We have shown (to our knowledge) the first results using optical intrinsic signal imaging to measure functional connectivity, and the first published mapping of functional connectivity in mice with resting-state hemodynamics. The findings of fcOIS were repeatable in time and also robust across multiple mice. These results satisfy our original goals of determining functional connections within the mouse brain in the resting state and of using the patterns of connections to generate a map of functionally distinct parcels. The functional neuroarchitecture found with fcOIS matches our expectations from previous studies in rats, primates, and humans as well as expectations that distinctions between functional regions should correspond to histological patterns 
Bilaterally symmetric functional connectivity is a prominent feature of our mapping results in visual, somatosensory, motor, frontal, cingulate, and retrosplenial cortices, as well as the olfactory bulb and the superior colliculus (these being all of the major parts of the brain within our field-of-view; for an equivalent human result see Salvador et al. 
). We did not observe widely distributed anterior-posterior functional connectivity, as typically found in humans (e.g., Fox et al. 
), possibly because large-scale, integrative functional processing is unlikely to occur in the mouse. The frontal, olfactory, and cingulate regions were all highly correlated with each other, and the visual cortices were weakly correlated with the superior colliculus. These patterns resemble those previously found in rats with fcMRI 
. Similar network patterns were obtained using the data driven SVD analysis. Improved statistical methodology is possible to more fully determine which correlations are the most significant and how their location varies between mice. The standard of practice in human fcMRI is to submit multi-subject datasets to statistical tests of significance using fixed effects 
or random effects 
analyses. However, such procedures depend on the availability of a standardized reference head space (known as an atlas) 
. Once standardized functional OIS atlasing methods for the mouse brain have been agreed upon, these statistical analyses would be straightforward extensions of the work in this paper and would be useful in many fcOIS applications.
Once we were able to demonstrate the presence of resting-state functional connectivity networks in the OIS data, our goal was to use this data to recreate the functional divisions within the mouse cortex and to recreate parcellations found in histological atlases. Our iterative parcellation scheme followed by clustering is able to divide the brain into networks in a data-driven manner. This method robustly parcellates the brain into similar functional regions as are found in the histological atlas 
In addition to the sensory and motor cortices, we also found functional connectivity (and associated parcellations) of higher-order cortical areas. Identifying these networks with resting-state neuroimaging is particularly noteworthy as developing task-paradigms to activate “cognitive” regions is difficult in the mouse. The olfactory, frontal, and cingulate cortices are all limbic areas 
, hence, it is expected that they would be highly correlated in the resting state. The functional network including the retrosplenial region most likely represents the murine equivalent of the primate default mode network 
. As in humans, mouse retrosplenial cortex, an evolutionary older structure, shows strong anti-correlations with more recently developed neocortical regions (e.g., somatomotor and visual cortex). The retrosplenial network in the mouse lacks certain human default-mode network components (e.g., dorsal medial prefrontal and lateral parietal cortex) but this is expected, as these regions are hypothesized to be later evolutionary additions 
While, in the present analysis, we have focused on comparisons of large-scale functional distinctions (e.g., between retrosplenial and somatomotor regions), future methodological development could provide robust finer distinctions (e.g., between subdivisions of visual cortex). That such further differentiation might be possible is suggested by the interesting finding that the multiple parcels in somatosensory cortex (as in ) correlate most highly with their putative contralateral homologue. Thus, for example, medial left somatosensory cortex correlates most highly with medial right somatosensory cortex (see ).
Several potential improvements of fcOIS correlation mapping technique can be identified. For example, in this paper we used only ΔHbO2
as a contrast. While previous functional connectivity studies with optical techniques have shown similar mapping results using different hemoglobin species as contrasts 
, the high resolution, event-related OIS functional mapping literature provides evidence for differences in the spatial extent of functional maps derived from different contrasts (HbO2
, which should be explored within the resting state. Optical imaging's ability to image multiple contrasts simultaneously can provide estimates of metabolic variables (such as oxygen extraction fraction and cerebral metabolic rate of oxygen) 
. The role of these parameters in functional connectivity (with their perhaps tighter coupling to the underlying neuronal physiology) remains to be studied.
