Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

S. Vidhusha (Department of Information Technology, Centre for Healthcare Technologies, SSN College of Engineering, India) and A. Kavitha (Department of Biomedical Engineering, Centre for Healthcare Technologies, SSN College of Engineering, India)
DOI: 10.4018/978-1-7998-3038-2.ch009
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Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.
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Autism is a brain disorder that involves multiple functional networks. Many researchers have indicated the variations present in the autistic individuals (Aghdam et al, 2018). While a growing number of studies indicate that functional connectivity analysis between selective brain regions can highlight pivotal differences in typical controls and autism patients, multiple studies indicate differences within the spectrum of autism (Asena, 2016). The autism spectrum is mainly categorized into low functioning and high functioning autism (DSM V, 2013). A few studies suggest strong connections in sub regions of the high functioning autistic brain with a deficiency in long range connectivity (Choi et al, 2015). According to (Deshpande et al, 2013) such differences might be explained by a combination of factors such as task-regression, field of view and low pass filtering.

Task based studies dominated functional neuroimaging till functional scans of resting subjects were acquired and the correlation of a seed (Wang, Y., 2014) defined in the frontal-parietal cortex with respect to the rest of the brain was computed. This revealed that even in the absence of a task, regions performing similar functions or regions that would be modulated by a task exhibit functional connectivity (Ding et al, 2006). Numerous studies have been experimented with EEG and MEG analysis that highlight variations between controls and autism subjects using brain signals (Blinowska, 2011).But, to intuitively signify the relative differences in the brain regions for low functioning and high functioning autistic individuals, has sparked an interest in analyzing the functional connectivity using resting state fMRI (rs-fMRI).

rs-fMRI studies have also confirmed the existence of a default mode network, that is particularly active during resting periods and whose activity diminishes while performing a task. This network has been found to include the posterior cingulate cortex (PCC), ventral medial prefrontal cortex (vMPFC), inferior parietal lobule (IPL), lateral temporal cortex (LTC), dorsal medial prefrontal cortex (dMPFC), and parahippocampal gyrus (PHC) (Dodel et al, 2005). Of these, posterior cingulate cortex, medial and lateral parietal cortex and medial prefrontal cortex were chosen (Torres, G., 2013) as the task negative regions (i.e. regions whose activity is lowered during the performance of an attention-demanding task) while from other fMRI studies (Friston et al., 1993), a set of task positive regions- intra parietal sulcus (IPS), frontal eye field (FEF) and middle temporal region (MT) were identified (Subbaraj, P. K et al, 2014) and from the connectivity based correlation and conjunction analyses (Goebel et al, 2003) these task positive and negative regions were found to be anti-correlated. The study focused on control subjects while also a comparison on the task positive and negative regions in autism subjects with those in the control has also been performed.

Functional connectivity provides a measure of temporal correlations between secluded physiological events (Kana et al, 2006). It is different from effective connectivity which depends on an apriori model for the cases of causal mode of relationship. Functional connectivity using fMRI (Chengaiyan, S., & Anandhan, K., 2015) was studied on the regions of motor cortex of resting state human brain using the relevance of product moment correlation of BOLD time courses. Functional connectivity measures can be tested for its implication and the reliability of functional connectivity among the nodes infers the way in which the integration of nodes (Van Den Heuvel et al, 2010) is allied in the functional network.

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