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Frasso G, Eilers PHC. Smooth deconvolution of low-field NMR signals. Anal Chim Acta 2024; 1287:341808. [PMID: 38182331 DOI: 10.1016/j.aca.2023.341808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 01/07/2024]
Abstract
BACKGROUND Low resolution nuclear magnetic resonance (LR-NMR) is a common technique to identify the constituents of complex materials (such as food and biological samples). The output of LR-NMR experiments is a relaxation signal which can be modelled as a type of convolution of an unknown density of relaxation times with decaying exponential functions, plus random Gaussian noise. The challenge is to estimate that density, a severely ill-posed problem. A complication is that non-negativity constraints need to be imposed in order to obtain valid results. SIGNIFICANCE AND NOVELTY We present a smooth deconvolution model for solution of the inverse estimation problem in LR-NMR relaxometry experiments. We model the logarithm of the relaxation time density as a smooth function using (adaptive) P-splines while matching the expected residual magnetisations with the observed ones. The roughness penalty removes the singularity of the deconvolution problem, and the estimated density is positive by design (since we model its logarithm). The model is non-linear, but it can be linearized easily. The penalty has to be tuned for each given sample. We describe an efficient EM-type algorithm to optimize the smoothing parameter(s). RESULTS We analyze a set of food samples (potato tubers). The relaxation spectra extracted using our method are similar to the ones described in the previous experiments but present sharper peaks. Using penalized signal regression we are able to accurately predict dry matter content of the samples using the estimated spectra as covariates.
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Affiliation(s)
- Gianluca Frasso
- Samotics BV, Bargelaan 200, 2333 CW, Leiden, the Netherlands.
| | - Paul H C Eilers
- Erasmus University Medical Center Rotterdam, the Netherlands.
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2
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Annen J, Frasso G, van der Lande GJM, Bonin EAC, Vitello MM, Panda R, Sala A, Cavaliere C, Raimondo F, Bahri MA, Schiff ND, Gosseries O, Thibaut A, Laureys S. Cerebral electrometabolic coupling in disordered and normal states of consciousness. Cell Rep 2023; 42:112854. [PMID: 37498745 DOI: 10.1016/j.celrep.2023.112854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 06/02/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023] Open
Abstract
We assess cerebral integrity with cortical and subcortical FDG-PET and cortical electroencephalography (EEG) within the mesocircuit model framework in patients with disorders of consciousness (DoCs). The mesocircuit hypothesis proposes that subcortical activation facilitates cortical function. We find that the metabolic balance of subcortical mesocircuit areas is informative for diagnosis and is associated with four EEG-based power spectral density patterns, cortical metabolism, and α power in healthy controls and patients with a DoC. Last, regional electrometabolic coupling at the cortical level can be identified in the θ and α ranges, showing positive and negative relations with glucose uptake, respectively. This relation is inverted in patients with a DoC, potentially related to altered orchestration of neural activity, and may underlie suboptimal excitability states in patients with a DoC. By understanding the neurobiological basis of the pathophysiology underlying DoCs, we foresee translational value for diagnosis and treatment of patients with a DoC.
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Affiliation(s)
- Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium.
| | | | - Glenn J M van der Lande
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Estelle A C Bonin
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Marie M Vitello
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Arianna Sala
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | | | - Federico Raimondo
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | | | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University Laval, Quebec City, QC, Canada
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3
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Panda R, López-González A, Gilson M, Gosseries O, Thibaut A, Frasso G, Cecconi B, Escrichs A, Deco G, Laureys S, Zamora-López G, Annen J. Whole-brain analyses indicate the impairment of posterior integration and thalamo-frontotemporal broadcasting in disorders of consciousness. Hum Brain Mapp 2023. [PMID: 37254960 DOI: 10.1002/hbm.26386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 06/01/2023] Open
Abstract
The study of the brain's dynamical activity is opening a window to help the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and the dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI data, fitted to a linear model of activity propagation. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to severe reduction of glucose metabolism as measured with [18 F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results shed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior.
