Imaging of functional connectivity in the mouse brain

Abstract

Functional neuroimaging (e.g., with fMRI) has been difficult to perform in mice, making it challenging to translate between human fMRI studies and molecular and genetic mechanisms. A method to easily perform large-scale functional neuroimaging in mice would enable the discovery of functional correlates of genetic manipulations and bridge with mouse models of disease. To satisfy this need, we combined resting-state functional connectivity mapping with optical intrinsic signal imaging (fcOIS). We demonstrate functional connectivity in mice through highly detailed fcOIS mapping of resting-state networks across most of the cerebral cortex. Synthesis of multiple network connectivity patterns through iterative parcellation and clustering provides a comprehensive map of the functional neuroarchitecture and demonstrates identification of the major functional regions of the mouse cerebral cortex. The method relies on simple and relatively inexpensive camera-based equipment, does not require exogenous contrast agents and involves only reflection of the scalp (the skull remains intact) making it minimally invasive. In principle, fcOIS allows new paradigms linking human neuroscience with the power of molecular/genetic manipulations in mouse models.

Figure 1. System for fcOIS.

(a) Illumination from sequentially flashing LEDs in four different wavelengths (478 nm, 588 nm, 610 nm, and 625 nm) arranged in a ring. Detection by an EMCCD camera is at 120 Hz (30 Hz after decoding of wavelengths). Crossed linear polarizers (not shown for simplicity) prevent artifacts from specular reflection off the skull. (b) A false color image of the mouse cortex generated from the red, yellow, and blue LED channels. The image shows the camera's field-of-view (approximately 1 cm²) of the mouse brain with the cerebral cortex visible through the skull from the olfactory bulb to the superior colliculus and far laterally on the convexity. In the corners, one can see the reflected skin flaps. The brain was manually segmented from the image providing a mask for fcOIS analysis.

Figure 2. Performing functional connectivity with OIS.

(a) Time traces (ΔHbO₂) for three cortical locations: left retrosplenial (blue), right retrosplenial (green), and right motor (red). The right and left retrosplenial are functionally related and show time traces with high correlation (r = 0.88), while the left retrosplenial and right motor cortices are anticorrelated (r = −0.47). (b) A functional connectivity map made by correlating the left retrosplenial seed (blue circle) with all other brain pixels. High correlation values show functional related regions, including the right retrosplenial (green circle), while other regions are negatively correlated, including the right motor cortex (red circle). Correlation values near zero are found in functionally unrelated regions (frontal and visual cortices).

Figure 3. Seed-based fcOIS.

Correlation maps for seeds chosen manually using the expected cortical positions of various functional areas (Mouse 1). Seed positions and sizes are shown with black circles. The scale for all correlation maps is from r = −1 to 1. Maps are shown overlaid on the “white light” image of the brain. Note the bilateral patterns for all seed locations.

Figure 4. Iterative parcellation of fcOIS data.

(a) The results of iterative parcellation using the first twenty singular vectors from the correlation matrix as an initial condition. We see clear delineation of a frontal/olfactory/cingulate (limbic) network (oranges), a motor network (reds), a somatosensory network (greens), a visual network (blue), the retrosplenial cortex (magenta), and the superior colliculus (light blues). Numbers on the parcels are arbitrary designations from the initial condition. (b) Dendrogram showing clustering of the parcels from their correlations. Each terminal branch is a parcel (numbered to match the parcellation image and color-coded based on functional assignments); parcels that are more closely related (i.e., that share similar correlation maps have branches that meet lower on the tree. Note the tight correlations within the frontal network, in turn connecting to first medial and then lateral motor areas. In total, there are main branches for all of the main networks we expect. (c) The Paxinos atlas applied to this mouse brain for comparison with the functional parcellation. (For the names of the different cytoarchitectural regions shown in the atlas, see Fig. S8).

Figure 5. Correlation matrix between parcels after iterative parcellation.

Each row and column corresponds to a parcel (labeled with a functional assignment, anatomic location, and a number that matches the scheme in Fig. 5). We see a block-diagonal pattern showing how the clustering has arranged the parcels into networks (dashed boxes shown for added visualization). Off-diagonal elements show the relationships between networks; in particular, note the anticorrelations between frontal and somatosensory and between retrosplenial and motor. The left parietal region correlates with both visual and somatosensory regions. Also note how each somatosensory parcel correlates most highly with its similarly named homologue in the opposite hemisphere.

Figure 6. fcOIS Parcellations in multiple mice.

Different networks have been color-coded (green for somatosensory; red, motor; orange, frontal/cingulate/olfactory; magenta, retrosplenial; blue, visual; gray-blue, parietal; light blue, superior colliculus; purple, inferior colliculus; pink, auditory). Note that overall the patterns are similar across all the mice though there are slight individual differences in borders of the functional areas.