Evan M. Gordon

Functional System and Areal Organization of a Highly Sampled Individual Human Brain

Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individuals systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.

On the Stability of BOLD fMRI Correlations

Measurement of correlations between brain regions (functional connectivity) using blood oxygen level dependent (BOLD) fMRI has proven to be a powerful tool for studying the functional organization of the brain. Recently, dynamic functional connectivity has emerged as a major topic in the resting-state BOLD fMRI literature. Here, using simulations and multiple sets of empirical observations, we confirm that imposed task states can alter the correlation structure of BOLD activity. However, we find that observations of dynamic BOLD correlations during the resting state are largely explained by sampling variability. Beyond sampling variability, the largest part of observed dynamics during rest is attributable to head motion. An additional component of dynamic variability during rest is attributable to fluctuating sleep state. Thus, aside from the preceding explanatory factors, a single correlation structure-as opposed to a sequence of distinct correlation structures-may adequately describe the resting state as measured by BOLD fMRI. These results suggest that resting-state BOLD correlations do not primarily reflect moment-to-moment changes in cognitive content. Rather, resting-state BOLD correlations may predominantly reflect processes concerned with the maintenance of the long-term stability of the brains functional organization.

Long-term neural and physiological phenotyping of a single human

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

Precision Functional Mapping of Individual Human Brains

Human functional MRI (fMRI) research primarily focuses on analyzing data averaged across groups, which limits the detail, specificity, and clinical utilityxa0of fMRI resting-state functional connectivity (RSFC) and task-activation maps. To push our understanding of functional brain organization to the level ofxa0individual humans, we assembled a novel MRI dataset containing 5xa0hr of RSFC data, 6xa0hr ofxa0task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults. Usingxa0these data, we generated ten high-fidelity, individual-specificxa0functional connectomes. This individual-connectome approach revealed several new typesxa0of spatial and organizational variability in brain networks, including unique network features and topologies that corresponded with structural and task-derived brain features. We arexa0releasing this highly sampled, individual-focused dataset asxa0a resource for neuroscientists, and we propose precision individual connectomics as a model for future work examining the organization of healthy and diseased individual human brains.

Individual-specific features of brain systems identified with resting state functional correlations

Abstract Recent work has made important advances in describing the large‐scale systems‐level organization of human cortex by analyzing functional magnetic resonance imaging (fMRI) data averaged across groups of subjects. However, new findings have emerged suggesting that individuals’ cortical systems are topologically complex, containing small but reliable features that cannot be observed in group‐averaged datasets, due in part to variability in the position of such features along the cortical sheet. This previous work has reported only specific examples of these individual‐specific system features; to date, such features have not been comprehensively described. Here we used fMRI to identify cortical system features in individual subjects within three large cross‐subject datasets and one highly sampled within‐subject dataset. We observed system features that have not been previously characterized, but 1) were reliably detected across many scanning sessions within a single individual, and 2) could be matched across many individuals. In total, we identified forty‐three system features that did not match group‐average systems, but that replicated across three independent datasets. We described the size and spatial distribution of each non‐group feature. We further observed that some individuals were missing specific system features, suggesting individual differences in the system membership of cortical regions. Finally, we found that individual‐specific system features could be used to increase subject‐to‐subject similarity. Together, this work identifies individual‐specific features of human brain systems, thus providing a catalog of previously unobserved brain system features and laying the foundation for detailed examinations of brain connectivity in individuals. HighlightsFeatures of brain systems identified in individuals are absent from group averages.These features were both reliable within a single subject and present across subjects.These features were observed across three independent datasets.Some subjects were “missing” system features, suggesting variable system connections.Matching system features between individuals increased inter‐individual similarity.

