4 research outputs found
Resting state functional connectivity correlates of inhibitory control in children with Attention-Deficit/Hyperactivity Disorder
No full textMotor inhibition is among the most commonly studied executive functions in Attention-Deficit/Hyperactivity Disorder (ADHD). Imaging studies using probes of motor inhibition such as the Stop Signal Task (SST) consistently demonstrate ADHD-related dysfunction within a right-hemisphere fronto-striatal network that includes inferior frontal gyrus and pre-supplementary motor area (pre-SMA). Beyond findings of focal hypo- or hyper-function, emerging models of ADHD psychopathology highlight disease-related changes in functional interactions between network components. Resting state fMRI (R-fMRI) approaches have emerged as powerful tools for mapping such interactions (i.e., resting state functional connectivity, RSFC), and for relating behavioral and diagnostic variables to network properties. We used R-fMRI data collected from 17 typically developing controls (TDC) and 17 age-matched children with ADHD (aged 8-13 years) to identify neural correlates of SST performance measured outside the scanner. We examined two related inhibition indices: stop signal reaction time (SSRT), indexing inhibitory speed, and stop signal delay (SSD), indexing inhibitory success. Using 11 fronto-striatal seed regions-of-interest, we queried the brain for relationships between RSFC and each performance index, as well as for interactions with diagnostic status. Both SSRT and SSD exhibited connectivity-behavior relationships independent of diagnosis. At the same time, we found differential connectivity-behavior relationships in children with ADHD relative to TDC. Our results demonstrate the utility of RSFC approaches for assessing brain/behavior relationships, and for identifying pathology-related differences in the contributions of neural circuits to cognition and behavior
Towards open sharing of task-based fMRI data: The OpenfMRI project
No full textThe large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function
A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets
No full textIn this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g. brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples including 1) quality control measures calculated from phantom data over time, 2) quality control data from human functional MRI data across various studies, scanners, sites, 3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e. data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger
The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry
No full textThe 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