Additionally, while we used the same functional connectivity frequency band as in previous human and rat studies, the dependence of murine fcOIS on temporal filtering remains a question for future investigation. The use of different frequency bands could potentially capture fast vs. slow correlations that reveal the structure of the brain's information processing, as has been recently attempted in human fcMRI 
. Additionally, different frequencies might capture information about different vascular compartments, similar to looking at different temporal windows in task-activation studies 
Numerous studies have also shown that functional connectivity persists, albeit in modified form, under anesthesia 
. We chose ketamine/xylazine because it is a relatively simple preparation and therefore well suited to a proof of principle demonstration of the ease of the fcOIS method. However, recent activation studies have shown that neurovascular coupling is more consistent under α-chlorolose 
. Further, recent fcMRI rat studies have shown improved functional connectivity mapping under α-chlorolose compared to isofluorane and medetomidine 
. We tested the stability of our fcOIS maps by splitting datasets into separately analyzed halves and observed good reproducibility in individual mice (see Figs. S3
). This consistency suggests stable depth of anesthesia over the duration during which these mice were imaged. Further studies with α-chlorolose may improve the precision of the functional border locations and reduce the modest differences in parcellations observed across imaging sessions (as in Fig. S7
Future work is also needed to address fcOIS accuracy through direct comparisons to histology. The most direct comparison would be with activation studies using somatosensory (e.g., whisker, forelimb, hindlimb), auditory, or visual stimuli, as in the seminal report of Biswal et al. 
. This evaluation would have the advantage of comparing two hemodynamically derived maps within the same mouse. However, a comprehensive mapping of all functional areas would be considerably involved, and some (such as cingulate) would be difficult to localize with stimulus paradigms.
Alternatively, comparison could be made to histological staining in order to comprehensively define all functional brain regions, which would be the gold standard for quantifying differences in brain organization between mice. In such an analysis, if fcOIS maps were to differ from the histological atlas, steps would be need to be taken to determine whether the divergence was due to variation in the arrangement of cytoarchitecture, noise in the imaging method, or functional connectivity borders differing from histological borders. Although the parameter space for both the optimization and validation of fcOIS is large, these studies will be critical in establishing a firm foundation for fcOIS as a tool for routine mouse neuroscience.
Additionally, a focus of current fMRI research is how closely functional and structural connectivity are connected. In humans, structural connectivity can be assessed only indirectly using diffusion tensor imaging (DTI). While studies comparing fcMRI and DTI have shown reasonable agreement 
, it is difficult for networks to be comprehensively assessed. In mice, neuronal connections could be directly visualized using invasive axonal tracing studies. Such studies, combined with fcOIS could help elucidate the role of multi-synaptic connections in resting-state functional connectivity and how functional networks evolve with the development of structural neural connections.
We expect that advances in MRI technology and methods will eventually allow fMRI-based functional connectivity mapping in mice. However, the need for high-field MRI scanners will most likely restrict its use to dedicated neuroimaging researchers and centers. In contrast, fcOIS provides a combination of high resolution, low cost, and ease of use (a simple intraperitoneal injection of anesthetic and no thinning of the skull) that should enable many laboratories that previously did not consider functional neuroimaging to connect with on-going studies of human disease. One physical limitation of OIS (due to light scattering) is the restriction of the field-of-view to the cortical surface (<1 mm), which precludes direct mapping of deep brain structures (e.g., the thalamus and hippocampus). Thus, we expect the two methods to eventually play a complementary role where interesting results can be found “at the benchside” using fcOIS, and then a subsequent fcMRI study could be done to visualize deep brain structures and compare with high-resolution anatomic scans 
In summary, we have demonstrated functional connectivity mapping with OIS in mice. Because we have determined that fcOIS is able to map both functional regions and their connections, this methodology should be a powerful tool for detecting when functional connectivity networks are disrupted (either in the distribution of the neuroarchitecture or in the pattern of connections). Thus, one could examine the functional consequences of disease models including genetic
disruptions. Imaging the development of neurodegenerative disease (e.g., Alzheimer's and Huntington's) in mouse could provide a less circumstantial link between the molecular mechanisms and the tendency for disease to target specific cortical networks
providing better insight into both pathophysiology and therapeutic targets
. We expect that fcOIS could be a useful tool to connect the intriguing neuroimaging results of human disease obtained through fcMRI with advances in mouse models.