This dissertation concerns the alteration of functional connectivity in the human brain through different patterns of somatosensory stimulation. In particular, I distinguish whether stimuli are regular (i.e., expected by the subject) or irregular (i.e., unexpected by the subject). An emerging theory of brain function known as Predictive Coding states that the brain is continuously creating an internal model of its environment that is constantly trying to predict what is going to happen. Expected sensory input leads to model consol- idation, while unexpected input leads to model update. In this context it is assumed that central neuronal processing differs significantly between these two cases. Furthermore, in my experiments, the stimulation is applied in two more variants which are also believed to be processed in completely different ways: consciously perceptible (suprathreshold) and imperceptible (subthreshold).
To measure functional connectivity in the acquired fMRI data, a method referred to as eigenvector centrality mapping (ECM) was chosen. This method has gained increasing attention in the fMRI community, as it represents a whole-brain approach that can be ap- plied for resting-state experiments. While there are a number of other centrality measures, each with their advantages and disadvantages, ECM stands out as being parameter-free and does not depend on prior assumptions. Similar to Google’s Pagerank algorithm, it assigns areas (“nodes”) in a network with a high centrality score that are closely connected to other central areas as well. Generally, increased connectivity is interpreted as of greater “importance” to the network. As there are different approaches on how to calculate ECM, I critically examine these and delve deeper into the method itself.
Three main research questions guided this study:
1. Is there a brain connectivity (ECM) alteration in the human brain for somatosensory stimulation that is pattern dependent (7 Hz irregular vs. regular)?
2. Is there a brain connectivity (ECM) alteration in the human brain for somatosensory stimulation that is intensity dependent (7 Hz suprathreshold vs. subthreshold)?
3. Are these different somatosensory stimulations (subthreshold, suprathreshold, irregular, regular) accompanied by a subsequent behavioral change?
Two experiments were conducted. In Experiment 1, participants were exposed to all four stimulation conditions consecutively. Results showed significant ECM alterations compared to the initial baseline, suggesting persisting effects. To counter this, Experiment 2 adopted a different approach. Here, individual stimulations were applied to separate groups, with an additional control group for comparison.
The results from Experiment 2 revealed that irregular stimulation compared to regular showed decreased connectivity in specific brain regions, aligning with the Predictive Coding theory. Suprathreshold stimulation showed increased connectivity in areas related to sensory input integration, possibly linked to conscious perception. Furthermore, all participants, regardless of stimulation type, showed heightened connectivity in somatosensory regions, suggesting a shared focus on tactile anticipation.
The behavioral session from Experiment 2 found that irregular suprathreshold stimulation led to a decreased sensitivity to near-threshold stimuli. However, this change wasn't mirrored in the functional connectivity data.
In conclusion, this research validated the differential neural processing of various somatosensory stimulations, supporting the Predictive Coding theory. The study also underscored the challenges and considerations in using ECM, particularly urging caution with methods that combine positive and negative correlations.:1 - Personal Motivation
2 - Introduction
3 - Background
3.1 Cognitive Neuroscience
3.2 Predictive Coding and the Free Energy Principle
3.3 Functional Magnetic Resonance Imaging (fMRI)
3.4 Blood-Oxygen Level Dependent (BOLD) Signal
3.5 Resting-State Functional Connectivity (RSFC)
3.6 Eigenvector Centrality Mapping (ECM)
3.7 Previous ECM Experiments - an Overview
3.8 Statistical Remarks
4 - Materials and Methods
4.1 Experimental Setup
4.1.1 Subjects
4.1.2 Experimental Procedures
4.1.2.1 Electrical stimulation
4.1.2.2 Absolute detection threshold examination
4.1.2.3 fMRI and behavioral data acquisition
4.2 fMRI Data Preprocessing
4.2.1 Prior Steps
4.2.2 Slice Time Correction
4.2.3 Motion Correction
4.2.4 Coregistration, Segmentation and Normalization
4.2.5 Spatial Filtering
4.2.6 Temporal Filtering
4.2.7 Grey Matter, White Matter and Cerebrospinal Fluid Masking
4.2.8 Nuisance Regression
4.3 ECM Approaches
4.4 Flexible Factorial Design
4.5 Seed-Based Functional Connectivity Analysis
5 - Results
5.1 Experiment 1
5.1.1 Detailed Results
5.1.2 Summary Experiment 1
5.2 Experiment 2 - fMRI Session
5.2.1 Detailed Results - ADD Approach
5.2.2 Detailed REsults - POS / NEG / ABS approach
5.2.3 Summary Experiment 2
5.3 ECM Approach Differences Illustrated by Examples
5.4 Seed-Based Functional Connectivity Analysis
5.5 Distribution of Voxel Timie Series Correlation Foefficients
5.6 Experiment 2 - Behavioral Session
6 - Discussion
6.1 Interpretation of Experimental Results
6.1.1 Experiment 1
6.1.2 Experimemnt 2 - fMRI Session
6.1.3 Experiment 2 - Behavioral Session
6.2 ECM Approaches
6.3 Further Considerations and Future Outlook
7 - Conclusion
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87193 |
| Date | 25 September 2023 |
| Creators | Witt, Jonas |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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