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Affiliation(s)
- Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
- University Hospital of Liège, Liège, Belgium
| | - Ane López-González
- Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
| | - Matthieu Gilson
- Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
- Institut des Neurosciences des Systemes, INSERM-AMU, Marseille, France
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
- University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
- University Hospital of Liège, Liège, Belgium
| | - Gianluca Frasso
- Wageningen Food Safety Research, Wageningen, The Netherlands
| | - Benedetta Cecconi
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
- University Hospital of Liège, Liège, Belgium
| | - Anira Escrichs
- Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
- Institució Catalana de la Recerça i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Victoria, Australia
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
- University Hospital of Liège, Liège, Belgium
- CERVO Research Center, Laval University, Québec, Quebec, Canada
| | - Gorka Zamora-López
- Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
| | - Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
- University Hospital of Liège, Liège, Belgium
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Iorio C, Frasso G, D’Ambrosio A, Siciliano R. Correction: Boosted-oriented probabilistic smoothing-spline clustering of series. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-022-00670-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Iorio C, Frasso G, D’Ambrosio A, Siciliano R. Boosted-oriented probabilistic smoothing-spline clustering of series. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-022-00665-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
AbstractFuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. However, a factor that influences the performance of fuzzy algorithms is the value of fuzzifier parameter. In this paper, we propose a fuzzy clustering procedure for data (time) series that does not depend on the definition of a fuzzifier parameter. It comes from two approaches, theoretically motivated for unsupervised and supervised classification cases, respectively. The first is the Probabilistic Distance clustering procedure. The second is the well known Boosting philosophy. Our idea is to adopt a boosting prospective for unsupervised learning problems, in particular we face with non hierarchical clustering problems. The global performance of the proposed method is investigated by various experiments.
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Marvin HJ, Hoenderdaal W, Gavai AK, Mu W, van den Bulk LM, Liu N, Frasso G, Ozen N, Elliott C, Manning L, Bouzembrak Y. Global media as an early warning tool for food fraud; an assessment of MedISys-FF. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Martens G, Kroupi E, Bodien Y, Frasso G, Annen J, Cassol H, Barra A, Martial C, Gosseries O, Lejeune N, Soria-Frisch A, Ruffini G, Laureys S, Thibaut A. Behavioral and electrophysiological effects of network-based frontoparietal tDCS in patients with severe brain injury: A randomized controlled trial. Neuroimage Clin 2020; 28:102426. [PMID: 32977212 PMCID: PMC7511767 DOI: 10.1016/j.nicl.2020.102426] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 08/31/2020] [Accepted: 09/08/2020] [Indexed: 12/31/2022]
Abstract
Behavioral and EEG effects of multifocal frontoparietal tDCS are investigated in patients with severe brain injury. No behavioral treatment effect was identified at the group level while EEG complexity increased in low frequency bands. Electrophysiological changes were not translated into behavioral changes at the group level.
Background Transcranial direct current stimulation (tDCS) may promote the recovery of severely brain-injured patients with disorders of consciousness (DOC). Prior tDCS studies targeted single brain regions rather than brain networks critical for consciousness recovery. Objective Investigate the behavioral and electrophysiological effects of multifocal tDCS applied over the frontoparietal external awareness network in patients with chronic acquired DOC. Methods Forty-six patients were included in this randomized double-blind sham-controlled crossover trial (median [interquartile range]: 46 [35 – 59] years old; 12 [5 – 47] months post injury; 17 unresponsive wakefulness syndrome, 23 minimally conscious state (MCS) and 6 emerged from the MCS). Multifocal tDCS was applied for 20 min using 4 anodes and 4 cathodes with 1 mA per electrode. Coma Recovery Scale-Revised (CRS-R) assessment and 10 min of resting state electroencephalogram (EEG) recordings were acquired before and after the active and sham sessions. Results At the group level, there was no tDCS behavioral treatment effect. However, following active tDCS, the EEG complexity significantly increased in low frequency bands (1–8 Hz). CRS-R total score improvement was associated with decreased baseline complexity in those bands. At the individual level, after active tDCS, new behaviors consistent with conscious awareness emerged in 5 patients. Conversely, 3 patients lost behaviors consistent with conscious awareness. Conclusion The behavioral effect of multifocal frontoparietal tDCS varies across patients with DOC. Electrophysiological changes were observed in low frequency bands but not translated into behavioral changes at the group level.
