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The Measure and Meaning of Structural Similarity in the Brain /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Measure and Meaning of Structural Similarity in the Brain // Isaac Scott Sebenius.
作者:	Sebenius, Isaac Scott,
面頁冊數:	1 electronic resource (163 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
標題:	Neurobiology. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32023243
ISBN:	9798286413027

The Measure and Meaning of Structural Similarity in the Brain /
Sebenius, Isaac Scott,

The Measure and Meaning of Structural Similarity in the Brain /Isaac Scott Sebenius. - 1 electronic resource (163 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

The concept of similarity has recently emerged as a central and often-overlooked organizing principle of brain network structure. At the same time, recent advances in structural MRI analytics have enabled large-scale mapping of anatomical similarity within individual brains. However, key questions remain that limit the current applicability and interpretability of research using similarity mapping approaches. What is the theoretical basis for using similarity to study brain network architecture? How should similarity be measured? What are its genomic influences, neurobiological implications, and clinical consequences? This thesis tackles these fundamental, unresolved questions about the measurement and meaning of structural similarity in the brain.In Chapter 1, I review the historical, technical, and theoretical foundations of structural MRI similarity. I begin by introducing the historical roots and technical methodology of MRI similarity analysis and comparing it with the distinct MRI-based approaches of structural covariance and tractography analysis. In particular, I focus on evaluating the evidence supporting two key assumptions that often underlie the interpretation of structural MRI similarity: (i) that MRI similarity represents architectonic similarity between cortical areas; and (ii) that similar areas are more likely to be axonally connected, as predicted by the homophily principle. Next, I contextualise this empirical work with two generative models of homophilic networks: an economic model of cost-constrained connectional homophily, and a heterochronic model of ontogenetically phased cortical maturation. I then review studies of the genetic and transcriptional architecture of MRI similarity in population-averaged and disorder-specific contexts, and developmental studies of normative cohorts and clinical studies of neurodevelopmental and neurodegenerative disorders. Finally, I prioritise knowledge gaps that must be addressed to consolidate structural MRI similarity as an accessible, valid marker of the architecture and connectivity of an individual brain network.Building on this foundational review, in Chapter 2I propose Morphometric INverse Divergence (MIND), a novel method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on N>11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry, and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI, suggesting that MIND can successfully identify relevant inter-individual variation. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography, offering strong new evidence for the close link between cortical transcription and MRI-estimated brain structural architecture. MIND network phenotypes were also more heritable and thereby represent promising candidates for genetic analysis of brain network organization. I argue that MIND network analysis provides a biologically-validated lens for cortical connectomics using readily-available MRI data.In Chapter 3, I study the common genetic variation of MIND structural similarity in order to shed fresh light on the organization and evolution of the cortex, the causal relationships between brain structure and function, and the pathogenesis of heritable disorders.

English

ISBN: 9798286413027Subjects--Topical Terms:

588707
Neurobiology.

The Measure and Meaning of Structural Similarity in the Brain /
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