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Vidal M, Onderdijk KE, Aguilera AM, Six J, Maes PJ, Fritz TH, Leman M. Cholinergic-related pupil activity reflects level of emotionality during motor performance. Eur J Neurosci 2024; 59:2193-2207. [PMID: 37118877 DOI: 10.1111/ejn.15998] [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: 10/13/2022] [Revised: 04/20/2023] [Accepted: 04/26/2023] [Indexed: 04/30/2023]
Abstract
Pupil size covaries with the diffusion rate of the cholinergic and noradrenergic neurons throughout the brain, which are essential to arousal. Recent findings suggest that slow pupil fluctuations during locomotion are an index of sustained activity in cholinergic axons, whereas phasic dilations are related to the activity of noradrenergic axons. Here, we investigated movement induced arousal (i.e., by singing and swaying to music), hypothesising that actively engaging in musical behaviour will provoke stronger emotional engagement in participants and lead to different qualitative patterns of tonic and phasic pupil activity. A challenge in the analysis of pupil data is the turbulent behaviour of pupil diameter due to exogenous ocular activity commonly encountered during motor tasks and the high variability typically found between individuals. To address this, we developed an algorithm that adaptively estimates and removes pupil responses to ocular events, as well as a functional data methodology, derived from Pfaffs' generalised arousal, that provides a new statistical dimension on how pupil data can be interpreted according to putative neuromodulatory signalling. We found that actively engaging in singing enhanced slow cholinergic-related pupil dilations and having the opportunity to move your body while performing amplified the effect of singing on pupil activity. Phasic pupil oscillations during motor execution attenuated in time, which is often interpreted as a measure of sense of agency over movement.
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Affiliation(s)
- Marc Vidal
- IPEM, Ghent University, Ghent, Belgium
- Department of Statistics and Operations Research, Institute of Mathematics, University of Granada, Granada, Spain
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Ana M Aguilera
- Department of Statistics and Operations Research, Institute of Mathematics, University of Granada, Granada, Spain
| | - Joren Six
- IPEM, Ghent University, Ghent, Belgium
| | | | - Thomas Hans Fritz
- IPEM, Ghent University, Ghent, Belgium
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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2
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Massara P, Asrar A, Bourdon C, Ngari M, Keown-Stoneman CDG, Maguire JL, Birken CS, Berkley JA, Bandsma RHJ, Comelli EM. New approaches and technical considerations in detecting outlier measurements and trajectories in longitudinal children growth data. BMC Med Res Methodol 2023; 23:232. [PMID: 37833647 PMCID: PMC10576311 DOI: 10.1186/s12874-023-02045-w] [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: 08/22/2022] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Growth studies rely on longitudinal measurements, typically represented as trajectories. However, anthropometry is prone to errors that can generate outliers. While various methods are available for detecting outlier measurements, a gold standard has yet to be identified, and there is no established method for outlying trajectories. Thus, outlier types and their effects on growth pattern detection still need to be investigated. This work aimed to assess the performance of six methods at detecting different types of outliers, propose two novel methods for outlier trajectory detection and evaluate how outliers affect growth pattern detection. METHODS We included 393 healthy infants from The Applied Research Group for Kids (TARGet Kids!) cohort and 1651 children with severe malnutrition from the co-trimoxazole prophylaxis clinical trial. We injected outliers of three types and six intensities and applied four outlier detection methods for measurements (model-based and World Health Organization cut-offs-based) and two for trajectories. We also assessed growth pattern detection before and after outlier injection using time series clustering and latent class mixed models. Error type, intensity, and population affected method performance. RESULTS Model-based outlier detection methods performed best for measurements with precision between 5.72-99.89%, especially for low and moderate error intensities. The clustering-based outlier trajectory method had high precision of 14.93-99.12%. Combining methods improved the detection rate to 21.82% in outlier measurements. Finally, when comparing growth groups with and without outliers, the outliers were shown to alter group membership by 57.9 -79.04%. CONCLUSIONS World Health Organization cut-off-based techniques were shown to perform well in few very particular cases (extreme errors of high intensity), while model-based techniques performed well, especially for moderate errors of low intensity. Clustering-based outlier trajectory detection performed exceptionally well across all types and intensities of errors, indicating a potential strategic change in how outliers in growth data are viewed. Finally, the importance of detecting outliers was shown, given its impact on children growth studies, as demonstrated by comparing results of growth group detection.
