567739 research-article2015
EEGXXX10.1177/1550059414567739Clinical EEG and NeuroscienceReinsberger et al
Article
Autonomic Changes in Psychogenic Nonepileptic Seizures: Toward a Potential Diagnostic Biomarker?
Clinical EEG and Neuroscience 2015, Vol. 46(1) 16–25 © EEG and Clinical Neuroscience Society (ECNS) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1550059414567739 eeg.sagepub.com
Claus Reinsberger1,2, Rani Sarkis1, Christos Papadelis3, Chiran Doshi3, David L. Perez4,5,6, Gaston Baslet1,4, Tobias Loddenkemper3, and Barbara A. Dworetzky1
Abstract Disturbances of the autonomic nervous system (ANS) are common in neuropsychiatric disorders. Disease specific alterations of both sympathetic and parasympathetic activity can be assessed by heart rate variability (HRV), whereas electrodermal activity (EDA) can assess sympathetic activity. In posttraumatic stress disorder (PTSD), parasympathetic HRV parameters are typically decreased and EDA is increased, whereas in major depressive disorder (MDD) and dissociation, both parasympathetic and sympathetic markers are decreased. ANS abnormalities have also been identified in psychogenic nonepileptic seizures (PNES) by using HRV, indicating lower parasympathetic activity at baseline. In addition to reviewing the current literature on ANS abnormalities in PTSD, MDD, and disorders with prominent dissociation, including borderline personality disorder (BPD), this article also presents data from a pilot study on EDA in patients with PNES. Eleven patients with PNES, during an admission to our epilepsy monitoring unit (EMU), were compared with 9 with generalized tonic-clonic seizures (GTCS). The area under the EDA curve, the number of EDA responses lasting longer than 2 seconds, and the number of EDA surges during sleep (sympathetic sleep storms) were calculated on ictal and interictal days by an automated algorithm. EDA changes in PNES patients did not follow a systematic pattern of sympathetic hyperarousal (like EDA after GTCS) but were more variable. How specific PNES semiologies, and/or underlying neuropsychiatric disorders, may influence ictal and interictal EDA patterns, and lead to a novel diagnostic biomarker remains to be evaluated in future larger studies. Keywords psychogenic nonepileptic seizures (PNES), autonomic nervous system (ANS), electrodermal activity (EDA), heart rate variability (HRV)
Introduction Disturbances of the ANS have been increasingly recognized in many neuropsychiatric disorders, including PNES. Initial observations such as tachycardia in PTSD, and similar conditions, are now supplemented with more sophisticated and detailed parameters of the ANS. These biomarkers not only contribute to a pathophysiological understanding of neuropsychiatric syndromes, but also serve as an adjunctive diagnostic tool. In this article, we provide a focused overview of various ANS biomarkers, and the current literature on ANS dysfunction in neuropsychiatric disorders, specifically PTSD, MDD, and dissociation, to contextualize the literature describing ANS disturbances in PNES. We also present pilot data on EDA in patients with PNES, and suggest future approaches.
Parameters of the ANS For a considerable time, the ANS has been of interest in neurological and neuropsychiatric diseases. It is widely recognized
that ANS dysfunction may be associated with many neurological disorders, especially conditions presenting with paroxysmal symptoms, including epilepsy. Autonomic disturbances in 1
Edward B. Bromfield Epilepsy Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA 2 Institute of Sports Medicine, University of Paderborn, Paderborn, Germany 3 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA 4 Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA 5 Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA 6 Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Corresponding Author: Claus Reinsberger, Institute of Sports Medicine, University of Paderborn, Warburger Straße 100, 33098 Paderborn, Germany. Email: [email protected] Full-color figures are available online at http://eeg.sagepub.com
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Reinsberger et al Table 1. Overview of Selected HRV Parameters.a Variable
Unit
Description Standard deviation of all NN intervals Standard deviation of the average in all 5-min segments of the entire recording The square root of the mean of the sum of the squares of differences between adjacent NN intervals Standard deviations of differences between adjacent NN intervals Total number of all NN intervals divided by the height of the histogram of all NN intervals measured on a discrete scale with bins of 1/128 seconds Power in the very low frequency range (≤0.04 Hz) Power in the low-frequency range (0.04-0.15 Hz) Power in the high-frequency range (0.15-0.4 Hz)
Time domain
SDNN SDANN RMSSD
ms ms ms
SDSD HRV triangular index VLF LF HF
ms rel
Frequency domain
ms2 ms2 ms2
Abbreviations: HF, high-frequency power; HRV, heart rate variability; LF, low-frequency power; NN, normal-to-normal; rel., relative; RMSSD, square root of the mean of the sum of the squares of differences between adjacent NN intervals; SDANN, standard deviation of the averages of NN intervals; SDNN, standard deviations of all normal-to-normal intervals; SDSD, standard deviation of differences between adjacent NN intervals; VLF, very low frequency power; VT, vagal tone. a After Malik et al (1996).4
epilepsy may be directly involved in the pathogenesis of the disorder, or aid our understanding of the relationship between a disorder and frequently associated comorbidities within the same or different organ systems.1,2
Heart Rate, Blood Pressure, Skin Temperature The most basic parameter for ANS assessment is heart rate (HR), which is controlled by both sympathetic and parasympathetic influences. Along with blood pressure and skin temperature, these parameters often indicate changes of ANS function and interaction between the antagonistic sympathetic and parasympathetic subsystems. For an in-depth analysis of ANS function and dysfunction, separate biomarkers of sympathetic or parasympathetic activity alone are usually of greater value than markers that are influenced by both subsystems. Among these are parameters derived by HRV and EDA.
