1
|
Fourdain S, Provost S, Tremblay J, Vannasing P, Doussau A, Caron-Desrochers L, Gaudet I, Roger K, Hüsser A, Dehaes M, Martinez-Montes E, Poirier N, Gallagher A. Functional brain connectivity after corrective cardiac surgery for critical congenital heart disease: a preliminary near-infrared spectroscopy (NIRS) report. Child Neuropsychol 2023; 29:1088-1108. [PMID: 36718095 DOI: 10.1080/09297049.2023.2170340] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 01/13/2023] [Indexed: 02/01/2023]
Abstract
Patients with congenital heart disease (CHD) requiring cardiac surgery in infancy are at high risk for neurodevelopmental impairments. Neonatal imaging studies have reported disruptions of brain functional organization before surgery. Yet, the extent to which functional network alterations are present after cardiac repair remains unexplored. This preliminary study aimed at investigating cortical functional connectivity in 4-month-old infants with repaired CHD, using resting-state functional near-infrared spectroscopy (fNIRS). After fNIRS signal frequency decomposition, we compared values of magnitude-squared coherence as a measure of connectivity strength, between 21 infants with corrected CHD and 31 healthy controls. We identified a subset of connections with differences between groups at an uncorrected statistical level of p < .05 while controlling for sex and maternal socioeconomic status, with most of these connections showing reduced connectivity in infants with CHD. Although none of these differences reach statistical significance after FDR correction, likely due to the small sample size, moderate to large effect sizes were found for group-differences. If replicated, these results would therefore suggest preliminary evidence that alterations of brain functional connectivity are present in the months after cardiac surgery. Additional studies involving larger sample size are needed to replicate our data, and comparisons between pre- and postoperative findings would allow to further delineate alterations of functional brain connectivity in this population.
Collapse
Affiliation(s)
- Solène Fourdain
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Sarah Provost
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Julie Tremblay
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | | | - Amélie Doussau
- Clinique d'investigation neurocardiaque (CINC), Sainte-Justine, Montreal University Hospital Center, Montreal, QC, Canada
| | - Laura Caron-Desrochers
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Isabelle Gaudet
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Kassandra Roger
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Alejandra Hüsser
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Mathieu Dehaes
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Montreal, QC, Canada
| | | | - Nancy Poirier
- Clinique d'investigation neurocardiaque (CINC), Sainte-Justine, Montreal University Hospital Center, Montreal, QC, Canada
- Department of Surgery, Faculty of Medicine, Université de Montreal, Montreal, QC, Canada
| | - Anne Gallagher
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| |
Collapse
|
2
|
Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Martinez-Montes E, Bosch-Bayard J, Bringas-Vega ML, Valdes-Sosa M, Valdes-Sosa PA. Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG. Sci Rep 2023; 13:11466. [PMID: 37454235 PMCID: PMC10349891 DOI: 10.1038/s41598-023-38513-y] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.
Collapse
Affiliation(s)
- Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Electrical Engineering, Central University "Marta Abreu" of Las Villas, Santa Clara, Cuba
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Rio, Cuba
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Jorge Bosch-Bayard
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
- McGill Centre for Integrative Neurosciences MCIN, Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Mitchell Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba.
| |
Collapse
|
3
|
Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Vega-Hernandez M, Wang Q, Bosch-Bayard J, Bringas-Vega ML, Martinez-Montes E, Valdes-Sosa MJ, Valdes-Sosa PA. Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning. Front Neurosci 2023; 17:978527. [PMID: 37008210 PMCID: PMC10050575 DOI: 10.3389/fnins.2023.978527] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 02/07/2023] [Indexed: 03/17/2023] Open
Abstract
Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.
Collapse
Affiliation(s)
- Deirel Paz-Linares
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Research Unit for Neurodevelopment, Institute of Neurobiology, Autonomous University of Mexico, Querétaro, Mexico
- Faculty of Electrical Engineering, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba
| | - Ariosky Areces-Gonzalez
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Faculty of Technical Sciences, University of Pinar del Río “Hermanos Saiz Montes de Oca”, Pinar del Rio, Cuba
| | - Ying Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qing Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge Bosch-Bayard
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maria L. Bringas-Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | | | - Mitchel J. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| |
Collapse
|
4
|
Martinez-Montes E, Garcia-Puente Y, Zanartu M, Prado-Gutierrez P. Chirp Analyzer for Estimating Amplitude and Latency of Steady-State Auditory Envelope Following Responses. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2744-2753. [PMID: 33085611 DOI: 10.1109/tnsre.2020.3032835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE The envelope following response (EFR) is a clinically relevant evoked potential, reflecting the synchronization of the auditory pathway to the temporal envelope of sounds. Since there is no standard analysis of this potential, we here aim at contrasting the relative accuracy of known time-frequency methods and new strategies for the reliable estimation of the EFR amplitude and latency. METHODS The EFR was estimated using explicit time-frequency methods: the Short-Term Fourier Transform (STFT) and the Morlet Continuous Wavelet Transform (CWT). Furthermore, the Chirp Analyzer (CA) was introduced as a new tool for the reliable estimation of the EFR. The applicability of the methods was tested in animal and human recordings. RESULTS Using simulated data for comparing the estimation performance by each method, we found that the CA is able to correctly estimate EFR amplitudes, without the typical bias observed in the STFT estimates. The CA is more robust to noise than the CWT method, although with higher sensitivity to the latency of the response. Thus, the estimation of the EFR amplitude with any of the methods, but especially with CA, should be corrected by using the estimated delay. Analysis of real data confirmed these results and showed that all methods offer estimated EFRs similar to those found in previous studies using the classical Fourier Analyzer. CONCLUSION AND SIGNIFICANCE The CA is a potential valuable tool for the analysis of the EFR, which could be extended for the estimation of oscillatory evoked potentials of other sensory modalities.
