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Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath 2023; 27:39-55. [PMID: 35262853 PMCID: PMC8904207 DOI: 10.1007/s11325-022-02592-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/25/2022] [Accepted: 03/02/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. METHOD The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. RESULTS Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. CONCLUSION Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
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Cesari M, Egger K, Stefani A, Bergmann M, Ibrahim A, Brandauer E, Högl B, Heidbreder A. Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures. Sleep 2023; 46:6862127. [PMID: 36455881 DOI: 10.1093/sleep/zsac288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 11/14/2022] [Indexed: 12/03/2022] Open
Abstract
Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 patients with idiopathic hypersomnia (IH) and 54 participants with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74 ± 0.04/0.79 ± 0.05 (NT1-vs-NT2), 0.89 ± 0.09/0.91 ± 0.08 (NT1-vs-IH), 0.93 ± 0.06/0.91 ± 0.07 (NT1-vs-sEDS), 0.88 ± 0.04/0.80 ± 0.07 (NT1-vs-rest), 0.65 ± 0.10/0.70 ± 0.09 (NT2-vs-IH), 0.72 ± 0.12/0.60 ± 0.10 (NT2-vs-sEDS), 0.54 ± 0.19/0.38 ± 0.13 (NT2-vs-rest), 0.57 ± 0.11/0.35 ± 0.18 (IH-vs-sEDS), 0.71 ± 0.08/0.35 ± 0.10 (IH-vs-rest) and 0.76 ± 0.08/0.71 ± 0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in patients with NT1 and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.
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Affiliation(s)
- Matteo Cesari
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Kristin Egger
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Ambra Stefani
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Melanie Bergmann
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Abubaker Ibrahim
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Elisabeth Brandauer
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgit Högl
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anna Heidbreder
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
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Deb R, An S, Bhat G, Shill H, Ogras UY. A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:5491. [PMID: 35897995 PMCID: PMC9371095 DOI: 10.3390/s22155491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
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Affiliation(s)
| | - Sizhe An
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
| | - Ganapati Bhat
- School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA;
| | - Holly Shill
- Lonnie and Muhammad Ali Movement Disorder Center, Phoenix, AZ 85013, USA;
| | - Umit Y. Ogras
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
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4
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Yang CY, Huang YZ. Parkinson’s Disease Classification Using Machine Learning Approaches and Resting-State EEG. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00695-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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5
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Baumgartner AJ, Kushida CA, Summers MO, Kern DS, Abosch A, Thompson JA. Basal Ganglia Local Field Potentials as a Potential Biomarker for Sleep Disturbance in Parkinson's Disease. Front Neurol 2021; 12:765203. [PMID: 34777232 PMCID: PMC8581299 DOI: 10.3389/fneur.2021.765203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/04/2021] [Indexed: 11/18/2022] Open
Abstract
Sleep disturbances, specifically decreases in total sleep time and sleep efficiency as well as increased sleep onset latency and wakefulness after sleep onset, are highly prevalent in patients with Parkinson's disease (PD). Impairment of sleep significantly and adversely impacts several comorbidities in this patient population, including cognition, mood, and quality of life. Sleep disturbances and other non-motor symptoms of PD have come to the fore as the effectiveness of advanced therapies such as deep brain stimulation (DBS) optimally manage the motor symptoms. Although some studies have suggested that DBS provides benefit for sleep disturbances in PD, the mechanisms by which this might occur, as well as the optimal stimulation parameters for treating sleep dysfunction, remain unknown. In patients treated with DBS, electrophysiologic recording from the stimulating electrode, in the form of local field potentials (LFPs), has led to the identification of several findings associated with both motor and non-motor symptoms including sleep. For example, beta frequency (13–30 Hz) oscillations are associated with worsened bradykinesia while awake and decrease during non-rapid eye movement sleep. LFP investigation of sleep has largely focused on the subthalamic nucleus (STN), though corresponding oscillatory activity has been found in the globus pallidus internus (GPi) and thalamus as well. LFPs are increasingly being recognized as a potential biomarker for sleep states in PD, which may allow for closed-loop optimization of DBS parameters to treat sleep disturbances in this population. In this review, we discuss the relationship between LFP oscillations in STN and the sleep architecture of PD patients, current trends in utilizing DBS to treat sleep disturbance, and future directions for research. In particular, we highlight the capability of novel technologies to capture and record LFP data in vivo, while patients continue therapeutic stimulation for motor symptoms. These technological advances may soon allow for real-time adaptive stimulation to treat sleep disturbances.
