Convergence of fMRI language paradigms across institutions offers the first step in providing a "Rosetta Stone" that provides a common reference point with which to compare and contrast the usefulness and reliability of fMRI data. From this common language task battery, future refinements and improvements are anticipated, particularly as objective measures of reliability become available. Some commonality of practice is a necessary first step to develop a foundation on which to improve the clinical utility of this field.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

Introduction : Early studies suggest that acute cerebrovascular events may be common in patients with coronavirus disease 2019 (COVID-19) and may be associated with a high mortality rate. Most cerebrovascular events described have been ischemic strokes, but both intracerebral hemorrhage and rarely cerebral venous sinus thrombosis (CVST) have also been reported. The diagnosis of CVST can be elusive, with wide-ranging and nonspecific presenting symptoms that can include headache or altered sensorium alone. Objective : To describe the presentation, barriers to diagnosis, treatment, and outcome of CVST in patients with COVID-19. Methods : We abstracted data on all patients diagnosed with CVST and COVID-19 from March 1 to August 9, 2020 at Boston Medical Center. Subsequently, we reviewed the literature and extracted all published cases of CVST in patients with COVID-19 from January 1, 2020 through August 9, 2020 and included all studies with case descriptions. Results : We describe the clinical features and management of CVST in 3 women with COVID-19 who developed CVST days to months after initial COVID-19 symptoms. Two patients presented with encephalopathy and without focal neurologic deficits, while one presented with visual symptoms. All patients were treated with intravenous hydration and anticoagulation. None suffered hemorrhagic complications, and all were discharged home. We identified 12 other patients with CVST in the setting of COVID-19 via literature search. There was a female predominance (54.5%), most patients presented with altered sensorium (54.5%), and there was a high mortality rate (36.4%). Conclusions : During this pandemic, clinicians should maintain a high index of suspicion for CVST in patients with a recent history of COVID-19 presenting with non-specific neurological symptoms such as headache to provide expedient management and prevent complications. The limited data suggests that CVST in COVID-19 is more prevalent in females and may be associated with high mortality.