Default mode network segregation and social deficits in autism spectrum disorder: Evidence from non-medicated children

Functional pathology of the default mode network is posited to be central to social-cognitive impairment in autism spectrum disorders (ASD). Altered functional connectivity of the default mode networks midline core may be a potential endophenotype for social deficits in ASD. Generalizability from prior studies is limited by inclusion of medicated participants and by methods favoring restricted examination of network function. This study measured resting-state functional connectivity in 22 8–13 year-old non-medicated children with ASD and 22 typically developing controls using seed-based and network segregation functional connectivity methods. Relative to controls the ASD group showed both under- and over-functional connectivity within default mode and non-default mode regions, respectively. ASD symptoms correlated negatively with the connection strength of the default mode midline core—medial prefrontal cortex–posterior cingulate cortex. Network segregation analysis with the participation coefficient showed a higher area under the curve for the ASD group. Our findings demonstrate that the default mode network in ASD shows a pattern of poor segregation with both functional connectivity metrics. This study confirms the potential for the functional connection of the midline core as an endophenotype for social deficits. Poor segregation of the default mode network is consistent with an excitation/inhibition imbalance model of ASD.

Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals.

Humans easily and flexibly complete a wide variety of tasks. To accomplish this feat, the brain appears to subtly adjust stable brain networks. Here, we investigate what regional factors underlie these modifications, asking whether networks are either altered atxa0(1) regions activated by a given task or (2) hubs that interconnect different networks. We used fMRI functional connectivity (FC) to compare networks during rest and three distinct tasks requiring semantic judgments, mental rotation, and visual coherence. We found that network modifications during these tasks were independently associated with both regional activation and network hubs. Furthermore, active and hub regions were associated with distinct patterns of network modification (differing in their localization, topography of FC changes, and variability across tasks), with activated hubs exhibiting patterns consistent with task control. These findingsxa0indicate that task goals modify brain networks through two separate processes linked to local brain function and network hubs.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.

Coupling between spontaneous pupillary fluctuations and brain activity relates to inattentiveness

Autonomic activity in neurological and psychiatric disorders is often dysregulated, particularly in the context of attentional behaviors. This suggests that interplay between the autonomic nervous system and aspects of the central nervous system subserving attention may be disrupted in these conditions. Better understanding these interactions and their relationship with individual variation in attentional behaviors could facilitate development of mechanistic biomarkers. We identified brain regions defined by trait‐sensitive central–autonomic coupling as a first step in this process. As spontaneous neural activity measured during the resting state is sensitive to phenotypic variability, unconfounded by task performance, we examined whether spontaneous fluctuations in brain activity and an autonomic measure, pupil diameter, were coupled during the resting state, and whether that coupling predicted individual differences in attentional behavior. By employing concurrent pupillometry and fMRI during the resting state, we observed positive coupling in regions comprising cingulo‐opercular, default mode, and fronto‐parietal networks, as well as negative coupling with visual and sensorimotor regions. Individuals less prone to distractibility in everyday behavior demonstrated stronger positive coupling in cingulo‐opercular regions often associated with sympathetic activity. Overall, our results suggest that individuals less prone to distractibility have tighter intrinsic coordination between specific brain areas and autonomic systems, which may enable adaptive autonomic shifts in response to salient environmental cues. These results suggest that incorporating autonomic indices in resting‐state studies should be useful in the search for biomarkers for neurological and psychiatric disorders.

Re-emergence of modular brain networks in stroke recovery

Studies of stroke have identified local reorganization in perilesional tissue. However, because the brain is highly networked, strokes also broadly alter the brains global network organization. Here, we assess brain network structure longitudinally in adult stroke patients using resting state fMRI. The topology and boundaries of cortical regions remain grossly unchanged across recovery. In contrast, the modularity of brain systems i.e. the degree of integration within and segregation between networks, was significantly reduced sub-acutely (nxa0=xa0107), but partially recovered by 3 months (nxa0=xa085), and 1 year (nxa0=xa067). Importantly, network recovery correlated with recovery from language, spatial memory, and attention deficits, but not motor or visual deficits. Finally, in-depth single subject analyses were conducted using tools for visualization of changes in brain networks over time. This exploration indicated that changes in modularity during successful recovery reflect specific alterations in the relationships between different networks. For example, in a patient with left temporo-parietal stroke and severe aphasia, sub-acute loss of modularity reflected loss of association between frontal and temporo-parietal regions bi-hemispherically across multiple modules. These long-distance connections then returned over time, paralleling aphasia recovery. This work establishes the potential importance of normalization of large-scale modular brain systems in stroke recovery.