Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

Objective The study of cortical gyrification in Alzheimer's disease (AD) could help to further understanding of the changes undergone in the brain during neurodegeneration. Here, we aimed to study brain gyrification differences between healthy controls (HC), mild cognitive impairment (MCI) patients, and AD patients, and explore how cerebral gyrification patterns were associated with memory and other cognitive functions. Methods We applied surface‐based morphometry techniques in 2 large, independent cross‐sectional samples, obtained from the Alzheimer's Disease Neuroimaging Initiative project. Both samples, encompassing a total of 1,270 participants, were analyzed independently. Results Unexpectedly, we found that AD patients presented a more gyrificated entorhinal cortex than HC. Conversely, the insular cortex of AD patients was hypogyrificated. A decrease in the gyrification of the insular cortex was also found in older HC participants as compared with younger HC, which argues against the specificity of this finding in AD. However, an increased degree of folding of the insular cortex was specifically associated with better memory function and semantic fluency, only in AD patients. Overall, MCI patients presented an intermediate gyrification pattern. All these findings were consistently observed in the two samples. Interpretation The marked atrophy of the medial temporal lobe observed in AD patients may explain the increased folding of the entorhinal cortex. We additionally speculate regarding alternative mechanisms that may also alter its folding. The association between increased gyrification of the insular cortex and memory function, specifically observed in AD, could be suggestive of compensatory mechanisms to overcome the loss of memory function. ANN NEUROL 2020 ANN NEUROL 2020;88:67–80

Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. Our aim is to describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population. Methods The Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program explores the potential to use RMT across a range of central nervous system disorders. Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the RADAR-CNS program. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments. Participants were followed-up for a maximum of 2 years. Results A total of 623 individuals with a history of MDD were enrolled in the study. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group. Our next steps are to illustrate the predictive value of these data, which will be the focus of our future data analysis aims.