Heart Rate Variability HRV refers to the interbeat variations of HR signals. It was first described by Hon and Lee3 in 1965 in fetuses, and since then several aspects of the interval and oscillations of intervals between heart beats have been described. At least 5-minute intervals/segments are used to measure and calculate markers for parasympathetic, possibly sympathetic or mixed activity.4 HRV can be described by measures of time or frequency (see Table 1).4 Examples of parameters in the time domain include the standard deviations of all normal-to-normal (NN) intervals (SDNN), the standard deviation of the averages of NN intervals in all 5-minute segments of the entire recording (SDANN), the square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), the standard deviation of differences between adjacent NN intervals (SDSD), and the total number of all NN intervals divided by the height of the histogram of all NN intervals, measured on a discrete scale (HRV triangular index). Since the late 1960s, frequency domain
methods have increasingly been used by applying spectral density analyses. Mainly, 3 spectral components (very low frequency [VLF, 26°C. To keep this effect constant, it is recommended to keep temperatures under lab conditions stable and at 0.02 μS/s, and the duration of EDR. In addition, we also determined surges of EDA during sleep (“sympathetic sleep storms”) as defined by Sano and Picard71 (≥3 EDR within 30 seconds and absence of motion in actigraphy). If storm epochs were adjacent or within five minutes of each other, they were combined into one single “storm.” Figure 1 presents 2 actual and 1 pseudo-sympathetic sleep storm, indicated by light red, on the first derivative of the EDA signal (Figure 1B). A pseudo-storm was defined as an event that satisfied all the criteria to be a sympathetic storm, but did not occur during sleep (motion algorithm detected motion activity in actigraphy data—see Figure 1D for the first storm). Differences between EDA variables of PNES and GTCS were assessed by performing a Wilcoxon rank sum test.
Results Clinical data of PNES and GTCS patients are presented in Table 2. Among the 11 PNES patients, 5 were MDD, 1 PTSD,
1 generalized anxiety disorder (GAD), 1 both PTSD and GAD, and 3 did not meet criteria for a neuropsychiatric disorder based on clinical psychiatric interviews. Interictally, no differences were found in the EDA measures between the 2 patient populations (Table 3). On event days, the longest EDR was found to be significantly greater in the GTCS group in comparison to the PNES group (P = .0074). No further significant differences were found, but values of several indices were highly variable within both groups. Some patients with PNES had EDRs that were higher than ictal GTCS values, but overall an expected amount of interindividual variability was found in all EDA parameters.12 The limited number of complete data sets and the inclusion of patients with subjective symptoms (yet highly indicative of PNES based on clinical history and EEG findings) precludes a more detailed and systematic statistical analysis comparing ictal and interictal values. However, all GTCS patients, for whom complete data sets were available, had an (mostly excessive) increase of the AUC of EDA on ictal days as compared with interictal values (patients 1, 2, 4, 5, and 7). In contrast, PNES patients either had mild increases (PNES patients 1 and 5) or decreases (patients 2 and 11), when AUC values are compared on ictal and interictal days. Interestingly, the patient with the largest increase (patient 1) presented with a major motor PNES semiology, while 1 patient with very mild increase (patient 5) and 2 patients with ictal AUC decrease presented with nonmotor or subjective PNES symptoms. Similarly, the number of EDRs increased in all but one GTCS patient from interictal to ictal days, but only in 1 of 3 PNES patients (and in that 1 case only from 0 to 1).
