Spontaneous electrical brain activity in vitro

Power-law identification at the network events distribution Maria Revythi

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What kind of activity do we study? What field do I study? Why is it important? Proposed approximation My first steps Difficulties Next steps

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Before brain was considered as an hierarchical neurons system.

Nowadays it is considered as a network with a lot of connections, characterized by parallel processing and serial functions. 3

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Hence, we study the spontaneous cortical activity during the growing and aging period. Our recordings come from ‘in vitro’ measurements, at cortical slices of mice. These recordings are local field potential (LFP) combined or many simultaneous recordings taken by multi electrode arrays.

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The

spontaneous activity produced by the isolated cortex, without electrical stimulation or pharmacological induction. Activity

that has the form of periodic synchronized discharges, that appeal like discrete events of potential variation (LFP measurements) with slow spontaneous oscillations(upstates/downstates). 5

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Study of the signals that come from measurements on isolated cortices observations on upstates(0) and blimps(1) Signal processing for each recording separately at first and then considering the recordings altogether Power-law detection

Start from the experiment RIG053A,with the following experiment conditions: 6

CH1 SLICE 11, S1 cortex AGE:34 DAYS (About 1 month)

CH2 SLICE 11, S1 and YFP AGE:510 DAYS (Exactly 17 months)

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Better understanding of the temporal dynamics of biological circuits. Neural circuits’ “inner life”, manifested by a high degree of organized spontaneous activity, gives a new dimension at neural circuit’s detection. As brain’s activity not only interacts with the sensory inputs, but also can even generate behavior in the absence of sensory stimuli, the brain is very interested in its own activity. 8

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Thus, it is a necessity to enter into these circuits and decipher their inner lives need for novel techniques such as

Computational and Theoretical Methods

Neuroscience ₊ Electrical Engineering ₌ Circuit Neuroscience

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The spontaneous cortical activity as an index of normal operation of cortical networks. Use of Power-law to compare the normal state of a cortex and the diseased one. Response to these research questions will help the anti-epileptic drugs discovery.

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Purpose: Power law detection through one

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Start from: Study of power through an

signal and then a double signal, three signals altogether and more.

upstate event(uppicks and downpicks). Comparison with power’s average. Upstate Correlation’s and autocorrelation’s Computation.

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Power spectral density computation: The total power P of a signal is the following time average: 

The purpose is to detect any periodicities in the data, by observing peaks at the frequencies corresponding to these periodicities. 

Graphic design where x axis will represent frequency for amplitude extraction (Hz) and y axis frequency for phase extraction:

Use of Laplacian transform in order to subtract the signal from an electrode by the average of its nearest neighbors for the above graphics.

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Under Laplacian transform, the signal from each electrode can be subtracted by the mean of its three or four nearest neighbors. This frequency analysis necessitates filtering

the broadband signal in different frequency ranges and extracting the phase and power of a lower- and a higher- frequency band, respectively.

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35 30 25

upstates(0)

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blimps(1)

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unclassified(-)

10 5 0 Channel 1

Channel 2

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Channel 1 displays the following Voltage variations (uppicks or downpicks):

13,9339-13,9343min:3 small downpicks only at CH1 at -50,-40 and-30μV 13,7037-13,7043min:2 small downpicks only at CH1at -90μV and -60μV 13,61-13,6105min:straight downpick at -536μV, also appears at CH2 at -477,18μV

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14,875-14,885min: many downpicks only at CH1 with the maximum of them at -112 μV (probably external noise) 14,6821-14,6824min: 3 small downpicks only at CH1 at -55, -37 and -33μV 14,596-14,6min: small fall of Voltage with an average of -25μV 14,582-14,5835min: 3 downpicks only at CH1 at -82,-53 and -84μV 14,4865-14,4875min: small uppick at 76,5μV at CH1 and at 85μV at CH2 14,48075-14,4808min: uppick at 336μV at CH1 and at 660μV at CH2 14,273-14,2734min: 2 small downpicks only at CH1 at -40 and -60μV 14,236-14,237min: 2 small downpicks only at CH1 at -40 and -50 μV

We also notice 2 blimps at 14,04 and 14,05min at CH1.

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• 15,686-15,6874min: small fall of Voltage with an average of -30μV • 15,639-15,641min: small fall of Voltage with an average of -30μV • 15,2405-15,242min: small fall of Voltage with an average of -20 μV • 15,1145-15,1165min: small fall of Voltage with an average of -20 μV • 15,024-15,025min: small fall of Voltage with an average of -20μV We also notice 2 blimps at 14,04 and 14,05min at CH1.

Channel 2 displays noticeable white noise as shown:

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Voltage(μV) -500

-1000 15,69

15,64

15,24

15,12

15,02

14,88

14,68

14,6

14,58

14,49

14,48

13,61 13,74 13,93 14,24 14,27

500

0 downpick1

downpick2

downpick3

uppick

Time(min)

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My first steps(5/6)

1st uppick

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My first steps(6/6)

first upstate’s powerlaw distribution

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Power Spectrum diagrams for frequency and time:

-For each voltage variation like ‘uppick’ or ‘downpick’ -For the amount of each signal -For all signals without pharmacological intervention -For all signals with pharmacological intervention 21

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For the amount of signals, avalanches (group of electrodes) selection. Study of criticality during different age periods.

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