Family dementia caregivers with suicidal ideation improve with mentalizing imagery therapy: Results from a pilot study.

Background: Family caregivers of persons living with dementia often experience increased depression and suicidal ideation (SI). However, the feasibility and impact of therapies on caregiver SI has remained largely unexplored. Mentalizing imagery therapy (MIT) helps reduce psychological symptoms through mindfulness and guided imagery. This pilot study examined the feasibility of participation by caregivers with SI in a randomized controlled trial (RCT) of MIT versus a psychosocial support group (SG), and the respective impact of group on SI, depression, and secondary outcomes. Methods: A secondary analysis of data from an RCT of 4-week MIT or SG for caregivers (n = 46) was performed, identifying SI (n = 23) and non-SI (n = 23) cohorts. Group attendance and home practice were compared between cohorts. In the SI cohort (total n = 23, MIT n = 11, SG n = 12), group differences in SI, depression, and secondary outcomes were evaluated post-group and at 4-month follow-up. Results: Attendance in both groups and home practice in MIT were similar between SI and non-SI cohorts. In the SI cohort, MIT evinced greater improvements relative to SG in SI (p=.02) and depression (p=.02) post-group, and other secondary outcomes at follow-up. Limitations: Limitations include small sample size and single-item assessments of SI from validated depression rating scales. Conclusions: Participation in an RCT was feasible for caregivers with SI. MIT resulted in important benefits for SI and depression, while SG showed no acute SI benefit. The role of MIT in improving SI should be confirmed with adequately powered trials, as effective therapies to address caregiver SI are critical.

Virtual mentalizing imagery therapy for Spanish language Latino family dementia caregivers: A feasibility and acceptability study.

Spanish speaking family caregivers of people living with dementia have limited supportive resources in Spanish. There are few validated, culturally acceptable virtual interventions for reducing these caregivers' psychological distress. We investigated the feasibility of a Spanish language adaptation of a virtual Mentalizing Imagery Therapy (MIT) program, which provides guided imagery and mindfulness training to reduce depression, increase mentalizing, and promote well-being. 12 Spanish-speaking family dementia caregivers received a 4-week virtual MIT program. Follow-up was obtained post group and at 4 months post baseline assessment. Feasibility, acceptability, and satisfaction with MIT were assessed. The primary psychological outcome was depressive symptoms; secondary outcomes included caregiver burden, dispositional mindfulness, perceived stress, well-being, interpersonal support, and neurological quality of life. Statistical analysis was performed with mixed linear models. Caregivers were 52 ± 8 (mean ± SD) years of age. 60% had a high school education or less. Participation in weekly group meetings was 100%. Home practice was performed on average 4 ± 1 times per week [range 2-5]. Satisfaction with MIT reached 19 ± 2 of a possible 20 points. Reduction in depression from baseline was observed by week three (p = 0.01) and maintained at 4 month follow-up (p = 0.05). There were significant improvements in mindfulness post-group, and in caregiver burden and well-being at 4 months. MIT was successfully adapted for Latino Spanish language family dementia caregivers within a virtual group environment. MIT is feasible and acceptable and may help reduce depressive symptoms and improve subjective well-being. Larger, randomized controlled trials of MIT should determine durability of effects and validate efficacy in this population.

Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study.

BACKGROUND: Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner. OBJECTIVE: We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated. METHODS: We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression. RESULTS: We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants' sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence. CONCLUSIONS: Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner.

An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor.

The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in the olfactory bulb. Any changes in the ambient odor of a fish's environment warrant a change in visual sensitivity and can trigger mating-like behavior in males due to increased GnRH signaling in the terminalis neuron. Behavioral experiments to study this phenomenon are commonly conducted in a controlled environment where a video of the fish is recorded over time before and after the application of chemicals to the water. Given the subtleties of behavioral change, trained biologists are currently required to annotate such videos as part of a study. This process of manually analyzing the videos is time-consuming, requires multiple experts to avoid human error/bias and cannot be easily crowdsourced on the Internet. Machine learning algorithms from computer vision, on the other hand, have proven to be effective for video annotation tasks because they are fast, accurate, and, if designed properly, can be less biased than humans. In this work, we propose to automate the entire process of analyzing videos of behavior changes in zebrafish by using tools from computer vision, relying on minimal expert supervision. The overall objective of this work is to create a generalized tool to predict animal behaviors from videos using state-of-the-art deep learning models, with the dual goal of advancing understanding in biology and engineering a more robust and powerful artificial information processing system for biologists.

Bridging the Gap Between Computational Photography and Visual Recognition.

What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step improve image interpretability for manual analysis or automatic visual recognition to classify scene content? While there have been important advances in the area of computational photography to restore or enhance the visual quality of an image, the capabilities of such techniques have not always translated in a useful way to visual recognition tasks. Consequently, there is a pressing need for the development of algorithms that are designed for the joint problem of improving visual appearance and recognition, which will be an enabling factor for the deployment of visual recognition tools in many real-world scenarios. To address this, we introduce the UG 2 dataset as a large-scale benchmark composed of video imagery captured under challenging conditions, and two enhancement tasks designed to test algorithmic impact on visual quality and automatic object recognition. Furthermore, we propose a set of metrics to evaluate the joint improvement of such tasks as well as individual algorithmic advances, including a novel psychophysics-based evaluation regime for human assessment and a realistic set of quantitative measures for object recognition performance. We introduce six new algorithms for image restoration or enhancement, which were created as part of the IARPA sponsored UG 2 Challenge workshop held at CVPR 2018. Under the proposed evaluation regime, we present an in-depth analysis of these algorithms and a host of deep learning-based and classic baseline approaches. From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.

An Extreme Value Theory Model of Cross-Modal Sensory Information Integration in Modulation of Vertebrate Visual System Functions.

We propose a computational model of vision that describes the integration of cross-modal sensory information between the olfactory and visual systems in zebrafish based on the principles of the statistical extreme value theory. The integration of olfacto-retinal information is mediated by the centrifugal pathway that originates from the olfactory bulb and terminates in the neural retina. Motivation for using extreme value theory stems from physiological evidence suggesting that extremes and not the mean of the cell responses direct cellular activity in the vertebrate brain. We argue that the visual system, as measured by retinal ganglion cell responses in spikes/sec, follows an extreme value process for sensory integration and the increase in visual sensitivity from the olfactory input can be better modeled using extreme value distributions. As zebrafish maintains high evolutionary proximity to mammals, our model can be extended to other vertebrates as well.