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Murphy N, Tamman AJF, Lijffijt M, Amarneh D, Iqbal S, Swann A, Averill LA, O'Brien B, Mathew SJ. Neural complexity EEG biomarkers of rapid and post-rapid ketamine effects in late-life treatment-resistant depression: a randomized control trial. Neuropsychopharmacology 2023; 48:1586-1593. [PMID: 37076582 PMCID: PMC10516885 DOI: 10.1038/s41386-023-01586-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 04/21/2023]
Abstract
Ketamine is an effective intervention for treatment-resistant depression (TRD), including late-in-life (LL-TRD). The proposed mechanism of antidepressant effects of ketamine is a glutamatergic surge, which can be measured by electroencephalogram (EEG) gamma oscillations. Yet, non-linear EEG biomarkers of ketamine effects such as neural complexity are needed to capture broader systemic effects, represent the level of organization of synaptic communication, and elucidate mechanisms of action for treatment responders. In a secondary analysis of a randomized control trial, we investigated two EEG neural complexity markers (Lempel-Ziv complexity [LZC] and multiscale entropy [MSE]) of rapid (baseline to 240 min) and post-rapid ketamine (24 h and 7 days) effects after one 40-min infusion of IV ketamine or midazolam (active control) in 33 military veterans with LL-TRD. We also studied the relationship between complexity and Montgomery-Åsberg Depression Rating Scale score change at 7 days post-infusion. We found that LZC and MSE both increased 30 min post-infusion, with effects not localized to a single timescale for MSE. Post-rapid effects of reduced complexity with ketamine were observed for MSE. No relationship was observed between complexity and reduction in depressive symptoms. Our findings support the hypothesis that a single sub-anesthetic ketamine infusion has time-varying effects on system-wide contributions to the evoked glutamatergic surge in LL-TRD. Further, changes to complexity were observable outside the time-window previously shown for effects on gamma oscillations. These preliminary results have clinical implications in providing a functional marker of ketamine that is non-linear, amplitude-independent, and represents larger dynamic properties, providing strong advantages over linear measures in highlighting ketamine's effects.
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Affiliation(s)
- Nicholas Murphy
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
| | - Amanda J F Tamman
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA.
| | - Marijn Lijffijt
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Dania Amarneh
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
| | - Sidra Iqbal
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Alan Swann
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Lynnette A Averill
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Brittany O'Brien
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
| | - Sanjay J Mathew
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
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Puglia MH, Slobin JS, Williams CL. The automated preprocessing pipe-line for the estimation of scale-wise entropy from EEG data (APPLESEED): Development and validation for use in pediatric populations. Dev Cogn Neurosci 2022; 58:101163. [PMID: 36270100 PMCID: PMC9586850 DOI: 10.1016/j.dcn.2022.101163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 01/13/2023] Open
Abstract
It is increasingly understood that moment-to-moment brain signal variability - traditionally modeled out of analyses as mere "noise" - serves a valuable functional role related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) - a measure of signal irregularity across temporal scales - is an increasingly popular analytic technique in human neuroscience calculated from time series such as electroencephalography (EEG) signals. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain's moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in data preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized EEG preprocessing and entropy estimation pipeline that adapts a critical modification to the original MSE algorithm, and generates reliable scale-wise entropy estimates capable of differentiating developmental stages and cognitive states. This novel pipeline - the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED.
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Affiliation(s)
- Meghan H. Puglia
- Correspondence to: University of Virginia Department of Neurology, PO Box 800834, Charlottesville, VA 22908, USA.
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On Conditional Tsallis Entropy. ENTROPY 2021; 23:e23111427. [PMID: 34828125 PMCID: PMC8620384 DOI: 10.3390/e23111427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 11/30/2022]
Abstract
There is no generally accepted definition for conditional Tsallis entropy. The standard definition of (unconditional) Tsallis entropy depends on a parameter α that converges to the Shannon entropy as α approaches 1. In this paper, we describe three proposed definitions of conditional Tsallis entropy suggested in the literature—their properties are studied and their values, as a function of α, are compared. We also consider another natural proposal for conditional Tsallis entropy and compare it with the existing ones. Lastly, we present an online tool to compute the four conditional Tsallis entropies, given the probability distributions and the value of the parameter α.
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van Noordt S, Willoughby T. Cortical maturation from childhood to adolescence is reflected in resting state EEG signal complexity. Dev Cogn Neurosci 2021; 48:100945. [PMID: 33831821 PMCID: PMC8027532 DOI: 10.1016/j.dcn.2021.100945] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/09/2021] [Accepted: 03/21/2021] [Indexed: 11/18/2022] Open
Abstract
Endogenous cortical fluctuations captured by electroencephalograms (EEGs) reflect activity in large-scale brain networks that exhibit dynamic patterns over multiple time scales. Developmental changes in the coordination and integration of brain function leads to greater complexity in population level neural dynamics. In this study we examined multiscale entropy, a measure of signal complexity, in resting-state EEGs in a large (N = 405) cross-sectional sample of children and adolescents (9–16 years). Our findings showed consistent age-dependent increases in EEG complexity that are distributed across multiple temporal scales and spatial regions. Developmental changes were most robust as the age gap between groups increased, particularly between late childhood and adolescence, and were most prominent over fronto-central scalp regions. These results suggest that the transition from late childhood to adolescence is characterized by age-dependent changes in the underlying complexity of endogenous brain networks.
