Reduced Repertoire of Cortical Microstates and Neuronal Ensembles in Medically Induced Loss of Consciousness.

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Experimental Model and Subject Details Animal subjects

Experiments were conducted with care and accordance with the Columbia University institutional animal care guidelines. Experiments were carried out on Thy1-GCaMP6F ( Dana et al., 2014 ) adult transgenic mice at postnatal age of 4–6 months. No animals were used for previous or subsequent experimentation. Food and water was provided ad libitum. All mice were kept at a 12 hour light/dark cycle. Human subjects, and Ethics Statement. Two human subjects were included in this study. Patient 1 was a 31-year-old male and patient 2 was a 64-year-old male. Both patients were undergoing neurosurgical resection of the anterior temporal lobe (patient 1: left; patient 2: right) in order to treat medically refractory mesial temporal lobe epilepsy. The University of Utah Institutional review board approved these experiments and both subjects provided informed consent prior to participating in the study. Method Details Animals, surgical procedures, and setup acclimatization. Prior to the actual experiment, mice were anesthetized with isoflurane (initial dose 2–3% partial pressure in air, then reduction to 1–1.5%). Right before surgery, all mice received carprofen (s.c.), enrofloxacin (s.c.), and dexamethasone (i.m.). Under sterile conditions, a small flap of skin above the skull was removed and a titanium head plate with a central foramen (7×7mm) was attached to the skull with dental cement above the left hemisphere. A small cranial aperture (around 2×2mm) was established above left somatosensory (coordinates from bregma: x 2.5mm, y −0.24mm, z −0.2mm) or visual cortex (from lambda x 2.5mm, y −0.02mm, z −0.2mm, one exp. over lateral visual cortex: from Lambda x 3 mm y 2.4 mm z −0.2) using a dental drill. Then, the craniotomy was covered with a thin glass cover slip (3×3mm, No. 0, Warner Instruments), which was fixed in place with a slim meniscus of silicon around the edge of the glass cover and finally cemented on the skull using small amounts of dental cement around the edge. A small section of skull (1×1mm) was left blank next to the cemented glass cover. For hippocampal imaging, a small area of cortex (around 1.5×1.5mm) above left CA1 was removed by gentle suction down to the external capsule, as described previously ( Dombeck et al., 2010 ). The site was repeatedly rinsed with sterile saline until no further bleeding could be observed. Then, a small UV-sterilized miniature glass plug (1.5×1.5mm, BK7 glass, obtained from BMV Optical), glued to the center of a thin glass coverslip (3×3mm, No. 0, Warner Instruments) with UV-sensitive glue, was carefully lowered onto the external capsule, and until the edges of the attached glass cover touched the skull surrounding the craniotomy. Finally, the plug was fixed in place with a slim meniscus of silicon around the edge of the glass cover and by applying small amounts of dental cement around the edge of the glass cover. Post-operatively, all mice received carprofen daily for 3 days. Over the following days, mice were accustomed to the experimenter, and the experimental setup until no signs of distress were present. Mice usually became rapidly acclimatized to the microscope and running on a wheel under head-restrained conditions over the course of 2–3 acclimatization sessions lasting 30 minutes each. Around two weeks after the implant of the glass cover slip or glass plug, on the day of the actual experiment, mice underwent brief surgery again. Using isoflurane as described above, a small burr hole was established in the area that had been left blank next to the cemented glass cover for access by a glass micropipette for LFP measurements (find a more detailed description under electrophysiology, below). A reference electrode was place over the right frontal cortex. Thereafter, mice were transferred to the microscope for the experiment. Experimental timeline in mice. In each experiment, animals were kept in head-restrained conditions yet allowed to move freely on the running wheel. Throughout each experiment, in addition to local population imaging, cortical activity was recorded by LFP measurements serving as an additional proxy of anesthetic depth aside from clinical assessment (breath rate, locomotion, responsiveness to tail pinching). After the first image and LFP series during wakefulness, mild anesthesia was established by delivery of low concentrations of isoflurane through a plastic cylinder positioned right in front of the mouse’s nose ( Fig. 1 A ). During mild anesthesia/low levels of isoflurane (0,5% partial pressure in air – ‘ppa’), mice remained responsive