Faculty Bibliography

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.

In this pilot study, amygdala connectivity related to trauma symptoms was explored using resting-state functional magnetic resonance imaging (R-fMRI) in 23 healthy adolescents ages 13-17 years with no psychiatric diagnoses. Adolescents completed a self-report trauma symptom checklist and a R-fMRI scan. We examined the relationship of trauma symptoms to resting-state functional connectivity of the amygdala. Increasing self-report of trauma symptoms by adolescents was associated with increasing functional connectivity with the right amygdala and a local limbic cluster and decreasing functional connectivity with the amygdala and a long-range frontoparietal cluster to the left amygdala, which can be a hallmark of immaturity. These pilot findings in adolescents provide preliminary evidence that even mild trauma symptoms can be linked to the configuration of brain networks associated with the amygdala.

Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that "micro" head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD>0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics - particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.

The brain's intrinsic functional architecture, revealed in correlated spontaneous activity, appears to constitute a faithful representation of its repertoire of evoked, extrinsic functional interactions. Here, using broad task contrasts to probe evoked patterns of coactivation, we demonstrate tight coupling between the brain's intrinsic and extrinsic functional architectures for default and task-positive regions, but not for subcortical and limbic regions or for primary sensory and motor cortices. While strong correspondence likely reflects persistent or recurrent patterns of evoked coactivation, weak correspondence may exist for regions whose patterns of evoked functional interactions are more adaptive and context dependent. These findings were independent of task. For tight task contrasts (e.g., incongruent vs. congruent trials), evoked patterns of coactivation were unrelated to the intrinsic functional architecture, suggesting that high-level task demands are accommodated by context-specific modulations of functional interactions. We conclude that intrinsic approaches provide only a partial understanding of the brain's functional architecture. Appreciating the full repertoire of dynamic neural responses will continue to require task-based functional magnetic resonance imaging approaches.

The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6-85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

Introduction: Recent work has demonstrated head motion contributes to artifactual differences in resting-state fMRI (R-fMRI) measures (Power et al., 2012a;Satterthwaite et al., 2012;Van Dijk et al., 2012). Here we explored how a broad array of R-fMRIbased intrinsic brain function measures are affected by head motion, and how such sensitivities and their test-retest (TRT) reliabilities are impacted by various motion correction strategies. Methods: After preprocessing publicly released developmental, young adult and TRT datasets, the following strategies were applied to correct head motion effects: regressing out 6 head motion parameters (Traditional 6), regressing out autoregressive models (Friston et al., 1996) (Friston 24), regressing out voxelspecific head motion regressors (Voxel-Specific 12), and data scrubbing at framewise displacement (FD) > 0.2 or 0.5mm. We then explored head motion effects and TRT reliability on amplitude of low frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity, voxel-mirrored homotopic connectivity, and functional connectivity of medial prefrontal cortex. Results: As previously suggested, head motion effects are stronger in developmental than adult data (Fig. 1 vs. Fig. 2). Among the measures, fALFF is least affected by head motion. Among head motion correction strategies, scrubbing at FD > 0.2 mm (Power et al., 2012b) cleared the most motion effect while creating artificial head motion effect in fALFF due to destruction of temporal structure. Scrubbing at FD > 0.2mm also diminished TRT reliability dramatically (Fig. 3); some subjects varied markedly in the number of time points excluded across sessions (e.g., (Figure Presented) 150 vs. 37). Importantly, head motion effects remained after all correction strategies (Figs. 1, 2) suggesting taking subject head motion into account at the group level is still necessary. Regressing out mean FD slightly decreased TRT reliability but preserved its structure (Fig. 4). Conclusion: Results suggest that head motion effects extend to all metrics when studying hyperkinetic populations. We suggest caution when using stringent scrubbing (e.g. FD > 0.2mm as recommend by Power et al. 2012b), as test-retest reliability can be compromised and frequency metrics made immeasurable. Correction for inter-individual differences in motion at the grouplevel appears to be necessary regardless of individual subject correction strategy