Feasibility of Remote Administration of the Uniform Data Set-Version 3 for Assessment of Older Adults With Mild Cognitive Impairment and Alzheimer's Disease.

OBJECTIVE: Assess the feasibility and concurrent validity of a modified Uniform Data Set version 3 (UDSv3) for remote administration for individuals with normal cognition (NC), mild cognitive impairment (MCI), and early dementia. METHOD: Participants (N = 93) (age: 72.8 [8.9] years; education: 15.6 [2.5] years; 72% female; 84% White) were enrolled from the Wake Forest ADRC. Portions of the UDSv3 cognitive battery, plus the Rey Auditory Verbal Learning Test, were completed by telephone or video within ~6 months of participant's in-person visit. Adaptations for phone administration (e.g., Oral Trails for Trail Making Test [TMT] and Blind Montreal Cognitive Assessment [MoCA] for MoCA) were made. Participants reported on the pleasantness, difficulty, and preference for each modality. Staff provided validity ratings for assessments. Participants' remote data were adjudicated by cognitive experts blinded to the in person-diagnosis (NC [N = 44], MCI [N = 35], Dementia [N = 11], or other [N = 3]). RESULTS: Remote assessments were rated as pleasant as in-person assessments by 74% of participants and equally difficult by 75%. Staff validity rating (video = 92%; phone = 87.5%) was good. Concordance between remote/in-person scores was generally moderate to good (r = .3 -.8; p < .05) except for TMT-A/OTMT-A (r = .3; p > .05). Agreement between remote/in-person adjudicated cognitive status was good (k = .61-.64). CONCLUSIONS: We found preliminary evidence that older adults, including those with cognitive impairment, can be assessed remotely using a modified UDSv3 research battery. Adjudication of cognitive status that relies on remotely collected data is comparable to classifications using in-person assessments.

Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis.

Previous studies in children with attention-deficit/hyperactivity disorder (ADHD) have observed functional brain network disruption on a whole-brain level, as well as on a sub-network level, particularly as related to the default mode network, attention-related networks, and cognitive control-related networks. Given behavioral findings that children with ADHD have more difficulty sustaining attention and more extreme moment-to-moment fluctuations in behavior than typically developing (TD) children, recently developed methods to assess changes in connectivity over shorter time periods (i.e., "dynamic functional connectivity"), may provide unique insight into dysfunctional network organization in ADHD. Thus, we performed a dynamic functional connectivity (FC) analysis on resting state fMRI data from 38 children with ADHD and 79 TD children. We used Hidden semi-Markov models (HSMMs) to estimate six network states, as well as the most probable sequence of states for each participant. We quantified the dwell time, sojourn time, and transition probabilities across states. We found that children with ADHD spent less total time in, and switched more quickly out of, anticorrelated states involving the default mode network and task-relevant networks as compared to TD children. Moreover, children with ADHD spent more time in a hyperconnected state as compared to TD children. These results provide novel evidence that underlying dynamics may drive the differences in static FC patterns that have been observed in ADHD and imply that disrupted FC dynamics may be a mechanism underlying the behavioral symptoms and cognitive deficits commonly observed in children with ADHD.