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Affiliation(s)
- Paraskevi Massara
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada.
| | - Arooj Asrar
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Celine Bourdon
- Translational Medicine Program, Hospital for Sick Children, Toronto, Canada
| | - Moses Ngari
- Kenya Medical Research Institute (KEMRI)/ Wellcome Trust Research Programme, Kilifi, Kenya
| | - Charles D G Keown-Stoneman
- Li KaShing Knowledge Institute, Unity Health Toronto, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Jonathon L Maguire
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada
- Li KaShing Knowledge Institute, Unity Health Toronto, Toronto, Canada
| | - Catherine S Birken
- Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, Canada
- Child Health Evaluative Services, Hospital for Sick Children, Toronto, Canada
| | - James A Berkley
- Kenya Medical Research Institute (KEMRI)/ Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Robert H J Bandsma
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada.
- Translational Medicine Program, Hospital for Sick Children, Toronto, Canada.
| | - Elena M Comelli
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada.
- Joannah and Brian Lawson Center for Child Nutrition, University of Toronto, Toronto, Canada.
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Boland J, Telesca D, Sugar C, Jeste S, Dickinson A, DiStefano C, Şentürk D. Central Posterior Envelopes for Bayesian Functional Principal Component Analysis. JOURNAL OF DATA SCIENCE : JDS 2023; 21:715-734. [PMID: 38883309 PMCID: PMC11178334 DOI: 10.6339/23-jds1085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Bayesian methods provide direct inference in functional data analysis applications without reliance on bootstrap techniques. A major tool in functional data applications is the functional principal component analysis which decomposes the data around a common mean function and identifies leading directions of variation. Bayesian functional principal components analysis (BFPCA) provides uncertainty quantification on the estimated functional model components via the posterior samples obtained. We propose central posterior envelopes (CPEs) for BFPCA based on functional depth as a descriptive visualization tool to summarize variation in the posterior samples of the estimated functional model components, contributing to uncertainty quantification in BFPCA. The proposed BFPCA relies on a latent factor model and targets model parameters within a mixed effects modeling framework using modified multiplicative gamma process shrinkage priors on the variance components. Functional depth provides a center-outward order to a sample of functions. We utilize modified band depth and modified volume depth for ordering of a sample of functions and surfaces, respectively, to derive at CPEs of the mean and eigenfunctions within the BFPCA framework. The proposed CPEs are showcased in extensive simulations. Finally, the proposed CPEs are applied to the analysis of a sample of power spectral densities (PSD) from resting state electroencephalography (EEG) where they lead to novel insights on diagnostic group differences among children diagnosed with autism spectrum disorder and their typically developing peers across age.
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Affiliation(s)
- Joanna Boland
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90025, USA
| | - Donatello Telesca
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90025, USA
| | - Catherine Sugar
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90025, USA
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90025, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90025, USA
| | - Shafali Jeste
- Division of Neurology, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - Abigail Dickinson
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90025, USA
| | - Charlotte DiStefano
- Division of Neurology, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - Damla Şentürk
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90025, USA
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90025, USA
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Gregori-Pla C, Zirak P, Cotta G, Bramon P, Blanco I, Serra I, Mola A, Fortuna A, Solà-Soler J, Giraldo Giraldo BF, Durduran T, Mayos M. How does obstructive sleep apnea alter cerebral hemodynamics? Sleep 2023; 46:zsad122. [PMID: 37336476 PMCID: PMC10424168 DOI: 10.1093/sleep/zsad122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/02/2023] [Indexed: 06/21/2023] Open
Abstract
STUDY OBJECTIVES We aimed to characterize the cerebral hemodynamic response to obstructive sleep apnea/hypopnea events, and evaluate their association to polysomnographic parameters. The characterization of the cerebral hemodynamics in obstructive sleep apnea (OSA) may add complementary information to further the understanding of the severity of the syndrome beyond the conventional polysomnography. METHODS Severe OSA patients were studied during night sleep while monitored by polysomnography. Transcranial, bed-side diffuse correlation spectroscopy (DCS) and frequency-domain near-infrared diffuse correlation spectroscopy (NIRS-DOS) were used to follow microvascular cerebral hemodynamics in the frontal lobes of the cerebral cortex. Changes in cerebral blood flow (CBF), total hemoglobin concentration (THC), and cerebral blood oxygen saturation (StO2) were analyzed. RESULTS We considered 3283 obstructive apnea/hypopnea events from sixteen OSA patients (Age (median, interquartile range) 57 (52-64.5); females 25%; AHI (apnea-hypopnea index) 84.4 (76.1-93.7)). A biphasic response (maximum/minimum followed by a minimum/maximum) was observed for each cerebral hemodynamic variable (CBF, THC, StO2), heart rate and peripheral arterial oxygen saturation (SpO2). Changes of the StO2 followed the dynamics of the SpO2, and were out of phase from the THC and CBF. Longer events were associated with larger CBF changes, faster responses and slower recoveries. Moreover, the extrema of the response to obstructive hypopneas were lower compared to apneas (p < .001). CONCLUSIONS Obstructive apneas/hypopneas cause profound, periodic changes in cerebral hemodynamics, including periods of hyper- and hypo-perfusion and intermittent cerebral hypoxia. The duration of the events is a strong determinant of the cerebral hemodynamic response, which is more pronounced in apnea than hypopnea events.
