High-order resting-state functional connectivity network for MCI classification: High-Order Correlation and FC Network

Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis

Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)

ObjectiveResting-state functional magnetic resonance imaging (rs-fMRI) has become an essential measure to investigate the human brain’s spontaneous activity and intrinsic functional connectivity. Several studies including our own previous work have shown that the brain controls the regulation of energy expenditure and food intake behavior. Accordingly, we expected different metabolic states to influence connectivity and activity patterns in neuronal networks.MethodsThe influence of hunger and satiety on rs-fMRI was investigated using three connectivity models (local connectivity, global connectivity and amplitude rs-fMRI signals). After extracting the connectivity parameters of 90 brain regions for each model, we used sequential forward floating selection strategy in conjunction with a linear support vector machine classifier and permutation tests to reveal which connectivity model differentiates best between metabolic states (hunger vs. satiety).ResultsWe found that the amplitude of rs-fMRI signals is slightly more precise than local and global connectivity models in order to detect resting brain changes during hunger and satiety with a classification accuracy of 81%.ConclusionThe amplitude of rs-fMRI signals serves as a suitable basis for machine learning based classification of brain activity. This opens up the possibility to apply this combination of algorithms to similar research questions, such as the characterization of brain states (e.g., sleep stages) or disease conditions (e.g., Alzheimer’s disease, minimal cognitive impairment)