Specificity of Quantitative Functional Brain Mapping with Arterial Spin-Labeling for Preoperative Assessment.

BACKGROUND AND PURPOSE: Arterial spin-labeling is a noninvasive MR imaging technique allowing direct and quantitative measurement of brain perfusion. Arterial spin-labeling is well-established in clinics for investigating the overall cerebral perfusion, but it is still occasionally employed during tasks. The typical contrast for functional MR imaging is blood oxygen level-dependent (BOLD) imaging, whose specificity could be biased in neurologic patients due to altered neurovascular coupling. This work aimed to validate the use of functional ASL as a noninvasive tool for presurgical functional brain mapping. This is achieved by comparing the spatial accuracy of functional ASL with transcranial magnetic stimulation as the criterion standard. MATERIALS AND METHODS: Twenty-eight healthy participants executed a motor task and received a somatosensory stimulation, while BOLD imaging and arterial spin-labeling were acquired simultaneously. Transcranial magnetic stimulation was subsequently used to define hand somatotopy. RESULTS: Functional ASL was found more adjacent to transcranial magnetic stimulation than BOLD imaging, with a significant shift along the inferior-to-superior direction. With respect to BOLD imaging, functional ASL was localized significantly more laterally, anteriorly, and inferiorly during motor tasks and pneumatic stimulation. CONCLUSIONS: Our results confirm the specificity of functional ASL in targeting the regional neuronal excitability. Functional ASL could be considered as a valid supplementary technique to BOLD imaging for presurgical mapping when spatial accuracy is crucial for delineating eloquent cortex.

Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study.

OBJECTIVE: To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). DESIGN: Cohort study with logistic regression analysis to combine predictors and treatment modality. SETTING: Subarachnoid Haemorrhage International Trialists' (SAHIT) data repository, including randomised clinical trials, prospective observational studies, and hospital registries. PARTICIPANTS: Researchers collaborated to pool datasets of prospective observational studies, hospital registries, and randomised clinical trials of SAH from multiple geographical regions to develop and validate clinical prediction models. MAIN OUTCOME MEASURE: Predicted risk of mortality or functional outcome at three months according to score on the Glasgow outcome scale. RESULTS: Clinical prediction models were developed with individual patient data from 10 936 patients and validated with data from 3355 patients after development of the model. In the validation cohort, a core model including patient age, premorbid hypertension, and neurological grade on admission to predict risk of functional outcome had good discrimination, with an area under the receiver operator characteristics curve (AUC) of 0.80 (95% confidence interval 0.78 to 0.82). When the core model was extended to a "neuroimaging model," with inclusion of clot volume, aneurysm size, and location, the AUC improved to 0.81 (0.79 to 0.84). A full model that extended the neuroimaging model by including treatment modality had AUC of 0.81 (0.79 to 0.83). Discrimination was lower for a similar set of models to predict risk of mortality (AUC for full model 0.76, 0.69 to 0.82). All models showed satisfactory calibration in the validation cohort. CONCLUSION: The prediction models reliably estimate the outcome of patients who were managed in various settings for ruptured intracranial aneurysms that caused subarachnoid haemorrhage. The predictor items are readily derived at hospital admission. The web based SAHIT prognostic calculator (http://sahitscore.com) and the related app could be adjunctive tools to support management of patients.