diff --git a/main.md b/main.md index 893f54e..00391a2 100644 --- a/main.md +++ b/main.md @@ -21,9 +21,9 @@ Optical techniques have long been used to monitor the functional dynamics in set Wide-field imaging of neuronal calcium flux offers mesoscale observation of cortical neural dynamics and allows for viewing the supracellular group organization between microscale (cell) and macroscale (tissue lobe) investigations; however, it is affected by issues common to optical imaging recordings. Body or facial movements can create large fluctuations in autofluorescence of the brain and blood vessels, which produce significant artifacts in the data. Vascular artifacts are commonly seen due to vasodynamics and the resulting changes in blood flow to meet the energy demands of surrounding tissue. Fluid exchange between vascular and neural tissue causes cortical hemodynamics, resulting in region specific changes of optical properties among cerebral lobes[^Ma:2016]. Further, though the skull is fixed to a specific location during the experiment, slight brain movements occur within the cranium, thereby influencing the recordings. Any optical property differences that originate from the experimental preparation may be highlighted in the dataset as signal due to changes in tissue contrast. -Eigendecompositions can be used to identify and filter components of signal[^Kozberg:2016][^Patel2015][^Pnevmatikakis2016], and present a flexible method of filtering that is not hardware dependent, and can be applied to any video dataset regardless of the recording hardware or parameters. Independent Component Analysis (ICA)[^Hyvarinen:2000] has been previously applied to fMRI and EEG data with varying success; for example, identifying both intrinsic connectivity networks rather than individual areas, and artifacts that represent large-scale effects rather than spatially localized effects 13–16. We hypothesize that this is due to the lower density of spatial sampling in fMRI and EEG data. Wide-field calcium imaging provides a unique combination of spatially and temporally resolved dynamics across the cortical surface, with scale ranging from complex activation patterns in high-order circuits, to discrete activations hundreds of micrometers in diameter, to whole cortical lobe activity patterns[^Ackman2014c][^Vanni2014]. Researchers have recorded wide field calcium dynamics at frame rates ranging from 5-100Hz[^Ackman2014c],17,18. In addition, spatial resolution varies between different researchers’ setups, but is typically in the range of 256x256 to 512x512 pixels (0.06 to 0.2 megapixels) for the entire cortical surface, and is often further spatially reduced for processing[^Ackman2014c],17,19. Selection of resolution is often dependent on the video observer’s perceived quality of the data or available computational resources, rather than a quantified comparison of signal content. +Eigendecompositions can be used to identify and filter components of signal[^Kozberg:2016][^Patel2015][^Pnevmatikakis2016], and present a flexible method of filtering that is not hardware dependent, and can be applied to any video dataset regardless of the recording hardware or parameters. Independent Component Analysis (ICA)[^Hyvarinen:2000] has been previously applied to fMRI and EEG data with varying success; for example, identifying both intrinsic connectivity networks rather than individual areas, and artifacts that represent large-scale effects rather than spatially localized effects[^Mckeown1998][^Pruim2015][^Parkes2018][^Beckmann2004]. We hypothesize that this is due to the lower density of spatial sampling in fMRI and EEG data. Wide-field calcium imaging provides a unique combination of spatially and temporally resolved dynamics across the cortical surface, with scale ranging from complex activation patterns in high-order circuits, to discrete activations hundreds of micrometers in diameter, to whole cortical lobe activity patterns[^Ackman2014c][^Vanni2014]. Researchers have recorded wide field calcium dynamics at frame rates ranging from 5-100Hz[^Ackman2014c][^Murphy2016][^Valley2020]. In addition, spatial resolution varies between different researchers’ setups, but is typically in the range of 256x256 to 512x512 pixels (0.06 to 0.2 megapixels) for the entire cortical surface, and is often further spatially reduced for processing[^Ackman2014c][^Murphy2016][^Allen2017]. Selection of resolution is often dependent on the video observer’s perceived quality of the data or available computational resources, rather than a quantified comparison of signal content. -It is common to use sensory stimulation to identify specific regions in the neocortex and align a reference map based on the location of these defined regions 19–21. Even if these maps are reliable for locating primary sensory areas, they often lack specificity for higher order areas, or even completely lack