Biology subjects

Motlaghian, S.

Publications and source records attributed to Motlaghian, S..

3 recordsLinked to original sources

bioRxiv2024

Explicitly Nonlinear Connectivity-Matrix Independent Component Analysis in Resting fMRI Data

Independent component analysis (ICA) is a widely used data-driven technique for investigating brain structure and function to extract intrinsic networks. However, the ability of ICA, a linear mixing model, to capture nonlinear relationships is inherently limited. While nonlinear ICA can be used to estimate nonlinear+ linear mixtures, it can be useful to study the degree to which there is nonlinearity above and beyond the widely studied linear resting networks. Here, we propose a way to divide the data into sources exhibiting linear-only or explicitly nonlinear dependencies in resting functional magnetic resonance imaging (fMRI) data. Such an approach can be very informative as it allows us to evaluate the degree to which a given network might be linear, nonlinear, or both linear and nonlinear. Here, we present an enhanced connectivity-domain ICA approach, connectivity-matrix ICA, incorporating normalized mutual information (NMI) after canceling the linear effects to measure explicitly nonlinear (EN) relationships within voxel connectivity. This integration enables the identification of brain spatial maps that exhibit pronounced explicitly nonlinear dependencies while excluding linear relationships. By eliminating linear dependencies and utilizing NMI, we discover highly structured resting networks that conventional functional connectivity methods would typically overlook. The results indicate that several maps show only linear or EN relationships, and the rest of the components display both linear and nonlinear patterns. We categorized these maps as linear-only, EN-only, and linear-EN maps. We also evaluate differences in the identified networks in a schizophrenia dataset. A significant global difference has been discovered between schizophrenia and controls in some linear-EN maps, such as the frontal lobe. Moreover, the temporal lobe and thalamus display linear group differences, while the visual and motor cortex display global differences in nonlinear relationships as their primary driver of these disparities. In sum, our findings emphasize the significance of accounting for explicitly nonlinear dependencies in functional connectivity analysis and demonstrate the effectiveness of the extended cmICA approach in revealing previously unrecognized brain dynamics.

neuroscience↗

bioRxiv2022

Explicitly Nonlinear Dynamic Functional Network Connectivity In Resting-State fMRI Data

Most dynamic functional connectivity in fMRI data is focused on linear correlations, and to our knowledge, no study has studied whole brain explicitly nonlinear dynamic relationships within the data. While some approaches have attempted to study overall connectivity more generally using flexible models, we are particularly interested in whether the non-linear relationships, above and beyond linear, are capturing unique information. This study thus proposes an approach to assess the explicitly nonlinear dynamic functional network connectivity derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information. Simulations showed the proposed method accurately estimated NMI over time, even within relatively short windows of data. Results on fMRI data included 151 schizophrenia patients, and 163 healthy controls showed three unique, highly structured, mostly long-range, functional states that also showed significant group differences. This analysis identifies a higher level of explicitly nonlinear dependencies in transient connectivity within the visual network in healthy controls compared to schizophrenia patients. In particular, nonlinear relationships tend to be more widespread than linear ones. We also find highly significant differences in the relative co-occurrence of linear and explicitly nonlinear states in HC and SZ, suggesting these may be an important aspect of the disorder. Overall, this work suggests that quantifying nonlinear dependencies of dynamic functional connectivity may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.

neuroscience↗

bioRxiv2021

Nonlinear Functional Network Connectivity In Resting fMRI Data

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (MI), that calculates the nonlinear correlation between different brain regions. We demonstrate our proposed approach using simulated data, then apply it to a dataset previously studied in (Damaraju et al., 2014). This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in nonlinear dependencies between schizophrenia patients and healthy controls particularly in visual cortex, with controls showing more nonlinearity in most cases. Certain domains, including cognitive control, and default mode, appeared much less nonlinear, whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Further, we propose a method that captures both linear and nonlinear effects in a boosted approach. This method increases the sensitivity to group differences in comparison to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

neuroscience↗