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Methods for the analysis and characterization of brain morphology from MRI images

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posted on 2024-09-03, 00:24 authored by Irene Brusini

Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure.

The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats’ aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival.

Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient’s level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer’s patients) that were characterized by different levels of hippocampal atrophy.

In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets.

List of scientific papers

I. Changes in brain architecture are consistent with altered fear processing in domestic rabbits. I. Brusini, M. Carneiro, C. Wang, C.J. Rubin, H. Ring, S. Afonso, J.A. Blanco-Aguiar, N. Ferrand, N. Rafati, R. Villafuerte, Ö. Smedby, P. Damberg, F. Hällböök, M. Fredrikson, L. Andersson. Proceedings of the National Academy of Sciences. 115 (28), pp. 7380- 7385. 2018.
https://doi.org/10.1073/pnas.1801024115

II. MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention. I. Brusini, E. MacNicol, E. Kim, Ö. Smedby, C. Wang, E. Westman, M. Veronese, F. Turkheimer, D. Cash. Neurobiology of Aging. 109, pp. 204-215. 2021.
https://doi.org/10.1016/j.neurobiolaging.2021.10.004

III. Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis. I. Brusini*, M. Platten*, R. Ouellette, F. Piehl, C. Wang, T. Granberg. Journal of Neuroimaging. 2022. *Shared first authorship.
https://doi.org/10.1111/jon.12972

IV. Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis. M. Platten, I. Brusini, O. Andersson, R. Ouellette, F. Piehl, C. Wang, T. Granberg. Journal of Neuroimaging. 31 (3), pp. 493-500. 2021.
https://doi.org/10.1111/jon.12838

V. Shape information improves the cross-cohort performance of deep learning-based segmentation of the hippocampus. I. Brusini, O. Lindberg, J. Muehlboeck, Ö. Smedby, E. Westman, C. Wang. Frontiers in Neuroscience. 14, p. 15. 2020.
https://doi.org/10.3389/fnins.2020.00015

History

Defence date

2022-03-25

Department

  • Department of Neurobiology, Care Sciences and Society

Publisher/Institution

Karolinska Institutet

Main supervisor

Wang, Chunliang

Co-supervisors

Westman, Eric; Smedby, Örjan; Wahlund, Lars-Olof

Publication year

2022

Thesis type

  • Doctoral thesis

ISBN

978-91-8040-138-8

Number of supporting papers

5

Language

  • eng

Original publication date

2022-03-04

Author name in thesis

Brusini, Irene

Original department name

Department of Neurobiology, Care Sciences and Society

Place of publication

Stockholm

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