Individual Differences in Human Brain Functional Network Organization

The human brain is organized at many spatial scales, including the level of areas and systems. Resting-state functional magnetic resonance imaging is a non-invasive technique that allows for the study of areal- and systems-level brain organization in vivo. Over two decades of research has sought to identify and characterize the functional communities that comprise the brain’s network architecture. Consequently, a convergent description of group-average functional network organization in healthy adults has emerged. Recent advances have allowed for the study of such organization in single individuals. Investigation of functional network organization in highly sampled individuals has revealed brain regions that deviate from the group-level description, i.e. individual differences in human brain functional network organization. This dissertation work characterizes individual differences in functional network organization, referred to as network variants, across a large sample of healthy adults. Network variants appear to be stable over time within an individual and organized systematically across individuals. They occur in characteristic cortical locations and associate with characteristic functional networks. Further, their task-evoked activity is consistent with their idiosyncratic functional network association. Finally, individuals may be sub-typed into one of two groups, where individuals in the same sub-group have a similar distribution of network variants. The sub-group phenomenon is heritable and relates to differences in neuropsychological measures of behavior. Network variants appear to be trait-like, functionally-relevant components of individual human brain functional network organization

Cognitive manipulation of brain electric microstates

EEG studies of wakeful rest have shown that there are brief periods in which global electrical brain activity on the scalp remains semi-stable (so-called microstates). Topographical analyses of this activity have revealed that much of the variance is explained by four distinct microstates that occur in a repetitive sequence. A recent fMRI study showed that these four microstates correlated with four known functional systems, each of which is activated by specific cognitive functions and sensory inputs. The present study used high density EEG to examine the degree to which spatial and temporal properties of microstates may be altered by manipulating cognitive task (a serial subtraction task vs. wakeful rest) and the availability of visual information (eyes open vs. eyes closed conditions). The hypothesis was that parameters of microstate D would be altered during the serial subtraction task because it is correlated with regions that are part of the dorsal attention functional system. It was also expected that the sequence of microstates would preferentially transition from all other microstates to microstate D during the task as compared to rest. Finally, it was hypothesized that the eyes open condition would significantly increase one or more microstate parameters associated with microstate B, which is associated with the visual system. Topographical analyses indicated that the duration, coverage, and occurrence of microstate D were significantly higher during the cognitive task compared to wakeful rest; in addition, microstate C, which is associated with regions that are part of the default mode and cognitive control systems, was very sensitive to the task manipulation, showing significantly decreased duration, coverage, and occurrence during the task condition compared to rest. Moreover, microstate B was altered by manipulations of visual input, with increased occurrence and coverage in the eyes open condition. In addition, during the eyes open condition microstates A and D had significantly shorter durations, while C had increased occurrence. Microstate D had decreased coverage in the eyes open condition. Finally, at least 15 microstates (identified via k-means clustering) were required to explain a similar amount of variance of EEG activity as previously published values. These results support important aspects of our hypotheses and demonstrate that cognitive manipulation of microstates is possible, but the relationships between microstates and their corresponding functional systems are complex. Moreover, there may be more than four primary microstates

BOLD cofluctuation \u27events\u27 are predicted from static functional connectivity

Recent work identified single time points ( events ) of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; ∼50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure

Data Quality Influences Observed Links Between Functional Connectivity and Behavior

A growing field of research explores links between behavioral measures and functional connectivity (FC) assessed using resting-state functional magnetic resonance imaging. Recent studies suggest that measurement of these relationships may be corrupted by head motion artifact. Using data from the Human Connectome Project (HCP), we find that a surprising number of behavioral, demographic, and physiological measures (23 of 122), including fluid intelligence, reading ability, weight, and psychiatric diagnostic scales, correlate with head motion. We demonstrate that "trait" (across-subject) and "state" (across-day, within-subject) effects of motion on FC are remarkably similar in HCP data, suggesting that state effects of motion could potentially mimic trait correlates of behavior. Thus, head motion is a likely source of systematic errors (bias) in the measurement of FC:behavior relationships. Next, we show that data cleaning strategies reduce the influence of head motion and substantially alter previously reported FC:behavior relationship. Our results suggest that spurious relationships mediated by head motion may be widespread in studies linking FC to behavior

Probabilistic mapping of human functional brain networks identifies regions of high group consensus

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show “core” (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations