Using Resting-State Functional MRI for Fine-Grained Mapping of Mouse Brain Functional Connectivity

The neuroimaging field has been revolutionized by the resting state functional MRI or rs-fMRI technique, (Biswal et al., 1995), highlighting a major shift towards the notion of combined whole brain functional networks in contrast to the process of identifying isolated functional brain regions triggered by some stimulus paradigm.

The rs-fMRI technique can detect spontaneous and low frequency (<0.1Hz) variations in Blood Oxygen Level Dependent (BOLD) signal along with their temporal correlations (Biswal, 2012), providing an exclusive non-invasive approach to view the intrinsic network of whole brain functional connectivity. The ability of the technique to make out the patterns of brain pathology was demonstrated over time (Castellazzi et al., 2014; Zhou et al., 2014; Nathan et al., 2015; Odish et al., 2015) and as a result, this method is now being employed in human brain studies (Sporns et al., 2005; Van Essen et al., 2013).

In the meantime, the characterization of strong and reliable resting state networks in mice has also gradually evolved. However, in spite of promising initial results (Jonckers et al., 2011) a key area that still remains underexplored is fine-grained mapping of the brain resting state networks in rodents. This fact is very surprising because this species is extensively used in experimental neuroscience studies for modeling neurological disorders in humans. Hence, shedding more light into the remodeling of brain resting state networks in these models could provide a better understanding of brain disorder pathophysiology and help establish imaging biomarkers.

However, when it comes to small animals, rs-fMRI presents many challenges that are related to the necessity of rapid, high-res­olution acquisition methods and at the same time ensures steady physiological parameters across the experiment. This article shows how CryoProbe – a new, advanced technology developed for mouse MRI – can be used to perform fine-grained mapping of the mouse brain functional “connectome” in a non-invasive manner. A signal to noise increase of a factor of less than 2 is obtained when probe and its electronics are cooled down.

Using this data, consistent intrinsic patterns of the mouse whole brain functional network architecture are identified. The approach used in this study depends on the blind source spatial segregation of the BOLD signal through group-level independent component analysis (ICA) together with graph theoretical analysis. It is shown that under a light medetomidine sedation (adapted from Pawela et al., 2009), the key modular properties identified from the human brain were simulated in the mouse and the network has the markings of an intricate small-world efficient architecture.

The highly connected brain areas in the mouse are also distinguished which emphasizes their significance as relays of the whole brain functional connectivity. This data is important in translational studies, closing the gap between clinical and preclinical studies of functional connectome remodeling.

Experimental protocol

Animal stabilization and sedation

First, rs-fMRI was carried out in C57BL/6N female mice (n = 13) aged 8 to 9 weeks. To induce moderate sedation, a subcutaneous (sc) bolus injection (0.3 mg medetomidine per kg body weight) containing the α2-adreno-receptor agonist medetomidine (MD – Domitor, Pfizer, Karlsruhe, Germany) was administered. After 15 minutes, a continuous sc infusion of MD was administered on the animals via a catheter located at the shoulder of the mouse (0.6 mg per kg body weight). Heart rate, respiration rate, body temperature, and blood oxygen saturation were continuously observed throughout the imaging procedure and scans were taken only at stable physiological parameters.

Magnets and coils

A Bruker Biospec 7T small bore animal system integrated with B-GA12S HP gradients is used to perform imaging. For data acquisition, a 1H mouse quadrature transmit/receive MRI CryoProbe along with ParaVision 5.1 software (Bruker BioSpin MRI) was employed.

Shimming procedure

In order to optimize the homogeneity of magnetic field, a localized shimming procedure is performed on a target volume (7.5 x 10.5 x 15) mm³ located within the mouse brain. This step is done using FastMap protocols and PRESS waterline spectroscopy procedure provided with the Bruker ParaVision 5.1 software.

Sequence parameters

For rs-fMRI acquisition {TE/TR = 10 ms/1700 ms}, single shot GE-EPI sequence was applied. Except or the cerebellum, the whole mouse brain was covered using 12 slices measuring a thickness of 0.7 mm.

With an acquisition matrix of 128 x 80 and a field of view (FOV) of 1.92 x 1.2 cm², the planar spatial resolution leads to 150 x 150 μm². For each run, 200 volumes were captured with slice acquisition in an interlaced manner. After bolus injection, rs-fMRI scans were carried out at an interval of 30 minutes.


The rs-fMRI data was preprocessed in Matlab and through Statistical Parametric Mapping (SPM8) fMRI tool. The process involved:

Using the same template, an extra brain mask was developed and applied to all datasets prior to further processing.