sub-regional divisions. This is especially true in areas with a high degree of interconnectedness and overlapping functionality, such as motor cortex 22. Moreover, there is evidence that the shape and location of higher order regions can vary from subject to subject 23,24. Improper map alignment or misinformed regional boundaries can lead to a loss in dynamic range between signals across a regional border. Thus, to extract the most information from a recorded dataset, the level of parcellation must reflect the quality and sources present within the data. Thus, a flexible data-driven method is necessary and must also respect functional boundaries of the cortex and be sensitive to age, genotype and individual variation. +It is common to use sensory stimulation to identify specific regions in the neocortex and align a reference map based on the location of these defined regions[^Allen2017][^Vanni2017][^Clancy2019]. Even if these maps are reliable for locating primary sensory areas, they often lack specificity for higher order areas, or even completely lack sub-regional divisions. This is especially true in areas with a high degree of interconnectedness and overlapping functionality, such as motor cortex[^Mountcastle:1997]. Moreover, there is evidence that the shape and location of higher order regions can vary from subject to subject[^Zhuang:2017][^Glasser2016]. Improper map alignment or misinformed regional boundaries can lead to a loss in dynamic range between signals across a regional border. Thus, to extract the most information from a recorded dataset, the level of parcellation must reflect the quality and sources present within the data. Thus, a flexible data-driven method is necessary and must also respect functional boundaries of the cortex and be sensitive to age, genotype and individual variation. Here we present an ICA-based workflow that isolates and filters artifacts from calcium imaging videos, with principled exploration of each component to identify each signal source necessary to reduce the contamination resulting from these physiological dynamics. Independent component analysis (ICA) is a nonparametric unsupervised machine learning technique that can identify each signal source in densely sampled (5.5 million pixels per frame) calcium imaging videos based on their spatially co-activating pixels and temporal properties. The global mean time course was initially subtracted and stored, thereby allowing ICA to decompose each signal distinct from global effects. The decomposition results in hundreds of neural source components per hemisphere that are distinctly de-mixed from artifact source signals. Our concurrent analysis of control wide-field imaging data corroborates the identification of artifact signal sources and gives insight into the structure of neuronal calcium dynamics across neocortex. @@ -32,7 +32,7 @@ Further, we explore the resolution-dependent effect of signal extraction on ICA ## Results -To record neural activity patterns in the cortex of awake behaving adult mice, we transcranially imaged fluorescence from a mouse that has the genetically encoded calcium indicator, GCaMP6s, expressed in all neurons under the control of the Snap25 promoter 25. We expose and illuminate the cranium with blue wavelength light and capture emitted green light with a sCMOS camera at high spatial resolution (2160x2560 pixels, 5.5 megapixels; ∼6.9 µm/pixel). To observe the spatiotemporal properties of the recorded activity patterns, we crop the video to only neural tissue, and compare the change in fluorescence over the mean fluorescence: ∆F/F over time (Fig. 1A-B). In order to identify eigenvectors associated with artifacts and hemodynamic responses, similar data was recorded and processed in three sets of age matched control mice: cx3cr1 GFP (microglia; mGFP), adhl1 GFP (astrocyte; aGFP), and the non-transgenic C57/black 6 (Bl6) mice. +To record neural activity patterns in the cortex of awake behaving adult mice, we transcranially imaged fluorescence from a mouse that has the genetically encoded calcium indicator, GCaMP6s, expressed in all neurons under the control of the Snap25 promoter[^Madisen:2015]. We expose and illuminate the cranium with blue wavelength light and capture emitted green light with a sCMOS camera at high spatial resolution (2160x2560 pixels, 5.5 megapixels; ∼6.9 µm/pixel). To observe the spatiotemporal properties of the recorded activity patterns, we crop the video to only neural tissue, and compare the change in fluorescence over the mean fluorescence: ∆F/F over time (Fig. 1A-B). In order to identify eigenvectors associated with artifacts and hemodynamic responses, similar data was recorded and processed in three sets of age matched control mice: cx3cr1 GFP (microglia; mGFP), adhl1 GFP (astrocyte; aGFP), and the non-transgenic C57/black 6 (Bl6) mice. @@ -283,13 +283,13 @@ We additionally quantified