Collapse
|
5
|
Sánchez-Catasús CA, Sanabria-Diaz G, Willemsen A, Martinez-Montes E, Samper-Noa J, Aguila-Ruiz A, Boellaard R, De Deyn PP, Dierckx RAJO, Melie-Garcia L. Subtle alterations in cerebrovascular reactivity in mild cognitive impairment detected by graph theoretical analysis and not by the standard approach. Neuroimage Clin 2017; 15:151-160. [PMID: 28529871 PMCID: PMC5429238 DOI: 10.1016/j.nicl.2017.04.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [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: 09/01/2016] [Revised: 03/10/2017] [Accepted: 04/19/2017] [Indexed: 01/07/2023]
Abstract
There is growing support that cerebrovascular reactivity (CVR) in response to a vasodilatory challenge, also defined as the cerebrovascular reserve, is reduced in Alzheimer's disease dementia. However, this is less clear in patients with mild cognitive impairment (MCI). The current standard analysis may not reflect subtle abnormalities in CVR. In this study, we aimed to investigate vasodilatory-induced changes in the topology of the cerebral blood flow correlation (CBFcorr) network to study possible network-related CVR abnormalities in MCI. For this purpose, four CBFcorr networks were constructed: two using CBF SPECT data at baseline and under the vasodilatory challenge of acetazolamide (ACZ), obtained from a group of 26 MCI patients; and two equivalent networks from a group of 26 matched cognitively normal controls. The mean strength of association (SA) and clustering coefficient (C) were used to evaluate ACZ-induced changes on the topology of CBFcorr networks. We found that cognitively normal adults and MCI patients show different patterns of C and SA changes. The observed differences included the medial prefrontal cortices and inferior parietal lobe, which represent areas involved in MCI's cognitive dysfunction. In contrast, no substantial differences were detected by standard CVR analysis. These results suggest that graph theoretical analysis of ACZ-induced changes in the topology of the CBFcorr networks allows the identification of subtle network-related CVR alterations in MCI, which couldn't be detected by the standard approach. Subtle alterations in cerebrovascular reactivity in MCI by graph theoretical analysis. Graph theoretical analysis seems to be sensitive to subtle abnormalities. The standard approach could be insufficient for capturing subtle abnormal changes.
Collapse
Affiliation(s)
- Carlos A Sánchez-Catasús
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands; Department of Nuclear Medicine, Center for Neurological Restoration (CIREN), Havana, Cuba.
| | - Gretel Sanabria-Diaz
- Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Antoon Willemsen
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands
| | | | - Juan Samper-Noa
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba; Hospital Carlos J. Finlay, Havana, Cuba
| | - Angel Aguila-Ruiz
- Department of Nuclear Medicine, Center for Neurological Restoration (CIREN), Havana, Cuba
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Peter P De Deyn
- Department of Neurology and Alzheimer Research Center, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Lester Melie-Garcia
- Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| |
Collapse
|
6
|
Lage-Castellanos A, Nieto JI, Quiñones I, Martinez-Montes E. A zero-training algorithm for EEG single-trial classification applied to a face recognition ERP experiment. Annu Int Conf IEEE Eng Med Biol Soc 2011; 2010:4209-12. [PMID: 21096895 DOI: 10.1109/iembs.2010.5627395] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper proposes a machine learning based approach to discriminate between EEG single trials of two experimental conditions in a face recognition experiment. The algorithm works using a single-trial EEG database of multiple subjects and thus does not require subject-specific training data. This approach supports the idea that zero-training classification and on-line detection Brain Computer Interface (BCI) systems are areas with a significant amount of potential.
Collapse
|
7
|
Beauchemin M, Gonzalez-Frankenberger B, Tremblay J, Vannasing P, Martinez-Montes E, Belin P, Beland R, Francoeur D, Carceller AM, Wallois F, Lassonde M. Mother and Stranger: An Electrophysiological Study of Voice Processing in Newborns. Cereb Cortex 2010; 21:1705-11. [DOI: 10.1093/cercor/bhq242] [Citation(s) in RCA: 79] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
8
|
Lippé S, Martinez-Montes E, Arcand C, Lassonde M. Electrophysiological study of auditory development. Neuroscience 2009; 164:1108-18. [PMID: 19665050 DOI: 10.1016/j.neuroscience.2009.07.066] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2007] [Revised: 05/18/2009] [Accepted: 07/18/2009] [Indexed: 10/20/2022]
|