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Affiliation(s)
- Alexander J Baumgartner
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Clete A Kushida
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael O Summers
- Department of Medicine, Division of Pulmonary, Critical Care, Sleep, and Allergy, University of Nebraska Medical Center, Omaha, NE, United States
| | - Drew S Kern
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO, United States
| | - Aviva Abosch
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, United States
| | - John A Thompson
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO, United States
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Brink-Kjær A, Cesari M, Sixel-Döring F, Mollenhauer B, Trenkwalder C, Mignot E, Sorensen HBD, Jennum P. Arousal Characteristics in Patients with Parkinson's Disease and Isolated Rapid Eye Movement Sleep Behavior Disorder. Sleep 2021; 44:6313215. [PMID: 34214165 DOI: 10.1093/sleep/zsab167] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/02/2021] [Indexed: 11/13/2022] Open
Abstract
STUDY OBJECTIVES Patients diagnosed with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) and Parkinson's disease (PD) have altered sleep stability reflecting neurodegeneration in brainstem structures. We hypothesize that neurodegeneration alters the expression of cortical arousals in sleep. METHODS We analyzed polysomnography data recorded from 88 healthy controls (HC), 22 iRBD patients, 82 de novo PD patients without RBD and 32 with RBD (PD+RBD). These patients were also investigated at a 2-year follow-up. Arousals were analyzed using a previously validated automatic system, which used a central EEG lead, electrooculography, and chin electromyography. Multiple linear regression models were fitted to compare group differences at baseline and change to follow-up for arousal index (ArI), shifts in electroencephalographic signals associated with arousals, and arousal chin muscle tone. The regression models were adjusted for known covariates affecting the nature of arousal. RESULTS In comparison to HC, patients with iRBD and PD+RBD showed increased ArI during REM sleep and their arousals showed a significantly lower shift in α-band power at arousals and a higher muscle tone during arousals. In comparison to HC, the PD patients were characterized by a decreased ArI in NREM sleep at baseline. ArI during NREM sleep decreased further at the 2-year follow-up, although not significantly. CONCLUSIONS Patients with PD and iRBD present with abnormal arousal characteristics as scored by an automated method. These abnormalities are likely to be caused by neurodegeneration of the reticular activation system due to alpha-synuclein aggregation.
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Affiliation(s)
- Andreas Brink-Kjær
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.,Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA
| | - Matteo Cesari
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Friederike Sixel-Döring
- Paracelsus-Elena Klinik, Kassel, Germany.,Department of Neurology, Philipps-University Marburg, Germany
| | - Brit Mollenhauer
- Paracelsus-Elena Klinik, Kassel, Germany.,Department of Neurology, University Medical Center Goettingen, Germany
| | - Claudia Trenkwalder
- Paracelsus-Elena Klinik, Kassel, Germany.,Department of Neurosurgery, University Medical Center, Goettingen, Germany
| | - Emmanuel Mignot
- Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA
| | - Helge B D Sorensen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Poul Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark
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Zahed H, Zuzuarregui JRP, Gilron R, Denison T, Starr PA, Little S. The Neurophysiology of Sleep in Parkinson's Disease. Mov Disord 2021; 36:1526-1542. [PMID: 33826171 DOI: 10.1002/mds.28562] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/02/2021] [Accepted: 02/16/2021] [Indexed: 12/14/2022] Open
Abstract
Sleep disturbances are among the most common nonmotor complications of Parkinson's disease (PD), can present in prodromal stages, and progress with advancing disease. In addition to being a symptom of neurodegeneration, sleep disturbances may also contribute to disease progression. Currently, limited options exist to modulate sleep disturbances in PD. Studying the neurophysiological changes that affect sleep in PD at the cortical and subcortical level may yield new insights into mechanisms for reversal of sleep disruption. In this article, we review cortical and subcortical recording studies of sleep in PD with a particular focus on dissecting reported electrophysiological changes. These studies show that slow-wave sleep and rapid eye movement sleep are both notably disrupted in PD. We further explore the impact of these electrophysiological changes and discuss the potential for targeting sleep via stimulation therapy to modify PD-related motor and nonmotor symptoms. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Hengameh Zahed
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | | | - Ro'ee Gilron
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Timothy Denison
- Institute of Biomedical Engineering and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
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8
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Cesari M, Stefani A, Mitterling T, Frauscher B, Schönwald SV, Högl B. Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring. Sleep Med 2021; 77:136-146. [PMID: 33360558 DOI: 10.1016/j.sleep.2020.11.033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/24/2020] [Accepted: 11/27/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND In current clinical practice, sleep is manually scored in discrete stages of 30-s duration. We hypothesize that modelling sleep automatically as continuous and dynamic process predicts healthy ageing better than traditional scoring. METHODS Sleep electroencephalography of 15 young healthy subjects (aged ≤40 years) was used to train the modelling method. Each 3-s sleep mini-epoch was modelled as a probabilistic combination of wakefulness, light and deep sleep. For 79 healthy sleepers (aged 20-77 years), 15 sleep features were derived from manual traditional scoring (manual features), 7 from the automatic modelling (automatic features) and 24 from a combination of automatic modelling with traditional scoring (combined features). Age was predicted with seven multiple linear regression models with i) manual, ii) automatic, iii) combined, iv) manual + automatic, v) manual + combined, vi) automatic + combined, and vii) manual + automatic + combined sleep features. Using the same seven sleep feature groups, two support vector machine and one random forest classifiers were used to discriminate younger (aged <47 years) from older subjects with fivefold cross-validation. Adjusted coefficients of determination (adj-R2) and average validation accuracy (ACC) were used to compare the linear models and the classifiers. RESULTS The linear model and the classifiers using only manual features achieved the lowest values of adjusted coefficient of determination and classification validation accuracy (adj-R2 = 0.295, ACC = 63.00% ± 16.22%) compared to the ones using automatic (adj-R2 = 0.354, ACC = 65.83% ± 9.39%), combined (adj-R2 = 0.321, ACC = 63.42% ± 8.78%), manual + automatic (adj-R2 = 0.416, ACC = 67.00% ± 8.60%), manual + combined (adj-R2 = 0.355, ACC = 72.17% ± 12.90%), automatic + combined (adj-R2 = 0.448, ACC = 65.92% ± 7.97%), and manual + automatic + combined sleep features (adj-R2 = 0.464, ACC = 70.92% ± 10.33%). CONCLUSIONS Continuous and dynamic sleep modelling captures healthy ageing better than traditional sleep scoring.