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Affiliation(s)
- Clara Gregori-Pla
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels(Barcelona), 08860, Spain
| | - Peyman Zirak
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels(Barcelona), 08860, Spain
| | - Gianluca Cotta
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels(Barcelona), 08860, Spain
| | - Pau Bramon
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels(Barcelona), 08860, Spain
| | - Igor Blanco
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels(Barcelona), 08860, Spain
| | - Isabel Serra
- Departament de Matemàtiques, Facultat de Ciències, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès (Barcelona), Spain
- Computer Architecture and Operating Systems, Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
| | - Anna Mola
- Sleep Unit, Department of Respiratory Medicine, Hospital de la Santa Creu i Sant Pau, C. de Sant Quintí, 89, 08041, Barcelona, Spain
| | - Ana Fortuna
- Sleep Unit, Department of Respiratory Medicine, Hospital de la Santa Creu i Sant Pau, C. de Sant Quintí, 89, 08041, Barcelona, Spain
| | - Jordi Solà-Soler
- Automatic Control Department (ESAII), Universitat Politècnica de Catalunya (UPC)-Barcelona Tech, 08028, Barcelona, Spain
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, 08019, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, 50018, Spain
| | - Beatriz F Giraldo Giraldo
- Automatic Control Department (ESAII), Universitat Politècnica de Catalunya (UPC)-Barcelona Tech, 08028, Barcelona, Spain
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, 08019, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, 50018, Spain
| | - Turgut Durduran
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels(Barcelona), 08860, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain
| | - Mercedes Mayos
- Sleep Unit, Department of Respiratory Medicine, Hospital de la Santa Creu i Sant Pau, C. de Sant Quintí, 89, 08041, Barcelona, Spain
- CIBER Enfermedades Respiratorias (CibeRes) (CB06/06), C. Montforte de Lemos 3-5, 28029, Madrid, Spain
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Lei X, Chen Z, Li H. Functional Outlier Detection for Density-Valued Data with Application to Robustify Distribution-to-Distribution Regression. Technometrics 2023. [DOI: 10.1080/00401706.2022.2164063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Xinyi Lei
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China
- Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China
- School of Civil Engineering, Harbin Institute of Technology, Harbin, China
| | - Zhicheng Chen
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China
- Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China
- School of Civil Engineering, Harbin Institute of Technology, Harbin, China
| | - Hui Li
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China
- Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China
- School of Civil Engineering, Harbin Institute of Technology, Harbin, China
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Staerman G, Adjakossa E, Mozharovskyi P, Hofer V, Sen Gupta J, Clémençon S. Functional anomaly detection: a benchmark study. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2022. [DOI: 10.1007/s41060-022-00366-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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7
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Band depth based initialization of K-means for functional data clustering. ADV DATA ANAL CLASSI 2022. [DOI: 10.1007/s11634-022-00510-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
AbstractThe k-Means algorithm is one of the most popular choices for clustering data but is well-known to be sensitive to the initialization process. There is a substantial number of methods that aim at finding optimal initial seeds for k-Means, though none of them is universally valid. This paper presents an extension to longitudinal data of one of such methods, the BRIk algorithm, that relies on clustering a set of centroids derived from bootstrap replicates of the data and on the use of the versatile Modified Band Depth. In our approach we improve the BRIk method by adding a step where we fit appropriate B-splines to our observations and a resampling process that allows computational feasibility and handling issues such as noise or missing data. We have derived two techniques for providing suitable initial seeds, each of them stressing respectively the multivariate or the functional nature of the data. Our results with simulated and real data sets indicate that our Functional Data Approach to the BRIK method (FABRIk) and our Functional Data Extension of the BRIK method (FDEBRIk) are more effective than previous proposals at providing seeds to initialize k-Means in terms of clustering recovery.