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Affiliation(s)
- Géraldine Martens
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium.
| | | | - Yelena Bodien
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA; Laboratory for Neuroimaging in Coma and Consciousness, Massachusetts General Hospital, Boston, MA, USA
| | - Gianluca Frasso
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium
| | - Helena Cassol
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium
| | - Alice Barra
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium
| | - Nicolas Lejeune
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre Hospitalier Neurologique William Lennox, Saint-Luc University Clinics, Université Catholique de Louvain, Belgium
| | | | | | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau2 - Centre intégré pluridisciplinaire de l'étude du cerveau, de la cognition et de la conscience, University Hospital of Liège, Liège, Belgium; Neuromodulation Center, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, USA
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8
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Annen J, Frasso G, Crone JS, Heine L, Di Perri C, Martial C, Cassol H, Demertzi A, Naccache L, Laureys S. Regional brain volumetry and brain function in severely brain-injured patients. Ann Neurol 2018; 83:842-853. [PMID: 29572926 DOI: 10.1002/ana.25214] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 03/15/2018] [Accepted: 03/16/2018] [Indexed: 11/07/2022]
Abstract
OBJECTIVE The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. METHODS Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. RESULTS Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. INTERPRETATION Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853.
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Affiliation(s)
- Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - Gianluca Frasso
- Faculty of Social Sciences, Quantitative Methods for Social Sciences, University of Liège, Liège, Belgium
| | | | - Lizette Heine
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,Auditory Cognition and Psychoacoustics Team, Lyon Neuroscience Research Center, Lyon, France
| | - Carol Di Perri
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium.,Centre for Clinical Brain Sciences UK Dementia Research Institute, Centre for Dementia Prevention, University of Edinburgh, Edinburgh, United Kingdom
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - Helena Cassol
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - Athena Demertzi
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,INSERM, U 1127, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, Hôpital Pitié-Salpêtrière, 47 bd de l'Hôpital, 75013, Paris, France
| | - Lionel Naccache
- INSERM, U 1127, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, Hôpital Pitié-Salpêtrière, 47 bd de l'Hôpital, 75013, Paris, France
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
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Annen J, Heine L, Ziegler E, Frasso G, Bahri M, Di Perri C, Stender J, Martial C, Wannez S, D'ostilio K, Amico E, Antonopoulos G, Bernard C, Tshibanda F, Hustinx R, Laureys S. Function-structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET. Hum Brain Mapp 2018; 37:3707-3720. [PMID: 27273334 DOI: 10.1002/hbm.23269] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 04/12/2016] [Accepted: 05/16/2016] [Indexed: 02/05/2023] Open
Abstract
A vast body of literature exists showing functional and structural dysfunction within the brains of patients with disorders of consciousness. However, the function (fluorodeoxyglucose FDG-PET metabolism)-structure (MRI-diffusion-weighted images; DWI) relationship and how it is affected in severely brain injured patients remains ill-defined. FDG-PET and MRI-DWI in 25 severely brain injured patients (19 Disorders of Consciousness of which 7 unresponsive wakefulness syndrome, 12 minimally conscious; 6 emergence from minimally conscious state) and 25 healthy control subjects were acquired here. Default mode network (DMN) function-structure connectivity was assessed by fractional anisotropy (FA) and metabolic standardized uptake value (SUV). As expected, a profound decline in regional metabolism and white matter integrity was found in patients as compared with healthy subjects. Furthermore, a function-structure relationship was present in brain-damaged patients between functional metabolism of inferior-parietal, precuneus, and frontal regions and structural integrity of the frontal-inferiorparietal, precuneus-inferiorparietal, thalamo-inferioparietal, and thalamofrontal tracts. When focusing on patients, a stronger relationship between structural integrity of thalamo-inferiorparietal tracts and thalamic metabolism in patients who have emerged from the minimally conscious state as compared with patients with disorders of consciousness was found. The latter finding was in line with the mesocircuit hypothesis for the emergence of consciousness. The findings showed a positive function-structure relationship within most regions of the DMN. Hum Brain Mapp 37:3707-3720, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- J Annen
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - L Heine
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - E Ziegler
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - G Frasso
- Faculty of Social Sciences, Quantitative Methods for Social Sciences, University of Liège, Liège, Belgium
| | - M Bahri
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - C Di Perri
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - J Stender
- University of Copenhagen, Copenhagen, Denmark
| | - C Martial
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - S Wannez
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - K D'ostilio
- Headache Research Unit, University of Liège, Liège, Belgium
| | - E Amico
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - G Antonopoulos
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - C Bernard
- University Hospital of Liège, Liège, Belgium
| | - F Tshibanda
- University Hospital of Liège, Liège, Belgium
| | - R Hustinx
- University Hospital of Liège, Liège, Belgium
| | - S Laureys
- Cyclotron Research Centre, University of Liège, Liège, Belgium. .,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium. .,University Hospital of Liège, Liège, Belgium.
