Andreotti, Jennifer (2014). The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging (Unpublished). (Dissertation, Faculty of Medicine)
Full text not available from this repository.The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions.
The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks.
To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.
Item Type: |
Thesis (Dissertation) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy > Psychiatric Neurophysiology [discontinued] 04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy 04 Faculty of Medicine > University Psychiatric Services |
UniBE Contributor: |
Andreotti, Jennifer |
Subjects: |
100 Philosophy > 150 Psychology 600 Technology > 610 Medicine & health |
Language: |
English |
Submitter: |
Jennifer Andreotti |
Date Deposited: |
13 Mar 2015 13:18 |
Last Modified: |
05 Dec 2022 14:41 |
URI: |
https://boris.unibe.ch/id/eprint/64015 |
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