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8
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Localization processes for functional data analysis. ADV DATA ANAL CLASSI 2022. [DOI: 10.1007/s11634-022-00512-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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9
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Data adaptive functional outlier detection: Analysis of the Paris bike sharing system data. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.04.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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10
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Elías A, Jiménez R, Paganoni AM, Sangalli LM. Integrated Depths for Partially Observed Functional Data. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2070171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Antonio Elías
- OASYS Group, Department of Applied Mathematics, Universidad de Málaga
| | - Raúl Jiménez
- Department of Statistics, University Carlos III of Madrid
| | - Anna M. Paganoni
- MOX Laboratory for Modeling and Scientic Computing, Dipartimento di Matematica, Politecnico di Milano
| | - Laura M. Sangalli
- MOX Laboratory for Modeling and Scientic Computing, Dipartimento di Matematica, Politecnico di Milano
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11
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Affiliation(s)
- Zhuo Qu
- Statistics Program, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - Marc G. Genton
- Statistics Program, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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LoMauro A, Colli A, Colombo L, Aliverti A. Breathing patterns recognition: A functional data analysis approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106670. [PMID: 35172250 DOI: 10.1016/j.cmpb.2022.106670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/27/2022] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The ongoing pandemic proved fundamental is to assess a subject's respiratory functionality and breathing pattern measurement during quiet breathing is feasible in almost all patients, even those uncooperative. Breathing pattern consists of tidal volume and respiratory rate in an individual assessed by data tracks of lung or chest wall volume over time. State-of-art analysis of these data requires operator-dependent choices such as individuation of local minima in the track, elimination of anomalous breaths and individuation of breath clusters corresponding to different breathing patterns. METHODS A semi-automatic, robust and reproducible procedure was proposed to pre-process and analyse respiratory tracks, based on Functional Data Analysis (FDA) techniques, to identify representative breath curve and the corresponding breathing patterns. This was achieved through three steps: 1) breath separation through precise localization of the minima of the volume trace; 2) functional outlier breaths detection according to time-duration, magnitude and shape; 3) breath clustering to identify different pattern of interest, through K-medoids with Alignment. The method was firstly validated on simulated tracks and then applied to real data in conditions of clinical interest: operational volume change, exercise, mechanical ventilation, paradoxical breathing and age. RESULTS The total error in the accuracy of minima detection and in was less than 5%; with the artificial outliers being almost completely removed with an accuracy of 99%. During incremental exercise and independently on the bike resistance level, five clusters were identified (quiet breathing; recovery phase; onset of exercise; maximal and intermediate levels of exercise). During mechanical ventilation, the procedure was able to separate the non-ventilated from the ventilatory-supported breathing and to identify the worsening of paradoxical breathing due to the disease progression and the breathing pattern changes in healthy subjects due to age. CONCLUSIONS We proposed a robust validated automatic breathing patterns identification algorithm that extracted representative curves that could be implemented in clinical practice for objective comparison of the breathing patterns within and between subjects. In all case studies the identified patterns proved to be coherent with the clinical conditions and the physiopathology of the subjects, therefore enforcing the potential clinical translational value of the method.
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Affiliation(s)
- A LoMauro
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, P.zza L. da Vinci 32; 20133 Milano, Italy.
| | - A Colli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, P.zza L. da Vinci 32; 20133 Milano, Italy
| | - L Colombo
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, P.zza L. da Vinci 32; 20133 Milano, Italy
| | - A Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, P.zza L. da Vinci 32; 20133 Milano, Italy
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Alemán-Gómez Y, Arribas-Gil A, Desco M, Elías A, Romo J. Depthgram: Visualizing outliers in high-dimensional functional data with application to fMRI data exploration. Stat Med 2022; 41:2005-2024. [PMID: 35118686 PMCID: PMC9305951 DOI: 10.1002/sim.9342] [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: 07/02/2021] [Revised: 12/10/2021] [Accepted: 01/17/2022] [Indexed: 11/28/2022]
Abstract
Functional magnetic resonance imaging (fMRI) is a non‐invasive technique that facilitates the study of brain activity by measuring changes in blood flow. Brain activity signals can be recorded during the alternate performance of given tasks, that is, task fMRI (tfMRI), or during resting‐state, that is, resting‐state fMRI (rsfMRI), as a measure of baseline brain activity. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. fMRI experiments from high‐resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to brain activity measurements over each voxel of the brain along the duration of the experiment. In this context, we propose novel visualization techniques for high‐dimensional functional data relying on depth‐based notions that enable computationally efficient 2‐dim representations of fMRI data, which elucidate sample composition, outlier presence, and individual variability. We believe that this previous step is crucial to any inferential approach willing to identify neuroscientific patterns across individuals, tasks, and brain regions. We present the proposed technique via an extensive simulation study, and demonstrate its application on a motor and language tfMRI experiment.