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Frasso G, Lambert P. Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone. Biostatistics 2016; 17:779-92. [PMID: 27324411 DOI: 10.1093/biostatistics/kxw027] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 05/16/2016] [Indexed: 11/13/2022] Open
Abstract
The 2014 Ebola outbreak in Sierra Leone is analyzed using a susceptible-exposed-infectious-removed (SEIR) epidemic compartmental model. The discrete time-stochastic model for the epidemic evolution is coupled to a set of ordinary differential equations describing the dynamics of the expected proportions of subjects in each epidemic state. The unknown parameters are estimated in a Bayesian framework by combining data on the number of new (laboratory confirmed) Ebola cases reported by the Ministry of Health and prior distributions for the transition rates elicited using information collected by the WHO during the follow-up of specific Ebola cases. The time-varying disease transmission rate is modeled in a flexible way using penalized B-splines. Our framework represents a valuable stochastic tool for the study of an epidemic dynamic even when only irregularly observed and possibly aggregated data are available. Simulations and the analysis of the 2014 Sierra Leone Ebola data highlight the merits of the proposed methodology. In particular, the flexible modeling of the disease transmission rate makes the estimation of the effective reproduction number robust to the misspecification of the initial epidemic states and to underreporting of the infectious cases.
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Affiliation(s)
- Gianluca Frasso
- Faculté des Sciences Sociales, Méthodes Quantitatives en Sciences Sociales, Université de Liège, Liège, BelgiumInstitut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Philippe Lambert
- Faculté des Sciences Sociales, Méthodes Quantitatives en Sciences Sociales, Université de Liège, Liège, BelgiumInstitut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
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11
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Frasso G, Jaeger J, Lambert P. Inference in dynamic systems using B-splines and quasilinearized ODE penalties. Biom J 2015; 58:691-714. [PMID: 26602190 DOI: 10.1002/bimj.201500082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Revised: 05/13/2015] [Accepted: 08/20/2015] [Indexed: 11/09/2022]
Abstract
Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems. We propose a smoothing approach regularized by a quasilinearized ODE-based penalty. Within the quasilinearized spline-based framework, the estimation reduces to a conditionally linear problem for the optimization of the spline coefficients. Furthermore, standard ODE compliance parameter(s) selection criteria are applicable. We evaluate the performances of the proposed strategy through simulated and real data examples. Simulation studies suggest that the proposed procedure ensures more accurate estimates than standard nonlinear least squares approaches when the state (initial and/or boundary) conditions are not known.