Independent component analysis

With the help of GIFT toolbox (Group ICA of fMRI Toolbox – v1.3i), Spatial Group Independent Component Analysis (ICA, Calhoun et al. 2001) was performed using Infomax algorithm. As this approach is purely driven by data, the BOLD signal is split into components that result from sources considered as being independent, and the voxels within a single component are regarded as func­tionally related to one another. The stochastic nature of the ICA method is well-known which for the first time made it possible to perform a pilot analysis to assess the various numbers of components through the ICASSO algorithm (Him­berg et al., 2004).

In this method, the ICA algorithm is run multiple times under somewhat different conditions (that is bootstrapping and/or different initial conditions) and the results of each run are considered to ultimately provide an averaged set of components. The tool not only helps to observe the component clustering but also gives a quantitative measure of the actual robustness of the components identified: the ‘quality index’ Iq (values ranging between 0 and 1). The quality index assessed the isolation and compactness of each cluster, making it possible to emphasize unstable IC (Independent Component) patterns.

The ICASSO algorithm provides quantitative measures of IC stability which also enabled the ultimate selection of a different number of components to be obtained by the algorithm. This represents an important step when ICA is used. A comparison of 40 and 100-ICASSO results are presented based on the former. On the template, all mean results obtained are shown as spatial color-coded z-maps and this color coding indicates the reliance of the time courses in all voxels as opposed to the mean time course of the individual component.

Partial correlation (PC) analysis

Based on the 100-ICASSO results, 8 components were barred as artifactual and a respective 92 x 92 adjacency partial correlation (PC) matrix was measured to assess the direct statistical relationship by controlling correlation that was mediated by other components (Smith et al., 2010). Next, negative correlations were additionally removed (separate analysis required) and the importance of correlation was evaluated by using a single sample t-test with thresholding at p<0.01. This leads to a weighted undirected matrix (WUM). A binary undirected matrix (BUM) was then produced to assess a number ranking according to importance of connections.

Whole brain modular structures

Considering the WUM, the mouse brain community structures were detected by graph theory as well as spectral partitioning technique including fine tuning (Newman, 2006) to verify the resulting configuration stability. The two major metrics of small-worldness – the minimum path length L and the clustering coef­ficient C – were measured (Watts and Strogatz, 1998) and the same was compared to the mean path length Lrand and clustering coefficient Crand of an arbitrary network with the equal number of edges, nodes, and degree distribution just like the rs-fMRI derived network. For small-world network configurations, as the human brain is depicted to be – the ratio L/Lrand is defined to be about 1 and the ratio C/Crand is greater than 1.

Results and discussion

Two functional areas related to cortical (Figure 1A) and subcortical (Figure 1B) networks detected with 100-ICASSO and 40-ICASSO were comparatively presented. The cortical resting-state cluster, located at the primary somatosensory cortex (SSC) that is anatomically well-defined; secondary somatosensory cortex; and barrel field is all representative of the bi-hemispherical or bilateral connectivity pattern acquired with 40-ICASSO, as shown in Figure 1 A-a.

Refining the analysis with 100-ICASSO, as shown in Figures 1 A-b to e, led to the segregation of the functional areas of somatosensory cortex. Unilateral connectivity patterns were unraveled, which suggests that the SSC may have different low frequency variations of the BOLD signal in both hemispheres. Therefore, when 100-ICASSO is used, resting-state networks differentiated in single components from 40-ICASSO were divided into many functional circuits.

In addition to being relevant for cortical areas, this was also a common feature of subcortical regions. Shown in Figure 1B is the division of the thalamic networks, from a single component acquired with 40-ICASSO (Figures 1 B-f), into 6 functional clusters produced with 100-ICASSO.

Figure 1. BOLD resting-state fMRI based cortical (A) and thalamic (B) mouse brain functional connectivity clusters, comparatively revealed by 40- (a, f) and 100-ICASSO (b–e and g–l). All images represent spatial color-coded z-maps of the independent components.

100-ICASSO also showed the segregation of functional cortical, dividing this brain area into anatomically meaningful areas as shown in Figure 2 - a) cingulate cortex, b) motor cortex, c) primary somatosensory cortex, d) insular cortex, e) piriform cortex). When performing research in animal models of brain disorders, this refinement with 100-ICASSO could be of major value for showing the functional abnormalities at particular cortical locations.

Figure 2. Fine-grained segregation of the cortical mouse brain functional connectivity revealed with 100-ICASSO. Bi- (a) and uni-lateral left and right ICs (b–e), matching well defined anatomical areas (see he atlas axial image - Paxinos and Franklin, 2001) are displayed.

The direct association between separate components through PC analysis was measured to further assess the connectional relation­ships of inter-components and to generate the connectivity matrix of the whole brain. The 92 non-artifactual resting state clusters (components) are included in the input for the correlation analysis.

