![]() If there is only slight variation between sessions, then the number of pixels lost by taking the intersection may be minimal, but for studies that include a large number of subjects, even with an ideal field-of-view, the number of pixels lost can become substantial. The only advantage of this method is simplicity. The most common method to account for variation has been to perform the final analysis only on the intersection of masks from all sessions ( i.e., only on those brain areas that were imaged in every session). Since the imaged region may vary between subjects, or even sessions from the same subject, an individual pixel might correspond to brain in one mouse but not in another. Lack of whole-brain coverage further complicates the process of combining data across multiple scans. Additionally, this approach is limiting because it does not offer flexibility to remove individual pixels within the larger field-of-view, for example to mask regions of low signal-to-noise due to overlying venous sinuses or optical defects in the cranial window. One important limitation of this method is that it relies significantly on operator judgement which, we hypothesize, causes unintended variability in border selection. Analysis of functional signals is then performed on pixels judged to be within the brain mask. In the existing literature, to segment the brain from these other tissue types, a single imaging frame is viewed, and the region corresponding to brain is traced manually. For optical neuroimaging in mice, the images consist of the dorsal surface of the brain as well as surrounding tissue ( e.g., skull, overlying veins, skin, and hair). Conversely, no reliable structural data exists in optical imaging with which to segment the data. Since every location in the brain is sampled in every subject, the data can be averaged or concatenated. Furthermore, in MRI, once the cortical surface is identified and the data transformed to an atlas, then analysis across subjects is relatively simple. For example, concurrent anatomic imaging and whole-brain coverage in MRI enables advanced brain segmentation techniques. ![]() While resting-state optical imaging algorithms are informed by fMRI, direct translation of the entire fMRI processing stream is not possible. These techniques, in turn, are stimulating development of new imaging biomarkers of neurologic disease in preclinical models. ![]() Recently, resting-state functional connectivity analysis has been adapted for use with optical intrinsic signal (OIS) imaging and for fluorescence imaging using voltage-sensitive dyes and genetically-encoded calcium indicators. The ability to assess brain functional integrity without task- or stimulus-paradigms is well-suited for both clinical populations and preclinical mouse models. A major analysis tool in this field is resting-state functional connectivity, which enables mapping of distributed brain networks using correlated hemodynamics in the absence of tasks. Optical functional neuroimaging holds promise to link mouse models of neurological disease to the insights about human neuroscience gained from functional magnetic resonance imaging (fMRI).
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