Rigorous and rapid evidence assessment in digital health with the evidence DEFINED framework.

Dozens of frameworks have been proposed to assess evidence for digital health interventions (DHIs), but existing frameworks may not facilitate DHI evidence reviews that meet the needs of stakeholder organizations including payers, health systems, trade organizations, and others. These organizations may benefit from a DHI assessment framework that is both rigorous and rapid. Here we propose a framework to assess Evidence in Digital health for EFfectiveness of INterventions with Evaluative Depth (Evidence DEFINED).

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification.

Background: Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.

Methods: Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period.

Alzheimer's disease and amnestic mild cognitive impairment weaken connections within the default-mode network: a multi-modal imaging study.

We applied a multi-modal imaging approach to examine structural and functional alterations in the default-mode network (DMN) that are associated with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), a transitional phase between healthy cognitive aging and dementia. Subjects included 10 patients with probable AD, 11 patients with aMCI, and 12 age- and education-matched normal controls (NC). Whole-brain resting-state functional, diffusion-weighted, and volumetric magnetic resonance imaging (MRI) data as well as 18F-fluorodeoxyglucose-based positron emission tomography (FDG-PET) data were acquired.

Disruption of limbic white matter pathways in mild cognitive impairment and Alzheimer's disease: a DTI/FDG-PET study.

Background: Alzheimer's disease (AD) and mild cognitive impairment (MCI) affect the limbic system, causing medial temporal lobe (MTL) atrophy and posterior cingulate cortex (PCC) hypometabolism. Additionally, diffusion tensor imaging (DTI) studies have demonstrated that MCI and AD involve alterations in cerebral white matter (WM) integrity.

Objectives: To test if (1) patients with MCI and AD exhibit decreases in the integrity of limbic WM pathways; (2) disconnection between PCC and MTL, manifested as disruption of the cingulum bundle, contributes to PCC hypometabolism during incipient AD.

Right-lateralized brain oscillations in human spatial navigation.

During spatial navigation, lesion and functional imaging studies suggest that the right hemisphere has a unique functional role. However, studies of direct human brain recordings have not reported interhemisphere differences in navigation-related oscillatory activity. We investigated this apparent discrepancy using intracranial electroencephalographic recordings from 24 neurosurgical patients playing a virtual taxi driver game.

Learning your way around town: how virtual taxicab drivers learn to use both layout and landmark information.

By having subjects drive a virtual taxicab through a computer-rendered town, we examined how landmark and layout information interact during spatial navigation. Subject-drivers searched for passengers, and then attempted to take the most efficient route to the requested destinations (one of several target stores). Experiment 1 demonstrated that subjects rapidly learn to find direct paths from random pickup locations to target stores.