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Affiliation(s)
- Yasser Alemán-Gómez
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Psychiatric Neuroscience, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland
| | - Ana Arribas-Gil
- Departamento de Estadística, Universidad Carlos III de Madrid, Getafe, Spain
| | - Manuel Desco
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Leganés, Spain.,Unidad de Medicina y Cirugía Experimental, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.,Unidad de Imagen Avanzada, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | | | - Juan Romo
- Departamento de Estadística, Universidad Carlos III de Madrid, Getafe, Spain
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A Geometric Perspective on Functional Outlier Detection. STATS 2021. [DOI: 10.3390/stats4040057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
We consider functional outlier detection from a geometric perspective, specifically: for functional datasets drawn from a functional manifold, which is defined by the data’s modes of variation in shape, translation, and phase. Based on this manifold, we developed a conceptualization of functional outlier detection that is more widely applicable and realistic than previously proposed taxonomies. Our theoretical and experimental analyses demonstrated several important advantages of this perspective: it considerably improves theoretical understanding and allows describing and analyzing complex functional outlier scenarios consistently and in full generality, by differentiating between structurally anomalous outlier data that are off-manifold and distributionally outlying data that are on-manifold, but at its margins. This improves the practical feasibility of functional outlier detection: we show that simple manifold-learning methods can be used to reliably infer and visualize the geometric structure of functional datasets. We also show that standard outlier-detection methods requiring tabular data inputs can be applied to functional data very successfully by simply using their vector-valued representations learned from manifold learning methods as the input features. Our experiments on synthetic and real datasets demonstrated that this approach leads to outlier detection performances at least on par with existing functional-data-specific methods in a large variety of settings, without the highly specialized, complex methodology and narrow domain of application these methods often entail.
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15
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Elías A, Jiménez R, Shang HL. On projection methods for functional time series forecasting. J MULTIVARIATE ANAL 2021. [DOI: 10.1016/j.jmva.2021.104890] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Ojo OT, Fernández Anta A, Lillo RE, Sguera C. Detecting and classifying outliers in big functional data. ADV DATA ANAL CLASSI 2021. [DOI: 10.1007/s11634-021-00460-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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17
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Affiliation(s)
- Paromita Dubey
- Department of Statistics, Stanford University, Stanford, CA
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18
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Khalemsky A, Gelbard R. ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time. ACM JOURNAL OF DATA AND INFORMATION QUALITY 2021. [DOI: 10.1145/3434778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.
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Affiliation(s)
- A. Khalemsky
- Information Systems Program, Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
| | - R. Gelbard
- Information Systems Program, Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
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Lopez-Pintado S, Qian K. A depth-based global envelope test for comparing two groups of functions with applications to biomedical data. Stat Med 2021; 40:1639-1652. [PMID: 33410197 PMCID: PMC9848787 DOI: 10.1002/sim.8861] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 09/01/2020] [Accepted: 11/02/2020] [Indexed: 01/21/2023]
Abstract
Functional data are commonly observed in many emerging biomedical fields and their analysis is an exciting developing area in statistics. Numerous statistical methods, such as principal components, analysis of variance, and linear regression, have been extended to functional data. The statistical analysis of functions can be significantly improved using nonparametric and robust estimators. New ideas of depth for functional data have been proposed in recent years and can be extended to image data. They provide a way of ordering curves or images from center-outward, and of defining robust order statistics in a functional context. In this paper we develop depth-based global envelope tests for comparing two groups of functions or images. In addition to providing global P-values, the proposed envelope test can be displayed graphically and indicates the specific portion(s) of the functional data (eg, in pixels or in time) that may have led to rejection of the null hypothesis. We show in a simulation study the performance of the envelope test in terms of empirical power and size in different scenarios. The proposed depth-based global approach has good power even for small differences and is robust to outliers. The methodology introduced is applied to test whether children with normal and low birth weight have similar growth pattern. We also analyzed a brain image dataset consisting of positron emission tomography scans of severe depressed patients and healthy controls. The global envelope test was used to find and visualize differences between the two groups.