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Affiliation(s)
- Matteo Cesari
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.
| | - Ambra Stefani
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Thomas Mitterling
- Department of Neurology 1, Kepler University Hospital, Johannes Kepler University, Linz, Austria
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Suzana V Schönwald
- Department of Neurology, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Birgit Högl
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
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Belkhiria C, Peysakhovich V. Electro-Encephalography and Electro-Oculography in Aeronautics: A Review Over the Last Decade (2010-2020). FRONTIERS IN NEUROERGONOMICS 2020; 1:606719. [PMID: 38234309 PMCID: PMC10790927 DOI: 10.3389/fnrgo.2020.606719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 11/17/2020] [Indexed: 01/19/2024]
Abstract
Electro-encephalography (EEG) and electro-oculography (EOG) are methods of electrophysiological monitoring that have potentially fruitful applications in neuroscience, clinical exploration, the aeronautical industry, and other sectors. These methods are often the most straightforward way of evaluating brain oscillations and eye movements, as they use standard laboratory or mobile techniques. This review describes the potential of EEG and EOG systems and the application of these methods in aeronautics. For example, EEG and EOG signals can be used to design brain-computer interfaces (BCI) and to interpret brain activity, such as monitoring the mental state of a pilot in determining their workload. The main objectives of this review are to, (i) offer an in-depth review of literature on the basics of EEG and EOG and their application in aeronautics; (ii) to explore the methodology and trends of research in combined EEG-EOG studies over the last decade; and (iii) to provide methodological guidelines for beginners and experts when applying these methods in environments outside the laboratory, with a particular focus on human factors and aeronautics. The study used databases from scientific, clinical, and neural engineering fields. The review first introduces the characteristics and the application of both EEG and EOG in aeronautics, undertaking a large review of relevant literature, from early to more recent studies. We then built a novel taxonomy model that includes 150 combined EEG-EOG papers published in peer-reviewed scientific journals and conferences from January 2010 to March 2020. Several data elements were reviewed for each study (e.g., pre-processing, extracted features and performance metrics), which were then examined to uncover trends in aeronautics and summarize interesting methods from this important body of literature. Finally, the review considers the advantages and limitations of these methods as well as future challenges.
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10
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Feng H, Chen L, Liu Y, Chen X, Wang J, Yu MWM, Huang B, Li SX, Chau SWH, Chan JWY, Chen J, Mok VCT, Wing YK, Zhang J. Rest‐Activity Pattern Alterations in Idiopathic
REM
Sleep Behavior Disorder. Ann Neurol 2020; 88:817-829. [DOI: 10.1002/ana.25853] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 07/17/2020] [Accepted: 07/19/2020] [Indexed: 01/22/2023]
Affiliation(s)
- Hongliang Feng
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Lauren Chen
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Yaping Liu
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Xinru Chen
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Jing Wang
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Mandy Wai Man Yu
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Bei Huang
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Shirley Xin Li
- Department of Psychology The University of Hong Kong Shatin, Hong Kong China
- The State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong, Pokfulam Hong Kong China
| | - Steven Wai Ho Chau
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Joey Wing Yan Chan
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Jie Chen
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Vincent Chung Tong Mok
- Margaret K.L. Cheung Research Centre in Management of Parkinsonism, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Yun Kwok Wing
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
| | - Jihui Zhang
- Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine The Chinese University of Hong Kong Shatin, Hong Kong China
- Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences Guangzhou Guangdong China
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11
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Mulam H, Mudigonda M. Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals. Proc Inst Mech Eng H 2020; 234:794-811. [PMID: 32615863 DOI: 10.1177/0954411920924496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human-computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg-Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.
