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). Designed for real-world use, the Evidence DEFINED Quick Start Guide may help streamline DHI assessment. A checklist is provided summarizing high-priority evidence considerations in digital health. Evidence-to-recommendation guidelines are proposed, specifying degrees of adoption that may be appropriate for a range of evidence quality levels. Evidence DEFINED differs from prior frameworks in its inclusion of unique elements designed for rigor and speed. Rigor is increased by addressing three gaps in prior frameworks. First, prior frameworks are not adapted adequately to address evidence considerations that are unique to digital health. Second, prior frameworks do not specify evidence quality criteria requiring increased vigilance for DHIs in the current regulatory context. Third, extant frameworks rarely leverage established, robust methodologies that were developed for non-digital interventions. Speed is achieved in the Evidence DEFINED Framework through screening optimization and deprioritization of steps that may have limited value. The primary goals of Evidence DEFINED are to a) facilitate standardized, rapid, rigorous DHI evidence assessment in organizations and b) guide digital health solutions providers who wish to generate evidence that drives DHI adoption.

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. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. RESULTS: Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. CONCLUSIONS: We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.

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. We carried out resting-state functional MRI-based functional connectivity and diffusion MRI-based structural connectivity analyses using isthmus of the cingulate cortex (ICC) and the subjacent white matter as the seeds. Whole-brain group and region of interest-based analyses demonstrated that AD weakens the structural and functional connections between ICC and other regions within the DMN, consistent with regional reduction of metabolic activity and atrophy within the DMN. A progressive weakening trend of these connections was also observed from NC to aMCI and then AD, although significant differences between aMCI and the other two groups were not found. Overall, based on both FDG-PET and MRI results, the DMN appears to serve as a window to understanding structural and functional brain changes associated with AD and aMCI.

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. METHODS: We measured fractional anisotropy (FA) and volume of the fornix and cingulum using DTI in 23 individuals with MCI, 21 with mild-to-moderate AD, and 16 normal control (NC) subjects. We also measured PCC metabolism using (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) in AD and MCI patients. RESULTS: Fornix FA and volume were reduced in MCI and AD to a similar extent. Descending cingulum FA was reduced in AD while volume was reduced in MCI and even more so in AD. Both FA and volume of the fornix and descending cingulum reliably discriminated between NC and AD. Fornix FA and descending cingulum volume also reliably discriminated between NC and MCI. Only descending cingulum volume reliably discriminated between MCI and AD. In the combined MCI-AD cohort, PCC metabolism directly correlated with both FA and volume of the descending cingulum. CONCLUSIONS: Disruption of limbic WM pathways is evident during both MCI and AD. Disconnection of the PCC from MTL at 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. When patients were virtually moving in the game, brain oscillations at various frequencies increased in amplitude compared with periods of virtual stillness. Using log-linear analysis, we analyzed the region and frequency specificities of this pattern and found that neocortical movement-related gamma oscillations (34-54 Hz) were significantly lateralized to the right hemisphere, especially in posterior neocortex. We also observed a similar right lateralization of gamma oscillations related to searching for objects at unknown virtual locations. Thus, our results indicate that gamma oscillations in the right neocortex play a special role in human spatial navigation.

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. Experiment 2 varied the degree to which landmark and layout cues were preserved across two successively learned towns. When spatial layout was preserved, transfer was low if only target stores were altered, and high if both target stores and surrounding buildings were altered, even though in the latter case all local views were changed. This suggests that subjects can rapidly acquire a survey representation based on the spatial layout of the town and independent of local views, but that subjects will rely on local views when present, and are harmed when associations between previously learned landmarks are disrupted. We propose that spatial navigation reflects a hierarchical system in which either layout or landmark information is sufficient for orienting and wayfinding; however, when these types of cues conflict, landmarks are preferentially used.