Preserved modular network organization in the sedated rat brain

Translation of resting-state functional connectivity (FC) magnetic resonance imaging (rs-fMRI) applications from human to rodents has experienced growing interest, and bears a great potential in pre-clinical imaging as it enables assessing non-invasively the topological organization of complex FC networks (FCNs) in rodent models under normal and various pathophysiological conditions. However, to date, little is known about the organizational architecture of FCNs in rodents in a mentally healthy state, although an understanding of the same is of paramount importance before investigating networks under compromised states. In this study, we characterized the properties of resting-state FCN in an extensive number of Sprague-Dawley rats (n = 40) under medetomidine sedation by evaluating its modular organization and centrality of brain regions and tested for reproducibility. Fully-connected large-scale complex networks of positively and negatively weighted connections were constructed based on Pearson partial correlation analysis between the time courses of 36 brain regions encompassing almost the entire brain. Applying recently proposed complex network analysis measures, we show that the rat FCN exhibits a modular architecture, comprising six modules with a high between subject reproducibility. In addition, we identified network hubs with strong connections to diverse brain regions. Overall our results obtained under a straight medetomidine protocol show for the first time that the community structure of the rat brain is preserved under pharmacologically induced sedation with a network modularity contrasting from the one reported for deep anesthesia but closely resembles the organization described for the rat in conscious state

Resting state fMRI reveals diminished functional connectivity in a mouse model of amyloidosis

INTRODUCTION: Functional connectivity (FC) studies have gained immense popularity in the evaluation of several neurological disorders, such as Alzheimer’s disease (AD). AD is a complex disorder, characterised by several pathological features. The problem with FC studies in patients is that it is not straightforward to focus on a specific aspect of pathology. In the current study, resting state functional magnetic resonance imaging (rsfMRI) is applied in a mouse model of amyloidosis to assess the effects of amyloid pathology on FC in the mouse brain. METHODS: Nine APP/PS1 transgenic and nine wild-type mice (average age 18.9 months) were imaged on a 7T MRI system. The mice were anesthetized with medetomidine and rsfMRI data were acquired using a gradient echo EPI sequence. The data were analysed using a whole brain seed correlation analysis and interhemispheric FC was evaluated using a pairwise seed analysis. Qualitative histological analyses were performed to assess amyloid pathology, inflammation and synaptic deficits. RESULTS: The whole brain seed analysis revealed an overall decrease in FC in the brains of transgenic mice compared to wild-type mice. The results showed that interhemispheric FC was relatively preserved in the motor cortex of the transgenic mice, but decreased in the somatosensory cortex and the hippocampus when compared to the wild-type mice. The pairwise seed analysis confirmed these results. Histological analyses confirmed the presence of amyloid pathology, inflammation and synaptic deficits in the transgenic mice. CONCLUSIONS: In the current study, rsfMRI demonstrated decreased FC in APP/PS1 transgenic mice compared to wild-type mice in several brain regions. The APP/PS1 transgenic mice had advanced amyloid pathology across the brain, as well as inflammation and synaptic deficits surrounding the amyloid plaques. Future studies should longitudinally evaluate APP/PS1 transgenic mice and correlate the rsfMRI findings to specific stages of amyloid pathology

Fisher z-transformed FC matrix of rat brain.

<p>a) Mean FC matrix (40 subjects) showing pairwise partial correlation coefficients obtained for 36 brain regions. b) Standard deviation matrix of the partial correlation coefficients. For abbreviations of brain regions see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106156#pone-0106156-g001" target="_blank">Figure 1</a>.</p

Rat brain parcellation scheme.

<p>Thirty six anatomically defined brain regions of interest, colored differently, are overlaid on the in-house rat MRI template. Distance to Bregma (in mm) is given at the top of each slice.</p

Modular organization of rat FCN and centrality of brain regions.

<p>a) Mean FC matrix rearranged with functional modules labeled along the major diagonal of the matrix. Six functional modules (sub-networks) of brain regions, as identified by the modularity partitioning algorithm (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106156#s2" target="_blank">methods</a>), are labeled in different colors. b) In the first row brain regions are shown in descending order of their generalized connection strength. Strong (strength>mean) brain areas are shown in red bars; the horizontal line indicates the mean strength. In the second row brain regions are shown in descending order of their generalized diversity value. Here, red bars show brain regions with strong connections as determined in the first row. Network hubs (i.e., nodes that are both strong and diverse) are indicated by green triangles. c) FCN modules are visualized on a schematic mid sagittal section of the rat brain. Network hubs are depicted by larger circles. For abbreviations of brain regions see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106156#pone-0106156-g001" target="_blank">Figure 1</a>. The colors used for the different modules are the same as those used in Figure 4a.</p

Modular organization in 4 different groups.

<p>Group plots with high modular similarity (low variability) in subjects randomly split into four groups (a–d) visualized on schematic mid sagittal sections of the rat brain. Network hubs are depicted by larger circles. The colors used for the sub-networks of the six functional modules, as identified by the modularity partitioning algorithm (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106156#s2" target="_blank">methods</a>), are analogue to those used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106156#pone-0106156-g004" target="_blank">Figure 4c</a>. For abbreviations of brain regions see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106156#pone-0106156-g001" target="_blank">Figure 1</a>.</p

Reproducibility of FC across subjects.

<p>The histograms (in blue) represent the distribution of the partial correlation coefficients (z-transformed) for each of the four randomly partitioned subject groups. Scatter plots indicating high correlation (r>0.85) of the pair-wise FC values between the groups suggest high reproducibility of FC at a group level.</p