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Table 2. Clinical Characteristics of the Cohort of 11 PNES Patients and 9 Epilepsy Patients. Age/Gender
Psychiatric Comorbidities
Medications
PNES patients 1 59/M
Nonea
2 3 4
58/F 35/F 25/F
None GAD MDD
5 6
47/F 51/M
7
31/F
8
55/F
None MDD, psychotic symptoms PTSD, bipolar disorder MDD
9 10 11
41/F 25/F 51/F
MDD, GAD, PTSD MDD PTSD, GAD
LEV, LTG BB Amitryptiline, mixed amphetamine and amphetamines, LTG, LZP
Epilepsy patients 1 51/M 2 25/M 3 67/M
None None MDD
4 5 6 7 8 9
None Dysthymia MDD GAD None GAD
DZP, VPA LEV, OXC Clonidine, duloxetine, LAC, quetiapine, VPA LAC, LTG, olanzapine, TPM LEV, ZNS Amitryptiline, LTG, quetiapine LEV, LAC LEV, LAC, PGB LEV, trazodone
58/M 52/F 44/F 34/F 53/M 36/F
BB, CLN, venlafaxine, TPM, trazodone CBZ, CLN, duloxetine, PGB LEV Levothyroxine, escitalopram, LZP,PGB,VPA GBP, LZP, OXC Venlafaxine BB, GBP, LEV, quetiapine, sertraline, VPA CLN, LEV, VPA
Classification/Symptoms Major motor: whole body shaking Subjective: light-headedness, strange sensation Dialeptic: staring Major motor: whole body shaking Subjective: headache/ not feeling herself Major motor: whole body shaking Major motor: whole body shaking Subjective/minor motor: dizziness, paresthesias, light-headedness, hand clenching Dialeptic: staring Major motor: whole body shaking Subjective: lightheadedness, feeling warm, shaky, unreal, and anxious
GTCS GTCS GTCS GTCS GTCS GTCS GTCS GTCS GTCS
Abbreviations: BB, beta blocker; CBZ, carbamazepine; CLN, clonazepam; DZP, diazepam; F, female; GAD, generalized anxiety disorder; GBP, gabapentin; GTCS, generalized tonic-clonic seizures; LAC, lacosamide; LEV, levetiracetam; LTG, lamotrigine; LZP, lorazepam; M, male; MDD, major depressive disorder; PGB, pregabalin; PTSD, posttraumatic stress disorder; TPM, topiramate; VPA, valproic acid; ZNS, zonisamide. a Antidepressive drugs were prescribed by outpatient providers, but the patient did not meet criteria for a psychiatric diagnosis at the time of the evaluation.
Conclusion The increase in the number of EDRs, the duration of the longest EDR and the AUC of EDA on ictal days supported the hypothesis that GTCS are associated with a more profound sympathetic arousal than PNES. In contrast to the previously reported EDA responses following GTCS,69 PNES are associated with less intense EDA response and often lack the typical large peak that is seen after GTCS, which may be utilized as a tool to differentiate between the 2 seizure types. Whether and how ictal autonomic parameters of PNES are different from baseline states in these patients will be evaluated in future studies, which should include more subjects and more complete datasets. Similarly, our dataset did not allow interpretation of EDA with respect to the underlying psychopathology or neuropsychiatric syndrome, because of our limited sample size. For instance, an ictal increase of AUC of EDA was found in 2 patients without evidence of a neuropsychiatric disorder at the
time of presentation, a remarkable decrease on ictal days was found in a patient with comorbid PTSD, and a slight decrease in a patient without a neuropsychiatric disorder. Inclusion of further patients will allow for the investigation of PNES subtypes with potentially distinct ANS peri-ictal patterns, as well as elucidate the association between psychiatric comorbidities and ANS patterns in PNES. It may also help clarify the influence of antidepressant medications on the ANS, specifically EDA. Trazodone, which may be associated with orthostatic hypotonia and arrhythmias, and amitriptyline, which has anticholinergic properties, may well influence EDA responses both ictally and interictally. The appearance and number of sleep storms in neurological and neuropsychiatric disorders is still poorly understood. Four of 5 patients in our study had an increase of sympathetic sleep storms during the night after a GTCS. Similarly 1 patient with PTSD, and 1 without neuropsychiatric disorder, showed this phenomenon the night after a PNES, while 2
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Reinsberger et al Table 3. EDA Variables in 11 PNES Patients and 9 Epilepsy Patients With Documented GTCS. No. of EDA Response ≥2 Seconds (Interictal/Ictal)
Longest EDA Response in Seconds (Interictal/Ictal)
No. of Sleep Storms (Interictal/Ictal)
0.13/1.40 20.17/19.70 —/0.004 0.006/— 0.11/0.26 —/35 8.31/— 3.57/— 12.89/— —/96.5 15.3/5.4
7/2 36/22 —/0 3/— 0/1 —/0 15/— 3/— 9/— —/42 0/0
2.75/2.88 5.13/6.13 —/1.88 2.13/— 0.88/2.13 —/1.38 5.36/— 3.50/— 2.75/— —/3.75 1.69/1.00
3/0 12/9 —/1 3/— 0/4 —/4 9/— 5/— 36/— —/28 0/3
23.03/42.07 1.05/11.18 73.42/— 0.64/8.59 2.13/2.91 0.01/— 5.56/11.93 2.4/— 6.76/—
95/454 1/19 104/— 0/33 16/13 0/— 55/34 5/— 0/—
5.63/7.63 2.00/4.80 7.40/— 1.60/7.50 3.60/10.25 1.50/— 7.50/8.63 2.90/— 0.50/—
6/33 0/5 10/— 1/4 3/5 0/— 20/5 2/— 0/—
AUC (Interictal/ Ictal) PNES patients 1 2 3 4 5 6 7 8 9 10 11 Epilepsy patients 1 2 3 4 5 6 7 8 9
AUC: Area under the EDA curve; EDA: electrodermal activity; GTCS: generalized tonic-clonic seizure; PNES: psychogenic non-epileptic seizure.