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Affiliation(s)
- Gianluca Frasso
- Faculté des Sciences Sociales, Méthodes Quantitatives en Sciences Sociales, Université de Liège, Boulevard du Rectorat 7, B-4000, Liège, Belgium
| | | | - Philippe Lambert
- Faculté des Sciences Sociales, Méthodes Quantitatives en Sciences Sociales, Université de Liège, Boulevard du Rectorat 7, B-4000, Liège, Belgium.,Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Belgium
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Rosas-Aguirre A, Erhart A, Llanos-Cuentas A, Branch O, Berkvens D, Abatih E, Lambert P, Frasso G, Rodriguez H, Gamboa D, Sihuincha M, Rosanas-Urgell A, D'Alessandro U, Speybroeck N. Modelling the potential of focal screening and treatment as elimination strategy for Plasmodium falciparum malaria in the Peruvian Amazon Region. Parasit Vectors 2015; 8:261. [PMID: 25948081 PMCID: PMC4429469 DOI: 10.1186/s13071-015-0868-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 04/21/2015] [Indexed: 12/05/2022] Open
Abstract
Background Focal screening and treatment (FSAT) of malaria infections has recently been introduced in Peru to overcome the inherent limitations of passive case detection (PCD) and further decrease the malaria burden. Here, we used a relatively straightforward mathematical model to assess the potential of FSAT as elimination strategy for Plasmodium falciparum malaria in the Peruvian Amazon Region. Methods A baseline model was developed to simulate a scenario with seasonal malaria transmission and the effect of PCD and treatment of symptomatic infections on the P. falciparum malaria transmission in a low endemic area of the Peruvian Amazon. The model was then adjusted to simulate intervention scenarios for predicting the long term additional impact of FSAT on P. falciparum malaria prevalence and incidence. Model parameterization was done using data from a cohort study in a rural Amazonian community as well as published transmission parameters from previous studies in similar areas. The effect of FSAT timing and frequency, using either microscopy or a supposed field PCR, was assessed on both predicted incidence and prevalence rates. Results The intervention model indicated that the addition of FSAT to PCD significantly reduced the predicted P. falciparum incidence and prevalence. The strongest reduction was observed when three consecutive FSAT were implemented at the beginning of the low transmission season, and if malaria diagnosis was done with PCR. Repeated interventions for consecutive years (10 years with microscopy or 5 years with PCR), would allow reaching near to zero incidence and prevalence rates. Conclusions The addition of FSAT interventions to PCD may enable to reach P. falciparum elimination levels in low endemic areas of the Amazon Region, yet the progression rates to those levels may vary substantially according to the operational criteria used for the intervention. Electronic supplementary material The online version of this article (doi:10.1186/s13071-015-0868-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Angel Rosas-Aguirre
- Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima 31, Peru. .,Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, 2000, Belgium. .,Research Institute of Health and Society (IRSS), Université catholique de Louvain, Brussels, 1200, Belgium.
| | - Annette Erhart
- Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, 2000, Belgium.
| | - Alejandro Llanos-Cuentas
- Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima 31, Peru.
| | - Oralee Branch
- Department of Medical Parasitology, New York University, New York, 10012, USA.
| | - Dirk Berkvens
- Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, 2000, Belgium.
| | - Emmanuel Abatih
- Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, 2000, Belgium.
| | - Philippe Lambert
- Research Institute of Health and Society (IRSS), Université catholique de Louvain, Brussels, 1200, Belgium. .,Institut des sciences humaines et sociales, Université de Liège, Liege, 4000, Belgium.
| | - Gianluca Frasso
- Institut des sciences humaines et sociales, Université de Liège, Liege, 4000, Belgium.
| | | | - Dionicia Gamboa
- Departamento de Ciencias Celulares y Moleculares, Facultad de Ciencias y Filosofia, Universidad Peruana Cayetano Heredia, Lima 31, Peru.
| | - Moisés Sihuincha
- Facultad de Medicina, Universidad Nacional Amazonia Peruana, Iquitos, Loreto, 160, Peru.
| | - Anna Rosanas-Urgell
- Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, 2000, Belgium.
| | - Umberto D'Alessandro
- Disease Control and Elimination, Medical Research Council Unit, Fajara, 220, The Gambia. .,London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.
| | - Niko Speybroeck
- Research Institute of Health and Society (IRSS), Université catholique de Louvain, Brussels, 1200, Belgium.
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Abstract
The L-curve is a tool for the selection of the regularization parameter in ill-posed inverse problems. It is a parametric plot of the size of the residuals vs that of the penalty. The corner of the L indicates the right amount of regularization. In the context of smoothing the L-curve is easy to compute and works surprisingly well, even for data with correlated noise. We present the theoretical background and applications to real data together with an alternative criterion for finding the corner automatically. We introduce as simplification, the V-curve, which replaces finding the corner of the L-curve by locating a minimum.
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Affiliation(s)
- Gianluca Frasso
- Institut des sciences humaines et sociales, Méthodes quantitatives en sciences sociales, Université de Liège, Belgium
| | - Paul HC Eilers
- Department of Biostatistics, Erasmus University Medical Centre, The Netherlands
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