Criteria for the diagnosis of vascular dementia (VaD) that are reliable, valid, and readily applicable in a variety of settings are urgently needed for both clinical and research purposes. To address this need, the Neuroepidemiology Branch of the National Institute of Neurological Disorders and Stroke (NINDS) convened an International Workshop with support from the Association Internationale pour la Recherche et l'Enseignement en Neurosciences (AIREN), resulting in research criteria for the diagnosis of VaD. Compared with other current criteria, these guidelines emphasize (1) the heterogeneity of vascular dementia syndromes and pathologic subtypes including ischemic and hemorrhagic strokes, cerebral hypoxic-ischemic events, and senile leukoencephalopathic lesions; (2) the variability in clinical course, which may be static, remitting, or progressive; (3) specific clinical findings early in the course (eg, gait disorder, incontinence, or mood and personality changes) that support a vascular rather than a degenerative cause; (4) the need to establish a temporal relationship between stroke and dementia onset for a secure diagnosis; (5) the importance of brain imaging to support clinical findings; (6) the value of neuropsychological testing to document impairments in multiple cognitive domains; and (7) a protocol for neuropathologic evaluations and correlative studies of clinical, radiologic, and neuropsychological features. These criteria are intended as a guide for case definition in neuroepidemiologic studies, stratified by levels of certainty (definite, probable, and possible). They await testing and validation and will be revised as more information becomes available.

Cavernous malformations are congenital abnormalities of the cerebral vessels that affect 0.5% to 0.7% of the population. They occur in two forms: a sporadic form characterized by isolated lesions, and a familial form characterized by multiple lesions with an autosomal dominant mode of inheritance. The management of patients with cavernous malformations, particularly those with the familial form of the disease, remains a challenge because little is known regarding the natural history. The authors report the results of an ongoing study in which six families afflicted by familial cavernous malformations have been prospectively followed with serial interviews, physical examinations, and magnetic resonance (MR) imaging at 6- to 12-month intervals. A total of 59 members of these six families were screened for protocol enrollment; 31 (53%) had MR evidence of familial cavernous malformations. Nineteen (61%) of these 31 patients were symptomatic, with seizures in 12 (39%), recurrent headaches in 16 (52%), focal sensory/motor deficits in three (10%), and visual field deficits in two (6%). Twenty-one of these 31 patients underwent at least two serial clinical and MR imaging examinations. A total of 128 individual cavernous malformations (mean 6.5 +/- 3.8 lesions/patient) were identified and followed radiographically. During a mean follow-up period of 2.2 years (range 1 to 5.5 years), serial MR images demonstrated 17 new lesions in six (29%) of the 21 patients; 13 lesions (10%) showed changes in signal characteristics, and five lesions (3.9%) changed significantly in size. The incidence of symptomatic hemorrhage was 1.1% per lesion per year. The results of this study demonstrate that the familial form of cavernous malformations is a dynamic disease; serial MR images revealed changes in the number, size, and imaging characteristics of lesions consistent with acute or resolving hemorrhage. It is believed that the de novo development of new lesions in this disease has not been previously reported. These findings suggest that patients with familial cavernous malformations require careful follow-up monitoring, and that significant changes in neurological symptoms warrant repeat MR imaging. Surgery should be considered only for lesions that produce repetitive or progressive symptoms. Prophylactic resection of asymptomatic lesions does not appear to be indicated.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes. Substantial clinical work has focused on computer-assisted diagnosis (CAD), whereas technical work in volumetric image analysis has focused primarily on segmentation. 3D convolutional neural networks (3D-CNNs) have primarily been used for supervised classification on 3D modeling and light detection and ranging (LiDAR) data. Here, we demonstrate a 3D-CNN architecture that performs weakly supervised classification to screen head CT images for acute neurologic events. Features were automatically learned from a clinical radiology dataset comprising 37,236 head CTs and were annotated with a semisupervised natural-language processing (NLP) framework. We demonstrate the effectiveness of our approach to triage radiology workflow and accelerate the time to diagnosis from minutes to seconds through a randomized, double-blinded, prospective trial in a simulated clinical environment.

The angiographic, computerized tomography (CT), and magnetic resonance imaging (MRI) findings were compared in 10 patients with a total of 16 pathologically verified cavernous angiomas. Only three lesions had abnormal vasculature in the form of venous pooling or a capillary blush. The CT scans were positive in seven patients and detected 14 lesions, while high-field strength (1.5 Tesla) MRI was positive in each case and demonstrated 27 distinct lesions. On T2-weighted MRI, the combination of a reticulated core of mixed signal intensity (SI) with a surrounding rim of decreased SI strongly suggests the diagnosis of a cavernous malformation. Smaller lesions appear as areas of decreased SI (black dots). The sensitivity of MRI is based on magnetic susceptibility and possibly diffusion effects related to field heterogeneity that is more conspicuous on high-field imaging and caused by the presence of excessive iron (hemosiderin).

Since several features on brain MR imaging are seen only in MSA-P, a simple diagnostic algorithm may improve the MR imaging diagnosis of MSA-P and PD.