Shown in Figure 3A and 3B are the relevant 92 x 92 PC coefficient matrix (WUM) which is averaged across all animals, with the relevant 3D representation of connectivity (negative correlations in blue, positive correlations in red, all the components are indicated by a dot at their center of mass). Shown in Figure 3C with Figure 3D are the BUM with the respective 3D representation.

Figure 3. Quantitative assessment of inter-component direct connectivity using partial correlation analysis and identification of the most connected brain regions. (A) Weighted undirected matrix (WUM) generated after correlating the time courses of the non-artifactual components obtained with group 100-ICASSO and respective 3D visualization (B). (C) Binary undirected matrix (BUM) reflecting the statistically significant positive correlations and 3D display (D). (E) Ranking of the most connected brain areas, considered as nodes of the functional connectivity. For identifying the brain regions in the graph see the complete abbreviation list. (F) Spatial maps of the rostro-dorsal hippocampus and of its strongest connections, as reflected by the partial correlation coefficient displayed between pairs of IC.

For each IC, an average number of 11.26 statistically related functional connections were measured. Among the 92 resting-state clusters, 42 showed a greater number of related connections than average; since each cluster overlaps a particular anatomical brain region, the method used here made it possible to rank the most connected brain areas, regarded as nodes of functional connectivity. Shown in Figure is data regarding the top 13 most connected mouse brain functional areas, which are ordered as per their respective number of related connections.

The most connected brain region was identified to be rostro-dorsal Hippocampus. Figure 3F shows a representative MR slice that defines the hippocampal network together with the respective spatial maps of seven of its strongest functional connections. An integral part of the limbic system, the Hippocampus showed the highest level of functional connectiv­ity with brain regions that play a key role in the regulation of learning, behavior, memory, and emotion. Retrosplenial cortex, amygdala, cingulum, and entorhinal cortex are part of these brain regions.

Also used as nodes were the 100-ICASSO functional clusters, which were interconnected through edges (inter-components strength from WUM) in the inspection of large-scale functional connectivity of mouse brain with graph theory. Topological properties of the global network were assessed using modularity, mean shortest path length, global clustering coefficient, and small-worldness. Using the data acquired from the WUM, a high modularity value (Q = 0.50) was obtained by segregating the whole brain network of the mouse into five modules. The high Q value obtained indicates a major modular structure of intrinsic connectional architecture of functional networks in the mouse brain.

The assessment of small-worldness properties of the functional connectivity of a C57BL/6N mouse brain, a high clustering coefficient value, along with a short minimum path length: C=3.08∗Crand and L = 1.11∗Lrand. Lastly, the ratio value σ = γ/λ = (C/Crand)/(L/Lrand) = 3.08/1.11 = 2.77 > 1, points to an optimal network configuration for local processing and global data transfer inside the mouse brain, in proportion to the small-worldness network configurations.

On comparison of the study data against the rat study by Liang et al. (2011), it was observed that higher clustering coefficient values γ = C/Crand = 3.08 in the mouse (vs. 1.7 in the rat, Liang et al., 2011), indicates a higher level of clustering data and thus segregation of functional areas within the mouse. In spite of different levels of consciousness during imaging, the ratio of minimum path length (L/Lrand = 1.11 in mice versus L/Lrand =1.08 in rat) and the number of modules (5 in mice versus 4 in rats with fine-tuning as per the spectral partitioning analysis explained by Newman, 2006) are observed to be extremely similar in both mice and rats – that is medetomidine sedated mice versus awake rats.

Yet, the network of mouse brains were able to form all its modules through an intricate combination of subcortical and cortical brain regions, while Liang et al. was able to detect modular struc­tures covering only the cortical ribbon of the rat brain.

The above results demonstrate the intricate structure and organization of the brain functional connectivity in rodents without affecting the global topo­logical characteristics of small-worldness network configurations and supporting specialized/segregated as well as integrated/distributed data processing (Bassett and Bullmore, 2006). Overall, this appears to be a standard feature of healthy human and rodent brains. Hence, the use of network topology readouts in animal models of brain disorders may help explain the biological substrates and mechanisms of human brain pathologies and thus bridge the gap between clinical and pre-clinical investigations.

Summary and outlook

Rs-fMRI is the only technique that allows non-invasive mapping of functional brain connectivity. In this article, a data acquisition and processing pipeline modeled for studying the rodent brain functional connectivity was provided. These results pave the way towards a collection of findings at the functional connectivity level and also provides a better understanding of structural network analyses through fiber tracking and diffusion tensor imaging (Harsan et al., 2013) as complementary methods for exposing the whole mouse brain “connectome”.


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Published on Sep 22, 2017


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