What does delta band tell us about cognitive processes: A mental

calculation studyStavros I. Dimitriadis,Nikolaos A. Laskaris, Vasso Tsirka,

Michael Vourkas, Sifis MicheloyannisElectronics Laboratory, Department of Physics, University of Patras, Patras 26500, GreeceArtificial Intelligence & Information Analysis Laboratory, Department of Informatics, Aristotle University, Thessaloniki, GreeceMedical Division (Laboratory L.Widιn), University of Crete, 71409 Iraklion/Crete, GreeceTechnical High School of Crete, Estavromenos, Iraklion, Crete, Greece

http://users.auth.gr/~stdimitr1

OutlineIntroduction-Multichannels EEG recordings-math calculations (comparison and multiplication)-delta activity (cognition and relation to the difficulty of the task -DMN (default mode network)

Methodology-four delta sub-bands (0.78 – 3.96 Hz)-Signal Power (SP) (derived via squaring and averaging the filtered EEG data)-network analysis (clustering coefficient (C) + path length (L))-Small-World theory (γ,λ,σ)

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Results

Conclusions

Outline

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Intro Method Results Conclusions

Mathematical thinking, as a cognitive process, activates local andspatially distributed cortical networks

Exact calculations are correlated with language function activating language specific regions located in the left hemisphere

During mental calculations different processes are necessary, such as the-recognition of the numbers in their Arabic form,-the comprehension of verbal representation of numbers, -the assignment of magnitudes to numerical quantities,-attention, memory, and other more specialized processes

During difficult math calculations, additional cortical regions, particularly of the left hemisphere, show increased activation. These calculations demand retrieval of simple mathematical fact

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Intro Method Results Conclusions

MotivationMost widely studied bands are theta, alpha, beta and lower gamma

Delta and higher gamma activity have been examined less due to artifact contamination

We intended not only to replicate the previous findings but also to promote the understanding about the significance of this slow band in math calculation

An increase of delta activity has been reported (Dolce & Waldeier,1974,Harmony et al., 1996)

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Intro Method Results Conclusions

Outline of our methodologyThe employment of narrow subbands gave us the opportunity to distinquish the actual brain activity from eye movement related activity, which is equally visible in all frequency range

Based on EEG activity and the network of electrodesthe notion of functional connectivity graph (FCG) topology , is utilized to identify different modes of brain’s self organization.

Network analysis is performed for each subject/task and the inter-task comparison reveals significant differences between the two cognitive tasks

Calculation of SP for each recording site

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Data acquisition: Math Experiment

Intro Method Results Conclusions

3 Conditions:ControlComparisonMultiplication

18 subjects30 EEG electrodesHorizontal and Vertical EOGTrial duration: 3 x 8 secondsSingle trial analysis

The recording was terminated when at least an EEG-trace without visible artifacts had been recorded for each condition7

Intro Method Results Conclusions

Using a zero-phase band-pass filter (3rd order Butterworth filter), signals were extracted within four different narrow bands (0.78-3.9 Hz was divided into non-overlapping subbands, each of 0.78 Hz width)

Filtering

Artifact CorrectionWorking individually for each subband and using EEGLAB (Delorme & Makeig,2004), artifact reduction was performed using ICA

-Components related to eye movement were identified based on their scalp topography which included frontal sites and their temporal course which followed the EOG signals.-Components reflecting cardiac activity were recognizedfrom the regular rythmic pattern in their time course widespreadin the corresponding ICA component. 8

Intro Method Results Conclusions

Signal Power (SP)Calculation of SP for each recording site

The SP values corresponding to each single electrode were contrasted for every subband and additionally the whole delta-band. Significant changes were captured via one-tailed paired t-tests (p < 0.001).

Τhe functional connectivity graph (FCG) describes coordinated brain activity

In order to setup the FCG, we have to establish connectionsbetween the nodes (i.e. the 30 EEG electrodes).

Phase synchronization, is a mode of neural synchronization, that can be easily quantified through EEG signals

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Intro Method Results Conclusions

Phase-locking Value (PLV)PLV quantifies the frequency-specific synchronization between two neuroelectric signals (Mormann et al., 2000 ; Lachaux et. al. 1999).

