RESUMO
About 50% of subjects infected with HIV present deficits in cognitive domains, which are known collectively as HIV associated neurocognitive disorder (HAND). The underlying synaptodendritic damage can be captured using resting state functional MRI, as has been demonstrated by a few earlier studies. Such damage may induce topological changes of brain connectivity networks. We test this hypothesis by capturing the functional interdependence of 90 brain network nodes using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized Radial Basis function (GRBF) neural networks. The network nodes are selected based on the regions defined in the Automated Anatomic Labeling (AAL) atlas. Each node is represented by the average time series of the voxels of that region. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We tested for differences in these properties in network graphs obtained for 10 subjects (6 male and 4 female, 5 HIV+ and 5 HIV-). Global network properties captured some differences between these subject cohorts, though significant differences were seen only with the clustering coefficient measure. Local network properties, such as local efficiency and the degree of connections, captured significant differences in regions of the frontal lobe, precentral and cingulate cortex amongst a few others. These results suggest that our method can be used to effectively capture differences occurring in brain network connectivity properties revealed by resting-state functional MRI in neurological disease states, such as HAND.
RESUMO
The use of functional Magnetic Resonance Imaging (fMRI) has provided interesting insights into our understanding of the brain. In clinical setups these scans have been used to detect and study changes in the brain network properties in various neurological disorders. A large percentage of subjects infected with HIV present cognitive deficits, which are known as HIV associated neurocognitive disorder (HAND). In this study we propose to use our novel technique named Mutual Connectivity Analysis (MCA) to detect differences in brain networks in subjects with and without HIV infection. Resting state functional MRI scans acquired from 10 subjects (5 HIV+ and 5 HIV-) were subject to standard pre-processing routines. Subsequently, the average time-series for each brain region of the Automated Anatomic Labeling (AAL) atlas are extracted and used with the MCA framework to obtain a graph characterizing the interactions between them. The network graphs obtained for different subjects are then compared using Network-Based Statistics (NBS), which is an approach to detect differences between graphs edges while controlling for the family-wise error rate when mass univariate testing is performed. Applying this approach on the graphs obtained yields a single network encompassing 42 nodes and 65 edges, which is significantly different between the two subject groups. Specifically connections to the regions in and around the basal ganglia are significantly decreased. Also some nodes corresponding to the posterior cingulate cortex are affected. These results are inline with our current understanding of pathophysiological mechanisms of HIV associated neurocognitive disease (HAND) and other HIV based fMRI connectivity studies. Hence, we illustrate the applicability of our novel approach with network-based statistics in a clinical case-control study to detect differences connectivity patterns.