Integrating Cytoarchitectonic Probabilities with MRI-Based Signal Intensities to Calculate Regional Volumes of Interest

Kurth, Florian; Jäncke, Lutz; Luders, Eileen (2018). Integrating Cytoarchitectonic Probabilities with MRI-Based Signal Intensities to Calculate Regional Volumes of Interest. In: Spalletta, Gianfranco; Piras, Fabrizio; Gili, Tommaso. Brain Morphometry. New York: Springer, 121-129.

Abstract

Hypotheses on specific brain structures are frequently assessed using anatomically defined regions of interest (ROIs) in structural T1-weighted images. The definition of these ROIs is often based on macroscopic landmarks (sulci, gyri, etc.) that do not necessarily coincide with the functional architecture of the brain. Microscopic labeling, on the other hand, enables a precise localization of anatomical boundaries. Thus, defining ROIs using cytoarchitectonic information might be a suitable alternative to the traditional landmark-based approach. In this chapter, we describe in detail how to perform such cytoarchitectonic ROI analyses by integrating voxel-wise cytoarchitectonic probabilities (using freely available maps created post mortem) with MR-based signal intensities (using standard T1-weighted data obtained in vivo). After elucidating common techniques to create ROIs (see Sect. 1), detailed information is provided with respect to the cytoarchitectonically defined probabilistic maps, i.e., the foundation for the proposed cytoarchitectonic ROI approach (see Sect. 2). The methodological aspects pertaining to this approach constitute the heart of the chapter and comprise three main steps: probability map selection, preprocessing, and integration (see Sect. 3). The chapter concludes with practical tips and helpful pointers for conducting a cytoarchitectonic ROI analysis (see Sect. 4).

Abstract

Hypotheses on specific brain structures are frequently assessed using anatomically defined regions of interest (ROIs) in structural T1-weighted images. The definition of these ROIs is often based on macroscopic landmarks (sulci, gyri, etc.) that do not necessarily coincide with the functional architecture of the brain. Microscopic labeling, on the other hand, enables a precise localization of anatomical boundaries. Thus, defining ROIs using cytoarchitectonic information might be a suitable alternative to the traditional landmark-based approach. In this chapter, we describe in detail how to perform such cytoarchitectonic ROI analyses by integrating voxel-wise cytoarchitectonic probabilities (using freely available maps created post mortem) with MR-based signal intensities (using standard T1-weighted data obtained in vivo). After elucidating common techniques to create ROIs (see Sect. 1), detailed information is provided with respect to the cytoarchitectonically defined probabilistic maps, i.e., the foundation for the proposed cytoarchitectonic ROI approach (see Sect. 2). The methodological aspects pertaining to this approach constitute the heart of the chapter and comprise three main steps: probability map selection, preprocessing, and integration (see Sect. 3). The chapter concludes with practical tips and helpful pointers for conducting a cytoarchitectonic ROI analysis (see Sect. 4).

Statistics

Citations

Dimensions.ai Metrics
1 citation in Web of Science®
2 citations in Scopus®