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Affiliation(s)
- Harikrishna Mulam
- Department of Electronics and Instrumentation Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Bachupally, Hyderabad, India
| | - Malini Mudigonda
- Department of Biomedical Engineering, University College of Engineering, Osmania University, Hyderabad, India
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12
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Cesari M, Christensen JAE, Muntean ML, Mollenhauer B, Sixel-Döring F, Sorensen HBD, Trenkwalder C, Jennum P. A data-driven system to identify REM sleep behavior disorder and to predict its progression from the prodromal stage in Parkinson's disease. Sleep Med 2020; 77:238-248. [PMID: 32798136 DOI: 10.1016/j.sleep.2020.04.010] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 04/04/2020] [Accepted: 04/10/2020] [Indexed: 11/18/2022]
Abstract
OBJECTIVES To investigate electroencephalographic (EEG), electrooculographic (EOG) and micro-sleep abnormalities associated with rapid eye movement (REM) sleep behavior disorder (RBD) and REM behavioral events (RBEs) in Parkinson's disease (PD). METHODS We developed an automated system using only EEG and EOG signals. First, automatic macro- (30-s epochs) and micro-sleep (5-s mini-epochs) staging was performed. Features describing micro-sleep structure, EEG spectral content, EEG coherence, EEG complexity, and EOG energy were derived. All features were input to an ensemble of random forests, giving as outputs the probabilities of having RBD or not (P (RBD) and P (nonRBD), respectively). A patient was classified as having RBD if P (RBD)≥P (nonRBD). The system was applied to 107 de novo PD patients: 54 had normal REM sleep (PDnonRBD), 26 had RBD (PD + RBD), and 27 had at least two RBEs without meeting electromyographic RBD cut-off (PD + RBE). Sleep diagnoses were made with video-polysomnography (v-PSG). RESULTS Considering PDnonRBD and PD + RBD patients only, the system identified RBD with accuracy, sensitivity, and specificity over 80%. Among the features, micro-sleep instability had the highest importance for RBD identification. Considering PD + RBE patients, the ones who developed definite RBD after two years had significantly higher values of P (RBD) at baseline compared to the ones who did not. The former were distinguished from the latter with sensitivity and specificity over 75%. CONCLUSIONS Our method identifies RBD in PD patients using only EEG and EOG signals. Micro-sleep instability could be a biomarker for RBD and for proximity of conversion from RBEs, as prodromal RBD, to definite RBD in PD patients.
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Affiliation(s)
- Matteo Cesari
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
| | - Julie A E Christensen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Glostrup, Denmark
| | | | - Brit Mollenhauer
- Paracelsus-Elena Klinik, Kassel, Germany; Department of Neurology, University Medical Center, Goettingen, Germany
| | - Friederike Sixel-Döring
- Paracelsus-Elena Klinik, Kassel, Germany; Department of Neurology, Philipps University, Marburg, Germany
| | - Helge B D Sorensen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Poul Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Glostrup, Denmark
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13
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Wang X, Gong G, Li N, Ding L. Use of multimodal physiological signals to explore pilots’ cognitive behaviour during flight strike task performance. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2020. [DOI: 10.1016/j.medntd.2020.100030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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14
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Mulam H, Mudigonda M. EOG-based eye movement recognition using GWO-NN optimization. BIOMED ENG-BIOMED TE 2020; 65:11-22. [PMID: 31393829 DOI: 10.1515/bmt-2018-0109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 01/25/2019] [Indexed: 11/15/2022]
Abstract
In recent times, the control of human-computer interface (HCI) systems is triggered by electrooculography (EOG) signals. Eye movements recognized based on the EOG signal pattern are utilized to govern the HCI system and do a specific job based on the type of eye movement. With the knowledge of various related examinations, this paper intends a novel model for eye movement recognition based on EOG signals by utilizing Grey Wolf Optimization (GWO) with neural network (NN). Here, the GWO is used to minimize the error function from the classifier. The performance of the proposed methodology was investigated by comparing the developed model with conventional methods. The results reveal the loftier performance of the adopted method with the error minimization analysis and recognition performance analysis in correspondence with varied performance measures such as accuracy, sensitivity, specificity, precision, false-positive rate (FPR), false-negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR) and the F1 score.
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Affiliation(s)
- Harikrishna Mulam
- Department of Electronics and Instrumentation Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Vignana Jyothi Nagar, Nizampet Rd, Pragathi Nagar, Hyderabad, Telangana 500090, India
| | - Malini Mudigonda
- Department of Biomedical Engieering, University College of Engineering, Osmania University, Osmania University Main Rd, Amberpet, Hyderabad, Telangana 500007, India
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15
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Olesen AN, Jennum P, Peppard P, Mignot E, Sorensen HBD. Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:1-4. [PMID: 30440296 DOI: 10.1109/embc.2018.8513080] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.
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16
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Melpignano A, Parrino L, Santamaria J, Gaig C, Trippi I, Serradell M, Mutti C, Riccò M, Iranzo A. Isolated rapid eye movement sleep behavior disorder and cyclic alternating pattern: is sleep microstructure a predictive parameter of neurodegeneration? Sleep 2019; 42:5536257. [DOI: 10.1093/sleep/zsz142] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/13/2019] [Indexed: 12/20/2022] Open
Abstract
Abstract
Objective
To evaluate the role of sleep cyclic alternating pattern (CAP) in patients with isolated REM sleep behavior disorder (IRBD) and ascertain whether CAP metrics might represent a marker of phenoconversion to a defined neurodegenerative condition.