without neuropsychiatric disorders had a decrease in storms. These data seem to support a prolonged sympathetic arousal after a GTCS, but the relationship between sleep storms and PNES remains to be elucidated. Overall, EDA changes in PNES patients did not follow a systematic sympathetic hyperarousal pattern like EDA after GTCS, but rather exhibited variable patterns. Specific PNES semiologies, and/or underlying neuropsychiatric disorders, may influence the ictal and interictal EDA pattern, and will be further evaluated by assessing these ANS values in larger samples. The small sample size in each group, and the high number of incomplete data sets, are notable limitations of this pilot study, which should be addressed in future research. It will also be useful to include a complex partial seizure control group, as the differentiation between CPS and PNES may often be clinically challenging. Our pilot data did not allow inclusion of a CPS group because of the low number of captured seizures.
Summary Changes in ANS physiology are commonly associated with neuropsychiatric conditions. As the methods and biosensors become more widely available, central changes in ANS in neuropsychiatric disorders, including PNES, will be further clarified (see Table 4 for an overview). Improved understanding of ANS disturbances in PNES will significantly contribute toward further classification of the pathophysiological mechanism behind this functional neurological symptom disorder. Future
Table 4. Overview of ANS Patterns in Select Neuropsychiatric Disorders and PNES. HF-HRV (Parasympathetic)
EDA (Sympathetic)
↓ ↓ ↓ ↓/↑ ↓/=
↑ ↓ ↓ ↑ ↑/ = / ↓
PTSD MDD Dissociation BPD PNES
Abbreviations: BPD, borderline personality disorder; EDA, electrodermal activity; HF-HRV, high-frequency power of heart rate variability; MDD, major depressive disorder; PNES, psychogenic nonepileptic seizures; PTSD, posttraumatic stress disorder.
research on ANS variables in PNES may offer a potential diagnostic biomarker, and further contribute to the classification of PNES into subgroups, which may help development of more specific diagnostic tests and treatments. Acknowledgments Biosensors used in this study were donated by Affectiva Inc.
Author Contributions CR, DLP, GB and BAD performed the literature research and wrote the manuscript. CR, RS, TL and BAD performed the study on EDA signals. CP and CD performed the computational analysis of EDA
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signals. RS performed the statistical analysis on EDA signals. All authors participated in editing the manuscript.
Declaration of Conflicting Interests The author(s) declared the following conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Reinsberger has received grants from the Epilepsy Foundation (EF#179308, not relevant to this article) and receives compensation for clinical consulting for SleepMed Inc (not relevant to this article). Dr Reinsberger also received grants from the Epilepsy Foundation (EF#213882, relevant to this article). Dr Sarkis received travel funding for an investigators meeting from Sunovion (not relevant to this article). Dr Papdelis and Mr Doshi have no disclosures. Dr Perez was funded by a NINDS R25NS065743-05S1 grant (not relevant to this article). Dr Baslet has no disclosures. Dr Loddenkemper serves on the Laboratory Accreditation Board for Long Term (Epilepsy and Intensive Care Unit) Monitoring, on the Council of the American Clinical Neurophysiology Society, on the American Board of Clinical Neurophysiology, as an Associate Editor for Seizure, and performs video electroencephalogram long-term monitoring, electroencephalograms, and other electrophysiological studies at Boston Children’s Hospital and bills for these procedures and evaluates pediatric neurology patients and bills for clinical care. He is part of a pending patent application to detect seizures. He receives research support from the American Epilepsy Society, the Epilepsy Foundation of America, the Center for Integration of Medicine and Innovative Technology, the Epilepsy Therapy Project, the Pediatric Epilepsy Research Foundation, Cure, Danny-Did Foundation, HHV-6 Foundation, and from investigator-initiated research grants from Lundbeck and Eisai. His wife, Karen Stannard, MD, is a practicing pediatric neurologist who evaluates pediatric neurology patients and bills for clinical care. Dr Dworetzky receives compensation for clinical consulting for SleepMed Inc and for Best Doctors (not relevant to this article).