to tail pinching. This responsiveness ceased completely once general anesthesia was achieved by increasing the dose of isoflurane to around 1,0% ppa. Then, burst suppression anesthesia was induced through another increase of isoflurane to around 1.5% , and maintained for 10 minutes. Finally, isoflurane delivery was halted, and imaging and lfp recordings continued while the animal was allowed to fully recover from anesthesia. Once the experiment was completed, animals were deeply anesthetized and sacrificed humanely. Infrared locomotion detection in mice. Locomotion was detected using an infrared sensor at the running wheel. The initially transparent running wheel was adapted to block the infrared light from passing through the wheel by using equally spaced strips of light absorbent tape ( Fig. 1A ). Thus, whenever the mouse would locomote, the light path between the light source underneath the wheel and the sensor on top of it would alternatingly get blocked or released rapidly. During each such transition (the longer the locomotion, the more transitions), the infrared sensor produced a large positive (from blocked to transparent) or negative (transparent to blocked) change in voltage that could be recorded at 1 kHz temporal resolution alongside the LFP using Prairie View Voltage Recording Software. Transitions could be easily extracted after an experiment to create a binary vector of locomotion or rest. For each event in the binary vector, 1 second of locomotion was counted, and the relative time of locomotion versus resting, for each experimental condition, was calculated. Two-photon calcium imaging in mice. Neural population activity was recorded by imaging changes of fluorescence with a two-photon microscope (Bruker; Billerica, MA) and a Ti:Sapphire laser (Chameleon Ultra II; Coherent) at 940 nm through a 25x objective (Olympus, water immersion, N.A. 1.05). Resonant galvanometer scanning and image acquisition (frame rate 30.206 fps, 512 × 512pixels, 150–250µm beneath the pial surface, or external capsule in the case of hippocampal imaging) were controlled by Prairie View Imaging software. Multiple datasets were acquired consecutively over the course of an experiment (in total 100,000–150,000 frames) with several momentary breaks interspersed for reasons of technical practicality. Two-photon image processing. Active cells were first identified visually in raw tiff movie files using ImageJ software ( Schneider et al., 2012 ). 10 minutes of imaging during each of the five anesthetic conditions (matched across animals by raw LFP, spectral power, and clinical parameters) were concatenated into one large movie tiff file spanning the entire experiment. A list of cell centroid spatial coordinates was obtained and used to initialize the recently described constrained nonnegative matrix factorization algorithm (CNMF) to extract calcium transients of all registered cells in MATLAB ( Pnevmatikakis et al., 2016 ; Yang et al., 2016 ). Prior to the initialization, tiff series were down sampled (averaged) from the original 30Hz temporal imaging resolution to 10Hz, and 512×512 pixel spatial resolution to 256×256 pixels. The CNMF algorithm finds spatiotemporal components based on pixels of high covariance around defined cell centroids while accounting for background fluorescence and minimizing signal noise. Based on the extracted fluorescence traces, ∆F/F signals are calculated for each cell using a sliding window (30 seconds). To derive binarized activity events from the ∆F/F signals, the ∆F/F is temporally deconvolved with the CNMF parameterized fluorescence decay. In addition, a temporal first derivative (slope) is independently obtained from the ∆F/F signals of individual cells. Then, the deconvolved signal and the derivative are thresholded at at least four standard deviations from the mean signal, respectively. At each time point, if both the devoncolved signal and first derivative exceed the threshold, a binary activity event is detected. The binary matrices obtained in this way, contained the recorded number of neurons across 30,000 frames of imaging (50 minutes), and represented the input matrices for t-SNE embedding/watershed segmentation, AP clustering, PCA, or Lempel-Ziv complexity analysis, as described below. Local field potential recordings in mice. For LFP measurements, a sharp glass micropipette (2–5 M) containing a silver chloride wire, back-filled with saline, was diagonally advanced into the cortex (30° angle) under visual control. The pi pette was lowered through a burr hole next to the glass cover slip, as described above, to a depth of around 200 µm beneath the pial surface. A reference electrode was positioned over the contralateral frontal cortex. LFP signals were amplified by use of a