthroughs via large sample size. However, utility of NIS is limited by the lack of admission NIHSS and 90-day modified Rankin score (mRS). This study creates estimates for stroke severity at admission and 90-day mRS using NIS data for acute ischemic stroke (AIS) patients treated with mechanical thrombectomy (MT).

METHODS

Three patient cohorts undergoing MT for AIS were utilized: Cohort 1 (N = 3729) and Cohort 3 (N = 1642) were derived from NIS data. Cohort 2 (N=293) was derived from a prospectively-maintained clinical registry. Using Cohort 1, Administrative Stroke Outcome Variable (ASOV) was created using disposition and mortality. Factors reflective of stroke severity were entered into a stepwise logistic regression predicting poor ASOV. Odds ratios were used to create the Administrative Data Stroke Scale (ADSS). Performances of ADSS and ASOV were tested using Cohort 2 and compared with admission NIHSS and 90-day mRS, respectively. ADSS performance was compared with All Patient Refined-Diagnosis Related Group (APR-DRG) severity score using Cohort 3.

RESULTS

Agreement of ASOV with 90-day mRS > 2 was fair (κ = 0.473). Agreement with 90-day mRS > 3 was substantial (κ = 0.687). ADSS significantly correlated (p < 0.001) with clinically-significant admission NIHSS > 15. ADSS performed comparably (AUC = 0.749) to admission NIHSS (AUC = 0.697) in predicting 90-day mRS > 2 and mRS > 3 (AUC = 0.767, 0.685, respectively). ADSS outperformed APR-DRG severity score in predicting poor ASOV (AUC = 0.698, 0.682, respectively).

CONCLUSION

We developed and validated measures of stroke severity at admission (ADSS) and outcome (ASOV, estimate for 90-day mRS > 3) to increase utility of NIS data in stroke research.

Activation of endothelial Wnt/β-catenin signaling by protective astrocytes repairs BBB damage in ischemic stroke

The role of astrocytes in dysregulation of blood-brain barrier (BBB) function following ischemic stroke is not well understood. Here, we investigate the effects of restoring the repair properties of astrocytes on the BBB after ischemic stroke. Mice deficient for NHE1, a pH-sensitive Na⁺/H⁺ exchanger 1, in astrocytes have reduced BBB permeability after ischemic stroke, increased angiogenesis and cerebral blood flow perfusion, in contrast to wild-type mice. Bulk RNA-sequencing transcriptome analysis of purified astrocytes revealed that ∼177 genes were differentially upregulated in mutant astrocytes, with Wnt7a mRNA among the top genes. Using a Wnt reporter line, we confirmed that the pathway was upregulated in cerebral vessels of mutant mice after ischemic stroke. However, administration of the Wnt/β-catenin inhibitor, XAV-939, blocked the reparative effects of Nhe1-deficient astrocytes. Thus, astrocytes lacking pH-sensitive NHE1 protein are transformed from injurious to "protective" by inducing Wnt production to promote BBB repair after ischemic stroke.

Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high-grade gliomas

OBJECTIVES

To determine the performance of pretreatment structural and arterial spin labelling (ASL) MRI in predicting p53 mutation in patients with high-grade gliomas (HGGs).

METHODS

Pre-treatment structural and ASL MRI were performed in 57 patients with histologically confirmed HGGs and information of p53 status. Whole-lesion histogram analysis of cerebral blood flow (CBF) images of the enhancing tumour and the peritumoral oedema in the HGGs were performed. Visually AcceSAble Rembrandt Images features were used as qualitative analysis. The differences of ASL histogram parameters and Visually AcceSAble Rembrandt Images features between HGGs with or without p53 mutation were analyzed with post hoc correction for multiple comparisons. LASSO regression was performed to select the optimal features that could predict p53 mutation, followed by receiver operating characteristic analysis to determine the predictive efficacy.

RESULTS

A total of 33 HGGs with p53 mutation and 24 without p53 mutation were included. HGGs with mutant p53 showed lower CBF_percentile5 and CBF_uniformity of the enhancing tumour (p < 0.05) and higher prevalence of the qualitative MRI feature of enhancing tumour crossing midline (ETCM) (p < 0.05) as compared with HGGs with wild-type p53. LASSO regression showed that the CBF_uniformity of the enhancing tumour and ETCM were predictive features for p53 mutation. CBF_uniformity showed an acceptable performance in predicting p53 mutation (area under the curve = 0.721), when combined with the feature of ETCM, its predictive efficacy was significantly improved (area under the curve = 0.814, p = 0.012).

CONCLUSION

An integrated pre-treatment structural and ASL MRI can help to predict p53 mutation in HGGs.

Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging

IMPORTANCE

Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke.

OBJECTIVES

To determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods.

DESIGN, SETTING, AND PARTICIPANTS

In this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019.

MAIN OUTCOMES AND MEASURES

Area under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of ≥0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion.

RESULTS

Among the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, -14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance.

CONCLUSIONS AND RELEVANCE

The deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.