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Affiliation(s)
- Sara Lopez-Pintado
- Department of Health Sciences, Northeastern University, Boston, Massachusetts
| | - Kun Qian
- Division of Biostatistics, Department of Population Health, Grossman School of Medicine, NYU Langone Health, New York, New York
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Harris T, Tucker JD, Li B, Shand L. Elastic Depths for Detecting Shape Anomalies in Functional Data. Technometrics 2020. [DOI: 10.1080/00401706.2020.1811156] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Trevor Harris
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL
| | | | - Bo Li
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL
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Affiliation(s)
- Harjit Hullait
- STOR-i Centre for Doctoral Training, Lancaster University, Lancaster, UK
| | - David S. Leslie
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Nicos G. Pavlidis
- Department of Management Science, Lancaster University, Lancaster, UK
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Dai W, Mrkvička T, Sun Y, Genton MG. Functional outlier detection and taxonomy by sequential transformations. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2020.106960] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Affiliation(s)
- Zonghui Yao
- Statistics Program King Abdullah University of Science and Technology Thuwal 23955‐6900 Saudi Arabia
| | - Wenlin Dai
- Institute of Statistics and Big Data Renmin University of China Beijing 100872 China
| | - Marc G. Genton
- Statistics Program King Abdullah University of Science and Technology Thuwal 23955‐6900 Saudi Arabia
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Lejeune C, Mothe J, Soubki A, Teste O. Shape-based outlier detection in multivariate functional data. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105960] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Krzyśko M, Smaga Ł. Robust multivariate functional discriminant coordinates. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2019.1580731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Mirosław Krzyśko
- Interfaculty Institute of Mathematics and Statistics, The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Kalisz, Poland
| | - Łukasz Smaga
- Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznań, Poland
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Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data. PLoS One 2020; 15:e0228154. [PMID: 31978151 PMCID: PMC6980495 DOI: 10.1371/journal.pone.0228154] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 01/09/2020] [Indexed: 12/21/2022] Open
Abstract
All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cut-offs and logic rules was designed. Five data cleaning methods for growth were tested with and without the addition of the algorithm and applied to five different longitudinal growth datasets: four uncleaned canine weight or height datasets and one pre-cleaned human weight dataset with randomly simulated errors. Prior to the addition of the algorithm, data cleaning based on non-linear mixed effects models was the most effective in all datasets and had on average a minimum of 26.00% higher sensitivity and 0.12% higher specificity than other methods. Data cleaning methods using the algorithm had improved data preservation and were capable of correcting simulated errors according to the gold standard; returning a value to its original state prior to error simulation. The algorithm improved the performance of all data cleaning methods and increased the average sensitivity and specificity of the non-linear mixed effects model method by 7.68% and 0.42% respectively. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains.
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29
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Rank tests for functional data based on the epigraph, the hypograph and associated graphical representations. ADV DATA ANAL CLASSI 2019. [DOI: 10.1007/s11634-019-00380-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Huang H, Sun Y. A Decomposition of Total Variation Depth for Understanding Functional Outliers. Technometrics 2019. [DOI: 10.1080/00401706.2019.1574241] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Huang Huang
- Statistical and Applied Mathematical Sciences Institute, Durham, NC
| | - Ying Sun
- CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Zirak P, Gregori-Pla C, Blanco I, Fortuna A, Cotta G, Bramon P, Serra I, Mola A, Solà-Soler J, Giraldo-Giraldo BF, Durduran T, Mayos M. Characterization of the microvascular cerebral blood flow response to obstructive apneic events during night sleep. NEUROPHOTONICS 2018; 5:045003. [PMID: 30681667 PMCID: PMC6215085 DOI: 10.1117/1.nph.5.4.045003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2018] [Accepted: 10/10/2018] [Indexed: 05/09/2023]
Abstract
Obstructive apnea causes periodic changes in cerebral and systemic hemodynamics, which may contribute to the increased risk of cerebrovascular disease of patients with obstructive sleep apnea (OSA) syndrome. The improved understanding of the consequences of an apneic event on the brain perfusion may improve our knowledge of these consequences and then allow for the development of preventive strategies. Our aim was to characterize the typical microvascular, cortical cerebral blood flow (CBF) changes in an OSA population during an apneic event. Sixteen patients (age 58 ± 8 years , 75% male) with a high risk of severe OSA were measured with a polysomnography device and with diffuse correlation spectroscopy (DCS) during one night of sleep with 1365 obstructive apneic events detected. All patients were later confirmed to suffer from severe OSA syndrome with a mean of 83 ± 15 apneas and hypopneas per hour. DCS has been shown to be able to characterize the microvascular CBF response to each event with a sufficient contrast-to-noise ratio to reveal its dynamics. It has also revealed that an apnea causes a peak increase of microvascular CBF ( 30 ± 17 % ) at the end of the event followed by a drop ( - 20 ± 12 % ) similar to what was observed in macrovascular CBF velocity of the middle cerebral artery. This study paves the way for the utilization of DCS for further studies on these populations.
