Studies

The Rockland Sample is currently comprised of data from four studies:

Cross-Sectional Lifespan Connectomics Study (Discovery)

Faced with the challenge of charting brain function and the origins of mental illness across the lifespan, the functional neuroimaging community is following the precedent set by molecular genetics in turning to discovery science. Once a distant goal, the 1000 Functional Connectomes Project (FCP) demonstrated the feasibility of conducting discovery science for human brain function. Capitalizing on the ease of data-sharing with resting state fMRI (R-fMRI), the FCP pooled datasets from over 1200 individuals independently collected at sites throughout the world. The FCP dataset immediately demonstrated the power of large-scale R-fMRI investigations, revealing widespread differences in the brain’s intrinsic functional architecture related to sex and age that are not easily detected with typical sample sizes (e.g., n = 20-100). The FCP effort represented the inauguration of effective, open data-sharing, with researchers who once struggled to obtain 20-30 datasets for analyses suddenly granted access to over 1200 datasets for methods innovation and data mining. Building on the initial success of the FCP, we recently called for the establishment of prospective data-sharing, using the pilot NKI-Rockland Sample, a phenotypically rich, life-span sample, as our prototype. With more than 300 phenotypic variables obtained across 26 psychiatric, behavioral, and cognitive assessment tools, the NKI- Rockland Sample exemplifies they type of data collection model ideally suited to allow neuroimaging methods to employ the data mining and discovery approach applied successfully in molecular genetics. Neuroimagers can now identify brain-behavior relationships and delineate their dynamic trajectories across the lifespan. Having established the feasibility of generating phenotypically rich datasets and openly sharing them with the scientific community on a prospective basis, the proposed work aims to generate a more carefully constructed lifespan sample, larger in scale, maximally representative of the community and sampled using appropriate strategies for the delineation of normative trajectories for metrics of the brain’s intrinsic functional architecture. Specifically, we propose to recruit 1000 participants (ages 6-85) over a four-year period, densely sampling early developmental and advanced aging periods, where age-related gradients of changes are maximal and model-fitting techniques are most prone to artifactual results. Recruitment and enrollment strategies will be carefully controlled to maximize the community representativeness of the sample and minimize biases commonly encountered with opportunistic recruiting. Seventy percent of the collected datasets will be randomly selected for discovery, while the remaining 30% will serve to rigorously test hypotheses generated during the discovery phase. Consistent with the model established for the pilot NKI-Rockland Sample, data generated as part of this proposal will be shared prospectively, on a weekly basis, via the FCP and the associated International Neuroimaging Data-sharing Initiative (INDI).

Longitudinal Developmental Connectomics Study (Longitudinal_Child)

Recent advances in magnetic resonance imaging (MRI) now allow the reliable mapping of functional and structural maturation in the human brain to derive analogs of height and weight growth charts. Such norms would allow early detection of pathologic process before clinically significant symptomatology onsets. Unfortunately, the datasets needed to develop comprehensive growth curves for brain function and structure do not yet exist, as technology has outpaced the collection of high quality longitudinal imaging data. Here, we propose to generate and share a large-scale, community-ascertained, structured multi-cohort, longitudinal sample from ages 6.0-20.5 yrs. This open access resource will allow the developmental trajectories of brain function and structure, as well as relationships with phenotypic measures to be delineated. We will generate at least 192 quality-controlled longitudinal datasets, evenly divided across 12 one-year age cohorts (6.0-17.9 yrs old at enrollment). Each dataset will contain 3 time-points separated by 15 months, with time-of-day and menstrual cycle phase controlled. State-of-the-art multiband imaging will be used to collect resting state functional MRI (R-fMRI) scans alternately optimized for temporal and spatial resolution. Multiband imaging will also enable the acquisition of 137-direction diffusion tensor imaging (DTI) in less than 7 minutes – thereby minimizing motion confounds. Arterial spin labeling perfusion measures will also provide additional quantitative information. Dimensional cognitive, behavioral, psychiatric, endocrine, immunologic, and metabolic phenotyping will be included. Genetic samples will be obtained and shared via the NIMH Genetics Repository. Given the paucity of data regarding the reliability of imaging measures in pediatric populations, we will also obtain retest scans for each participant within 2 weeks of their initial scan session. Careful determination of reliability is a prerequisite for determination of eventual clinical applicability, and especially pertinent in pediatric populations where risk of artifact and physiological variations are greatest. The proposed work builds on the Nathan Kline Institute-Rockland Sample (NKI-RS), a cross-sectional sample of brain development, maturation and aging spanning ages 6-85 years, currently funded to recruit and assess 1000 community-ascertained participants using multiband imaging-based R-fMRI, DTI, and deep phenoytyping. Building upon the NKI-RS effort will minimize startup costs, infrastructure and design needs, and maximize the focus on follow-up data collection. Consistent with the NKI-RS protocol, anonymized datasets will be shared weekly on a prospective (pre-publication) basis via The 1000 Functional Connectomes Project (FCP)/ International Neuroimaging Data-sharing Initiative (INDI) (www.nitrc.org). Additionally, any analysis methods and software developed in the course of the project will be made fully available on publication.