Methods
Sixty-seven IRBD patients were included and classified into patients who phenoconverted to a neurodegenerative disease (RBD converters: converter REM sleep behavior disorder [cRBD]; n = 34) and remained disease-free (RBD non-converters: non-converter REM sleep behavior disorder [ncRBD]; n = 33) having a similar follow-up duration. Fourteen age- and gender-balanced healthy controls were included for comparisons.
Results
Compared to controls, CAP rate and CAP index were significantly decreased in IRBD mainly due to a decrease of A1 phase subtypes (A1 index) despite an increase in duration of both CAP A and B phases. The cRBD group had significantly lower values of CAP rate and CAP index when compared with the ncRBD group and controls. A1 index was significantly reduced in both ncRBD and cRBD groups compared to controls. When compared to the ncRBD group, A3 index was significantly decreased in the cRBD group. The Kaplan-Meier curve applied to cRBD estimated that a value of CAP rate below 32.9% was related to an average risk of conversion of 9.2 years after baseline polysomnography.
Conclusion
IRBD is not exclusively a rapid eye movement (REM) sleep parasomnia, as non-rapid eye movement (non-REM) sleep microstructure can also be affected by CAP changes. Further studies are necessary to confirm that a reduction of specific CAP metrics is a marker of neurodegeneration in IRBD.
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Affiliation(s)
- Andrea Melpignano
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Liborio Parrino
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Joan Santamaria
- Neurology Service, Multidisciplinary Sleep Unit, Universitat de Barcelona, IDIBAPS, CIBERNED, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Carles Gaig
- Neurology Service, Multidisciplinary Sleep Unit, Universitat de Barcelona, IDIBAPS, CIBERNED, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Irene Trippi
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Monica Serradell
- Neurology Service, Multidisciplinary Sleep Unit, Universitat de Barcelona, IDIBAPS, CIBERNED, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Carlotta Mutti
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Matteo Riccò
- AUSL-IRCCS di Reggio Emilia-Department of Public Health; Service for Occupational Health and Safety on the Workplaces, Parma, Italy
| | - Alex Iranzo
- Neurology Service, Multidisciplinary Sleep Unit, Universitat de Barcelona, IDIBAPS, CIBERNED, Hospital Clinic de Barcelona, Barcelona, Spain
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17
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Ferini-Strambi L, Fasiello E, Sforza M, Salsone M, Galbiati A. Neuropsychological, electrophysiological, and neuroimaging biomarkers for REM behavior disorder. Expert Rev Neurother 2019; 19:1069-1087. [PMID: 31277555 DOI: 10.1080/14737175.2019.1640603] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Introduction: Rapid eye movement (REM) sleep behavior disorder (RBD) is a REM sleep parasomnia characterized by dream enacting behaviors allowed by the loss of physiological atonia during REM sleep. This disorder is recognized as a prodromal stage of neurodegenerative disease, in particular Parkinson's Disease (PD) and Dementia with Lewy Bodies (DLB). Therefore, a timely identification of biomarkers able to predict an early conversion into neurodegeneration is of utmost importance. Areas covered: In this review, the authors provide updated evidence regarding the presence of neuropsychological, electrophysiological and neuroimaging markers in isolated RBD (iRBD) patients when the neurodegeneration is yet to come. Expert opinion: Cognitive profile of iRBD patients is characterized by the presence of impairment in visuospatial abilities and executive function that is observed in α-synucleinopathies. However, longitudinal studies showed that impaired executive functions, rather than visuospatial abilities, augmented conversion risk. Cortical slowdown during wake and REM sleep suggest the presence of an ongoing neurodegenerative process paralleled by cognitive decline. Neuroimaging findings showed that impairment nigrostriatal dopaminergic system might be a good marker to identify those patients at higher risk of short-term conversion. Although a growing body of evidence the identification of biomarkers still represents a critical issue in iRBD.
