Faculty Bibliography

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

Caregiver maltreatment induces vulnerability to later-life psychopathology. Clinical and preclinical evidence suggest changes in prefrontal and limbic circuitry underlie this susceptibility. We examined this question using a rat model of maternal maltreatment and methods translated from humans, resting-state functional magnetic resonance imaging (R-fMRI). Rat pups were reared by mothers provided with insufficient or abundant bedding for nest building from postnatal (PN) days 8 to 12 and underwent behavioral assessments of affect-related behaviors (forced swim, sucrose preference and social interaction) in adolescence (PN45) and early adulthood (PN60). R-fMRI sessions were conducted under light anesthesia at both ages. Offspring reared with insufficient bedding (that is, maltreated) displayed enduring negative affective behaviors. Amygdala-prefrontal cortex (PFC) functional connectivity increased significantly from adolescence to adulthood in controls, but not in maltreated animals. We computed the fractional amplitude of low-frequency fluctuations (fALFF), an index of intrinsic brain activity, and found that fALFF in medial prefrontal cortex and anterior cingulate cortex (MPFC/ACC) increased significantly with age in controls but remained unchanged in maltreated animals during adolescence and adulthood. We used a seed-based analysis to explore changes in functional connectivity between this region and the whole brain. Compared with controls, maltreated animals demonstrated reduced functional connectivity between MPFC/ACC and left caudate/putamen across both ages. Functional connectivity between MPFC/ACC and right caudate/putamen showed a group by age interaction: decreased in controls but increased in maltreated animals. These data suggest that maltreatment induces vulnerability to psychopathology and is associated with differential developmental trajectories of prefrontal and subcortical circuits underlying affect regulation.

Connectomics has enhanced our understanding of neurocognitive development and decline by the integration of network sciences into studies across different stages of the human life span. However, these studies commonly occurred independently, missing the opportunity to test integrated models of the dynamical brain organization across the entire life span. In this review article, we survey empirical findings in life-span connectomics and propose a generative framework for computationally modeling the connectome over the human life span. This framework highlights initial findings that across the life span, the human connectome gradually shifts from an 'anatomically driven' organization to one that is more 'topological'. Finally, we consider recent advances that are promising to provide an integrative and systems perspective of human brain plasticity as well as underscore the pitfalls and challenges.

Impairment in social cognition, including emotion recognition, has been extensively studied in both Autism Spectrum Disorders (ASD) and Schizophrenia (SZ). However, the relative patterns of deficit between disorders have been studied to a lesser degree. Here, we applied a social cognition battery incorporating both auditory (AER) and visual (VER) emotion recognition measures to a group of 19 high-functioning individuals with ASD relative to 92 individuals with SZ, and 73 healthy control adult participants. We examined group differences and correlates of basic auditory processing and processing speed. Individuals with SZ were impaired in both AER and VER while ASD individuals were impaired in VER only. In contrast to SZ participants, those with ASD showed intact basic auditory function. Our finding of a dissociation between AER and VER deficits in ASD relative to Sz support modality-specific theories of emotion recognition dysfunction. Future studies should focus on visual system-specific contributions to social cognitive impairment in ASD.

Alteration in self-perception is a salient feature in major depression. Hyperactivity of anterior cortical midline regions has been implicated in this phenomenon in depressed adults. Here, we extend this work to depressed adolescents during a developmental time when neuronal circuitry underlying the sense of self matures by using task-based functional magnetic resonance imaging (fMRI) and connectivity analyses. Twenty-three depressed adolescents and 18 healthy controls (HC) viewed positive and negative trait words in a scanner and judged whether each word described them ('self' condition) or was a good trait to have ('general' condition). Self-perception scores were based on participants' endorsements of positive and negative traits during the fMRI task. Depressed adolescents exhibited more negative self-perceptions than HC. Both groups activated cortical midline regions in response to self-judgments compared to general-judgments. However, depressed adolescents recruited the posterior cingulate cortex/precuneus more for positive self-judgments. Additionally, local connectivity of the dorsal medial prefrontal cortex was reduced during self-reflection in depressed adolescents. Our findings highlight differences in self-referential processing network function between depressed and healthy adolescents and support the need for further investigation of brain mechanisms associated with the self, as they may be paramount to understanding the etiology and development of major depressive disorder.

Transcranial magnetic stimulation (TMS) is a powerful investigational tool for in vivo manipulation of regional or network activity, with a growing number of potential clinical applications. Unfortunately, the vast majority of targeting strategies remain limited by their reliance on non-realistic brain models and assumptions that anatomo-functional relationships are 1:1. Here, we present an integrated framework that combines anatomically realistic finite element models of the human head with resting functional MRI to predict functional networks targeted via TMS at a given coil location and orientation. Using data from the Human Connectome Project, we provide an example implementation focused on dorsolateral prefrontal cortex (DLPFC). Three distinct DLPFC stimulation zones were identified, differing with respect to the network to be affected (default, frontoparietal) and sensitivity to coil orientation. Network profiles generated for DLPFC targets previously published for treating depression revealed substantial variability across studies, highlighting a potentially critical technical issue.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals' resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.