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EEGXXX10.1177/1550059414567739Clinical EEG and NeuroscienceReinsberger et al
Article
Autonomic Changes in Psychogenic Nonepileptic Seizures: Toward a Potential Diagnostic Biomarker?
Clinical EEG and Neuroscience 2015, Vol. 46(1) 16–25 © EEG and Clinical Neuroscience Society (ECNS) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1550059414567739 eeg.sagepub.com
Claus Reinsberger1,2, Rani Sarkis1, Christos Papadelis3, Chiran Doshi3, David L. Perez4,5,6, Gaston Baslet1,4, Tobias Loddenkemper3, and Barbara A. Dworetzky1
Abstract Disturbances of the autonomic nervous system (ANS) are common in neuropsychiatric disorders. Disease specific alterations of both sympathetic and parasympathetic activity can be assessed by heart rate variability (HRV), whereas electrodermal activity (EDA) can assess sympathetic activity. In posttraumatic stress disorder (PTSD), parasympathetic HRV parameters are typically decreased and EDA is increased, whereas in major depressive disorder (MDD) and dissociation, both parasympathetic and sympathetic markers are decreased. ANS abnormalities have also been identified in psychogenic nonepileptic seizures (PNES) by using HRV, indicating lower parasympathetic activity at baseline. In addition to reviewing the current literature on ANS abnormalities in PTSD, MDD, and disorders with prominent dissociation, including borderline personality disorder (BPD), this article also presents data from a pilot study on EDA in patients with PNES. Eleven patients with PNES, during an admission to our epilepsy monitoring unit (EMU), were compared with 9 with generalized tonic-clonic seizures (GTCS). The area under the EDA curve, the number of EDA responses lasting longer than 2 seconds, and the number of EDA surges during sleep (sympathetic sleep storms) were calculated on ictal and interictal days by an automated algorithm. EDA changes in PNES patients did not follow a systematic pattern of sympathetic hyperarousal (like EDA after GTCS) but were more variable. How specific PNES semiologies, and/or underlying neuropsychiatric disorders, may influence ictal and interictal EDA patterns, and lead to a novel diagnostic biomarker remains to be evaluated in future larger studies. Keywords psychogenic nonepileptic seizures (PNES), autonomic nervous system (ANS), electrodermal activity (EDA), heart rate variability (HRV)
Introduction Disturbances of the ANS have been increasingly recognized in many neuropsychiatric disorders, including PNES. Initial observations such as tachycardia in PTSD, and similar conditions, are now supplemented with more sophisticated and detailed parameters of the ANS. These biomarkers not only contribute to a pathophysiological understanding of neuropsychiatric syndromes, but also serve as an adjunctive diagnostic tool. In this article, we provide a focused overview of various ANS biomarkers, and the current literature on ANS dysfunction in neuropsychiatric disorders, specifically PTSD, MDD, and dissociation, to contextualize the literature describing ANS disturbances in PNES. We also present pilot data on EDA in patients with PNES, and suggest future approaches.
Parameters of the ANS For a considerable time, the ANS has been of interest in neurological and neuropsychiatric diseases. It is widely recognized
that ANS dysfunction may be associated with many neurological disorders, especially conditions presenting with paroxysmal symptoms, including epilepsy. Autonomic disturbances in 1
Edward B. Bromfield Epilepsy Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA 2 Institute of Sports Medicine, University of Paderborn, Paderborn, Germany 3 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA 4 Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA 5 Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA 6 Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Corresponding Author: Claus Reinsberger, Institute of Sports Medicine, University of Paderborn, Warburger Straße 100, 33098 Paderborn, Germany. Email: [email protected] Full-color figures are available online at http://eeg.sagepub.com
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Reinsberger et al Table 1. Overview of Selected HRV Parameters.a Variable
Unit
Description Standard deviation of all NN intervals Standard deviation of the average in all 5-min segments of the entire recording The square root of the mean of the sum of the squares of differences between adjacent NN intervals Standard deviations of differences between adjacent NN intervals Total number of all NN intervals divided by the height of the histogram of all NN intervals measured on a discrete scale with bins of 1/128 seconds Power in the very low frequency range (≤0.04 Hz) Power in the low-frequency range (0.04-0.15 Hz) Power in the high-frequency range (0.15-0.4 Hz)
Time domain
SDNN SDANN RMSSD
ms ms ms
SDSD HRV triangular index VLF LF HF
ms rel
Frequency domain
ms2 ms2 ms2
Abbreviations: HF, high-frequency power; HRV, heart rate variability; LF, low-frequency power; NN, normal-to-normal; rel., relative; RMSSD, square root of the mean of the sum of the squares of differences between adjacent NN intervals; SDANN, standard deviation of the averages of NN intervals; SDNN, standard deviations of all normal-to-normal intervals; SDSD, standard deviation of differences between adjacent NN intervals; VLF, very low frequency power; VT, vagal tone. a After Malik et al (1996).4
epilepsy may be directly involved in the pathogenesis of the disorder, or aid our understanding of the relationship between a disorder and frequently associated comorbidities within the same or different organ systems.1,2
Heart Rate, Blood Pressure, Skin Temperature The most basic parameter for ANS assessment is heart rate (HR), which is controlled by both sympathetic and parasympathetic influences. Along with blood pressure and skin temperature, these parameters often indicate changes of ANS function and interaction between the antagonistic sympathetic and parasympathetic subsystems. For an in-depth analysis of ANS function and dysfunction, separate biomarkers of sympathetic or parasympathetic activity alone are usually of greater value than markers that are influenced by both subsystems. Among these are parameters derived by HRV and EDA.
