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Chan HL, Low I, Chen LF, Chen YS, Chu IT, Hsieh JC. A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea. J Neural Eng 2021; 18. [PMID: 33691295 DOI: 10.1088/1741-2552/abed83] [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: 10/27/2020] [Accepted: 03/10/2021] [Indexed: 12/30/2022]
Abstract
Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.
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Affiliation(s)
- Hui-Ling Chan
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Intan Low
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Li-Fen Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.,Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yong-Sheng Chen
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ian-Ting Chu
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jen-Chuen Hsieh
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
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Borges G, Brusamarello V. Sensor fusion methods for reducing false alarms in heart rate monitoring. J Clin Monit Comput 2015; 30:859-867. [PMID: 26439831 DOI: 10.1007/s10877-015-9786-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 09/29/2015] [Indexed: 11/29/2022]
Abstract
Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.
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Affiliation(s)
- Gabriel Borges
- Electrical Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, 90035-190, Brazil.
| | - Valner Brusamarello
- Electrical Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, 90035-190, Brazil
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Pineda-Pardo JA, Bruña R, Woolrich M, Marcos A, Nobre AC, Maestú F, Vidaurre D. Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of conditions of mild cognitive impairment. Neuroimage 2014; 101:765-77. [PMID: 25111472 PMCID: PMC4312351 DOI: 10.1016/j.neuroimage.2014.08.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Revised: 07/30/2014] [Accepted: 08/01/2014] [Indexed: 01/18/2023] Open
Abstract
Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corresponding functional connections. We applied beamformer source reconstruction to the resting state MEG recordings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was obtained for each subject, and time series were assigned to each of the regions. The fiber densities between the regions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introducing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups. We propose an anatomy-driven method for functional connectivity estimation in MEG. Structural prior contributes to a better representation of the functional connectivity. The proposed method is shown to be useful as a biomarker for classification of MCI.
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Affiliation(s)
- José Angel Pineda-Pardo
- Laboratory of Neuroimaging, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain; Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Mark Woolrich
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
| | - Alberto Marcos
- The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom; Department of Neurology, Hospital Clínico San Carlos, Madrid, Spain.
| | - Anna C Nobre
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom.
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Diego Vidaurre
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
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Parsons CE, Young KS, Mohseni H, Woolrich MW, Thomsen KR, Joensson M, Murray L, Goodacre T, Stein A, Kringelbach ML. Minor structural abnormalities in the infant face disrupt neural processing: a unique window into early caregiving responses. Soc Neurosci 2013; 8:268-74. [PMID: 23659740 DOI: 10.1080/17470919.2013.795189] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Infant faces elicit early, specific activity in the orbitofrontal cortex (OFC), a key cortical region for reward and affective processing. A test of the causal relationship between infant facial configuration and OFC activity is provided by naturally occurring disruptions to the face structure. One such disruption is cleft lip, a small change to one facial feature, shown to disrupt parenting. Using magnetoencephalography, we investigated neural responses to infant faces with cleft lip compared with typical infant and adult faces. We found activity in the right OFC at 140 ms in response to typical infant faces but diminished activity to infant faces with cleft lip or adult faces. Activity in the right fusiform face area was of similar magnitude for typical adult and infant faces but was significantly lower for infant faces with cleft lip. This is the first evidence that a minor change to the infant face can disrupt neural activity potentially implicated in caregiving.
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Mohseni HR, Smith PP, Parsons CE, Young KS, Hyam JA, Stein A, Stein JF, Green AL, Aziz TZ, Kringelbach ML. MEG can map short and long-term changes in brain activity following deep brain stimulation for chronic pain. PLoS One 2012; 7:e37993. [PMID: 22675503 PMCID: PMC3366994 DOI: 10.1371/journal.pone.0037993] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2012] [Accepted: 05/01/2012] [Indexed: 11/18/2022] Open
Abstract
Deep brain stimulation (DBS) has been shown to be clinically effective for some forms of treatment-resistant chronic pain, but the precise mechanisms of action are not well understood. Here, we present an analysis of magnetoencephalography (MEG) data from a patient with whole-body chronic pain, in order to investigate changes in neural activity induced by DBS for pain relief over both short- and long-term. This patient is one of the few cases treated using DBS of the anterior cingulate cortex (ACC). We demonstrate that a novel method, null-beamforming, can be used to localise accurately brain activity despite the artefacts caused by the presence of DBS electrodes and stimulus pulses. The accuracy of our source localisation was verified by correlating the predicted DBS electrode positions with their actual positions. Using this beamforming method, we examined changes in whole-brain activity comparing pain relief achieved with deep brain stimulation (DBS ON) and compared with pain experienced with no stimulation (DBS OFF). We found significant changes in activity in pain-related regions including the pre-supplementary motor area, brainstem (periaqueductal gray) and dissociable parts of caudal and rostral ACC. In particular, when the patient reported experiencing pain, there was increased activity in different regions of ACC compared to when he experienced pain relief. We were also able to demonstrate long-term functional brain changes as a result of continuous DBS over one year, leading to specific changes in the activity in dissociable regions of caudal and rostral ACC. These results broaden our understanding of the underlying mechanisms of DBS in the human brain.
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Affiliation(s)
- Hamid R. Mohseni
- University Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Institute of Biomedical Engineering, School of Engineering Science, University of Oxford, Oxford, United Kingdom
- Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
- Department of Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
- Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom
| | - Penny P. Smith
- Institute of Biomedical Engineering, School of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Christine E. Parsons
- University Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
- Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom
| | - Katherine S. Young
- University Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
- Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom
| | - Jonathan A. Hyam
- Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
| | - Alan Stein
- University Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - John F. Stein
- Department of Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
| | - Alexander L. Green
- Department of Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
| | - Tipu Z. Aziz
- Department of Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
| | - Morten L. Kringelbach
- University Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
- Department of Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
- Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom
- * E-mail:
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