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Affiliation(s)
- Stefon van Noordt
- Azrieli Centre for Autism Research, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada; Department of Psychology, Brock University, St. Catharines, Ontario, Canada.
| | - Teena Willoughby
- Department of Psychology, Brock University, St. Catharines, Ontario, Canada
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Puglia MH, Krol KM, Missana M, Williams CL, Lillard TS, Morris JP, Connelly JJ, Grossmann T. Epigenetic tuning of brain signal entropy in emergent human social behavior. BMC Med 2020; 18:244. [PMID: 32799881 PMCID: PMC7429788 DOI: 10.1186/s12916-020-01683-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 06/26/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND How the brain develops accurate models of the external world and generates appropriate behavioral responses is a vital question of widespread multidisciplinary interest. It is increasingly understood that brain signal variability-posited to enhance perception, facilitate flexible cognitive representations, and improve behavioral outcomes-plays an important role in neural and cognitive development. The ability to perceive, interpret, and respond to complex and dynamic social information is particularly critical for the development of adaptive learning and behavior. Social perception relies on oxytocin-regulated neural networks that emerge early in development. METHODS We tested the hypothesis that individual differences in the endogenous oxytocinergic system early in life may influence social behavioral outcomes by regulating variability in brain signaling during social perception. In study 1, 55 infants provided a saliva sample at 5 months of age for analysis of individual differences in the oxytocinergic system and underwent electroencephalography (EEG) while listening to human vocalizations at 8 months of age for the assessment of brain signal variability. Infant behavior was assessed via parental report. In study 2, 60 infants provided a saliva sample and underwent EEG while viewing faces and objects and listening to human speech and water sounds at 4 months of age. Infant behavior was assessed via parental report and eye tracking. RESULTS We show in two independent infant samples that increased brain signal entropy during social perception is in part explained by an epigenetic modification to the oxytocin receptor gene (OXTR) and accounts for significant individual differences in social behavior in the first year of life. These results are measure-, context-, and modality-specific: entropy, not standard deviation, links OXTR methylation and infant behavior; entropy evoked during social perception specifically explains social behavior only; and only entropy evoked during social auditory perception predicts infant vocalization behavior. CONCLUSIONS Demonstrating these associations in infancy is critical for elucidating the neurobiological mechanisms accounting for individual differences in cognition and behavior relevant to neurodevelopmental disorders. Our results suggest that an epigenetic modification to the oxytocin receptor gene and brain signal entropy are useful indicators of social development and may hold potential diagnostic, therapeutic, and prognostic value.
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Affiliation(s)
- Meghan H Puglia
- Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA.
- Department of Neurology, University of Virginia, P.O. Box 800834, Charlottesville, VA, 22908, USA.
| | - Kathleen M Krol
- Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA
- Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Manuela Missana
- Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
- Department of Early Child Development and Culture, Leipzig University, 04109, Leipzig, Germany
| | - Cabell L Williams
- Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA
| | - Travis S Lillard
- Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA
| | - James P Morris
- Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA
| | - Jessica J Connelly
- Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA
| | - Tobias Grossmann
- Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA
- Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
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Eroğlu G, Gürkan M, Teber S, Ertürk K, Kırmızı M, Ekici B, Arman F, Balcisoy S, Özgüz V, Çetin M. Changes in EEG complexity with neurofeedback and multi-sensory learning in children with dyslexia: A multiscale entropy analysis. APPLIED NEUROPSYCHOLOGY-CHILD 2020; 11:133-144. [DOI: 10.1080/21622965.2020.1772794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Günet Eroğlu
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Mert Gürkan
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Serap Teber
- Medical Faculty, Child Neurology Department, Ankara University, Ankara, Turkey
| | | | | | - Barış Ekici
- Özel Çocuk Nörolojisi Kliniği, Istanbul, Turkey
| | - Fehim Arman
- Neurology Department, Acıbadem Hastanesi Kadıköy, Istanbul, Turkey
| | - Selim Balcisoy
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Volkan Özgüz
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Müjdat Çetin
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
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Hasegawa C, Takahashi T, Yoshimura Y, Nobukawa S, Ikeda T, Saito DN, Kumazaki H, Minabe Y, Kikuchi M. Developmental Trajectory of Infant Brain Signal Variability: A Longitudinal Pilot Study. Front Neurosci 2018; 12:566. [PMID: 30154695 PMCID: PMC6102372 DOI: 10.3389/fnins.2018.00566] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 07/27/2018] [Indexed: 11/13/2022] Open
Abstract
The infant brain shows rapid neural network development that considerably influences cognitive and behavioral abilities in later life. Reportedly, this neural development process can be indexed by estimating neural signal complexity. However, the precise developmental trajectory of brain signal complexity during infancy remains elusive. This study was conducted to ascertain the trajectory of magnetoencephalography (MEG) signal complexity from 2 months to 3 years of age in five infants using multiscale entropy (MSE), which captures signal complexity at multiple temporal scales. Analyses revealed scale-dependent developmental trajectories. Specifically, signal complexity predominantly increased from 5 to 15 months of age at higher temporal scales, whereas the complexity at lower temporal scales was constant across age, except in one infant who showed decreased complexity. Despite a small sample size limiting this study’s power, this is the first report of a longitudinal investigation of changes in brain signal complexity during early infancy and is unique in its application of MSE analysis of longitudinal MEG data during infancy. The results of this pilot study may serve to further our understanding of the longitudinal changes in the neural dynamics of the developing infant brain.
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Affiliation(s)
- Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | | | - Yuko Yoshimura
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.,Faculty of Education, Kanazawa University, Kanazawa, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Daisuke N Saito
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Hirokazu Kumazaki
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Yoshio Minabe
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis. ENTROPY 2017. [DOI: 10.3390/e19120680] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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