Supplementary Figure S1: Histological analysis of kainate-treated animals

Transcription

1 Supplementary Figure S1: Histological analysis of kainate-treated animals Nissl stained coronal or horizontal sections were made from kainate injected (right) and saline injected (left) animals at different antero-posterior locations along the hippocampal formation and entorhinal cortex: (a) 1.5mm posterior to bregma, (b) near the kainate injection site at 2.0mm posterior to bregma, (c) close to the site of optical fiber placement at 2.5mm posterior to bregma, and (d) medial entorhinal cortex layers I-VI. Key features of the intrahippocampal kainate model can be appreciated here, including cellular morphology changes, dramatic granule cell dispersion, CA1 pyramidal cell loss, and changes in entorhinal cortical morphology, particularly in layer III. Scale bar, a-c 1mm; d 250µm. Images were obtained in a grid pattern with a 10x objective and panels a-c were stitched together using a Stitching plugin for ImageJ 1. DG: dentate gyrus. CA: cornu ammonis.

2 min[max((s n ),max(max(s n-1 ),max(s n-2 ))] - max[min(s n ),min(min(s n-1 ),min(s n-2 ))] G s1 (up) G s2 (down) G = 0.8 Integrator AC thrshld x Amplitude Correlation Samples 60 samples per frame Remove DC and calc STD Slow Integrator Fast Integrator PSLOW PFAST Ratio G f1 (up) G f2 (down) threshold threshold Fast/Slow Fast Power Drop pos x NSpike thrshld x pos level min # pos Peak Detection (pos and levels) Spike Width/Distance Narrow Spike Detection Remove Spike DC x pos thrshld x pos Min width min # threshold pos Level Crossing Detection (pos and levels) Average Distance/ STD Spike Ratio Remove DC f1 f2 FFT 512 pts f3 f4 range f1:f2 range f3:f4 pos Min # of spikes Sum(abs(f1:f2)) / Sum(abs(f3:f4)) threshold threshold threshold Frequency Band Ratio Supplementary Figure S2: Custom closed-loop seizure detection software signal flow diagram. The digitized data (sampled at 500 Hz) from each animal was processed one frame (60 samples, 120 ms) at a time. After removing the DC-offset, the signal was fed into several signal processing blocks, each performing a separate detection algorithm and producing a trigger output. The trigger outputs were then incorporated, in various animal specific logical AND/OR-combinations, to obtain the final detection/no detection decision. Further explanation of the relevant calculations is provided in Supplementary Methods.

3 Supplementary Figure S3: Behavioral seizures over the recording period of an example animal. In the example animal used for triggering and seizure analysis (see Supplementary Note), all behavioral seizures recorded over the entire intervention period were plotted. Note that both the seizure frequency and duration vary, regardless of whether they occurred during a light (blue triangles) or no light (red circles) condition. Also note that fewer seizures occurred when light was activated, indicating that intervention prevented the progression of seizures to behavioral stages.

4 Supplementary Note 1 Assessment of seizure detection program In order to assess the triggering rate, the detection rate, and the false positive rate, we conducted a detailed analysis of the triggers and seizures in an animal which showed the highest number of behavioral seizures. This analysis is split into two sections. The first examines the triggering during the behavioral seizure experimental phase. The second examines the triggering during a prior experimental phase which was used to analyze the effect of bilateral light delivery on electrographic only seizures. Behavioral seizure experimental phase Total behavioral seizures: 95 Total correctly timed triggers for behavioral seizures (i.e., after onset of electrographic seizure, but prior to overt behavior): 91 Total behavioral seizures with incorrectly timed triggers: 4 91/95 = 95.8% correct detection rate 4/95 = 4.2% incorrectly timed triggers 0/95 = 0% complete false ative rate Rate of triggers during this experimental period (NB: this includes triggering on electrographic only seizures) Total time recorded: 1,268.9 hours Total triggers during this time: 9,169 Total no light triggers during this time: 4,570 Total yes light triggers during this time: 4,599 Percent of triggers with light: 50.2% Average trigger rate: 0.12/minute For those correctly timed behavioral seizures, seizures that occurred despite light stimulation closely resembled seizures that occurred with no light stimulation in terms of time before trigger (17.3±2.0s for triggers with light, 18.9±.9s for triggers without light, p=0.53 with student s t test), post-trigger duration (48.8±3.1s with light and 52.8±2.5s with no light; p=0.31 with student s t test), and time from trigger to emergence of behavior (17.9±2.1s with light and 23.2±2.1 without light, p=0.10 with student s t test). Time frame examined for the effect of bilateral stimulation on electrographic seizures Total time: 12hrs Total triggers: 103 Total correct triggers: 102 Total incorrect triggers: 1 rate: 8.6/hr (about 1 every 7minutes) False positive rate: <1% (0.97%)

