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1.
Figure 1

Figure 1. From: Imaging large scale neural activity with cellular resolution in awake mobile mice.

Awake mouse two-photon microscopy experimental apparatus. A) A two-photon microscope is used to image through the cranial window of an awake behaving mouse that is able to maneuver on an air-supported free-floating Styrofoam ball that acts as a spherical treadmill. Optical computer mice are used to record mouse locomotion by quantifying treadmill movement. B) Images of the disassembled (i) and assembled (top-view (ii) and side-view (iii)) head-plate used for cranial window imaging and mouse head restraint.

Daniel A. Dombeck, et al. Neuron. ;56(1):43-57.
2.
Figure 6

Figure 6. From: Imaging large scale neural activity with cellular resolution in awake mobile mice.

Estimation of error rates for detecting cell activity related calcium transients. A–D) Histograms of positive (black) and negative (gray) going fluorescence transients of varying durations and amplitudes (in units of baseline σ). E) Quantification of error rates for detecting cell activity obtained from the ratio of the number of negative to positive events for given event amplitudes and durations. F) Negative (gray) and positive (black) event triggered average of mouse running speed. Note the sharp increase in running speed at time 0 sec (event onset) indicating a correlation between negative going fluorescence transients and the onset of running and a causal relationship between brain motion and negative going events.

Daniel A. Dombeck, et al. Neuron. ;56(1):43-57.
3.
Figure 7

Figure 7. From: Imaging large scale neural activity with cellular resolution in awake mobile mice.

Imaging astrocytic population activity in sensory cortex of awake behaving mice. Ai) False color projection-image of the 3 minute long time series (~250 µm deep); astrocytes and neurons were loaded with Calcium Green-AM (green channel) but SR101 labeled only astrocytes (red channel). B) Fluorescence traces for the neuropil and 9 astrocytic structures outlined in Aii obtained after the time series was motion corrected. Running speed, air puff stimulus and brain displacements are also shown. C) Correlation coefficients of astrocytic structure fluorescence versus running speed. The structures were numbered throughout this figure in descending correlation coefficient order. D) Running onset triggered average of fluorescence for 5 structures with varying amounts of running correlation.

Daniel A. Dombeck, et al. Neuron. ;56(1):43-57.
4.
Figure 2

Figure 2. From: Imaging large scale neural activity with cellular resolution in awake mobile mice.

Image sequences and quantification of brain motion caused by mouse movements. A,C) Frames from a two-photon time-series (unprocessed data, no motion correction) of YFP expressing cortical neurons in adult YFP (A, ~200 µm deep) and SR101 labeled cortical astrocytes in adolescent WT (C, ~200 µm deep) mice during periods of resting and running. The three components (X,Y and Z) of brain motion (displacement) were quantified and coplotted with running speed for YFP (Bi) and WT (Di) mice. Note that individual astrocytic processes can be followed in all frames of C. Larger amplitude brain motion can be seen in YFP mice in a running onset triggered average of Y-displacement (Bii) compared to the smaller amplitude brain motion seen in a running triggered y-displacement average for younger WT mice (Dii). E) A plot of brain motion versus running acceleration for WT mice. Positive mouse running accelerations lead to the largest brain displacements, while little displacement is seen during deceleration or constant running velocity.

Daniel A. Dombeck, et al. Neuron. ;56(1):43-57.
5.
Figure 5

Figure 5. From: Imaging large scale neural activity with cellular resolution in awake mobile mice.

Line-scan and fast transient recordings in sensory cortex of awake mice. A) Image of neurons loaded with Calcium Green-AM (green channel) and astrocytes with SR101 (red channel). B) Fluorescence traces of the neurons shown in A from a time series recorded at 64ms/frame. C) Image of neurons loaded with Calcium Green-AM (green channel) and astrocytes with SR101 (red channel). The gray line represents the line-scanning position. D) Intensity versus time line-scan image (Trial 1) of the Calcium Green channel from the position shown in C during which an air puff stimulus was applied to the mouse. E) 4 trials of line-scan recorded fluorescence traces for the 4 neurons labeled in C. Traces are aligned to the stimulus timing (red dashed line). Mouse running speed is shown in the last column. Motion correction was not applied to the line scan recordings. Note the fast increases at the onset and the slower exponential offset decay of the fluorescence transients in all traces as well as the summation of transients in B) neuron 2 and E) neuron 2, trial 3 (arrowheads).

Daniel A. Dombeck, et al. Neuron. ;56(1):43-57.
6.
Figure 4

Figure 4. From: Imaging large scale neural activity with cellular resolution in awake mobile mice.

Imaging neural population activity in sensory cortex of awake behaving mice. Ai) False color time-projection image of the 5 minute long time series (~150 µm deep); neurons were loaded with Calcium Green-AM (green channel) and were negative for the astrocytic marker SR101 (red channel). B) Fluorescence traces for the neuropil and 34 neurons outlined in Aii obtained after the time series was motion corrected. Running speed, air puff stimulus and brain displacements are also shown. Red segments indicate >3σ positive going fluorescence transients with <5% false positive error rate (Figure 6E). C) An expanded view of the fluorescence activity of 4 neurons. D) Correlation coefficients of neuron fluorescence versus running speed. The neurons were numbered throughout this figure in descending correlation coefficient order. E) Running onset triggered average of fluorescence for 7 neurons with varying amounts of running correlation. F) Neuron-Neuron fluorescence activity correlation coefficients.

Daniel A. Dombeck, et al. Neuron. ;56(1):43-57.
7.
Figure 3

Figure 3. From: Imaging large scale neural activity with cellular resolution in awake mobile mice.

Motion Correction using a Hidden Markov Model. A) A simplified demonstration of the corruption created by a sample moving during frame acquisition by laser scanning microscopy. i) The first half of the frame is acquired with the sample held fixed at one position. After the green line is scanned, the sample makes a sudden movement. ii) The second half of the frame is acquired with the sample displaced. iii) A reference image of the sample as it would have been acquired with no displacements. iv) An image as it would have been acquired if the sample had undergone the displacement as indicated between i and ii. Lines between images indicate the relative Y position of line scans. B) Simulation of laser scanning microscopy of a sample undergoing continuous motion displacement. Blue lines between sample (i) and simulated data (ii) indicate mean Y position of each line scan. Partial reconstruction (iii) of the sample is possible by estimating the relative position of each line to the standard raster scanning pattern. Red lines between simulated data and reconstruction indicate the prediction of a HMM of the Y position of each line scan. There are sections of sample over which the beam did not pass, and so blank lines are an unavoidable result of brain motion causing non-uniform spatial sampling C) Displacements of the sample during the simulated frame acquisition. Actual displacements are indicated in red and black. Displacements predicted by the algorithm are shown in blue and green and are slightly vertically offset for clarity. D) i) Two hypothetical examples of sequences of offsets considered in the HMM model. Transitions between offsets are indicated with arrows colored with respect to the exponential model illustrated in ii. The circles at each offset position have been colored with respect to the fit of the line to the sample at that offset, as illustrated in iii. Although the fit at 4a is the most probable offset for line 4, the path through 4b is more probable because it has smaller incremental offsets. ii) Illustration of the exponential model of the probability of a transition in offset from one line to the next. Probabilities shown here are for a space constant λ=0.5, which produced the overall most probable prediction shown in C. iii) Relative fit of line 40 to the sample over a range of offsets.

Daniel A. Dombeck, et al. Neuron. ;56(1):43-57.

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