Multiclamp 700B amplifier (Axon Instruments, Sunnyvale, CA), low-pass filtered (300Hz, Multiclamp 700B commander software, Axon Instruments), digitized at 1 kHz (Bruker) and recorded using Prairie View Voltage Recording Software along with calcium imaging. Single Unit Activity and LFP data acquisition and pre-processing in humans. Human electrophysiological data were acquired from a Utah-style microelectrode array implanted in each subject’s middle temporal gyrus, approximately 3 cm from the temporal pole. Detailed surgical methods are described in House et al ( House et al., 2006 ). Data from these microelectrodes were acquired at 30,000 samples per second and pseudo-differentially amplified by 10 using an FDA-approved neural signal processing system (Blackrock Microsystems, Salt Lake City, UT). Continuous recordings were acquired from the microelectrode arrays while anesthesia was maintained at different anesthetic depths. Signals from the microelectrode arrays were segregated into two data streams: single unit activity (SUA), and local field potentials (LFP). SUA was acquired by first band-pass filtering the voltages recorded on each microelectrode between 300 and 3,000 Hz (4th order butterworth filter). This high pass filtered signal was thresholded at −3.5 times its root mean square, and 48 samples around each threshold crossing were retained for spike sorting. Spike sorting was carried out in a semi-supervised fashion on a feature space of the first three principal components, and clustering using the T-distributed expectation maximization algorithm ( Shoham et al., 2003 ). The time stamps of well-isolated single units were retained for further analysis in a binary matrix in which one dimension represented the activity of a single unit and the other dimension represented time at 1000 samples per second. LFP data were acquired by non-causal low pass filtering (500 Hz fir filter) the voltage recorded on each microelectrode and averaging across channels. This mean LFP across the array was then down-sampled to 1000 samples per second. Analysis of LFP spectral power in mice or human subjects. LFP low frequency spectral power (1–4Hz) was calculated with a 1 Hz temporal resolution. A Fast-Fourier transform (FFT) was carried out, and the spectral power was calculated as the squared absolute value of the complex output of the FFT. Finally, spectral power was averaged across the low frequency range. T-SNE and watershed segmentation (t-SNE/WS). We identified cortical microstates of the neuronal population using an un-supervised nonlinear embedding method, t-Distributed Stochastic Neighbor Embedding (t-SNE) ( van der Maaten, 2008 ). T-SNE was performed on active frames in population raster plots of neural activity derived from two-photon calcium imaging in mice, or microelectrode array SUA in humans, as described above. In both mice and humans, temporal down-sampling was used so that in all datasets, one “frame” corresponded to 100ms of neural activity. Using the perplexity value with the lowest optimization error for each dataset (a value range from ~5–200 was tested), and an initial reduction to 25 dimensions of activity using principal component analysis (PCA), t-SNE was applied across 1000 repititions to produce a robust two-dimensional embedding space that could be conveniently visualized ( Fig. 1G ). This 2D embedding space of an entire experiment, in which every data point represents the activity of the entire recorded neural population per individual frame, was used for watershed segmentation (WS) in order to separate clusters of similar activity ( Suppl. Fig. 1A ). To this end, a density map was generated. A probability density function was calculated in the embedding space by convolving the embedded points with a Gaussian kernel; the standard deviation of the Gaussian was chosen to be ¼0 of the maximum value in the embedding space. To segment the density map, local maxima were identified in the density map, a binary map containing peak positions was generated, and peak points were dilated by three pixels. A distance map of the binary image was generated and inverted, and the peak positions were set to be minimum. Watershedding was performed on the inverted distance map, and the boundaries were defined with the resulting watershed segmentation. Advantages and shortcomings of t-SNE: It is a nonlinear method for dimensionality reduction that preserves local structures well, and is robust against noise. It is widely used in different fields including neuroscience and genetics. The method is well-suited for visualizing data. As it is a tool only for dimensionality reduction and visualization, it has to be combined with additional methods for further analysis (e.g. watershed segmentation). Depending on the