Functional and effective connectivity study of the human brain topology using a novel unifying framework - Rashid, Muhammad Hakimi Mohd (2023)
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.