Publication: Functional and effective connectivity study of the human brain topology using a novel unifying framework
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Date
2023-01
Authors
Rashid, Muhammad Hakimi Mohd
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Abstract
There has been a rapid expansion of neuroscientific research employing brain connectivity
analysis. Among these are studies unravelling the neural mechanisms of brain
diseases and treatments. Of interest, the therapeutic effects of various auditory stimulus
have been demonstrated in several studies. However, neural mechanisms of these
therapy remain elusive. In parallel, many new connectivity measures have been developed,
adding to the ever-growing connectivity tools. Attempts have been made to
classify them according to different taxonomies. To date, however, no general framework
has been developed to unify these measures. Thus, this study aimed to build a
unifying framework of various connectivity measures. The study also aimed to build
a novel connectivity measure using the framework as a template and then compared it
with other established measures on an open-source dataset. The study also sought to
introduce a statistical approach for testing both the absence and the presence of experimental
effects, which was then used to investigate the effects of listening to several auditory
stimuli on emotion-processing brain network topology. A unifying framework
was devised in the language of category theory by identifying common underlying
structures shared among connectivity measures and assembling them into a single category
called connectivity theory. A novel connectivity measure called the complexity
of the amplitude envelope correlation (CAEC) was developed. Functional connectivity
among brain regions involved in face processing was estimated using CAEC,
and two network measures were derived: transitivity and global efficiency. Both network measures were compared with the network measures derived using established
connectivity measures: amplitude envelope correlation (AEC) and imaginary part of
coherency (ICOH). Additionally, a cross-over study investigating the effects of 8 different
auditory stimuli on emotion-processing network topology in 30 healthy subjects
was conducted. Three network measures were used: mean weighted degree, transitivity
and global efficiency. The posterior distribution of differences between the
resting state and each stimulus was estimated to test for the absence and presence of
effects. Equivalence region was defined as differences in network measures between
pre- and post-stimuli resting MEG.Within the proposed framework, connectivity measures
were shown to be models of connectivity theory. The first experiment showed
that CAEC produced a different network topology compared to AEC and ICOH for the
same stimulus. Overall, the effects of experimental manipulations on network topology
were shown to be dependent on the connectivity measure used. In the second
experiment, the network measures across all auditory stimuli were equivalent to that
of the resting state in all frequency bands. The categorical framework unifies connectivity
measures and provides a template for developing a novel connectivity measure,
thus illuminating further work on constructing new connectivity measures. The first
experiment indicated that connectivity analysis results should be interpreted based on
the utilised connectivity measure. As shown in the second experiment, the novel statistical
approach was able to test for the absence of experimental effects. This approach
would be valuable in expanding the direction of future neuroimaging studies.