Abstract

Precision Functional Mapping in Obsessive-Compulsive Disorder Using Dense Sampling Scanning

Vaghi M, Shim S, Ali Rios J, Bissett P, Mukunda P, Rodriguez C, Poldrack R
61st Annual Meeting of the American College of Neuropsychopharmacology. 2022.

Background: One of the roadblocks in translating imaging research findings into clinical practice biomarkers is represented by shortcomings of traditional approaches. These generally entail analysis of limited data from each subject. Accordingly, inferences are based on cross-subjects averages which might obscure patterns of brain organization specific to each individual. Additionally, by focusing on an individual time point there is limited understanding of whether observed effects are stable over time or state dependent.

Here, we used a recently pioneered ‘dense sampling’ scanning acquisition where imaging data are collected on the same subject repeatedly over time, resulting in more data per individual. This experimental design enables conclusions to be drawn at the individual level and to capture brain fluctuations in the intermediate timescale of weeks to months, which might bear relevance to fluctuations in symptoms. We examined magnitude and anatomical distributions of network variability across subjects and sessions from seven high-quality, highly sampled patients with Obsessive Compulsive Disorder (OCD). Imaging data were collected while subjects performed cognitive tasks as well as during resting-state, but results presented here pertain to resting-state only.

Methods: Data were collected from 7 patients with a diagnosis of OCD (4 males; age: 22-48). For each subject, data were acquired on 10 sessions, each on a separate day, approximately one week apart. To minimize within-subject variability due to external factors, scans were performed at fixed times of the day, with rare variations due to participant or scanner’s scheduling constraints. Each session entailed psychiatric evaluation as well as functional MRI. Overall, this resulted in approximately 6 h (3 h resting-state) of functional imaging per subject. MRI data were acquired on a research-dedicated GE 3 T MRI scanner using a 32-channel head coil. All functional imaging scans were performed using a multi-echo, multi-band, gradient-echo EPI sequence. Additionally, a T1 and a T2-weighted image were obtained for each subject. Imaging data were pre-processed via fMRIPrep 22.0.0 and denoised with Tedana. Functional connectivity (FC) was measured via time-series correlations among cortical regions parcellated using the Yeo’s 17 networks template. Accordingly, a FC connectivity matrix was obtained for each subject and session. Effects were calculated per subject and were compared with one another using paired two-tailed t tests, with p values FDR corrected for the number of comparisons.

Results: Group and individual effects on network similarity were investigated by calculating correlations among FC connectivity matrixes. To quantify group effects, correlations between FC connectivity matrixes from different individuals and sessions were inspected. This analysis indicated a shared common basic structure, as functional networks from different individuals and sessions showed substantial similarity (mean Spearman’s correlation = 0.76). However, networks from the same individual and different sessions were even more like each other, with an additional effect of mean Spearman’s correlation = 0.86 over the group effect, demonstrating a large influence of individual identity on functional networks. We also investigated within-subject variability across sessions by computing, for each subject, the standard deviation of the correlation between each parcel-pair across all 10 sessions. Average variability across all correlations showed a pattern of relatively larger variability in visual, somato-motor and dorsal attentional regions, which is in line with analogous studies in healthy subjects. However, in contrast to studies in healthy subjects, variability in these networks was not significantly different from the one observed in executive control areas, which have been traditionally implicated in OCD.

Conclusions: We find that, in patients with OCD, functional networks are dominated by common organizational principles as well as prominent individual features. These results mirror previous investigations in healthy subjects and extend it to patients with OCD highlighting the importance of individualized approaches for studying properties of brain organization. In contrast to previous reports in healthy subjects, variability was similar in processing and executive control regions, setting the stage for relating longitudinal dynamics of brain functions to behavioral and psychiatric symptoms variability.