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Objective: The purpose of this study was to investigate if vessel‐wall magnetic resonance imaging (VW‐MRI) could differentiate among primary headaches disorders, such as migraine and cluster headache (CH), and detect the presence of neurogenic inflammation. Background: The pathophysiology of primary headaches disorders is complex and not completely clarified. The activation of nociceptive trigeminal afferents through the release of vasoactive neuropeptides, termed "neurogenic inflammation," has been hypothesized. VW‐MRI can identify vessel wall changes, reflecting the inflammatory remodeling of the vessel walls despite different etiologies. Methods: In this case series, we enrolled seven patients with migraine and eight patients with CH. They underwent a VW‐MRI study before and after the intravenous administration of contrast medium, during and outside a migraine attack or cluster period. Two expert neuroradiologists analyzed the magnetic resonance imaging (MRI) studies to identify the presence of vessel wall enhancement or other vascular abnormalities. Results: Fourteen out of 15 patients had no enhancement. One out of 15, with migraine, showed a focal parietal enhancement in the intracranial portion of a vertebral artery, unmodified during and outside the attack, thus attributable to atherosclerosis. No contrast enhancement attributable to neurogenic inflammation was observed in VW‐MRI, both during and outside the attack/cluster in all patients. Moreover, MRI angiography registered slight diffuse vasoconstriction in one of seven patients with migraine during the attack and in one of eight patients with cluster headache during the cluster period; both patients had taken triptans as symptomatic therapy for pain. Conclusions: These preliminary results suggest that VW‐MRI studies are negative in patients with primary headache disorders even during migraine attacks or cluster periods. The VW‐MRI studies did not detect signs of neurogenic inflammation in the intracranial intradural vessels of patients with migraine or CH. [ABSTRACT FROM AUTHOR]

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis. [ABSTRACT FROM AUTHOR]

Background: Hemorrhage is the most devastating complication of brain arteriovenous malformations (bAVMs), and to date, there is still concern about the needing for treatment in case of unruptured and asymptomatic bAVM. In fact, the morbidity and mortality of treatments may exceed that of the AVM's natural history. None of the classifications and scores for bAVM allows to predict the risk of bleeding. In this study, we aimed to identify the angio-architectural characteristics of brain AVMs associated with bleeding. Methods: We retrospectively evaluated all consecutive patients diagnosed with cerebral AVMs, between January 2010 and December 2019 from our prospective bAVM database. Univariate and multivariate logistic regression analysis were used to evaluate relationships between angio-architectural features of ruptured and unruptured bAVMs. Results: Of the 143 retrieved bAVMs, 65 were unruptured and 78 were ruptured. The univariate logistic regression analysis demonstrated statistically significant differences into angio-architectural features of unruptured and ruptured bAVMs. The multivariate logistic regression analysis fitted well (p =.113) with a good discrimination capacity (ROC = 0.83) of three independent angio-architectural features mainly related to bleeding in bAVMs: a smaller diameter of the nidus (p <.001), the absence of venous drainage alterations (p =.047), of the presence of prenidal aneurysms (p =.005). Conclusions: In our study, several features resulted related to an increased probability of rupture for bAVMs, among which the more relevant were a small diameter of the nidus, the absence of venous drainage alterations, and the presence of prenidal aneurysms. [ABSTRACT FROM AUTHOR]

Background: In idiopathic normal pressure hydrocephalus (iNPH), gait and balance impairment is the most frequent symptom, and it is often associated with a higher fall risk. In a prior study, the anterior callosal angle (ACA) was validated as a reliable marker to discriminate iNPH from Alzheimer's disease and healthy controls. However, the potential correlation between the ACA with clinical symptoms and functional outcomes has not been assessed. The objective of this study is to determine the utility of the ACA in predicting gait improvement after ventriculoperitoneal (VP) shunting. Methods: Patients with probable iNPH who underwent shunt placement at a single institution were prospectively enrolled from May 2015 to May 2019. Patients were assessed preoperatively and at 6 months postoperatively following a standard clinical and MRI protocol. Callosal angle (CA) and ACA were calculated from 3 T MRI preoperatively and at 6 months postoperatively. CA and ACA were tested for correlation with clinical scores. Results: Forty-seven patients with probable INPH who completed 6-month postoperative follow-up were enrolled in the study. Baseline ACA was significantly correlated with preoperative fall risk, gait, and balance impairment assessed with Tinetti POMA scale. Additionally, baseline ACA differentiated patients who experienced improvement at Tinetti POMA scale after surgery. Conclusions: The baseline ACA is a useful neuroradiological marker to differentiate patients by fall risk and has significant correlation with the improvement in gait and balance impairment following surgery. This study demonstrated that the ACA may be a complementary tool to the CA in predicting shunt responsiveness in iNPH. [ABSTRACT FROM AUTHOR]