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Multimodal Parcellations and Extensive Behavioral Profiling Tackling the Hippocampus Gradient - PubMed

  • ️Tue Jan 01 2019

Multimodal Parcellations and Extensive Behavioral Profiling Tackling the Hippocampus Gradient

Anna Plachti et al. Cereb Cortex. 2019.

Abstract

The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ventral-dorsal dimension, which contrasts with the anterior-posterior functional differentiation. The variety of maps has raised the need for an integrative multimodal view. We applied connectivity-based parcellation to 1) intrinsic connectivity 2) task-based connectivity, and 3) structural covariance, as complementary windows into structural and functional differentiation of the hippocampus. Strikingly, while functional properties (i.e., intrinsic and task-based) revealed similar partitions dominated by an anterior-posterior organization, structural covariance exhibited a hybrid pattern reflecting both functional and cytoarchitectonic subdivision. Capitalizing on the consistency of functional parcellations, we defined robust functional maps at different levels of partitions, which are openly available for the scientific community. Our functional maps demonstrated a head-body and tail partition, subdivided along the anterior-posterior and medial-lateral axis. Behavioral profiling of these fine partitions based on activation data indicated an emotion-cognition gradient along the anterior-posterior axis and additionally suggested a self-world-centric gradient supporting the role of the hippocampus in the construction of abstract representations for spatial navigation and episodic memory.

Keywords: anterior–posterior; gradient; map; medial temporal lobe; structural covariance.

© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Figures

Figure 1.
Figure 1.

Hippocampal mapping based on histology, structural MRI segmentation, and CBP method. (Images reproduced with permission from publishers.)

Figure 2.
Figure 2.

The effect of denoising on RSFC-CBP and on voxel functional properties. (A) Most stable hippocampal parcellations across all levels of partition (k = 2–7) were obtained with FIX + WM/CSF, GSR, and WM/CSF regression as denoising approaches. Bars indicate mean ARI (±standard errors). Independent of denoising technique the highest stability was acquired for 6 clusters. All comparisons were statistically significant. (B) Seed voxels’ time course similarity was reduced after the application of denoising. No significant difference was observed between FIX + GSR and FIX + WM/CSF, whereas all the other comparisons were significant. (C) Denoising resulted in an increase in seed voxels’ dissimilarity in comparison to uncleaned data. FIX-related strategies demonstrated the strongest effect of connectivity profile dissimilarity. (D) FIX + WM/CSF showed the highest stability across all levels of partition (k = 2–7) compared with other denoising techniques. Mean ARI (±standard errors). All comparisons were statistically significant.

Figure 3.
Figure 3.

Hippocampus partitions based on SC, MACM, RSFC, and cytoarchitecture mapping. Mean ARI (±standard error). All comparisons between cluster solutions showed significant differences.

Figure 4.
Figure 4.

Functional multimodal maps across different granularities showing differentiation along the anterior–posterior and the medial–lateral dimensions.

Figure 5.
Figure 5.

Anterior to posterior characterization with BrainMap.

Figure 6.
Figure 6.

Anterior to posterior characterization with NeuroSynth.

Figure 7.
Figure 7.

Emotion–cognition and self-world-centric functional gradient along the anterior–posterior axis. Lateral clusters display an emotion–cognition gradient yielded with BrainMap and a self-world-centric gradient found with NeuroSynth.

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