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Background: To gain a comprehensive understanding of the altered sensory processing in patients with migraine, in this study, we developed an electroencephalography (EEG) protocol for examining brainstem and cortical responses to sensory stimulation. Furthermore, machine learning techniques were employed to identify neural signatures from evoked brainstem-cortex activation and their interactions, facilitating the identification of the presence and subtype of migraine., Methods: This study analysed 1,000-epoch-averaged somatosensory evoked responses from 342 participants, comprising 113 healthy controls (HCs), 106 patients with chronic migraine (CM), and 123 patients with episodic migraine (EM). Activation amplitude and effective connectivity were obtained using weighted minimum norm estimates with spectral Granger causality analysis. This study used support vector machine algorithms to develop classification models; multimodal data (amplitude, connectivity, and scores of psychometric assessments) were applied to assess the reliability and generalisability of the identification results from the classification models., Results: The findings revealed that patients with migraine exhibited reduced amplitudes for responses in both the brainstem and cortical regions and increased effective connectivity between these regions in the gamma and high-gamma frequency bands. The classification model with characteristic features performed well in distinguishing patients with CM from HCs, achieving an accuracy of 81.8% and an area under the curve (AUC) of 0.86 during training and an accuracy of 76.2% and an AUC of 0.89 during independent testing. Similarly, the model effectively identified patients with EM, with an accuracy of 77.5% and an AUC of 0.84 during training and an accuracy of 87% and an AUC of 0.88 during independent testing. Additionally, the model successfully differentiated patients with CM from patients with EM, with an accuracy of 70.5% and an AUC of 0.73 during training and an accuracy of 72.7% and an AUC of 0.74 during independent testing., Conclusion: Altered brainstem-cortex activation and interaction are characteristic of the abnormal sensory processing in migraine. Combining evoked activity analysis with machine learning offers a reliable and generalisable tool for identifying patients with migraine and for assessing the severity of their condition. Thus, this approach is an effective and rapid diagnostic tool for clinicians., (© 2024. The Author(s).)

Objectives: This study aimed to compare clinical and perfusion imaging profiles in acute ischemic stroke with large vessel occlusion (AIS-LVO) between patients with intracranial atherosclerotic disease (ICAD) and non-ICAD who underwent endovascular treatment (EVT)., Methods: Data from AIS-LVO patients over the anterior circulation undergoing EVT across two stroke centers were retrospectively analyzed. Clinical profiles and perfusion parameters from automated processing of perfusion imaging were compared between ICAD and non-ICAD groups. Ischemic core was defined as relative cerebral blood flow < 30 % on CT perfusion or apparent diffusion coefficient ≤ 620 × 10 -6 mm 2 /s on MR diffusion weighted imaging., Results: A total of 111 patients were included (46 ICAD, 65 non-ICAD). The ICAD group exhibited a higher male proportion (60.9 % vs. 35.4 %), more M1 segment occlusions (78.3 % vs. 56.9 %), lower atrial fibrillation rates (17.4 % vs. 63.1 %), and lower baseline NIH Stroke Scale (NIHSS) scores (median [IQR]: 13 [8.75-18] vs. 15 [10-21]) at presentation compared to non-ICAD (all p < 0.05). However, there was no difference in NIHSS scores at discharge or in good functional outcomes (modified Rankin Scale 0-2) at 3 months between the two groups. ICAD patients also had smaller median ischemic core volumes (0 [IQR 0-9.7] vs. 4.4 [0-21.6] ml, p = 0.038), smaller median Tmax >6s tissue volulmes (89.3 [IQR 51.1-147.1] vs. 124.4 [80.5-178.6] ml, p = 0.017) and lower median HIR (hypoperfusion intensity ratio defined as Tmax >10s divided by Tmax >6s; 0.28 [IQR 0.09-0.42] vs. 0.44 [0.24-0.60], p = 0.003). Higher baseline NIHSS scores correlated with larger Tmax >6s lesion volumes as well as higher HIR value in non-ICAD patients, but not in ICAD patients., Conclusions: In anterior circulation of AIS-LVO, ICAD patients exhibited distinct clinical presentations and perfusion imaging characteristics when compared to non-ICAD patients. Perfusion imaging profiles may serve as indicators for identifying ICAD patients before EVT., Competing Interests: Declaration of competing interest Hung-Yu Liu, Jeremy J. Heit, Nicole Yuen, Chung-Han Yang, Michael Mlynash, Aroosa Zamarud, Ronda Lun and Maarten G. Lansberg declare no conflicts of interest. Gregory W. Albers has served as a consultant for Genentech and iSchemaView., (Copyright © 2024. Published by Elsevier Inc.)