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Neuroimage. 2015 Dec;123:11-21. doi: 10.1016/j.neuroimage.2015.07.090. Epub 2015 Aug 18.

Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification.

Author information

1
Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland. Electronic address: valerio.zerbi@hest.ethz.ch.
2
Institute for Biomedical Engineering, University and ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zurich, Switzerland.
3
Institute for Biomedical Engineering, University and ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zurich, Switzerland; Neuroscience Center Zurich, University and ETH Zurich, Winterthurer-Str. 190, 8057 Zurich, Switzerland; Institute of Pharmacology and Toxicology, University of Zurich, Winterthurer-Str. 190, 8057 Zurich, Switzerland.
4
Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland; Neuroscience Center Zurich, University and ETH Zurich, Winterthurer-Str. 190, 8057 Zurich, Switzerland.

Abstract

The use of resting state fMRI (rs-fMRI) in translational research is a powerful tool to assess brain connectivity and investigate neuropathology in mouse models. However, despite encouraging initial results, the characterization of consistent and robust resting state networks in mice remains a methodological challenge. One key reason is that the quality of the measured MR signal is degraded by the presence of structural noise from non-neural sources. Notably, in the current pipeline of the Human Connectome Project, a novel approach has been introduced to clean rs-fMRI data, which involves automatic artifact component classification and data cleaning (FIX). FIX does not require any external recordings of physiology or the segmentation of CSF and white matter. In this study, we evaluated the performance of FIX for analyzing mouse rs-fMRI data. Our results showed that FIX can be easily applied to mouse datasets and detects true signals with 100% accuracy and true noise components with very high accuracy (>98%), thus reducing both within- and between-subject variability of rs-fMRI connectivity measurements. Using this improved pre-processing pipeline, maps of 23 resting state circuits in mice were identified including two networks that displayed default mode network-like topography. Hierarchical clustering grouped these neural networks into meaningful larger functional circuits. These mouse resting state networks, which are publicly available, might serve as a reference for future work using mouse models of neurological disorders.

KEYWORDS:

Artifact detection; Data cleaning; Hierarchical clustering; Independent component analysis; Mouse; Resting state fMRI

[Indexed for MEDLINE]

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