As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

Understanding how risk genes cumulatively impair brain function in schizophrenia could provide critical insights into its pathophysiology. Working memory impairment in schizophrenia has been associated with abnormal dopamine signaling in the prefrontal cortex, which is likely under complex genetic control. The catechol-O-methyltransferase (COMT) 158Val 3 Met polymorphism (rs4680), which affects the availability of prefrontal dopamine signaling, consistently stratifies prefrontal activation during working memory performance. However, the low-dopamine COMT 158Val allele does not confer increased risk for schizophrenia, and its effects on prefrontal function are not specific to the disorder. In the setting of other genetic variants influencing prefrontal dopamine signaling, COMT 158Val 3 Met genotype may exert disease-specific effects. A second polymorphism, methylenetetrahydrofolate reductase (MTHFR) 677C 3 T (rs1801133), has been associated with overall schizophrenia risk and executive function impairment in patients, and may influence dopamine signaling through mechanisms upstream of COMT effects. We found that the hypofunctional 677T variant was associated with decreased working memory load-dependent activation in the prefrontal and insular cortices in 79 schizophrenia patients, but not in 75 demographically matched healthy controls. Further, significant MTHFR ؋ COMT genotype interactions were observed, which differed by diagnostic group: Reduced prefrontal activation was associated with the 677T and 158Val alleles in patients, but with 677C/C and 158Met/Met genotype in controls. These findings are consistent with epistatic effects of the COMT and MTHFR polymorphisms on prefrontal dopamine signaling, and suggest that in schizophrenia patients, the MTHFR 677T allele exacerbates prefrontal dopamine deficiency. The findings also suggest the importance of weighing COMT effects on prefrontal function within the context of MTHFR genotype.dopamine ͉ folate ͉ functional MRI ͉ genetics ͉ methylation

A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining.

Single task analysis methods of functional MRI brain data, though useful, are not able to evaluate the joint information between tasks. Data fusion of multiple tasks that probe different cognitive processes provides knowledge of the joint information and may be important in order to better understand the complex disorders such as schizophrenia. In this article, we introduce a simple but effective technique to fuse two tasks by computing the histogram of correlations for all possible combinations of whole brain voxels. The approach was applied to data derived from healthy controls and patients with schizophrenia from four different tasks, auditory oddball (target), auditory oddball (novel), Sternberg working memory, and sensorimotor. It was found that in four out of six task combinations patients’ intertask correlations were more positively correlated than controls’, in one combination the controls showed more positive correlations and in another there was no significant difference. The robustness of this result was checked with several testing techniques. The four task combinations for which patients had more positive correlation occurred at different scanning sessions and the task combination that showed the opposite result occurred within the same scanning session. Brain regions that showed high intertask correlations were found for both groups and regions that correlated differently between the two groups were identified. The approach introduced finds interesting results and new differential features that cannot be achieved through traditional methods.

In 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 from over 10,000 data sets 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.