size of the dataset to be analyzed, t-SNE can be resource heavy. Affinity propagation clustering (APC). Affinity propagation clustering is a method to cluster data points by identifying a subset of representative examples and iteratively refining their cluster members through optimizing an objective function that describes the net similarity ( Frey and Dueck, 2007 ). This method operates on the pairwise similarity matrix between all pairs of data points, and identifies the exemplers based on an input preference vector, then automatically determines the number of clusters. Here, we used a Matlab module to perform affinity propagation in both mice and human data [ https://www.psi.toronto.edu/index.php?q=affinity%20propagation ]. As we had no a priori knowledge about pairwise similarity between any set of two datapoints, we ran the script without sparse capacity. In both observed and shuffled datasets, we set the preference vector to ones. For consistency with t-SNE, we removed all the frames with no active neurons before the clustering procedure. In this paper, a cluster identified by t-SNE/WS or APC, is called a “microstate”. Advantages and shortcomings of APC: APC is a primary clustering method. It does not require a cluster number as input but defines the cluster number automatically. The preference vector is an intuitive parameter, and the method is robust against noise. Similarly to t-SNE/WS, it can be resource heavy depending on the size of the dataset. Lempel-Ziv-Welch Complexity (LZWC). The Lempel-Ziv Complexity, or sequence complexity, measures the complexity of a finite sequence of symbols by way of trying to compress the sequence ( Ziv and Lempel, 1978 ). Here, the Lempel-Ziv-Welch (LZW) algorithm was used for this purpose, which is an encoding algorithm that works by creating a dictionary of common substrings ( Welch, 1984 ). The Lempel-Ziv-Welch algorithm is an improved implementation over the initial algorithm proposed for calculating the LZC, in terms of computation cost. LZW was applied to raster firing matrices of recorded cellular calcium transients in mice or SUA in humans ( Suppl. Fig. 3D ). To arrive at its encoded form, first the binary matrix was transformed into a vector that was scanned by the LZW algorithm. A Matlab module was used to calculate LZWC [ https://www.mathworks.com/matlabcentral/fileexchange/4899-lzw-compression-algorithm ]. To make the results of the LZW algorithm comparable across experiments, and therefore independent of the number of imaged neurons per animal or single units recorded in the two human subjects, an upper and lower bound was established. This was done by running the LZW algorithm on a vector of the same size but with all zeros, as well as taking the average of random vectors of the same size. These were used to establish lower and upper bounds between complete order and randomness, respectively, and then normalize the length of the compressed vector of neural activity to arrive at a final value between 0 and 1, so that: n e u r a l a c t i v i t y c o m p r e s s e d − o r d e r e d a c t i v i t y c o m p r e s s e d r a n d o m a c t i v i t y c o m p r e s s e d − o r d e r e d a c t i v i t y c o m p r e s s e d Advantages and shortcomings of LZWC: The method is fast, and can be done online. It is not a clustering method, and does not in itself define microstates, as by the nature of its algorithm it exclusively measures the ‚compressionability’ ofa dataset. In comparison to t-SNE/WS, APC, and PCA, It is not too robust against noise, and is vulnerale to temporal segregation of recorded signals. It is a practical analytical approach, but should in the optimal case, depending on the question addressed, be combined with other neural activity feature extraction methods.

Quantification and Statistical analysis

Unless stated otherwise, all values reported in this paper represent means ± s.e.m.. To determine statistical significance of differences between mean values measured under different experimental conditions in mice (n=7 animals, 5 conditions: awake, mild anesthesia, surgical anesthesia, deep anesthesia, and wake-up), 1-way anova was carried out (4 degrees of freedom), followed by a multiple comparison Bonferroni correction. When two groups were statistically compared ( Fig. 1J ), a Mann-Whitney (MW) test was used. Regarding statistical differences between observed and corresponding randomized numbers of unique microstates across experimental conditions in both mice and human subjects, observed single values were compared to a distribution of 100 values derived from randomized surrogate datasets (performed for t-SNE/WS, APC, and PCA). If the observed value was smaller/bigger than 95% of the randomized values, statistical significance was reached (p