whether detected regions were similar across map comp Wide-field calcium imaging has grown in popularity in the last decade due to advances in genetically encoded calcium indicators, however, the methods used to isolate neural signal sources are underdeveloped 29. Here we use an ICA-based algorithm that overcomes many of these limitationsEigendecomposition algorithms have been essential to understand signals across neuroscience. Another recent eigendecomposition pipeline has been developed to explore the functional activities across wide-field imaging of the cortex, but is limited by the use of a reference map and was not able to separate artifact signals from neural activations 29. The methods presented here are able to achieve similar expository results with artifact removal, allowing researchers to explore datasets of any age, treatment, genotype, or strain that would be impeded by the use of a reference map. -High resolution imaging of mesoscale cortical calcium dynamics combined with data-driven decomposition using ICA results in an optimized extraction of neural source signals. We have built de novo anour own Independent Component Analysis (ICA) based pipeline to that can not onlynot only achieve (isolate?, identify? sub-regional neural components, but also can to show utilization of components to quantify the impact on data quality based on recording parameters, to improve data quality through removal of artifacts, and to build domain maps based on the limitations of the fluorescent signal sources. We demonstrate that these methods provide precise isolation and filtration of video artifacts due to movement, optical deformations, or blood vessel dynamics while recovering cortical source signals with minimal alteration. Our lab and another have successfully implemented an ICA-based filtration to isolate the neural signal from artifacts 30,31. This approach can either be used alone, or in conjunction with techniques to correct calcium dynamics from tissue hemodynamics[^Ma:2016],18. +High resolution imaging of mesoscale cortical calcium dynamics combined with data-driven decomposition using ICA results in an optimized extraction of neural source signals. We have built de novo anour own Independent Component Analysis (ICA) based pipeline to that can not onlynot only achieve (isolate?, identify? sub-regional neural components, but also can to show utilization of components to quantify the impact on data quality based on recording parameters, to improve data quality through removal of artifacts, and to build domain maps based on the limitations of the fluorescent signal sources. We demonstrate that these methods provide precise isolation and filtration of video artifacts due to movement, optical deformations, or blood vessel dynamics while recovering cortical source signals with minimal alteration. Our lab and another have successfully implemented an ICA-based filtration to isolate the neural signal from artifacts 30,31. This approach can either be used alone, or in conjunction with techniques to correct calcium dynamics from tissue hemodynamics[^Ma:2016][^Valley2020]. Signal separation from mesoscale calcium dynamics recorded across the cortical surface was the most complete at the highest spatial resolution tested (pixel size of 6.9 µm/px). Our recordings consisted of large sets of densely sampled image frames having at least 12 bits of dynamic range across pixel intensities. Temporal resolution had less of an effect on ICA signal separation; we found that a 10Hz sampling rate was sufficient for spatial segregation. These metrics for signal quality are automatically generated by our algorithm, and can be used to optimize signal collection on any given experimental setup. The number of components identified is highly stable after recording sufficient duration of dynamics, and provides a metric for spatial complexity of neural signals across the neocortex. Compared with the high density optical recordings we used here, other neurophysiological techniques remain limited in the number of available spatial samples; as such, the effect of signal recording resolution on ICA decomposition of neural signal sources had not previously been reported. The data rebuild of identified neural components with mean filtration is a statistically valid process for isolating neural signals. We hypothesize that these sampling conditions coupled with a strong neuronal GCaMP signal-to-noise ratio optimizes ICA’s signal de-mixing ability to functionally isolate discrete patches of cerebral cortex from other physiological signals. In control recordings lacking a calcium sensor to report neuronal dynamics, high quality isolation of signal components was not attained given equivalent video sampling conditions. The dynamics of vascular and neural tissues are energetically and thus physiologically linked and the interplay between the hemodynamic responses