Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real‐world digital phenotyping

Human Brain Mapping, Journal Year: 2024, Volume and Issue: 45(4)

Published: March 1, 2024

Abstract A primary goal of neuroscience is to understand the relationship between brain and behavior. While magnetic resonance imaging (MRI) examines structure function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real‐world settings. Combining these two technologies may bridge gap imaging, physiology, real‐time behavior, enhancing generalizability laboratory clinical findings. However, use MRI data from PADs outside scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews Meta‐Analysis systematic literature review that identifies analyzes current state research on integration PADs. PubMed Scopus were automatically searched using keywords covering various techniques Abstracts screened only include articles collected PAD environment. Full‐text screening was then conducted ensure included combined quantitative with PADs, yielding 94 selected papers total N = 14,778 subjects. Results reported as cross‐frequency tables sampling methods patterns identified through network analysis. Furthermore, maps studies synthesized according measurement modalities used. demonstrate feasibility integrating across study designs, patient control populations, age groups. The majority published combines functional, T1‐weighted, diffusion weighted physical activity sensors, ecological momentary assessment sleep. further highlights specific regions frequently correlated distinct MRI‐PAD combinations. These combinations enable in‐depth how influence each other. Our potential constructing brain–behavior models extend beyond into contexts.

Language: Английский

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Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 30, 2024

Abstract Chronic disease management and follow-up are vital for realizing sustained patient well-being optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions longitudinal monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting analyzing data from wearables presents several challenges, such as entry errors, non-wear periods, missing data, artifacts. In this work, we explore these analysis challenges using two real-world datasets (mBrain21 ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, an optimized pipeline detecting periods. Additionally, propose a visualization-oriented approach validate processing pipelines scalable tools tsflex Plotly-Resampler. Lastly, present bootstrapping methodology evaluate the variability of wearable-derived features presence partially segments. Prioritizing transparency reproducibility, provide open access our detailed code examples, facilitating adaptation future research. conclusion, contributions actionable approaches improving collection analysis.

Language: Английский

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Improving wearable-based seizure prediction by feature fusion using growing network

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

A bstract The unpredictability of seizures is one the most compromising features reported by people with epilepsy. Non-stigmatizing and easy-to-use wearable devices may provide information to predict based on physiological data. We propose a patient-agnostic seizure prediction method that identifies group-level patterns across data from multiple patients. employ supervised long-short-term networks (LSTMs) add unsupervised deep canonically correlated autoencoders (DCCAE) 24-hour using time-of-day information. fuse these three techniques growing neural network, allowing incremental learning. Our all improves accuracy over baseline LSTM 7.3%, 74.4% 81.7%, averaged patients, outperforms in 84% Compared all-at-once fusion, network 9.5%. analyze impact preictal duration, quality, clinical variables performance.

Language: Английский

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Maternal depression during the perinatal period and its relationship with emotion regulation in young adulthood: An fMRI study in a prenatal birth cohort

Psychological Medicine, Journal Year: 2025, Volume and Issue: 55

Published: Jan. 1, 2025

Abstract Background Maternal perinatal mental health is essential for optimal brain development and of the offspring. We evaluated whether maternal depression during period early life offspring might be selectively associated with altered function emotion regulation those may further correlate physiological responses typical use strategies. Methods Participants included 163 young adults (49% female, 28–30 years) from ELSPAC prenatal birth cohort who took part in its neuroimaging follow-up had complete data life. depressive symptoms were measured mid-pregnancy, 2 weeks, 6 months, 18 months after birth. Regulation negative affect was studied using functional magnetic resonance imaging, concurrent skin conductance response (SCR) heart rate variability (HRV), assessment strategy. Results weeks interacted sex showed a relationship greater right frontal cluster women. Moreover, this mediated between suppression emotions adult women (ab = 0.11, SE 0.05, 95% CI [0.016; 0.226]). The strategy also as sociated SCR HRV. Conclusions These findings suggest that predisposes female to maladaptive skills particularly adulthood.

Language: Английский

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Uncovering the potential of smartphones for behavior monitoring during migraine follow-up

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 18, 2025

Migraine is a neurological disorder that affects millions of people worldwide. It one the most debilitating disorders which leads to many disability-adjusted life years. Conventional methods for investigating migraines, like patient interviews and diaries, suffer from self-reporting biases intermittent tracking. This study aims leverage smartphone-derived data as an objective tool examining relationship between migraines various human behavior aspects. By utilizing built-in sensors monitoring phone interactions, we gather derive metrics such keyboard usage, application interaction, physical activity levels, ambient light conditions, sleep patterns. We perform statistical analysis testing investigate whether there difference in user behavioral aspects during headache non-headache periods. Our 362 headaches reveals differences light, use leisure apps, number keystrokes periods exploratory shows on hand it possible monitor using smartphone interaction only. On other can observe work step towards objectively measure effects migraine has people's lives.

