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Opie GM, Hughes JM, Puri R. Age-related differences in how the shape of alpha and beta oscillations change during reaction time tasks. Neurobiol Aging 2024; 142:52-64. [PMID: 39153461 DOI: 10.1016/j.neurobiolaging.2024.08.001] [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: 10/24/2023] [Revised: 07/25/2024] [Accepted: 08/06/2024] [Indexed: 08/19/2024]
Abstract
While the shape of cortical oscillations is increasingly recognised to be physiologically and functionally informative, its relevance to the aging motor system has not been established. We therefore examined the shape of alpha and beta band oscillations recorded at rest, as well as during performance of simple and go/no-go reaction time tasks, in 33 young (23.3 ± 2.9 years, 27 females) and 27 older (60.0 ± 5.2 years, 23 females) adults. The shape of individual oscillatory cycles was characterised using a recently developed pipeline involving empirical mode decomposition, before being decomposed into waveform motifs using principal component analysis. This revealed four principal components that were uniquely influenced by task and/or age. These described specific dimensions of shape and tended to be modulated during the reaction phase of each task. Our results suggest that although oscillation shape is task-dependent, the nature of this effect is altered by advancing age, possibly reflecting alterations in cortical activity. These outcomes demonstrate the utility of this approach for understanding the neurophysiological effects of ageing.
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Affiliation(s)
- George M Opie
- Discipline of Physiology, School of Biomedicine, The University of Adelaide, Adelaide, Australia.
| | - James M Hughes
- School of Mechanical Engineering, The University of Adelaide, Adelaide, Australia
| | - Rohan Puri
- School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
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2
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Lancia L. Instantaneous phase of rhythmic behaviour under volitional control. Hum Mov Sci 2024; 96:103249. [PMID: 39047306 DOI: 10.1016/j.humov.2024.103249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 06/18/2024] [Accepted: 06/21/2024] [Indexed: 07/27/2024]
Abstract
The phase of signals representing cyclic behavioural patterns provides valuable information for understanding the mechanisms driving the observed behaviours. Methods usually adopted to estimate the phase, which are based on projecting the signal onto the complex plane, have strict requirements on its frequency content, which limits their application. To overcome these limitations, input signals can be processed using band-pass filters or decomposition techniques. In this paper, we briefly review these approaches and propose a new one. Our approach is based on the principles of Empirical Mode Decomposition (EMD), but unlike EMD, it does not aim to decompose the input signal. This avoids the many problems that can occur when extracting a signal's components one by one. The proposed approach estimates the phase of experimental signals that have one main oscillatory component modulated by slower activity and perturbed by weak, sparse, or random activity at faster time scales. We illustrate how our approach works by estimating the phase dynamics of synthetic signals and real-world signals representing knee angles during flexion/extension activity, heel height during gait, and the activity of different organs involved in speech production.
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Affiliation(s)
- Leonardo Lancia
- Laboratoire Parole et Langage, Aix-Marseille Université / CNRS, 5 av. Pasteur, 13100 Aix-en-Provence, France.
