Babatunde Adeyemo

Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations

The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.

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.

Decoding of MSTd population activity accounts for variations in the precision of heading perception.

Humans and monkeys use both vestibular and visual motion (optic flow) cues to discriminate their direction of self-motion during navigation. A striking property of heading perception from optic flow is that discrimination is most precise when subjects judge small variations in heading around straight ahead, whereas thresholds rise precipitously when subjects judge heading around an eccentric reference. We show that vestibular heading discrimination thresholds in both humans and macaques also show a consistent, but modest, dependence on reference direction. We used computational methods (Fisher information, maximum likelihood estimation, and population vector decoding) to show that population activity in area MSTd predicts the dependence of heading thresholds on reference eccentricity. This dependence arises because the tuning functions for most neurons have a steep slope for directions near straight forward. Our findings support the notion that population activity in extrastriate cortex limits the precision of both visual and vestibular heading perception.

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.

Individual Variability of the System-Level Organization of the Human Brain

Abstract Recent functional magnetic resonance imaging‐based resting‐state functional connectivity analyses of group average data have characterized large‐scale systems that represent a high level in the organizational hierarchy of the human brain. These systems are likely to vary spatially across individuals, even after anatomical alignment, but the characteristics of this variance are unknown. Here, we characterized large‐scale brain systems across two independent datasets of young adults. In these individuals, we were able to identify brain systems that were similar to those described in the group average, and we observed that individuals had consistent topological arrangement of the system features present in the group average. However, the size of system features varied across individuals in systematic ways, such that expansion of one feature of a given system predicted expansion of other parts of the system. Individual‐specific systems also contained unique topological features not present in group average systems; some of these features were consistent across a minority of individuals. These effects were observed even after controlling for data quality and for the accuracy of anatomical registration. The variability characterized here has important implications for cognitive neuroscience investigations, which often assume the functional equivalence of aligned brain regions across individuals.

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.

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Highlights • SVMs classified 6 versus 12 month-old infants above chance based on fcMRI data alone.• We carefully accounted for the effects of fcMRI motion artifact.• These results coincide with a period of dramatic change in infant development.• Two interpretations about connections supporting this age categorization are given.

Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

Abstract Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method – support vector machine (SVM) classification – to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8–15 yrs) and 42 unaffected controls (age, IQ, in‐scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

Loss of white matter integrity reflects tau accumulation in Alzheimer disease defined regions

Objective White matter (WM) projections were assessed from Alzheimer disease (AD) gray matter regions associated with β-amyloid (Aβ), tau, or neurodegeneration to ascertain relationship between WM structural integrity with Aβ and/or tau deposition. Methods Participants underwent diffusion tensor imaging (DTI), PET Aβ ([18F]AV-45 [florbetapir]), and PET tau ([18F]AV-1451 [flortaucipir]) imaging. Probabilistic WM summary and individual tracts were created from either a composite or individual gray matter seed regions derived from Aβ, tau, and neurodegeneration. Linear regressions were performed for Aβ, age, tau and WM hyperintensities (WMH) to predict mean diffusivity (MD) or fractional anisotropy (FA) from the corresponding WM summaries or tracts. Results Our cohort was composed of 59 cognitively normal participants and 10 cognitively impaired individuals. Aβ was not associated with DTI metrics in WM summary or individual tracts. Age and WMH strongly predicted MD and FA in several WM regions, with tau a significant predictor of MD only in the anterior temporal WM. Conclusion Tau, not Aβ, was associated with changes in anterior temporal WM integrity. WMH, a proxy for vascular damage, was strongly associated with axonal damage, but tau independently contributed to the model, suggesting an additional degenerative mechanism within tracts projecting from regions vulnerable to AD pathology. WM decline was associated with early tau accumulation, and further decline may reflect tau propagation in more advanced stages of AD.

Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles for brain MRI. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, this estimation may be corrupted by factitious effects owing to main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and a comparison ‘single-shot’ dataset from Oregon Health & Science University. We show unequivocally that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with degraded quality of functional MRI. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects. Subsequently, we demonstrate that utilizing this filter improves post-processing data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.