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Affiliation(s)
- Peyman Zirak
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Clara Gregori-Pla
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Barcelona, Spain
- Address all correspondence to: Clara Gregori-Pla, E-mail:
| | - Igor Blanco
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ana Fortuna
- Hospital de la Santa Creu i Sant Pau, Department of Respiratory Medicine, Sleep Unit, Barcelona, Spain
| | - Gianluca Cotta
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Pau Bramon
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Isabel Serra
- Centre de Recerca Matemàtica (CRM), Bellaterra, Spain
| | - Anna Mola
- Hospital de la Santa Creu i Sant Pau, Department of Respiratory Medicine, Sleep Unit, Barcelona, Spain
| | - Jordi Solà-Soler
- Universitat Politècnica de Catalunya (UPC)-Barcelona Tech, Department of Automatic Control (ESAII), Barcelona, Spain
- The Barcelona Institute of Science and Technology, Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Beatriz F. Giraldo-Giraldo
- Universitat Politècnica de Catalunya (UPC)-Barcelona Tech, Department of Automatic Control (ESAII), Barcelona, Spain
- The Barcelona Institute of Science and Technology, Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Turgut Durduran
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Mercedes Mayos
- Hospital de la Santa Creu i Sant Pau, Department of Respiratory Medicine, Sleep Unit, Barcelona, Spain
- CIBER Enfermedades Respiratorias (CibeRes) (CB06/06), Madrid, Spain
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35
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Affiliation(s)
- Wenlin Dai
- Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Marc G. Genton
- Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Rousseeuw PJ, Raymaekers J, Hubert M. A Measure of Directional Outlyingness With Applications to Image Data and Video. J Comput Graph Stat 2018. [DOI: 10.1080/10618600.2017.1366912] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
| | | | - Mia Hubert
- Department of Mathematics, KU Leuven, Leuven, Belgium
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Giovannella M, Ibañez D, Gregori-Pla C, Kacprzak M, Mitjà G, Ruffini G, Durduran T. Concurrent measurement of cerebral hemodynamics and electroencephalography during transcranial direct current stimulation. NEUROPHOTONICS 2018; 5:015001. [PMID: 29392156 PMCID: PMC5784784 DOI: 10.1117/1.nph.5.1.015001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 12/20/2017] [Indexed: 05/05/2023]
Abstract
Transcranial direct current stimulation (tDCS) is currently being used for research and treatment of some neurological and neuropsychiatric disorders, as well as for improvement of cognitive functions. In order to better understand cerebral response to the stimulation and to redefine protocols and dosage, its effects must be monitored. To this end, we have used functional diffuse correlation spectroscopy (fDCS) and time-resolved functional near-infrared spectroscopy (TR-fNIRS) together with electroencephalography (EEG) during and after stimulation of the frontal cortex. Twenty subjects participated in two sessions of stimulation with two different polarity montages and twelve also underwent a sham session. Cerebral blood flow and oxyhemoglobin concentration increased during and after active stimulation in the region under the stimulation electrode while deoxyhemoglobin concentration decreased. The EEG spectrum displayed statistically significant power changes across different stimulation sessions in delta (2 to 4 Hz), theta (4 to 8 Hz), and beta (12 to 18 Hz) bands. Results suggest that fDCS and TR-fNIRS can be employed as neuromonitors of the effects of transcranial electrical stimulation and can be used together with EEG.
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Affiliation(s)
- Martina Giovannella
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Address all correspondence to: Martina Giovannella, E-mail:
| | | | - Clara Gregori-Pla
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Michal Kacprzak
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | | | - Giulio Ruffini
- Starlab, Barcelona, Spain
- Neuroelectrics Barcelona, Barcelona, Spain
| | - Turgut Durduran
- ICFO-Institut de Ciències Fotòniques, Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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38
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López-Pintado S, Wrobel J. Robust non-parametric tests for imaging data based on data depth. Stat (Int Stat Inst) 2017. [DOI: 10.1002/sta4.168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Sara López-Pintado
- Department of Biostatistics; Columbia University, Mailman School of Public Health; New York NY 10032 USA
| | - Julia Wrobel
- Department of Biostatistics; Columbia University, Mailman School of Public Health; New York NY 10032 USA
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Affiliation(s)
- Stanislav Nagy
- Department of Mathematics, KU Leuven, Belgium
- Department of Probability and Mathematical Statistics, Charles University, Czech Republic
| | | | - Daniel Hlubinka
- Department of Probability and Mathematical Statistics, Charles University, Czech Republic
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40
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Component-wise outlier detection methods for robustifying multivariate functional samples. Stat Pap (Berl) 2017. [DOI: 10.1007/s00362-017-0953-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Abstract
Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window method for detecting trials with outliers in intra-participant time-series data. Outliers were detected through two stages for the strides (mean 38 cycles) from treadmill running. Cycles were removed in stage 1 for one-dimensional (spatial) outliers at each time point using the median absolute deviation, and in stage 2 for two-dimensional (spatial–temporal) outliers using a moving window standard deviation. Significance levels of the t-statistic were used for scaling. Fewer cycles were removed with smaller scaling and smaller window size, requiring more stringent scaling at stage 1 (mean 3.5 cycles removed for 0.0001 scaling) than at stage 2 (mean 2.6 cycles removed for 0.01 scaling with a window size of 1). Settings in the supplied Matlab code should be customised to each data set, and outliers assessed to justify whether to retain or remove those cycles. The method is effective in identifying trials with outliers in intra-participant time series data.