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Affiliation(s)
- Luigi Ferini-Strambi
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
| | - Elisabetta Fasiello
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
| | - Marco Sforza
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
| | - Maria Salsone
- Institute of Molecular Bioimaging and Physiology, National Research Council , Catanzaro , Italy
| | - Andrea Galbiati
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
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18
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Cesari M, Christensen JA, Sixel-Döring F, Trenkwalder C, Mayer G, Oertel WH, Jennum P, Sorensen HB. Validation of a new data-driven automated algorithm for muscular activity detection in REM sleep behavior disorder. J Neurosci Methods 2019; 312:53-64. [DOI: 10.1016/j.jneumeth.2018.11.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 11/07/2018] [Accepted: 11/19/2018] [Indexed: 01/13/2023]
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19
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Stefani A, Holzknecht E, Högl B. Clinical neurophysiology of REM parasomnias. HANDBOOK OF CLINICAL NEUROLOGY 2019; 161:381-396. [DOI: 10.1016/b978-0-444-64142-7.00062-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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20
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Koch H, Jennum P, Christensen JAE. Automatic sleep classification using adaptive segmentation reveals an increased number of
rapid eye movement
sleep transitions. J Sleep Res 2018; 28:e12780. [DOI: 10.1111/jsr.12780] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 08/28/2018] [Accepted: 09/18/2018] [Indexed: 12/11/2022]
Affiliation(s)
- Henriette Koch
- Danish Center for Sleep MedicineDepartment of Clinical Neurophysiology, Rigshospitalet Glostrup Denmark
| | - Poul Jennum
- Danish Center for Sleep MedicineDepartment of Clinical Neurophysiology, Rigshospitalet Glostrup Denmark
| | - Julie A. E. Christensen
- Danish Center for Sleep MedicineDepartment of Clinical Neurophysiology, Rigshospitalet Glostrup Denmark
- Biomedical Engineering, Department of Electrical Engineering Technical University of Denmark Kongens Lyngby Denmark
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21
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Pham TD. Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features. J Neurosci Methods 2018; 307:194-202. [PMID: 29859213 DOI: 10.1016/j.jneumeth.2018.05.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 05/25/2018] [Accepted: 05/27/2018] [Indexed: 01/17/2023]
Abstract
BACKGROUND Identifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder research, because it is of therapeutic and economic benefits to optimal intervention and management of the disease. NEW METHOD This paper presents a nonlinear dynamics approach, including recurrence plots, recurrence quantification analysis, fuzzy recurrence plots, and scalable recurrence networks for visualization, classification, and characterization of keystroke time series obtained from healthy control (HC) and early-stage PD subjects. RESULTS Several differentiative properties for characterizing early PD and HC subjects can be obtained from fuzzy recurrence plots (FRPs) and scalable recurrence networks. Comparison with existing methods: cross-validation results obtained from FRP-based texture are highest among other methods. The method of fuzzy recurrence plots outperforms other existing methods for classification of HC and PD subjects. CONCLUSIONS Features extracted from the nonlinear dynamics analysis of the keystroke time series are found to be very effective for machine learning and the properties of the scalable recurrence networks have the potential to be utilized as physiologic markers of the disease.
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Affiliation(s)
- Tuan D Pham
- Department of Biomedical Engineering, Linkoping University, Linkoping 58183, Sweden.
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22
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Bertocci MA, Bebko G, Versace A, Iyengar S, Bonar L, Forbes EE, Almeida JRC, Perlman SB, Schirda C, Travis MJ, Gill MK, Diwadkar VA, Sunshine JL, Holland SK, Kowatch RA, Birmaher B, Axelson DA, Frazier TW, Arnold LE, Fristad MA, Youngstrom EA, Horwitz SM, Findling RL, Phillips ML. Reward-related neural activity and structure predict future substance use in dysregulated youth. Psychol Med 2017; 47:1357-1369. [PMID: 27998326 PMCID: PMC5576722 DOI: 10.1017/s0033291716003147] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Identifying youth who may engage in future substance use could facilitate early identification of substance use disorder vulnerability. We aimed to identify biomarkers that predicted future substance use in psychiatrically un-well youth. METHOD LASSO regression for variable selection was used to predict substance use 24.3 months after neuroimaging assessment in 73 behaviorally and emotionally dysregulated youth aged 13.9 (s.d. = 2.0) years, 30 female, from three clinical sites in the Longitudinal Assessment of Manic Symptoms (LAMS) study. Predictor variables included neural activity during a reward task, cortical thickness, and clinical and demographic variables. RESULTS Future substance use was associated with higher left middle prefrontal cortex activity, lower left ventral anterior insula activity, thicker caudal anterior cingulate cortex, higher depression and lower mania scores, not using antipsychotic medication, more parental stress, older age. This combination of variables explained 60.4% of the variance in future substance use, and accurately classified 83.6%. CONCLUSIONS These variables explained a large proportion of the variance, were useful classifiers of future substance use, and showed the value of combining multiple domains to provide a comprehensive understanding of substance use development. This may be a step toward identifying neural measures that can identify future substance use disorder risk, and act as targets for therapeutic interventions.