Heart Rate Variability HRV refers to the interbeat variations of HR signals. It was first described by Hon and Lee3 in 1965 in fetuses, and since then several aspects of the interval and oscillations of intervals between heart beats have been described. At least 5-minute intervals/segments are used to measure and calculate markers for parasympathetic, possibly sympathetic or mixed activity.4 HRV can be described by measures of time or frequency (see Table 1).4 Examples of parameters in the time domain include the standard deviations of all normal-to-normal (NN) intervals (SDNN), the standard deviation of the averages of NN intervals in all 5-minute segments of the entire recording (SDANN), the square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), the standard deviation of differences between adjacent NN intervals (SDSD), and the total number of all NN intervals divided by the height of the histogram of all NN intervals, measured on a discrete scale (HRV triangular index). Since the late 1960s, frequency domain
methods have increasingly been used by applying spectral density analyses. Mainly, 3 spectral components (very low frequency [VLF, 26°C. To keep this effect constant, it is recommended to keep temperatures under lab conditions stable and at 0.02 μS/s, and the duration of EDR. In addition, we also determined surges of EDA during sleep (“sympathetic sleep storms”) as defined by Sano and Picard71 (≥3 EDR within 30 seconds and absence of motion in actigraphy). If storm epochs were adjacent or within five minutes of each other, they were combined into one single “storm.” Figure 1 presents 2 actual and 1 pseudo-sympathetic sleep storm, indicated by light red, on the first derivative of the EDA signal (Figure 1B). A pseudo-storm was defined as an event that satisfied all the criteria to be a sympathetic storm, but did not occur during sleep (motion algorithm detected motion activity in actigraphy data—see Figure 1D for the first storm). Differences between EDA variables of PNES and GTCS were assessed by performing a Wilcoxon rank sum test.
Results Clinical data of PNES and GTCS patients are presented in Table 2. Among the 11 PNES patients, 5 were MDD, 1 PTSD,
1 generalized anxiety disorder (GAD), 1 both PTSD and GAD, and 3 did not meet criteria for a neuropsychiatric disorder based on clinical psychiatric interviews. Interictally, no differences were found in the EDA measures between the 2 patient populations (Table 3). On event days, the longest EDR was found to be significantly greater in the GTCS group in comparison to the PNES group (P = .0074). No further significant differences were found, but values of several indices were highly variable within both groups. Some patients with PNES had EDRs that were higher than ictal GTCS values, but overall an expected amount of interindividual variability was found in all EDA parameters.12 The limited number of complete data sets and the inclusion of patients with subjective symptoms (yet highly indicative of PNES based on clinical history and EEG findings) precludes a more detailed and systematic statistical analysis comparing ictal and interictal values. However, all GTCS patients, for whom complete data sets were available, had an (mostly excessive) increase of the AUC of EDA on ictal days as compared with interictal values (patients 1, 2, 4, 5, and 7). In contrast, PNES patients either had mild increases (PNES patients 1 and 5) or decreases (patients 2 and 11), when AUC values are compared on ictal and interictal days. Interestingly, the patient with the largest increase (patient 1) presented with a major motor PNES semiology, while 1 patient with very mild increase (patient 5) and 2 patients with ictal AUC decrease presented with nonmotor or subjective PNES symptoms. Similarly, the number of EDRs increased in all but one GTCS patient from interictal to ictal days, but only in 1 of 3 PNES patients (and in that 1 case only from 0 to 1).
