No abstract

) for a scientific commentary on this article.A GGGGCC repeat expansion in C9orf72 leads to frontotemporal dementia and/or amyotrophic lateral sclerosis. Diverse pathological features have been identified, and their disease relevance remains much debated. Here, we describe two illuminating patients with frontotemporal dementia due to the C9orf72 repeat expansion. Case 1 was a 65-year-old female with behavioural variant frontotemporal dementia accompanied by focal degeneration in subgenual anterior cingulate cortex, amygdala, and medial pulvinar thalamus. At autopsy, widespread RNA foci and dipeptide repeat protein inclusions were observed, but TDP-43 pathology was nearly absent, even in degenerating brain regions. Case 2 was a 74-year-old female with atypical frontotemporal dementia-motor neuron disease who underwent temporal lobe resection for epilepsy 5 years prior to her first frontotemporal dementia symptoms. Archival surgical resection tissue contained RNA foci, dipeptide repeat protein inclusions, and loss of nuclear TDP-43 but no TDP-43 inclusions despite florid TDP-43 inclusions at autopsy 8 years after first symptoms. These findings suggest that C9orf72-specific phenomena may impact brain structure and function and emerge before first symptoms and TDP-43 aggregation. Keywords: frontotemporal dementia; protein aggregation; TDP-43 Abbreviations: ALS = amyotrophic lateral sclerosis; FTD = frontotemporal dementia; mPULV = medial pulvinar nucleus of the thalamus; NCI = neuronal cytoplasmic inclusion

IMPORTANCE Misdiagnosis of epilepsy is common. Video electroencephalogram provides a definitive diagnosis but is impractical for many patients referred for evaluation of epilepsy.OBJECTIVE To evaluate the accuracy of outpatient smartphone videos in epilepsy. DESIGN, SETTING, AND PARTICIPANTSThis prospective, masked, diagnostic accuracy study (the OSmartViE study) took place between August 31, 2015, and August 31, 2018, at 8 academic epilepsy centers in the United States and included a convenience sample of 44 nonconsecutive outpatients who volunteered a smartphone video during evaluation and subsequently underwent video electroencephalogram monitoring. Three epileptologists uploaded videos for physicians from the 8 epilepsy centers to review. MAIN OUTCOMES AND MEASURES Measures of performance (accuracy, sensitivity, specificity, positive predictive value, and negative predictive value) for smartphone video-based diagnosis by experts and trainees (the index test) were compared with those for history and physical examination and video electroencephalogram monitoring (the reference standard). RESULTS Forty-four eligible epilepsy clinic outpatients (31 women [70.5%]; mean [range] age, 45.1 [20-82] years) submitted smartphone videos (530 total physician reviews). Final video electroencephalogram diagnoses included 11 epileptic seizures, 30 psychogenic nonepileptic attacks, and 3 physiologic nonepileptic events. Expert interpretation of a smartphone video was accurate in predicting a video electroencephalogram monitoring diagnosis of epileptic seizures 89.1% (95% CI, 84.2%-92.9%) of the time, with a specificity of 93.3% (95% CI, 88.3%-96.6%). Resident responses were less accurate for all metrics involving epileptic seizures and psychogenic nonepileptic attacks, despite greater confidence. Motor signs during events increased accuracy. One-fourth of the smartphone videos were correctly diagnosed by 100% of the reviewing physicians, composed solely of psychogenic attacks. When histories and physical examination results were combined with smartphone videos, correct diagnoses rose from 78.6% to 95.2%. The odds of receiving a correct diagnosis were 5.45 times greater using smartphone video alongside patient history and physical examination results than with history and physical examination alone (95% CI, 1.01-54.3; P = .02). CONCLUSIONS AND RELEVANCEOutpatient smartphone video review by experts has predictive and additive value for diagnosing epileptic seizures. Smartphone videos may reliably aid psychogenic nonepileptic attacks diagnosis for some people.

BackgroundThere is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting.MethodsUsing three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions.ResultsA consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R 2 > 0.83). The three datasets showed high predictive accuracy for this log–log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log–log predicted 94% of the correct ranges while the RR50 predicted 77%.ConclusionReliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.

Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years. Encompassing the fields of science and engineering, analytics, health care, business, and government, this report explores the promise that wearable biosensors, along with integrated mobile apps, hold for improving the quality of patient care and clinical outcomes. The discussion focuses on groundbreaking device innovation, data optimization and validation, commercial platform integration, clinical implementation and regulation, and the broad societal implications of using mobile health technologies.