and neural signals is known 32,33. Even so, we found that neural GCaMP components comprise discrete units across the cortex. In tissue expressing a contrast agent, such as GFP, the optical hemodynamics are enhanced and result in widespread regional effects among the cerebral lobes from control animals. Wavelet analysis on the global mean and individual neural components show the dominant signals extracted from GCaMP animals as being in a faster frequency range than cortical hemodynamics (>1 Hz). Our results indicate that neural GCaMP signals heavily outweigh the neocortical hemodynamic signals in decomposed independent components of densely sampled wide-field calcium imaging videos. -Maximal segmentation of the cortex was achieved after 20 minutes of spontaneous recordings, resulting in specific domain maps generated from individual animals. We describe a method for using these ICA-based components to perform data-driven mapping of the captured cortical dynamics, resulting in a superior isolation of the various signal sources on the cortical surface. Sufficient numbers of activations resulted in a fully segmented cortex with higher density of domains in primary sensory regions. Detected units vary in shape and size across the cortical surface, and have features that resemble known cortical morphology. These maps help elucidate changes in functional structure across the cortical surface across different experimental groups known to change cortical functional or spatial structure 34. Interestingly, these maximal segmented maps may highlight the limitations of this imaging technique theoretically outlined in the field 35. +Maximal segmentation of the cortex was achieved after 20 minutes of spontaneous recordings, resulting in specific domain maps generated from individual animals. We describe a method for using these ICA-based components to perform data-driven mapping of the captured cortical dynamics, resulting in a superior isolation of the various signal sources on the cortical surface. Sufficient numbers of activations resulted in a fully segmented cortex with higher density of domains in primary sensory regions. Detected units vary in shape and size across the cortical surface, and have features that resemble known cortical morphology. These maps help elucidate changes in functional structure across the cortical surface across different experimental groups known to change cortical functional or spatial structure 34. Interestingly, these maximal segmented maps may highlight the limitations of this imaging technique theoretically outlined in the field[^Waters2020]. Functional imaging in unanesthetized, behaving animals gives insight into the nature of physiological processes; however, nontrivial challenges arise during such sessions with intermixed sets of time varying signals. The methods presented here address the most common issues in analyzing large wide-field mesoscale datasets, including filtration of vessel artifacts, spatial mapping, and optimized time series analysis. This work demonstrates that signal components having maximal statistical independence captured in sufficiently sampled mono-chromatic calcium flux videos exhibit a combination of spatiotemporal features that allow machine classification of signal type. Implementation of automated machine classifiers for neural signals is practical given densely captured arrays of spatially and temporally variant data gathered from individual subjects .With these tools, neuroscientists can easily collect and analyze high quality neural dynamics across the cortical surface, allowing the investigation of complex networks at unprecedented scale. @@ -430,20 +430,32 @@ Statistical significance was calculated using OLS models from statsmodel.formula [^Hyvarinen:2000]: Hyvärinen A, Oja E. Independent component analysis: Algorithms and applications. Neural Netw. (2000). 13:411–30. pmid:10946390 +[^Mckeown1998]: Mckeown MJ, Makeig S, Brown GG, Jung T-P, Kindermann SS, Bell AJ, et al. 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Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. (2004). 23:137–52. doi:10.1109/TMI.2003.822821 + +[^Valley2020]: Valley MT, Moore MG, Zhuang J, Mesa N, Castelli D, Sullivan D, et al. Separation of hemodynamic signals from GCaMP fluorescence measured with wide-field imaging. J Neurophysiol. (2020). 123:356–366. doi:10.1152/jn.00304.2019 pmid:31747332 + +[^Allen2017]: Allen WE, Kauvar IV, Chen MZ, Richman EB, Yang SJ, Chan K, et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron. (2017). 94:891–907.e6. doi:10.1016/j.neuron.2017.04.017 + +[^Vanni2017]: Vanni MP, Chan AW, Balbi M, Silasi G, Murphy TH. Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules. J Neurosci. (2017). 37:7513–7533. doi:10.1523/JNEUROSCI.3560-16.2017 + +[^Clancy2019]: Clancy KB, Orsolic I, Mrsic-Flogel TD. 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