Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI
ABSTRACT Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between functional and effective connectivity. To attain this goal, we proposed a novel approach based on the partial correlation matrix. In this study, we further validate the use of partial correlation analysis by employing a large-scale, neurobiologically realistic neural network model to generate simulated data that we analyze with both structural equation modeling (SEM) and the partial correlation approach. Unlike real experimental data, where the interregional anatomical links are not necessarily known, the links between the nodes of the network model are fully specified, and thus provide a standard against which to judge the results of SEM and partial correlation analyses. Our results show that partial correlation analysis from the data alone exhibits patterns of effective connectivity that are similar to those found using SEM, and both are in agreement with respect to the underlying neuroarchitecture. Our findings thus provide a strong validation for the partial correlation method.
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ABSTRACT: Many psychiatric disorders are associated with abnormal resting-state functional connectivity between pairs of brain regions, although it remains unclear whether the fault resides within the pair of regions themselves or other regions connected to them. Identifying the source of dysfunction is crucial for understanding the etiology of different disorders. Using pathway- and network-based techniques to analyze resting-state functional magnetic imaging data from a large population of patients with attention deficit hyperactivity disorder (239 patients, 251 controls), major depression (39 patients, 37 controls), and schizophrenia (69 patients, 62 controls), we show for the first time that only network-based cross-correlation identifies significant functional connectivity changes in all 3 disorders which survive correction. This demonstrates that the primary source of dysfunction resides not in the regional pairs themselves but in their external connections. Combining pathway and network-based functional-connectivity analysis, we established that, in all 3 disorders, the counterparts of pairs of regions in the opposite hemisphere contribute 60-76% to altered functional connectivity, compared with only 17-21% from the regions themselves. Thus, a transdiagnostic feature is of abnormal functional connectivity between brain regions produced via their contralateral counterparts. Our results demonstrate an important role for contralateral counterpart regions in contributing to altered regional connectivity in psychiatric disorders.Cerebral Cortex 08/2014; DOI:10.1093/cercor/bhu173 · 8.31 Impact Factor
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ABSTRACT: Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system. How to choose the right tuning parameter is a fundamental and difficult problem in learning the sparse system. In this paper, by treating the the tuning parameter as an additional dimension, persistent homological structures over the parameter space is introduced and explored. The structures are then further exploited in speeding up the computation using the proposed soft-thresholding technique. The topological structures are further used as multivariate features in the tensor-based morphometry (TBM) in characterizing white matter alterations in children who have experienced severe early life stress and maltreatment. These analyses reveal that stress-exposed children exhibit more diffuse anatomical organization across the whole white matter region.08/2014; DOI:10.1109/TMI.2015.2416271