Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease

Abstract

Functional brain connectivity measured with functional magnetic resonance imaging (fMRI) is a popular technique for investigating neural organization in both healthy subjects and patients with mental illness. Despite a rapidly growing body of literature, however, functional connectivity research has yet to deliver biomarkers that can aid psychiatric diagnosis or prognosis at the single-subject level. One impediment to developing such practical tools has been uncertainty regarding the ratio of intra- to interindividual variability in functional connectivity; in other words, how much variance is state- versus trait-related. Here, we review recent evidence that functional connectivity profiles are both reliable within subjects and unique across subjects, and that features of these profiles relate to behavioral phenotypes. Together, these results suggest the potential to discover reliable correlates of present and future illness and/or response to treatment in the strength of an individual's functional brain connections. Ultimately, this work could help develop personalized approaches to psychiatric illness.

Keywords: biomarker; fMRI; functional connectivity; individual variation; prediction.

Figure 1.. Schematic of functional connectivity analysis. (A) 268-Node functional brain atlas covering cortical, subcortical, and cerebellar structures. This atlas was defined using a groupwise clustering algorithm on resting-state data from healthy adults. The algorithm groups voxels into nodes with maximally coherent timecourses. (B) An example of two blood-oxygen level dependent (BOLD) signal timecourses from a pair of nodes “ i ” (red) and “ j ” (green). The similarity of these two signals is measured using Pearson correlation (r); a high correlation coefficient implies a strong functional connection. (C) Correlating the timecourses of all possible pairs of nodes produces a symmetric 268 x 268 connectivity matrix. Connectivity matrices can be calculated using data from a single subject and a single scan session, such that each individual has a unique matrix associated with a particular scan condition. A 268-node atlas produces a matrix with 35 778 unique elements; this set of correlation strengths is what is referred to here as a “functional connectivity (FC) profile.”

Figure 2.. Identification accuracies across pairs of rest and task conditions. Color-coded matrix displaying identification accuracy between all 18 possible database-target pairs of rest and task sessions, expressed as the fraction of correctly predicted identities (number of successful trials out of a total of n=126 subjects). While identification was most successful in the rest-rest condition pair, accuracy remained quite high even across changes in cognitive state induced by different task demands. Note that chance in all cases is approximately 0.8. Em, emotion; ID, identification; Lg, language; Mt, motor; R1, first rest session (day 1); R2, second rest session (day 2); WM, working memory. Adapted from results described in reference 27.

Figure 3.. Group contrasts versus dimensional approaches. (A) An example of a traditional contrast in an observed brain measurement (eg, strength of a functional connection or network) between patients and controls (n=20 in each group). The difference between group means is significant at α<0.05 according to a two-tailed t-test, but individual data points are highly overlapping. If a new subject is brought in (red circle) with a known brain measurement, this overlap makes it difficult to predict diagnostic status. (B) An example of a dimensional approach, in which a phenotype is objectively measured in subjects both with and without a diagnosis and all subjects are placed on the same axis, revealing a clear association between the brain measurement and phenotypic measurement. The phenotypic variable could be performance on a task, score on self-report or clinician-rated scale, future illness status, response to an intervention, or any other continuous measurement. In contrast to (A), if a new subject is brought in with a known brain measurement (red circle), it is straightforward to generate a phenotype prediction for this subject using the regression model built on the original data set.