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

Abstract

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

Keywords: EMA; MRI; actigraph; ambulatory assessment; diffusion tensor imaging; diffusion weighted imaging; digital phenotyping; ecological momentary assessments; fMRI; functional magnetic resonance imaging; magnetic resonance imaging; physical activity; physiology; portable device; sleep; smart monitoring; smartphone; smartwatch; structural magnetic resonance imaging; wearable.

FIGURE 1

Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) 2020 guidelines flow chart showing the search strategy and selection process for this review.

FIGURE 2

Sample mean age and age distribution of studies included in the systematic review. The study (y‐axis) was ordered by the mean age (dot) from each study. If the mean was not available, the median was used and plotted. If neither the mean nor the median were available, the average between the minimum and maximum were used. Red represents papers that included patients.

FIGURE 3

Characteristics of the studies included in the systematic review. (a) Distribution of sample sizes in the reviewed papers. (b) Distribution of excluded subjects according to the source of the issue, which could be related to magnetic resonance imaging (MRI), the device and its data collection, or other factors (e.g., unknown medical problems or phobias). (c) Number of days during which a device was used to collect data. The devices are organized into two groups based on the level of interaction required from the subject: active (some interaction is needed, such as answering a question) or passive (no interaction is required, such as heart rate measurements). (d) Number of studies employing a device based on its function to measure specific human behavior or physiological variables. (e) Diagnoses investigated in the included study sample. The studied illnesses were grouped according to their type. The number of studies that investigate an illness according to its group is shown.

FIGURE 4

Network of co‐occurrences between portable automatic devices and magnetic resonance imaging (MRI) techniques. Link thickness represents the frequency of a particular combination in the selected research papers, with thicker links indicating higher frequency. Node size corresponds to the number of papers using a specific device or technique. (a) Nodes are color‐coded based on category: MRI (green), physiology (orange), and behavior (purple). (b) Link communities and node overlaps in the network. Links are colored according to the detected link communities that are also indicated by the shaded areas. Node positions are the same as shown in (a)).

FIGURE 5

Frequency of used magnetic resonance imaging (MRI) techniques and portable automatic devices over time. (a) Colors indicate the number of papers employing a specific device and MRI technique. Panels (b)–(d)indicate the number of times a device has been used in combination with T1/T2‐weighted MRI, fMRI, DWI, and angiography.

FIGURE 6

Brain areas reported across the most common magnetic resonance imaging‐portable automatic devices (MRI‐PAD) combinations. The colors represent the number of studies that reported a specific brain area as statistically significant for (a) T1‐weighted MRI alone, (b) T1‐weighted MRI and physical activity, (c) functional MRI (fMRI) alone, (d) fMRI and physical activity, (e) fMRI and EMA/ESM, and (f) fMRI and sleep.

FIGURE 7

Meta‐analytical brain maps of areas reported using the most common functional magnetic resonance imaging‐portable automatic devices (fMRI‐PAD) combinations. For each study, we extracted the reported statistically significant region of interest (ROI) coordinates. Then, for each combination, we run a meta‐analysis if at least four studies reported coordinates. Significant clusters were found for studies combining (a) fMRI and EMA/ESM and (b) fMRI and sleep sensors.