Language: Английский

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The Future of Virology Diagnostics Using Wearable Devices Driven by Artificial Intelligence

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 473 - 504

Published: Jan. 10, 2025

The utilization of the wearable devices (WDs) that are enhanced by artificial intelligence (AI) can have a notable potential in healthcare. This chapter aimed to provide an overview applications AI-driven WDs enhancing early detection and management virus infections. First, we presented examples highlight capabilities very monitoring infections such as COVID-19. In addition, provided on utility machine learning algorithms analyze large data for signs We also overviewed enable real-time surveillance effective outbreak management. showed how this be achieved via collection analysis diverse WDs' across various populations. Finally, discussed challenges ethical issues comes with virology diagnostics, including concerns about privacy security well issue equitable access.

Language: Английский

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Distress detection in VR environment using Empatica E4 wristband and Bittium Faros 360

Frontiers in Physiology, Journal Year: 2025, Volume and Issue: 16

Published: March 5, 2025

Distress detection in virtual reality systems offers a wealth of opportunities to improve user experiences and enhance therapeutic practices by catering individual physiological emotional states. This study evaluates the performance two wearable devices, Empatica E4 wristband Faros 360, detecting distress motion-controlled interactive environment. Subjects were exposed baseline measurement VR scenes, one non-interactive interactive, involving problem-solving distractors. Heart rate measurements from both including mean heart rate, root square successive differences, subject-specific thresholds, utilized explore intensity frequency. Both sensors adequately captured signals, with demonstrating higher signal-to-noise ratio consistency. While correlation coefficients moderately positive between data, indicating linear relationship, small absolute error values suggested good agreement measuring rate. Analysis occurrence during scene revealed that devices detect more high- medium-level occurrences compared scene. Device-specific factors emphasized due differences detected events devices.

Language: Английский

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The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

JMIR mhealth and uhealth, Journal Year: 2023, Volume and Issue: 11, P. e45103 - e45103

Published: Sept. 8, 2023

Abstract Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology free-living conditions. The All of Us Research Program (AoURP), a National Institutes Health initiative that collects health-related information from participants the United States, has expanded its data collection include DHT Fitbit devices. This offers researchers an unprecedented opportunity examine large cohort alongside biospecimens electronic records. However, there are existing challenges sources error need be considered before using device AoURP. In this viewpoint, we reliability potential associated with available through AoURP Researcher Workbench outline actionable strategies mitigate missingness noise. We begin by discussing noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, (3) movement motion artifacts, proceed discuss data. then methods such noise end considering how future enhancements AoURP’s inclusion new types would impact usability Although considerations suggested literature tailored toward AoURP, recommendations broadly applicable wearable DHTs

Language: Английский

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A dual in-person and remote assessment approach to developing digital endpoints relevant to autism and co-occurring conditions: protocol for a multi-site observational study (Preprint)

Published: Jan. 21, 2025

Research priorities for autistic people include developing effective interventions the numerous challenges affecting their daily living, e.g., mental health problems, sleep difficulties, and social wellbeing. However, clinical research progress is limited by a lack of validated objective measures that represent target outcomes improvement. Digital technologies, including wearable devices smartphone applications, provide opportunities to develop novel may reflect everyday experience complement key assessments. little known about acceptability feasibility implementing digital data collection in this population. Our endpoints relevant outcomes, research, communication, sleep, health, using both in-person remote (i.e., at home) procedures. In particular, protocol aims implement evaluate usability, acceptability, adherence such procedures, as well explore properties certain resulting measures. Eligible non-autistic participants AIMS Longitudinal European Autism Project (LEAP) were invited participate digitally augmented Diagnostic Observation Schedule-2 (ADOS-2) 28-day measurement (RM) involving wearing Fitbit device, downloading passive app, two active reporting apps. The first LEAP study enrolled September 2021 (in-person component) March 2022 (RM component). To date, 190 have taken part ADOS-2 component, 86 been protocol. Recruitment now complete with some RM ongoing until August 2025. Preliminary analysis, exploration metrics, pipeline development speech analysis measures, framework coding qualitative data, has started. Results are expected be submitted publication from February This lays important groundwork understanding remotely implemented procedures capture meaningful domains improving life people.

Language: Английский

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Quantifying and controlling the impact of regression to the mean on randomized controlled trials in epilepsy

Epilepsia, Journal Year: 2023, Volume and Issue: 64(10), P. 2635 - 2643

Published: July 28, 2023

Randomized controlled trials (RCTs) in epilepsy for drug treatments are plagued by high costs. One potential remedy is to reduce placebo response via better control over regression the mean (RTM). Here, RTM represents an initial observed seizure rate higher than long-term average, which gradually settles closer resulting apparent treatment. This study used simulation clarify relationship between eligibility criteria and RTM.

Language: Английский

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