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Cited by 253Functional Brain Connectivity StudiesOptical Imaging and Spectroscopy TechniquesAdvanced MRI Techniques and Applications

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

Imaging of Functional Connectivity in the Mouse Brain

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White, Brian R., Bauer, Adam Q., Snyder, Abraham Z., Schlaggar, Bradley L., Lee, Jin-Moo, Culver, Joseph P.·2011·PLoS ONE
DOI 10.1371/journal.pone.0016322PMC PMC3024435
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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.

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Figures

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 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.CC BY · PubMed Central
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 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).CC BY · PubMed Central
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 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.CC BY · PubMed Central
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 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).CC BY · PubMed Central
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 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.CC BY · PubMed Central
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.
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.CC BY · PubMed Central

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Research works printed but not linked: 5

  1. A Devor, 2003, Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex., Neuron, 39, 353, 10.1016/S0896-6273(03)00403-3 · DOI · Paper page
  2. A Devor, 2005, Coupling of the cortical hemodynamic response to cortical and thalamic neuronal activity., PNAS, 102, 3822, 10.1073/pnas.0407789102 · DOI
  3. MD Fox, 2007, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging., Nature Reviews Neuroscience, 8, 700, 10.1038/nrn2201 · DOI · Paper page
  4. MD Fox, 2009, The Global Signal and Observed Anticorrelated Resting State Brain Networks., Journal of Neurophysiology, 101, 3270, 10.1152/jn.90777.2008 · DOI · Paper page
  5. AK Dunn, 2005, Spatial extent of oxygen metabolism and hemodynamic changes during functional activation of the rat somatosensory cortex., NeuroImage, 27, 279, 10.1016/j.neuroimage.2005.04.024 · DOI · Paper page

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Metadata

Authors
White, B. R., Bauer, A. Q., Snyder, A. Z., Schlaggar, B. L., Lee, J.-M., & Culver, J. P.
Affiliations
Washington University in St. Louis (US)
Corresponding author
Culver, Joseph P.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2011-01-20
Volume
6
Issue
1
Pages
e16322
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0016322
PMC
PMC3024435
OpenAlex
W2065496603
Cited by
253
References
60
Citations by year
2026: 7 · 2025: 15 · 2024: 18 · 2023: 25 · 2022: 16 · 2021: 15 · 2020: 15 · 2019: 22 · 2018: 15 · 2017: 30 · 2016: 16 · 2015: 9 · 2014: 23 · 2013: 16 · 2012: 7
FWCI
7.96
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Institutes of Health
  • National Institute on Drug Abuse (T90DA022871)
  • National Institute of Neurological Disorders and Stroke (P01 NS32636, R01NS084028, P01NS032636)
  • National Institute of Biomedical Imaging and Bioengineering (R01EB008085, R01EB009233)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords
functional connectivity, mouse brain, resting-state networks, intrinsic signal imaging, mouse models of human disease, non-invasive brain imaging, camera-based imaging
MeSH terms
Animals, Brain Mapping, Cerebral Cortex, Equipment Design, Magnetic Resonance Imaging, Methods, Neural Pathways, Mice, Optical Devices

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