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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (3): 297-306    DOI: 10.31083/JIN-170056
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QEEG-based neural correlates of decision making in a well-trained eight year-old chess player
Abolfazl Alipour1, 2, Sahar Seifzadeh3, Hadi Aligholi2, 4, Mohammad Nami2, 4, 5, *()
1 Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
2 Neuroscience Laboratory-NSL (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran;
3 Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
4 Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
5 Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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Abstract  

The neurocognitive substrates of decision making in the context of chess has appealed to the interest of investigators for decades. Expert and beginner chess players are hypothesized to employ different functional brain networks when involved in episodes of critical decision making while playing chess. Cognitive capacities including, but not restricted to, pattern recognition, visuospatial search, reasoning, planning, and decision making are perhaps the key determinants of the reward and judgment decisions made during chess games. Meanwhile, the precise neural correlates of decision making in this context has largely remained elusive. Quantitative electroencephalography is an investigative tool possessing an appropriate temporal resolution for the study of the neural correlates of cognitive tasks at a cortical level. A 22-channel electroencephalography setup and digital polygraphy were employed in the investigation of a well-trained eight-year old boy while engaged in playing chess against a computer. Quantitative analyses mapped and source-localized electroencephalography signals. Analyses indicated a lower power spectral density for higher frequency bands in the right hemisphere during decision making related epochs. Moreover, in the given subject, the information flow of decision making blocks tended to move from posterior towards anterior brain regions.

Key words:  Chess      decision making      QEEG      electroencephalography      power spectra      functional connectivity     
Submitted:  09 September 2017      Accepted:  13 October 2017      Published:  15 August 2018     
*Corresponding Author(s):  Mohammad Nami     E-mail:  torabinami@sums.ac.ir

Cite this article: 

Abolfazl Alipour, Sahar Seifzadeh, Hadi Aligholi, Mohammad Nami. QEEG-based neural correlates of decision making in a well-trained eight year-old chess player. Journal of Integrative Neuroscience, 2018, 17(3): 297-306.

URL: 

https://jin.imrpress.com/EN/10.31083/JIN-170056     OR     https://jin.imrpress.com/EN/Y2018/V17/I3/297

Fig. 1.  Data acquisition setup. Subject engaged in chess against a computer. The subject reported six episodes of critical decision making during which a real-time EEG recording was marked and subsequently grand averaged in brain mapping. The galvanic skin conductance (GSC) and heart rate variability (HRV) were simultaneously monitored during the procedure with special markings indicating decision making episodes.

Fig. 2.  Spiderweb chart plot of absolute power values for the theta frequency band upon decision making (orange) and resting state (blue) QEEG data. The analysis revealed higher theta power upon decision making task in F8-T3, F4-C3, T4-P3, C4-P3 and T6-O1 dipoles compared to resting state. Values are presented in $ \mu $V$ ^2 $. # p < 0.05, ## p < 0.01 and ### p < 0.001. FP: frontopolar, C: central, F: frontal, P: parietal, T: temporal and O: occipital.

Fig. 3.  Spiderweb chart plot of absolute power values for the alpha frequency band upon decision making (orange) and during resting state (blue) QEEG data. Analysis revealed higher alpha power during decision making task at F4-C3 dipoles compared with the resting state, ## p < 0.01. The F8-T3 dipole, however, showed the alpha power to dominate the resting state when compared to task-positive epochs, * p < 0.05. Values are presented as $ \mu $V$^2$. FP: frontopolar, C: central, F: frontal, P: parietal, T: temporal and O: occipital.

Fig. 4.  Spiderweb chart plot of absolute power values for the beta frequency band upon decision making (orange) and during resting state (blue) QEEG data. Analysis revealed higher beta power during the resting state when compared with DM epochs at F8-T3, F8-F4, F4-C4, C4-P4, and FP2-F3. * p < 0.05, ** p < 0.01, ## p < 0.01. Values are presented in $ \mu $V$^2$. FP: frontopolar, C: central, F: frontal, P: parietal, T: temporal and O: occipital.

Fig. 5.  QEEG topographical spectral brain maps. Analysis demonstrated absolute power values across spectra during resting state (section (A), upper panel) and decision making QEEG data (panel (B), upper panel). Resting state QEEG centroparietal activity is compatible with default mode network activity. Grand average for the six decision making blocks (section (B)) suggest lower than expected beta power in the anterior brain region rather than in the occipital and centroparietal areas. The lower panels in sections (A) and (B) give resting state and decision making beta coherence maps indicating a frontal beta hypocoherence during task-positive rather than resting states.

Fig. 6.  Autonomic response during the decision making task. Panels (A)-(C) give heart rate variability (HRV), pNN50 (number of pairs of successive NN intervals that differ by more than 50 ms), and GSR (in $ \mu $Siemens). Yellow color indicates the decision making (DM) blocks from which EEG data were extracted and analyzed. Despite no significant difference in GSR between DM and non-DM blocks, there was an apparent increase in HRV and PNN50 in the DM blocks. This suggests involvement of an autonomic component when the subject was engaged in the DM task.

Fig. 7.  Information flow between major sources and sinks during critical decision making. (A) (left to right): Position of information sources in sagittal, coronal, and transverse sections. (B): Color matrix for information flow between all signal sources. Each square represents the information flow difference between resting and DM states for a specific pair of signal sources. For example, the red elements (four, three) indicate increased information flow and causal effect of signal source three on signal source four during DM epochs. (C): Position of information sources in 3D space. (D) (left to right): Position of "information sinks’’ in sagittal, coronal, and transverse sections. (E): Reciprocal color matrix (versus (B)) for information flow between all signal sources. (F): Position of information sinks in 3D space. Sources and sinks were identified by thresholding the magnitude of their causal effect. Sources of information flow during DM blocks were localized at components 3, 4, 13, 14, 15 and 16 (anatomically linked to the left vmPFC, right occipital and right medial temporal cortices); the information flow sinks were localized at components 9, 11, 19 and 22 (anatomically linked to the left OFC, left PPC and right IPS).

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