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Kaboodvand N, Karimi H, Iravani B. Preparatory activity of anterior insula predicts conflict errors: integrating convolutional neural networks and neural mass models. Sci Rep 2024; 14:16682. [PMID: 39030222 PMCID: PMC11271609 DOI: 10.1038/s41598-024-67034-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024] Open
Abstract
Preparatory brain activity is a cornerstone of proactive cognitive control, a top-down process optimizing attention, perception, and inhibition, fostering cognitive flexibility and adaptive attention control in the human brain. In this study, we proposed a neuroimaging-informed convolutional neural network model to predict cognitive control performance from the baseline pre-stimulus preparatory electrophysiological activity of core cognitive control regions. Particularly, combined with perturbation-based occlusion sensitivity analysis, we pinpointed regions with the most predictive preparatory activity for proactive cognitive control. We found that preparatory arrhythmic broadband neural dynamics in the right anterior insula, right precentral gyrus, and the right opercular part of inferior frontal gyrus (posterior ventrolateral prefrontal cortex), are highly predictive of prospective cognitive control performance. The pre-stimulus preparatory activity in these regions corresponds to readiness for conflict detection, inhibitory control, and overall elaborate attentional processing. We integrated the convolutional neural network with biologically inspired Jansen-Rit neural mass model to investigate neurostimulation effects on cognitive control. High-frequency stimulation (130 Hz) of the left anterior insula provides significant cognitive enhancement, especially in reducing conflict errors, despite the right anterior insula's higher predictive value for prospective cognitive control performance. Thus, effective neurostimulation targets may differ from regions showing biomarker activity. Finally, we validated our theoretical finding by evaluating intrinsic neuromodulation through neurofeedback-guided volitional control in an independent dataset. We found that left anterior insula was intrinsically modulated in real-time by volitional control of emotional valence, but not arousal. Our findings further highlight central role of anterior insula in orchestrating proactive cognitive control processes, positioning it at the top of hierarchy for cognitive control.
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Affiliation(s)
- Neda Kaboodvand
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Hanie Karimi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Behzad Iravani
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.
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Guo X, He S, Geng X, Yao P, Wiest C, Nie Y, Tan H, Wang S. Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease. Neurobiol Dis 2024; 197:106519. [PMID: 38685358 PMCID: PMC7616028 DOI: 10.1016/j.nbd.2024.106519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/26/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024] Open
Abstract
Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.
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Affiliation(s)
- Xuanjun Guo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Xinyi Geng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Pan Yao
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100094 Beijing, China; School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100049 Beijing, China
| | - Christoph Wiest
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Yingnan Nie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Shouyan Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China; Shanghai Engineering Research Center of AI & Robotics, Fudan University, Shanghai, China; Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China.
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Fabus MS, Woolrich MW, Warnaby CW, Quinn AJ. Understanding Harmonic Structures Through Instantaneous Frequency. IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2022; 3:320-334. [PMID: 36172264 PMCID: PMC9491016 DOI: 10.1109/ojsp.2022.3198012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/06/2022] [Indexed: 06/16/2023]
Abstract
The analysis of harmonics and non-sinusoidal waveform shape in time-series data is growing in importance. However, a precise definition of what constitutes a harmonic is lacking. In this paper, we propose a rigorous definition of when to consider signals to be in a harmonic relationship based on an integer frequency ratio, constant phase, and a well-defined joint instantaneous frequency. We show this definition is linked to extrema counting and Empirical Mode Decomposition (EMD). We explore the mathematics of our definition and link it to results from analytic number theory. This naturally leads to us to define two classes of harmonic structures, termed strong and weak, with different extrema behaviour. We validate our framework using both simulations and real data. Specifically, we look at the harmonic structures in shallow water waves, the FitzHugh-Nagumo neuronal model, and the non-sinusoidal theta oscillation in rat hippocampus local field potential data. We further discuss how our definition helps to address mode splitting in nonlinear time-series decomposition methods. A clear understanding of when harmonics are present in signals will enable a deeper understanding of the functional roles of non-sinusoidal oscillations.
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Affiliation(s)
- Marco S. Fabus
- Nuffield Deparment of Clinical NeurosciencesUniversity of OxfordOxfordOX1 2JDU.K.
| | | | - Catherine W. Warnaby
- Nuffield Deparment of Clinical NeurosciencesUniversity of OxfordOxfordOX1 2JDU.K.
| | - Andrew J. Quinn
- Department of PsychiatryUniversity of OxfordOxfordOX1 2JDU.K.
