Can brain state be manipulated to emphasize individual differences in functional connectivity?

Abstract

While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.

Keywords: Brain state; Functional connectivity; Human Connectome Project; Individual differences; Resting state; Scan condition; Task; fMRI.

Fig. 1.. What is the optimal brain state for measuring individual differences?

A thought experiment to illustrate how between-subject variability can be dissociated from single-subject identifiability. Consider images of three individuals with a ground-truth similarity of r=0.50 (average of the correlation coefficients between all three pairs of image RBG values; middle box), but this is a latent value and not directly observable. Instead, individual differences are measured in various experimental conditions, each of which is associated with different levels of between-subject similarity (measured with r) and individual-subject identifiability (a subjective metric reflecting ease of recognition). A condition that makes subjects look maximally different (lower left) is not ideal for studying individual differences, since it may destroy key features of each individual. On the other hand, increasing similarity across subjects at the expense of obscuring any individual features (lower right) is also not ideal. Thus, the optimal brain state for measuring individual differences is likely one that evokes some level of divergence across subjects while stabilizing the most important features of each subject. Such a state may evoke either an overall increase in between-subject variability (upper left, “caricature” condition) or, somewhat paradoxically, a decrease in overall between-subject variability (upper right, “selective enhancement” condition), as long as the key individual features are preserved or even enhanced. Image credits: Presidential photographs, whitehouse.gov; Clinton caricature, www.flickr.com/photos/donkeyhotey/10964745624/; Bush caricature, www.flickr.com/photos/donkeyhotey/29513525751/;Obama caricature, www.flickr.com/photos/donkeyhotey/5601868538/. All caricatures provided under Creative Commons Attribution 2.0 Generic license.

Fig. 2.. Hypothetical depictions of within- versus between-subject variability as a function of brain state.

Each color (red, green, blue) is a subject, each of whom is measured in three different brain states (a, b, c). In the left panel, while states can push subjects around their personal “connectome space,” these spaces are non-overlapping. Thus, subject accounts for more of the variance than state, and subjects should always be perfectly identifiable. In the second scenario (right panel), the variance associated with state expands such that individual connectome spaces do overlap, and in some states a given subject might be mistaken for someone else (for example, subject 1 for subject 2 in state c).

Fig. 3.. Individuals look more similar during certain brain states than others.

(a) Between-subject similarity as a function of scan condition. Values were derived from the upper triangle of a 716×716 subjects×subjects correlation matrix, representing the correlation (Fisher-transformed r value) between all possible pairs of subjects’ whole-brain functional connectivity profiles in each respective condition. Whiskers denote 1.5*interquartile range. (b) Histograms showing the distribution of performance on the four tasks that measured accuracy on a continuous scale from 0 to 1. Horizontal axis is accuracy; vertical axis is subject count (n=716 for each condition). (c) Sex-condition interaction on between-subject similarity. Green denotes female-to-female correlations; orange denotes male-to-male correlations. See Table 1 for condition abbreviations.

Fig. 4.. Within-individual similarity varies between pairs of brain states.

(a) Within-subject correlations between connectivity matrices acquired during each of 36 pairs of conditions. Each cell i,j represents the mean within-subject correlation between the connectivity patterns in condition i and condition j. Lower triangle is based on connectivity matrices calculated using equal amounts of data per condition. Upper triangle is based on connectivity matrices calculated using all available data per condition (see Table 1 for scan durations). (b) Estimating how similar each state is to all other states within individuals. For each condition for each subject, correlations to all eight other conditions are averaged (thus each subject contributes one value to the boxplot per condition). See Table 1 for condition abbreviations.

Fig. 5.. Identification of individual subjects across brain states.

(a) Extension of the identification experiments described in Finn et al. (2015) using n=716 subjects each scanned during nine conditions. Identification (ID) rate denotes the overall accuracy of an iterative algorithm that attempts to match a given subject’s connectivity matrix from one condition (target) to the matrix of the same subject from a different condition (database). The roles of target and database may then be reversed, resulting in 72 possible identification experiments between rest-rest, rest-task and task-task scan pairs. Chance rate is approximately 0.001 (1/716). (b) Mean database ID rate (average across each row of the matrix in panel (a)) plotted against mean between-subject correlation (Fisher-transformed z-score; cf. Fig. 3a) for all nine conditions. Conditions that make subjects look more similar tend to make better databases for identification experiments (r(7)=0.82, p=0.007). See Table 1 for condition abbreviations.