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Clinical EEG and Neuroscience 46(1)
Table 2. Clinical Characteristics of the Cohort of 11 PNES Patients and 9 Epilepsy Patients. Age/Gender
Psychiatric Comorbidities
Medications
PNES patients 1 59/M
Nonea
2 3 4
58/F 35/F 25/F
None GAD MDD
5 6
47/F 51/M
7
31/F
8
55/F
None MDD, psychotic symptoms PTSD, bipolar disorder MDD
9 10 11
41/F 25/F 51/F
MDD, GAD, PTSD MDD PTSD, GAD
LEV, LTG BB Amitryptiline, mixed amphetamine and amphetamines, LTG, LZP
Epilepsy patients 1 51/M 2 25/M 3 67/M
None None MDD
4 5 6 7 8 9
None Dysthymia MDD GAD None GAD
DZP, VPA LEV, OXC Clonidine, duloxetine, LAC, quetiapine, VPA LAC, LTG, olanzapine, TPM LEV, ZNS Amitryptiline, LTG, quetiapine LEV, LAC LEV, LAC, PGB LEV, trazodone
58/M 52/F 44/F 34/F 53/M 36/F
BB, CLN, venlafaxine, TPM, trazodone CBZ, CLN, duloxetine, PGB LEV Levothyroxine, escitalopram, LZP,PGB,VPA GBP, LZP, OXC Venlafaxine BB, GBP, LEV, quetiapine, sertraline, VPA CLN, LEV, VPA
Classification/Symptoms Major motor: whole body shaking Subjective: light-headedness, strange sensation Dialeptic: staring Major motor: whole body shaking Subjective: headache/ not feeling herself Major motor: whole body shaking Major motor: whole body shaking Subjective/minor motor: dizziness, paresthesias, light-headedness, hand clenching Dialeptic: staring Major motor: whole body shaking Subjective: lightheadedness, feeling warm, shaky, unreal, and anxious
GTCS GTCS GTCS GTCS GTCS GTCS GTCS GTCS GTCS
Abbreviations: BB, beta blocker; CBZ, carbamazepine; CLN, clonazepam; DZP, diazepam; F, female; GAD, generalized anxiety disorder; GBP, gabapentin; GTCS, generalized tonic-clonic seizures; LAC, lacosamide; LEV, levetiracetam; LTG, lamotrigine; LZP, lorazepam; M, male; MDD, major depressive disorder; PGB, pregabalin; PTSD, posttraumatic stress disorder; TPM, topiramate; VPA, valproic acid; ZNS, zonisamide. a Antidepressive drugs were prescribed by outpatient providers, but the patient did not meet criteria for a psychiatric diagnosis at the time of the evaluation.
Conclusion The increase in the number of EDRs, the duration of the longest EDR and the AUC of EDA on ictal days supported the hypothesis that GTCS are associated with a more profound sympathetic arousal than PNES. In contrast to the previously reported EDA responses following GTCS,69 PNES are associated with less intense EDA response and often lack the typical large peak that is seen after GTCS, which may be utilized as a tool to differentiate between the 2 seizure types. Whether and how ictal autonomic parameters of PNES are different from baseline states in these patients will be evaluated in future studies, which should include more subjects and more complete datasets. Similarly, our dataset did not allow interpretation of EDA with respect to the underlying psychopathology or neuropsychiatric syndrome, because of our limited sample size. For instance, an ictal increase of AUC of EDA was found in 2 patients without evidence of a neuropsychiatric disorder at the
time of presentation, a remarkable decrease on ictal days was found in a patient with comorbid PTSD, and a slight decrease in a patient without a neuropsychiatric disorder. Inclusion of further patients will allow for the investigation of PNES subtypes with potentially distinct ANS peri-ictal patterns, as well as elucidate the association between psychiatric comorbidities and ANS patterns in PNES. It may also help clarify the influence of antidepressant medications on the ANS, specifically EDA. Trazodone, which may be associated with orthostatic hypotonia and arrhythmias, and amitriptyline, which has anticholinergic properties, may well influence EDA responses both ictally and interictally. The appearance and number of sleep storms in neurological and neuropsychiatric disorders is still poorly understood. Four of 5 patients in our study had an increase of sympathetic sleep storms during the night after a GTCS. Similarly 1 patient with PTSD, and 1 without neuropsychiatric disorder, showed this phenomenon the night after a PNES, while 2
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Reinsberger et al Table 3. EDA Variables in 11 PNES Patients and 9 Epilepsy Patients With Documented GTCS. No. of EDA Response ≥2 Seconds (Interictal/Ictal)
Longest EDA Response in Seconds (Interictal/Ictal)
No. of Sleep Storms (Interictal/Ictal)
0.13/1.40 20.17/19.70 —/0.004 0.006/— 0.11/0.26 —/35 8.31/— 3.57/— 12.89/— —/96.5 15.3/5.4
7/2 36/22 —/0 3/— 0/1 —/0 15/— 3/— 9/— —/42 0/0
2.75/2.88 5.13/6.13 —/1.88 2.13/— 0.88/2.13 —/1.38 5.36/— 3.50/— 2.75/— —/3.75 1.69/1.00
3/0 12/9 —/1 3/— 0/4 —/4 9/— 5/— 36/— —/28 0/3
23.03/42.07 1.05/11.18 73.42/— 0.64/8.59 2.13/2.91 0.01/— 5.56/11.93 2.4/— 6.76/—
95/454 1/19 104/— 0/33 16/13 0/— 55/34 5/— 0/—
5.63/7.63 2.00/4.80 7.40/— 1.60/7.50 3.60/10.25 1.50/— 7.50/8.63 2.90/— 0.50/—
6/33 0/5 10/— 1/4 3/5 0/— 20/5 2/— 0/—
AUC (Interictal/ Ictal) PNES patients 1 2 3 4 5 6 7 8 9 10 11 Epilepsy patients 1 2 3 4 5 6 7 8 9
AUC: Area under the EDA curve; EDA: electrodermal activity; GTCS: generalized tonic-clonic seizure; PNES: psychogenic non-epileptic seizure.