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Affiliation(s)
- David R. Mullineaux
- School of Sport and Exercise Science, College of Social Sciences, University of Lincoln, Lincoln, UK
| | - Gareth Irwin
- Cardiff School of Sport, Cardiff Metropolitan University, Cardiff, UK
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Tzeng S, Hennig C, Li YF, Lin CJ. Dissimilarity for functional data clustering based on smoothing parameter commutation. Stat Methods Med Res 2017; 27:3492-3504. [PMID: 28535712 PMCID: PMC5723154 DOI: 10.1177/0962280217710050] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Many studies measure the same type of information longitudinally on the same subject at multiple time points, and clustering of such functional data has many important applications. We propose a novel and easy method to implement dissimilarity measure for functional data clustering based on smoothing splines and smoothing parameter commutation. This method handles data observed at regular or irregular time points in the same way. We measure the dissimilarity between subjects based on varying curve estimates with pairwise commutation of smoothing parameters. The intuition is that smoothing parameters of smoothing splines reflect the inverse of the signal-to-noise ratios and that when applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. Our method takes into account the estimation uncertainty using smoothing parameter commutation and is not strongly affected by outliers. It can also be used for outlier detection. The effectiveness of our proposal is shown by simulations comparing it to other dissimilarity measures and by a real application to methadone dosage maintenance levels.
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Affiliation(s)
- ShengLi Tzeng
- 1 Department of Public Health, China Medical University, Taiwan
| | - Christian Hennig
- 2 Department of Statistical Science, University College London, UK
| | - Yu-Fen Li
- 1 Department of Public Health, China Medical University, Taiwan
| | - Chien-Ju Lin
- 3 MRC Biostatistics Unit, University of Cambridge, UK
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Affiliation(s)
- Ramón Flores
- Departamento de Geometría y Topología, Universidad of de Sevilla-IMUS, Sevilla, Spain
| | - Rosa Lillo
- Departamento de Estadística, Universidad Carlos III de Madrid, Getafe, Spain
| | - Juan Romo
- Departamento de Estadística, Universidad Carlos III de Madrid, Getafe, Spain
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Mechmeche I, Mitiche A, Ouakrim Y, De Guise JA, Mezghani N. Data correction to determine a representative pattern of a set of 3D knee kinematic measurements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:884-887. [PMID: 28268465 DOI: 10.1109/embc.2016.7590842] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of this study is to determine a representative pattern of a set of three dimensional (3D) knee kinematic measurement curves recorded throughout several trials with a patient walking on a treadmill. The measurements are knee angles, (namely joint angles) with respect to the sagittal, frontal, and transverse planes, as a function of time during a gait cycle. Two serious difficulties met while extracting a representative pattern from the trials are that the curves possess phase variability and there are outliers. We propose a scheme which first removes outliers using the modified band depth index method, and follows with phase variability reduction by curve registration. This scheme leads to retaining the mean curve of the corrected set of curves, as the most representative.
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45
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Depth-based nonparametric description of functional data, with emphasis on use of spatial depth. Comput Stat Data Anal 2017. [DOI: 10.1016/j.csda.2016.07.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Kuhnt S, Rehage A. An angle-based multivariate functional pseudo-depth for shape outlier detection. J MULTIVARIATE ANAL 2016. [DOI: 10.1016/j.jmva.2015.10.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ieva F, Paganoni AM. Discussion of “multivariate functional outlier detection” by M. Hubert, P. Rousseeuw and P. Segaert. STAT METHOD APPL-GER 2015. [DOI: 10.1007/s10260-015-0303-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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50
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Discussion of Multivariate functional outlier detection by M. Hubert, P. Rousseeuw and P. Segaert. STAT METHOD APPL-GER 2015. [DOI: 10.1007/s10260-015-0323-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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