We obtain the phase of each signal using the Hilbert transform.(t, n) is the phase difference φ1(t, n) - φ2(t, n) between the signals.

PLV measures the inter-trial variability of this phase difference at t.If the phase difference varies little across the trials, PLV is close to 1; otherwise is close to 0

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PLV procedure for a pair of electrodes

Intro Method Results Conclusions

Adopted from Lachaux et al,1999

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0.9

0.6

Building the FCG

The process is repeated for every electrode, creating a complete graph.

Establishing links for a single electrode

Intro Method Results Conclusions

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Surrogate AnalysisIntro Method Results Conclusion

s

-To detect significant connections, we utilized surrogate data to form a distribution of PLI values, for each electrode-pair separately, that corresponds to the case in which there is no functional coupling

- Functional connections that showed significant differences,with respect to the distribution of PLI values generatedby a randomization procedure corresponding to each electrode pair,were only considered.

-Since our analysis was based on a single sweep, we shuffled the time series of the second electrode for each pair (in contrast to the case of multiple trials where one shuffles the trials of the second electrode as described in Lachaux et al., 2000).

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-Finally, the original PLI values were compared against the emerged baseline distribution (surrogate data) and this comparison was expressed via a p-value which was set at p < 0.001.

Intro Method Results Conclusions

Surrogate Analysis

-Graph edges where the above criterion was not met, were assigned a zero-weighted link.

5%

Nonparametric Null Distribution

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Intro Method Results Conclusions

Network AnalysisThe clustering coefficient C of network is defined as :

Ni ii

ihjiGhjihjhij

kk

www

NC

)1(

)(1

3/1

,,,

in which ki is the degree of the current node

The characteristic path length L is defined (through integration across all nodes) as:

Ni ii

ihjiGhjihjhij

kk

www

NC

)1(

)(1

3/1

,,,

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Intro Method Results Conclusions

Small – World network measures-We rewired each network 1000 times using the algorithmproposed in (Maslov & Sneppen, 2002)

-Derived Cr and Lr as the averages correspondingto the ensemble of randomized graphs. The two (normalized)ratios γ= C/Cr and λ= L/Lr were used in the summarizing measure of “small-worldness”, defined as σ= γ/λ, which becomes greater than 1 in the case of networks with small-world topology

-The above described computations were performed foreach subject separately

- Results were employed to detect systematic trends, via statistical comparison among different tasks (comparison–control, multiplication–control,comparison–multiplication), across subjects.

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Intro Method Results Conclusions

Small – World network measuresThe three graph metrics (γ,λ and σ ) were contrasted, for every subband, in a similar manner via paired t-test.

Recording Montage-The selected data had been originally recorded based on a montage using a common reference electrode.

-Since, this selection could influence significantly the subsequent computations of SP and phase-synchrony we additionally re-reference the data.

-Using average-reference, we repeated the above described calculations.

- We mention the observed differences and commonalities.17

Intro Method Results Conclusions

Signal Power (SP)

Bi-color (black and grey) circles denote the sites where significant increase is observed before, but not after average re-referencing

Αll subbands show widespread SP-increase during mathHigher SP values of delta rhythm are seen over regions of the left hemisphere during multiplication in contrast to the number comparison 18

Intro Method Results Conclusions

Network Analysis

The (normalized) clustering coefficient and the (normalized) path length are higher during multiplication, for networks corresponding to those subbands which showed significant inter-task SP-differences.

These findings could be considered as the result of increased nodal (local) activity and less efficient remote connections within the corresponding brainnetworks.

Simultaneously, all subbands show small-world network characteristics i.e. optimum organization.

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ConclusionsWe investigate brain activity in four sub-bands during math calculation.

We characterize EEG recorded brain activity (Related to any particular cognitive task and the four sub-bands)

Based on functional connectivity graphs

Artifact contamination (occulographic and myographic activity)can be overcomed using sub-bands + ICA.

Our methodology offers novel knowledge about delta activity during math calculation and the nodal organization of the related FCGs.

The changes in SP and network organization related todelta rhythm could be possibly explained as the results of inhibitorymechanisms reflecting the deactivation of the default network.

Intro Method Results Conclusions

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