Real-Time Neurofeedback (Neurofeedback)

The default network (DN) is a distributed pattern of brain regions associated with spontaneous cognition, internalized thought and emotional regulation that are consistently deactivated during the performance of goal- driven cognitive tasks. Failure to appropriately activate or deactivate the DN during performing of cognitive tasks is increasingly being implicated in psychiatric illness, with little specificity regarding disorderor attention to symptom domain. Further challenges arise from limitations of task-based and resting state functional MRI imaging approaches that have left the field with little insight into te nature of DN dysregulation (i.e. inability to modulate DN activity as opposed to the tendency to do so) in the various disorders. Consistent with the Research Domain Criteria Project (R-DoC), the proposed work capitalizes on recent innovations in real-time fMRI (RT-fMRI) based neurofeedback to provide a dimensional profile of DN regulation that can be linked to cognitive and psychiatric phenotyping profiles, as well as underlying brain architecture. Specifically, we propose a multi-faceted imaging study that characterizes DN regulation using a combination of neurofeedback RT-fMRI, to assess an individual’s ability to modulate the DN, and task-based fMRI activation and deactivation (i.e., the self-referential processing task and the multi-source interference, respectively) to assess an individuals tendency to modulate the DN. Consistent with the “agnostic” approach promoted by R- DoC, we focus on a community-ascertained sample of 180 adults (ages: 25-40 years old), using minimally restrictive psychiatric exclusion criteria. The comprehensive phenotyping protocol established by the Nathan Kline Institute Rockland Sample (NKI-RS) will be used to characterize a range of psychiatric and cognitive domains. Successful completion of the proposed work will serve to: 1) Establish the relationship between DN modulation capacity as measured by RT-fMRI and DN modulation tendency as measured by task-related DN activation and deactivations, 2) link multidimensional imaging-based DN modulation and phenotypic profiles, and 3) link multidimensional DN modulation profiles to the brain’s functional and structural architecture, as assessed by resting state fMRI and diffusion tensor imaging.

Adult Longitudinal Study (Longitudinal_Adult)

Having established the feasibility of generating phenotypically rich datasets and openly sharing them with the scientific community on a prospective basis though efforts of the Nathan Kline Institute-Rockland Sample (NKI-RS) program, the proposed work aims to generate a carefully constructed 4-year longitudinal sample of over 232 high-quality data sets between enrollment ages 38-71.9. We anticipate no more than 437 participants will be required at enrollment to yield this number of high-quality complete data sets. Through a high-quality phenotypic characterization protocol including gold standard cardiovascular fitness (CF) evaluation with advanced neuroimaging, we propose to (1) Delineate aging trajectories of brain functional and structural architectures, (2) Test for the relationship between CF and brain aging, and (3) Test for relationships between aging trajectories of brain functional and structural architectures and other phenotypic indices. Consistent with the NKI-RS model, all data generated through the proposed work will be openly shared pre-publication through HIPAA-compliant platforms including the Collaborative Informatics and Neuroimaging Suite (COINS) as well as the 1000 Functional Connectomes Project.