Building bridges: simultaneous multimodal neuroimaging approaches for exploring the organization of brain networks

Abstract

Brain organization is evident across spatiotemporal scales as well as from structural and functional data. Yet, translating from micro- to macroscale (vice versa) as well as between different measures is difficult. Reconciling disparate observations from different modes is challenging because each specializes within a restricted spatiotemporal milieu, usually has bounded organ coverage, and has access to different contrasts. True intersubject biological heterogeneity, variation in experiment implementation (e.g., use of anesthesia), and true moment-to-moment variations in brain activity (maybe attributable to different brain states) also contribute to variability between studies. Ultimately, for a deeper and more actionable understanding of brain organization, an ability to translate across scales, measures, and species is needed. Simultaneous multimodal methods can contribute to bettering this understanding. We consider four modes, three optically based: multiphoton imaging, single-photon (wide-field) imaging, and fiber photometry, as well as magnetic resonance imaging. We discuss each mode as well as their pairwise combinations with regard to the definition and study of brain networks.

Keywords: brain networks; fiber photometry; magnetic resonance imaging; multiphoton imaging; simultaneous imaging; single-photon imaging.

Fig. 1

Simultaneous one-photon (wide-field) imaging and two-photon imaging. (a) A schematic of the light path of the two-photon imaging microscope (left). The FOV of the one-photon imaging setup is indicated by a green box. Behavioral data are collected using an auxiliary camera (middle). A schematic of the surgery—skull thinning, cranial window, and placement of a small prism—is shown in the in-lay (upper right). (b) A schematic of the light path of the one-photon imaging microscope (left). The FOV of the multiphoton imaging setup is indicated by the orange box. Example data are shown (right) (adapted with permission from Refs.  and 44).

Fig. 2

Simultaneous one-photon optical imaging and fMRI. (a) A schematic for unimodal one-photon optical imaging (left) and an adaptation for acquiring these data within the MR scanner (right). (b) An overview of the multimodal setup. (c) Example anatomical images for multimodal registration and functional data from unilateral hind-paw stimulation (adapted with permission from Ref. 83).

Fig. 3

Linking across spatiotemporal scales—from (sub)cellular to the whole-brain—and ultimately to behavior. (a) Each neuron can be classified based on a variety of attributes (top left). In this toy example, neurons are labeled with a fluorescent indicator “A.” To further define neurons of “type-A,” they can be grouped by whether they project to two remote brain regions: “B” and “C.” Further, neurons of type-A (that project to region B or C) may (+) or may not (−) exhibit modulated activity during animal locomotion. Together, these three attributes (fluorescence, projection, and modulation with locomotion) can define the phenotype of a cell. To access these attributes can require more than one imaging mode (bottom left) and an understanding of how behavior modulates activity (bottom right). In our example, regions B and C may be deeper in the brain than one- or two-photon imaging can access. The activity of individual cells within a typical multiphoton microscope FOV (top right) sums to approximate coarser measures (b). Understanding how to translate short- and long-range circuit and network function from cellular to mesoscale concert activity, as well as how these measures (e.g., functional connectivity) reflect behavior, will come with help from simultaneous multimodal implementations. (b) Single-cell measures (top) in a two-photon FOV can be summed to approximate measures accessible by one-photon imaging (bottom). Relationships between cells (two-photon) or regions (one-photon) can be summarized using a correlation (connectivity) matrix; a measure of activity synchrony (or asynchrony) between all region pairs. Connectivity matrices can be computed from different epochs of an experiment. In our example, epochs of locomotion or rest using data from different modes. Extending this understanding to the whole brain (c) will also require the implementation of complementary optical approaches and fMRI. For access to deep brain regions (like B and C), either fMRI or fiber photometry can be implemented; the relative coverage and FOV of the imaging technologies discussed here are summarized in (c) side (left) and top (right) view. Like optical imaging data, fMRI data can be summarized using a connectivity matrix (middle), which can help to translate complementary information across spatiotemporal scales.