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Silva L, Dias M, Folgado D, Nunes M, Namburi P, Anthony B, Carvalho D, Carvalho M, Edelman E, Gamboa H. Respiratory Inductance Plethysmography to Assess Fatigability during Repetitive Work. SENSORS 2022; 22:s22114247. [PMID: 35684868 PMCID: PMC9185634 DOI: 10.3390/s22114247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/27/2022] [Accepted: 05/30/2022] [Indexed: 11/16/2022]
Abstract
Cumulative fatigue during repetitive work is associated with occupational risk and productivity reduction. Usually, subjective measures or muscle activity are used for a cumulative evaluation; however, Industry 4.0 wearables allow overcoming the challenges observed in those methods. Thus, the aim of this study is to analyze alterations in respiratory inductance plethysmography (RIP) to measure the asynchrony between thorax and abdomen walls during repetitive work and its relationship with local fatigue. A total of 22 healthy participants (age: 27.0 ± 8.3 yrs; height: 1.72 ± 0.09 m; mass: 63.4 ± 12.9 kg) were recruited to perform a task that includes grabbing, moving, and placing a box in an upper and lower shelf. This task was repeated for 10 min in three trials with a fatigue protocol between them. Significant main effects were found from Baseline trial to the Fatigue trials (p < 0.001) for both RIP correlation and phase synchrony. Similar results were found for the activation amplitude of agonist muscle (p < 0.001), and to the muscle acting mainly as a joint stabilizer (p < 0.001). The latter showed a significant effect in predicting both RIP correlation and phase synchronization. Both RIP correlation and phase synchronization can be used for an overall fatigue assessment during repetitive work.
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Affiliation(s)
- Luís Silva
- Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal; (M.D.); (D.F.); (H.G.)
- Correspondence:
| | - Mariana Dias
- Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal; (M.D.); (D.F.); (H.G.)
| | - Duarte Folgado
- Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal; (M.D.); (D.F.); (H.G.)
- Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal;
| | - Maria Nunes
- Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal;
| | - Praneeth Namburi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (P.N.); (B.A.); (E.E.)
- MIT.nano Immersion Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Brian Anthony
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (P.N.); (B.A.); (E.E.)
- Device Realization Laboratory, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Diogo Carvalho
- Faculty of Medicine, Rīga Stradiņš University, 16 Dzirciema iela, LV-1007 Rīga, Latvia;
| | - Miguel Carvalho
- Campus de Azurém, Minho University, 4800-058 Guimarães, Portugal;
| | - Elazer Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (P.N.); (B.A.); (E.E.)
- Brigham and Women’s Hospital, Cardiovascular Division, 75 Francis Street, Boston, MA 02115, USA
| | - Hugo Gamboa
- Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal; (M.D.); (D.F.); (H.G.)
- Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal;
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Papadopoulos S, Bonaiuto J, Mattout J. An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces. Front Neurosci 2022; 15:824759. [PMID: 35095410 PMCID: PMC8789741 DOI: 10.3389/fnins.2021.824759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/21/2021] [Indexed: 01/11/2023] Open
Abstract
The development of reliable assistive devices for patients that suffer from motor impairments following central nervous system lesions remains a major challenge in the field of non-invasive Brain-Computer Interfaces (BCIs). These approaches are predominated by electroencephalography and rely on advanced signal processing and machine learning methods to extract neural correlates of motor activity. However, despite tremendous and still ongoing efforts, their value as effective clinical tools remains limited. We advocate that a rather overlooked research avenue lies in efforts to question neurophysiological markers traditionally targeted in non-invasive motor BCIs. We propose an alternative approach grounded by recent fundamental advances in non-invasive neurophysiology, specifically subject-specific feature extraction of sensorimotor bursts of activity recorded via (possibly magnetoencephalography-optimized) electroencephalography. This path holds promise in overcoming a significant proportion of existing limitations, and could foster the wider adoption of online BCIs in rehabilitation protocols.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Bron, France
- *Correspondence: Sotirios Papadopoulos,
| | - James Bonaiuto
- University Lyon 1, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Bron, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
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