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Affiliation(s)
- M A Bertocci
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - G Bebko
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - A Versace
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - S Iyengar
- Department of Statistics,University of Pittsburgh,Pittsburgh, PA,USA
| | - L Bonar
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - E E Forbes
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - J R C Almeida
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - S B Perlman
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - C Schirda
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - M J Travis
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - M K Gill
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - V A Diwadkar
- Department of Psychiatry and Behavioral Neuroscience,Wayne State University,Detroit, MI,USA
| | - J L Sunshine
- Department of Radiology,University Hospitals Case Medical Center/Case Western Reserve University,Cleveland, OH,USA
| | - S K Holland
- Cincinnati Children's Hospital Medical Center, University of Cincinnati,Cincinnati, OH,USA
| | - R A Kowatch
- Department of Psychiatry and Behavioral Health,Ohio State University,Columbus, OH,USA
| | - B Birmaher
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
| | - D A Axelson
- Department of Psychiatry and Behavioral Health,Ohio State University,Columbus, OH,USA
| | - T W Frazier
- Pediatric Institute,Cleveland Clinic,Cleveland, OH,USA
| | - L E Arnold
- Department of Psychiatry and Behavioral Health,Ohio State University,Columbus, OH,USA
| | - M A Fristad
- Department of Psychiatry and Behavioral Health,Ohio State University,Columbus, OH,USA
| | - E A Youngstrom
- Department of Psychology,University of North Carolina at Chapel Hill,Chapel Hill, NC,USA
| | - S M Horwitz
- Department of Child and Adolescent Psychiatry,New York University School of Medicine,New York, NY,USA
| | - R L Findling
- Department of Psychiatry,Johns Hopkins University,Baltimore, MD,USA
| | - M L Phillips
- Department of Psychiatry,Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh,Pittsburgh, PA,USA
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23
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Bertocci MA, Bebko G, Dwojak A, Iyengar S, Ladouceur CD, Fournier JC, Versace A, Perlman SB, Almeida JRC, Travis MJ, Gill MK, Bonar L, Schirda C, Diwadkar VA, Sunshine JL, Holland SK, Kowatch RA, Birmaher B, Axelson D, Horwitz SM, Frazier T, Arnold LE, Fristad MA, Youngstrom EA, Findling RL, Phillips ML. Longitudinal relationships among activity in attention redirection neural circuitry and symptom severity in youth. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:336-345. [PMID: 28480336 DOI: 10.1016/j.bpsc.2016.06.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Changes in neural circuitry function may be associated with longitudinal changes in psychiatric symptom severity. Identification of these relationships may aid in elucidating the neural basis of psychiatric symptom evolution over time. We aimed to distinguish these relationships using data from the Longitudinal Assessment of Manic Symptoms (LAMS) cohort. METHODS Forty-one youth completed two study visits (mean=21.3 months). Elastic-net regression (Multiple response Gaussian family) identified emotional regulation neural circuitry that changed in association with changes in depression, mania, anxiety, affect lability, and positive mood and energy dysregulation, accounting for clinical and demographic variables. RESULTS Non-zero coefficients between change in the above symptom measures and change in activity over the inter-scan interval were identified in right amygdala and left ventrolateral prefrontal cortex. Differing patterns of neural activity change were associated with changes in each of the above symptoms over time. Specifically, from Scan1 to Scan2, worsening affective lability and depression severity were associated with increased right amygdala and left ventrolateral prefrontal cortical activity. Worsening anxiety and positive mood and energy dysregulation were associated with decreased right amygdala and increased left ventrolateral prefrontal cortical activity. Worsening mania was associated with increased right amygdala and decreased left ventrolateral prefrontal cortical activity. These changes in neural activity between scans accounted for 13.6% of the variance; that is 25% of the total explained variance (39.6%) in these measures. CONCLUSIONS Distinct neural mechanisms underlie changes in different mood and anxiety symptoms overtime.
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Affiliation(s)
- Michele A Bertocci
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Genna Bebko
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Amanda Dwojak
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | | | - Cecile D Ladouceur
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Jay C Fournier
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Amelia Versace
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Susan B Perlman
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | | | - Michael J Travis
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Mary Kay Gill
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Lisa Bonar
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Claudiu Schirda
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University
| | - Jeffrey L Sunshine
- University Hospitals Case Medical Center/Case Western Reserve University
| | - Scott K Holland
- Cincinnati Children's Hospital Medical Center, University of Cincinnati
| | | | - Boris Birmaher
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
| | - David Axelson
- The Research Institute at Nationwide Children's Hospital
| | - Sarah M Horwitz
- Department of Child Psychiatry, New York University School of Medicine
| | | | - L Eugene Arnold
- Department of Psychiatry and Behavioral Health, Ohio State University
| | | | - Eric A Youngstrom
- Department of Psychology, University of North Carolina at Chapel Hill
| | - Robert L Findling
- University Hospitals Case Medical Center/Case Western Reserve University.,Department of Psychiatry, Johns Hopkins University
| | - Mary L Phillips
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh
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24
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Predicting clinical outcome from reward circuitry function and white matter structure in behaviorally and emotionally dysregulated youth. Mol Psychiatry 2016; 21:1194-201. [PMID: 26903272 PMCID: PMC4993633 DOI: 10.1038/mp.2016.5] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 10/09/2015] [Accepted: 12/02/2015] [Indexed: 12/20/2022]
Abstract
Behavioral and emotional dysregulation in childhood may be understood as prodromal to adult psychopathology. Additionally, there is a critical need to identify biomarkers reflecting underlying neuropathological processes that predict clinical/behavioral outcomes in youth. We aimed to identify such biomarkers in youth with behavioral and emotional dysregulation in the Longitudinal Assessment of Manic Symptoms (LAMS) study. We examined neuroimaging measures of function and white matter in the whole brain using 80 youth aged 14.0 (s.d.=2.0) from three clinical sites. Linear regression using the LASSO (Least Absolute Shrinkage and Selection Operator) method for variable selection was used to predict severity of future behavioral and emotional dysregulation measured by the Parent General Behavior Inventory-10 Item Mania Scale (PGBI-10M)) at a mean of 14.2 months follow-up after neuroimaging assessment. Neuroimaging measures, together with near-scan PGBI-10M, a score of manic behaviors, depressive behaviors and sex, explained 28% of the variance in follow-up PGBI-10M. Neuroimaging measures alone, after accounting for other identified predictors, explained ~1/3 of the explained variance, in follow-up PGBI-10M. Specifically, greater bilateral cingulum length predicted lower PGBI-10M at follow-up. Greater functional connectivity in parietal-subcortical reward circuitry predicted greater PGBI-10M at follow-up. For the first time, data suggest that multimodal neuroimaging measures of underlying neuropathologic processes account for over a third of the explained variance in clinical outcome in a large sample of behaviorally and emotionally dysregulated youth. This may be an important first step toward identifying neurobiological measures with the potential to act as novel targets for early detection and future therapeutic interventions.