without neuropsychiatric disorders had a decrease in storms. These data seem to support a prolonged sympathetic arousal after a GTCS, but the relationship between sleep storms and PNES remains to be elucidated. Overall, EDA changes in PNES patients did not follow a systematic sympathetic hyperarousal pattern like EDA after GTCS, but rather exhibited variable patterns. Specific PNES semiologies, and/or underlying neuropsychiatric disorders, may influence the ictal and interictal EDA pattern, and will be further evaluated by assessing these ANS values in larger samples. The small sample size in each group, and the high number of incomplete data sets, are notable limitations of this pilot study, which should be addressed in future research. It will also be useful to include a complex partial seizure control group, as the differentiation between CPS and PNES may often be clinically challenging. Our pilot data did not allow inclusion of a CPS group because of the low number of captured seizures.
Summary Changes in ANS physiology are commonly associated with neuropsychiatric conditions. As the methods and biosensors become more widely available, central changes in ANS in neuropsychiatric disorders, including PNES, will be further clarified (see Table 4 for an overview). Improved understanding of ANS disturbances in PNES will significantly contribute toward further classification of the pathophysiological mechanism behind this functional neurological symptom disorder. Future
Table 4. Overview of ANS Patterns in Select Neuropsychiatric Disorders and PNES. HF-HRV (Parasympathetic)
EDA (Sympathetic)
↓ ↓ ↓ ↓/↑ ↓/=
↑ ↓ ↓ ↑ ↑/ = / ↓
PTSD MDD Dissociation BPD PNES
Abbreviations: BPD, borderline personality disorder; EDA, electrodermal activity; HF-HRV, high-frequency power of heart rate variability; MDD, major depressive disorder; PNES, psychogenic nonepileptic seizures; PTSD, posttraumatic stress disorder.
research on ANS variables in PNES may offer a potential diagnostic biomarker, and further contribute to the classification of PNES into subgroups, which may help development of more specific diagnostic tests and treatments. Acknowledgments Biosensors used in this study were donated by Affectiva Inc.
Author Contributions CR, DLP, GB and BAD performed the literature research and wrote the manuscript. CR, RS, TL and BAD performed the study on EDA signals. CP and CD performed the computational analysis of EDA
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Clinical EEG and Neuroscience 46(1)
signals. RS performed the statistical analysis on EDA signals. All authors participated in editing the manuscript.
Declaration of Conflicting Interests The author(s) declared the following conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Reinsberger has received grants from the Epilepsy Foundation (EF#179308, not relevant to this article) and receives compensation for clinical consulting for SleepMed Inc (not relevant to this article). Dr Reinsberger also received grants from the Epilepsy Foundation (EF#213882, relevant to this article). Dr Sarkis received travel funding for an investigators meeting from Sunovion (not relevant to this article). Dr Papdelis and Mr Doshi have no disclosures. Dr Perez was funded by a NINDS R25NS065743-05S1 grant (not relevant to this article). Dr Baslet has no disclosures. Dr Loddenkemper serves on the Laboratory Accreditation Board for Long Term (Epilepsy and Intensive Care Unit) Monitoring, on the Council of the American Clinical Neurophysiology Society, on the American Board of Clinical Neurophysiology, as an Associate Editor for Seizure, and performs video electroencephalogram long-term monitoring, electroencephalograms, and other electrophysiological studies at Boston Children’s Hospital and bills for these procedures and evaluates pediatric neurology patients and bills for clinical care. He is part of a pending patent application to detect seizures. He receives research support from the American Epilepsy Society, the Epilepsy Foundation of America, the Center for Integration of Medicine and Innovative Technology, the Epilepsy Therapy Project, the Pediatric Epilepsy Research Foundation, Cure, Danny-Did Foundation, HHV-6 Foundation, and from investigator-initiated research grants from Lundbeck and Eisai. His wife, Karen Stannard, MD, is a practicing pediatric neurologist who evaluates pediatric neurology patients and bills for clinical care. Dr Dworetzky receives compensation for clinical consulting for SleepMed Inc and for Best Doctors (not relevant to this article).
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