Characterization of fear conditioning and fear extinction by analysis of electrodermal activity

Faghih, Rose T.; Stokes, Patrick A.; Marin, Marie‐France; Zsido, Rachel G.; Zorowitz, Sam; Rosenbaum, Blake L.; Song, Hyohak; Milad, Mohammed R.; Dougherty, Darin D.; Eskandar, Emad N.; Widge, Alik S.; Brown, Emery N.; Barbieri, Riccardo

doi:10.1109/embc.2015.7320204

Cited by 37 publications

(26 citation statements)

References 16 publications

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“…Our results link directly the physiology of sweat glands and the statistical structure of the EDA data collected at the skin surface. Current detailed signal processing methods for EDA analysis require significant computational complexity ( 6 – 10 ). However, looking to the physiology provided a principled framework by which to drastically reduce model dimensionality—all of our models had only one, two, or three parameters—and increase the accuracy of the data description.…”

Section: Discussionmentioning

confidence: 99%

“…For the deconvolution methods, a single pulse shape is assumed, and the EDA signal is represented as the convolution of this pulse shape with neural inputs. For this technique, the neural input is deconvolved from the EDA while simultaneously fitting pulse shape parameters ( 6 – 10 ). Hence, deconvolution methods report the occurrence times and amplitudes of the pulse events.…”

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confidence: 99%

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Point process temporal structure characterizes electrodermal activity

Subramanian

Barbieri

Brown

2020

Proc. Natl. Acad. Sci. U.S.A.

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Electrodermal activity (EDA) is a direct readout of the body’s sympathetic nervous system measured as sweat-induced changes in the skin’s electrical conductance. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Standardized EDA data analysis methods are readily available. However, none considers an established physiological feature of EDA. The sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process. An integrate-and-fire process modeled as a Gaussian random walk with drift diffusion yields an inverse Gaussian model as the interpulse interval distribution. Therefore, we chose an inverse Gaussian model as our principal probability model to characterize EDA interpulse interval distributions. To analyze deviations from the inverse Gaussian model, we considered a broader model set: the generalized inverse Gaussian distribution, which includes the inverse Gaussian and other diffusion and nondiffusion models; the lognormal distribution which has heavier tails (lower settling rates) than the inverse Gaussian; and the gamma and exponential probability distributions which have lighter tails (higher settling rates) than the inverse Gaussian. To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 h of quiet wakefulness. Each of the 11 time series was accurately described by an inverse Gaussian model measured by Kolmogorov–Smirnov measures. Our broader model set offered a useful framework to enhance further statistical descriptions of EDA. Our findings establish that a physiologically based inverse Gaussian probability model provides a parsimonious and accurate description of EDA.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Point process temporal structure characterizes electrodermal activity

Subramanian

Barbieri

Brown

2020

Proc. Natl. Acad. Sci. U.S.A.

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“…For the deconvolution methods, a single pulse shape is assumed for each subject, and the EDA signal is represented as the convolution of this pulse shape with neural inputs. Therefore, the neural input is deconvolved from the EDA while also simultaneously fitting the optimal pulse shape parameters (5)(6)(7)(8)(9). Hence, the deconvolution methods report the occurrence times and amplitudes of the pulse events.…”

Section: Introductionmentioning

confidence: 99%

Point Process Temporal Structure Characterizes Electrodermal Activity

Subramanian

Barbieri

Brown

2020

Preprint

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Electrodermal activity (EDA) is a read-out of the body's sympathetic nervous system measured as sweatinduced changes in the electrical conductance properties of the skin. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality and emotional states. Standardized EDA data analysis methods are readily available. However, none considers two established physiological features of EDA: 1) sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process; 2) inter-pulse interval times vary depending upon the local physiological state of the skin. Based on the anatomy and physiology that underlie feature 1, we postulate that inverse Gaussian probability models would accurately describe EDA inter-pulse intervals. Given feature 2, we postulate that under fluctuating local physiological states, the inter-pulse intervals would follow mixtures of inverse Gaussian models, that can be represented as lognormal models if the conditions favor longer intervals (heavy tails) or by gamma models if the conditions favor shorter intervals (light tails). To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 to 2 hours of quiet wakefulness. We assess the tail behavior of the probability models by computing their settling rates. All data series were accurately described by one or more of the models: two by inverse Gaussian models; five by lognormal models and three by gamma models. These probability models suggest a highly succinct point process framework for real-time tracking of sympatheticallymediated changes in physiological state.

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“…Our group is now 1 year into a 5year program to test that theory. 2 We have defined initial metrics for the transdiagnostic states [126][127][128] and designed novel devices that can use them to drive treatment [129,130].…”

Section: Responsive Closed-loop Stimulationmentioning

confidence: 99%

Deep Brain Stimulation for Treatment-Refractory Mood and Obsessive-Compulsive Disorders

Widge

Dougherty

2015

Curr Behav Neurosci Rep

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Deep brain stimulation (DBS) is an invasive therapy in which electrodes are surgically placed into the brain and deliver continuous electrical stimulation. It was originally developed for Parkinson's disease and other movement disorders. As evidence emerged that certain mental disorders involved abnormal function of specific brain nuclei, multiple investigators launched trials of DBS for mental disorders, particularly major depression (MDD) and obsessive-compulsive disorder (OCD). While open-label results were promising, recent large well-designed clinical trials in MDD have failed. We review the result of major DBS studies to date in psychiatry and what they have taught us about DBS' safety and efficacy. We further review a variety of animal and human neuroscience studies that have started to shed light on DBS' mechanisms of action at multiple brain targets. From these, we identify major trends that are likely to drive psychiatric DBS development in the coming decade, including Bclosed-loop^responsive stimulation, biomarker-based patient selection, and a better modeling of phenotypic heterogeneity within mental disorders. We conclude that on balance, DBS remains promising as a psychiatric treatment, but recent evidence highlights a clear need for further development and a better understanding of mechanisms.

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Characterization of fear conditioning and fear extinction by analysis of electrodermal activity

Cited by 37 publications

References 16 publications

Point process temporal structure characterizes electrodermal activity

Point process temporal structure characterizes electrodermal activity

Point Process Temporal Structure Characterizes Electrodermal Activity

Deep Brain Stimulation for Treatment-Refractory Mood and Obsessive-Compulsive Disorders

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