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25
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Christensen JAE, Jennum P, Koch H, Frandsen R, Zoetmulder M, Arvastson L, Christensen SR, Sorensen HBD. Sleep stability and transitions in patients with idiopathic REM sleep behavior disorder and patients with Parkinson’s disease. Clin Neurophysiol 2016; 127:537-543. [DOI: 10.1016/j.clinph.2015.03.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Revised: 03/05/2015] [Accepted: 03/10/2015] [Indexed: 11/26/2022]
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26
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Christensen JAE, Nikolic M, Warby SC, Koch H, Zoetmulder M, Frandsen R, Moghadam KK, Sorensen HBD, Mignot E, Jennum PJ. Sleep spindle alterations in patients with Parkinson's disease. Front Hum Neurosci 2015; 9:233. [PMID: 25983685 PMCID: PMC4416460 DOI: 10.3389/fnhum.2015.00233] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Accepted: 04/11/2015] [Indexed: 01/04/2023] Open
Abstract
The aim of this study was to identify changes of sleep spindles (SS) in the EEG of patients with Parkinson's disease (PD). Five sleep experts manually identified SS at a central scalp location (C3-A2) in 15 PD and 15 age- and sex-matched control subjects. Each SS was given a confidence score, and by using a group consensus rule, 901 SS were identified and characterized by their (1) duration, (2) oscillation frequency, (3) maximum peak-to-peak amplitude, (4) percent-to-peak amplitude, and (5) density. Between-group comparisons were made for all SS characteristics computed, and significant changes for PD patients vs. control subjects were found for duration, oscillation frequency, maximum peak-to-peak amplitude and density. Specifically, SS density was lower, duration was longer, oscillation frequency slower and maximum peak-to-peak amplitude higher in patients vs. controls. We also computed inter-expert reliability in SS scoring and found a significantly lower reliability in scoring definite SS in patients when compared to controls. How neurodegeneration in PD could influence SS characteristics is discussed. We also note that the SS morphological changes observed here may affect automatic detection of SS in patients with PD or other neurodegenerative disorders (NDDs).
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Affiliation(s)
- Julie A E Christensen
- Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark Kongens Lyngby, Denmark ; Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup University Hospital Glostrup, Denmark ; Stanford Center for Sleep Sciences and Medicine, Psychiatry and Behavioral Sciences, Stanford University Palo Alto, CA, USA
| | - Miki Nikolic
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup University Hospital Glostrup, Denmark
| | - Simon C Warby
- Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital of Montréal, University of Montréal Montréal, QC, Canada
| | - Henriette Koch
- Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark Kongens Lyngby, Denmark ; Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup University Hospital Glostrup, Denmark ; Stanford Center for Sleep Sciences and Medicine, Psychiatry and Behavioral Sciences, Stanford University Palo Alto, CA, USA
| | - Marielle Zoetmulder
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup University Hospital Glostrup, Denmark ; Department of Neurology, Bispebjerg Hospital Copenhagen, Denmark
| | - Rune Frandsen
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup University Hospital Glostrup, Denmark
| | - Keivan K Moghadam
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna Bologna, Italy
| | - Helge B D Sorensen
- Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark Kongens Lyngby, Denmark
| | - Emmanuel Mignot
- Stanford Center for Sleep Sciences and Medicine, Psychiatry and Behavioral Sciences, Stanford University Palo Alto, CA, USA
| | - Poul J Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup University Hospital Glostrup, Denmark ; Center for Healthy Ageing, University of Copenhagen Copenhagen, Denmark
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Koch H, Christensen JA, Frandsen R, Zoetmulder M, Arvastson L, Christensen SR, Jennum P, Sorensen HB. Automatic sleep classification using a data-driven topic model reveals latent sleep states. J Neurosci Methods 2014; 235:130-7. [DOI: 10.1016/j.jneumeth.2014.07.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2014] [Revised: 06/29/2014] [Accepted: 07/01/2014] [Indexed: 01/28/2023]
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