5 Off-line analysis yielded 104 seizures during this time frame. Total number of seizures missed on-line (versus off-line): 2 Percent of seizures missed on-line (versus off-line): 1.9% Supplementary Methods Custom closed-loop seizure detection software design Custom-designed software for data acquisition, laser triggering, and analysis was created in MATLAB (version R1011b) in conjunction with the MATLAB Data Acquisition and Image Acquisition Toolboxes. Data was sampled from the digitizer at 500Hz, buffered, and processed one frame at a time, with each frame consisting of 60 samples (120ms). DC offset was removed before further processing. Criteria available for use in tuning the triggering algorithm to specific animals signals, either alone or in combination, were designed to identify key features of the signal, and both the combination of criteria and the values for each were specified by the experimenter individually for each animal. These criteria belonged to three general categories, and are detailed below: signal power properties, spike features, and frequency properties. Signal Power properties The power of the EEG signal was used both directly to create inclusion or exclusion criteria and also indirectly to determine amplitude thresholds for other criteria. Those criteria utilizing the signal power are described below. Threshold calculations Some of the trigger criteria required the user to set level-dependent thresholds with respect to the EEG baseline (including for AmplitudeCorrelation, Power Ratio, Fast/Slow Ratio, Spikes, and Narrow Spikes). These thresholds were obtained by first calculating the standard deviation of the signal for each 120 ms frame. This number was then fed into two first order unity gain IIR-filter integrators (a Slow integrator and a Fast integrator). The feedback coefficient of each filter was selected based on the difference between the current input and the current value of the filter. Different rise and fall times could be implemented for each integrator, both the slow and fast integrator, by using level dependent feedback coefficients. The general equation for both the Fast and the Slow integrator is:

6 The value of STDF was then used for those criteria requiring level-dependent thresholds by scaling STDF by a user-specified gain factor suitable to set thresholds for each criteria being used. The Slow integrator was used to threshold Spikes, Narrow Spikes, and AmplitudeCorrelation, while the Fast integrator was used for Fast Power Drop, and both the Fast and Slow integrators were used for Fast/Slow Ratio. Amplitude Correlation This algorithm was based on an autocorrelation method similar to the strategy used by White et al. (2006) 2. However, the subsequent summation and filtering was performed over a shorter time window to enable faster real-time detection of seizures and was computed once per frame (0.12s) as follows: The computed AmplitudeCorrelation AC of each frame was fed into a first order integrator (low pass filter) and the result (ACFiltered) was used for triggering as follows: Fast/slow ratio This value was typically used as an exclusion criterion to prevent triggering in cases where the power of the signal changed very quickly, indicating a likely high amplitude movement artifact rather than seizure activity. The two power integrators (Fast and Slow) described above (threshold calculations) were updated once per frame (0.12s) as follows: The condition was then true when:

7 Fast Power Drop This value was utilized similarly to the Fast/slow power ratio above as an exclusion criterion, but required only the Fast integrator described above, and the trigger was satisfied when: Spike features The features of individual spikes and spike trains were useful in identifying and isolating electrographic seizure activity. Individual spikes were detected using thresholds set with the methods described above. Then the following criteria utilizing spike features of the detected spikes were used: Spike Ratio In many cases, a spike train with very regular interspike intervals indicated a seizure. The number of threshold crossings within a sliding time window (the length of which was set by the experimenter) was used to select spikes of a minimum amplitude and rate. Only those spikes located more than the minimum distance (50 ms) apart were considered. When the number of spikes exceeded the minimum number specified by the experimenter, the regularity of the spikes (the inverse of the coefficient of variation of the inter-spike interval) was evaluated as follows: The trigger occurred when SpikeRatio exceeded the desired threshold. These measurements could be performed on positive and ative spikes as separate conditions. Narrow Spikes d to repetitive movement artifacts such as those seen during grooming, chewing, and scratching, the shapes of spikes involved in electrographic seizure activity were often characteristically narrow. As with the Spike Ratio above, both positive and ative spikes above threshold could be analyzed as separate criteria. For the narrow spike condition, the width of each spike was calculated at a user-defined amplitude, expressed as a fraction of the detected peak amplitude of the spike (eg spike halfwidth). Only those spikes satisfying the specified maximum width and the specified minimum interspike interval were included. The trigger condition became true when the number of valid spikes exceeded an experimenter-defined minimum number during the specified time window. Frequency properties The energy within certain frequency ranges was often relatively stronger or weaker during seizures, allowing for the use of frequency analysis to identify seizure activity, as described here: Frequency Band Ratio The relative contribution of the power of specific bands of frequencies to the frequency spectrum of the signal could be used to quickly identify seizure activity in many cases. First, an FFT (Fast Fourier Transform) was computed using the last 512 samples (1.024 s).

8 The experimenter could adjust the values of f 1-4 to maximize the change in the value of FBR at the time of the seizure. The trigger then occurred when the value of FBR exceeded or dropped below the experimenter-defined threshold. Supplementary References 31. Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25, (2009). 32. White, A. M. et al. Efficient unsupervised algorithms for the detection of seizures in continuous EEG recordings from rats after brain injury. J Neurosci Methods 152, (2006).