Presentation Number P 342
Poster Session 3
September 20, 2013 / 15:15-15:15 / Room: Exhibit Hall B

Revealing the mouse brain functional connectivity patterns with resting state functional MRI (rsfMRI)

Anna Mechling1, Neele Saskia Huebner1,2, Hsu-Lei Lee1, Juergen Hennig1, Dominik von Elverfeldt1, Laura Adela Harsan1, 1Medical Physics, University Medical Center Freiburg, Freiburg, Germany; 2Biology Department, Albert-Ludwigs-University, Freiburg, Germany. Contact e-mail: anna.mechling@uniklinik-freiburg.de

A non-invasive insight into the brain's intrinsic connectional architecture of functional networks has only become possible since the development of resting-state functional magnetic resonance imaging (rsfMRI) (1). In humans, the default mode functional networks and their alterations in pathologies are intensively studied (2). However, this field is largely unexplored in the animal brain, perhaps because of methodological challenges related to the necessity of stable and comparable physiological parameters during the imaging sessions of all investigated animals, but also due to the confounding effects of anesthetic agents. To bridge this gap, we have systematically investigated here the functional connectional architecture of the mouse brain by implementing a robust, non-invasive methodological framework for the acquisition and analysis of mouse brain rsfMRI data. Our study, performed at high magnetic field (7T) and using the CryoProbe technology for the acquisition of the MR signal, demonstrate robust and reproducible patterns of mouse brain functional connectivity. First, we comparatively used 40 vs 100 independent component analysis (ICA) to identify elementary functional clusters of the mouse brain. The pure use of group ICA was extended allowing to statistically determining the reproducibility of the results via ICASSO (3) (20 repetitions of the ICA, varying initial conditions, bootstrapping and clustering - see also annex). Fig 1 (a) exemplifies a resting state network identified with 40 components group ICASSO in the anatomically well-defined somatosensory cortex (S1 and S2) areas, functionally linking these brain regions in both hemispheres (bilateral activation). Extending the analysis to 100 components, the ICASSO approach (Fig 1, b-e) was able to segregate the cortical somatosensory (SSC) network into four individual parts, separating the primary (S1) and secondary (S2) SSC and depicting unilateral (uni-hemispherical) patterns of activation. This was not only relevant for cortical regions but it was a generalized feature of subcortical areas as well. Thalamic networks clustered in one component with 40-ICASSO, were clearly split-up with 100-ICASSO into six sub-networks, corresponding to well defined thalamic nuclei. The connectional relationships between the elementary clusters obtained with ICASSO was further evaluated by partial correlation analysis and graph theory, and used to construct a graph of whole-brain neural network and to identify the most important mouse brain connectional hubs. Among them we mention the hippocampus, dorsal thalamic nuclei, cingulate and retrosplenial cortices, somatosensory cortical areas, amygdala or the hypothalamus. This whole mouse brain neuronal network exhibited the typical features of small-worldness and strong community structures seen in the human brain. Therefore our study provides a functional atlas of networks architecture of the mouse brain and demonstrates that this organization conserves fundamental topological properties that are also seen in the human brain (2). Refs:(1)Biswal BB, NeuroImage-62, 2012; (2)van den Heuvel MP et al., HBM-30, 2009; (3)Himberg et al., NeuroImage-22, 2004

images/P_342_A.jpg
Mouse somatosensory cortex functional connectivity maps resulting form 40 (a) and 100 (b) components group ICA (ICASSO). The single cortical component (a) obtained with 40-ICASSO (bilateral pattern) is segregated into 4 meaningful components (b) when using 100-ICASSO, showing both inter- and intra- hemispherical separation of the somatosensory cortex.