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Characterization of affective states by pupillary dynamics and autonomic correlates.

Onorati F, Barbieri R, Mauri M, Russo V, Mainardi L - Front Neuroeng (2013)

Bottom Line: ECG, RESP, and PD data from 13 normal subjects were recorded during a memory recall paradigm, and processed with spectral and cross-spectral analysis.A strong linear coupling was found between the variables, due to the influence of RESP on both PD and HRV within the High Frequency (HF) band, from 0.15 to 0.45 Hz.Most importantly, our results point at PD features as possible candidates for characterizing basic emotional stimuli.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano Milan, Italy ; Massachusetts Institute of Technology Cambridge, MA, USA ; Massachusetts General Hospital Boston, MA, USA ; Behavior and Brain Lab, IULM University Milan, Italy.

ABSTRACT
With the recent advent of new recording devices and an easier access to signal processing tools, researchers are increasingly exploring and studying the Pupil Dilation (PD) signal. Recently, numerous studies pointed out the relations between PD dynamics and psychophysiological states. Although it is well known that PD is controlled by the Autonomic Nervous System (ANS), and ANS responses are related to emotional events/stimuli, the relationship between emotional states and PD is still an open issue. The aim of this study is to define the statistical properties of the PD signal, to understand its relation with ANS correlates such as Heart Rate Variability (HRV) and respiration (RESP), and to explore if PD could provide information for the evaluation of the psychophysiological response of ANS to affective triggering events. ECG, RESP, and PD data from 13 normal subjects were recorded during a memory recall paradigm, and processed with spectral and cross-spectral analysis. Our results demonstrate that variability indices extracted from fast PD oscillations, not observable through standard cardiorespiratory identification in the frequency domain, would be able to discern psychophysiological responses elicited by basic emotional stimuli. A strong linear coupling was found between the variables, due to the influence of RESP on both PD and HRV within the High Frequency (HF) band, from 0.15 to 0.45 Hz. Most importantly, our results point at PD features as possible candidates for characterizing basic emotional stimuli.

No MeSH data available.


Related in: MedlinePlus

Coherence analysis for PD and RR signal for the subject “sbj05,” during “Anger” event (left), and coherence analysis for PD and RESP for the subject “sbj15,” during “Happiness” event (right). On the first row, the PSD of RR (left) and RESP (right) are depicted; on the second row, the PSDs of the PD for the two analysis are shown; on the third row, the plots show the Coherence between RR series and PD (left), and the Coherence between RESP signal and PD (right).
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Figure 5: Coherence analysis for PD and RR signal for the subject “sbj05,” during “Anger” event (left), and coherence analysis for PD and RESP for the subject “sbj15,” during “Happiness” event (right). On the first row, the PSD of RR (left) and RESP (right) are depicted; on the second row, the PSDs of the PD for the two analysis are shown; on the third row, the plots show the Coherence between RR series and PD (left), and the Coherence between RESP signal and PD (right).

Mentions: An example of Coherence analysis between PD, RR intervals and RESP is presented in Figure 5. The analysis between PD and the RR signal is shown on the left, while the analysis between PD and RESP is on the right.


Characterization of affective states by pupillary dynamics and autonomic correlates.

Onorati F, Barbieri R, Mauri M, Russo V, Mainardi L - Front Neuroeng (2013)

Coherence analysis for PD and RR signal for the subject “sbj05,” during “Anger” event (left), and coherence analysis for PD and RESP for the subject “sbj15,” during “Happiness” event (right). On the first row, the PSD of RR (left) and RESP (right) are depicted; on the second row, the PSDs of the PD for the two analysis are shown; on the third row, the plots show the Coherence between RR series and PD (left), and the Coherence between RESP signal and PD (right).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3818468&req=5

Figure 5: Coherence analysis for PD and RR signal for the subject “sbj05,” during “Anger” event (left), and coherence analysis for PD and RESP for the subject “sbj15,” during “Happiness” event (right). On the first row, the PSD of RR (left) and RESP (right) are depicted; on the second row, the PSDs of the PD for the two analysis are shown; on the third row, the plots show the Coherence between RR series and PD (left), and the Coherence between RESP signal and PD (right).
Mentions: An example of Coherence analysis between PD, RR intervals and RESP is presented in Figure 5. The analysis between PD and the RR signal is shown on the left, while the analysis between PD and RESP is on the right.

Bottom Line: ECG, RESP, and PD data from 13 normal subjects were recorded during a memory recall paradigm, and processed with spectral and cross-spectral analysis.A strong linear coupling was found between the variables, due to the influence of RESP on both PD and HRV within the High Frequency (HF) band, from 0.15 to 0.45 Hz.Most importantly, our results point at PD features as possible candidates for characterizing basic emotional stimuli.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano Milan, Italy ; Massachusetts Institute of Technology Cambridge, MA, USA ; Massachusetts General Hospital Boston, MA, USA ; Behavior and Brain Lab, IULM University Milan, Italy.

ABSTRACT
With the recent advent of new recording devices and an easier access to signal processing tools, researchers are increasingly exploring and studying the Pupil Dilation (PD) signal. Recently, numerous studies pointed out the relations between PD dynamics and psychophysiological states. Although it is well known that PD is controlled by the Autonomic Nervous System (ANS), and ANS responses are related to emotional events/stimuli, the relationship between emotional states and PD is still an open issue. The aim of this study is to define the statistical properties of the PD signal, to understand its relation with ANS correlates such as Heart Rate Variability (HRV) and respiration (RESP), and to explore if PD could provide information for the evaluation of the psychophysiological response of ANS to affective triggering events. ECG, RESP, and PD data from 13 normal subjects were recorded during a memory recall paradigm, and processed with spectral and cross-spectral analysis. Our results demonstrate that variability indices extracted from fast PD oscillations, not observable through standard cardiorespiratory identification in the frequency domain, would be able to discern psychophysiological responses elicited by basic emotional stimuli. A strong linear coupling was found between the variables, due to the influence of RESP on both PD and HRV within the High Frequency (HF) band, from 0.15 to 0.45 Hz. Most importantly, our results point at PD features as possible candidates for characterizing basic emotional stimuli.

No MeSH data available.


Related in: MedlinePlus