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Amoruso L, García AM, Pusil S, Timofeeva P, Quiñones I, Carreiras M. Decoding bilingualism from resting-state oscillatory network organization. Ann N Y Acad Sci 2024; 1534:106-117. [PMID: 38419368 DOI: 10.1111/nyas.15113] [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] [Indexed: 03/02/2024]
Abstract
Can lifelong bilingualism be robustly decoded from intrinsic brain connectivity? Can we determine, using a spectrally resolved approach, the oscillatory networks that better predict dual-language experience? We recorded resting-state magnetoencephalographic activity in highly proficient Spanish-Basque bilinguals and Spanish monolinguals, calculated functional connectivity at canonical frequency bands, and derived topological network properties using graph analysis. These features were fed into a machine learning classifier to establish how robustly they discriminated between the groups. The model showed excellent classification (AUC: 0.91 ± 0.12) between individuals in each group. The key drivers of classification were network strength in beta (15-30 Hz) and delta (2-4 Hz) rhythms. Further characterization of these networks revealed the involvement of temporal, cingulate, and fronto-parietal hubs likely underpinning the language and default-mode networks (DMNs). Complementary evidence from a correlation analysis showed that the top-ranked features that better discriminated individuals during rest also explained interindividual variability in second language (L2) proficiency within bilinguals, further supporting the robustness of the machine learning model in capturing trait-like markers of bilingualism. Overall, our results show that long-term experience with an L2 can be "brain-read" at a fine-grained level from resting-state oscillatory network organization, highlighting its pervasive impact, particularly within language and DMN networks.
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Affiliation(s)
- Lucia Amoruso
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
| | - Adolfo M García
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Sandra Pusil
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Polina Timofeeva
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
| | - Ileana Quiñones
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Manuel Carreiras
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
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Laguna A, Pusil S, Bazán À, Zegarra-Valdivia JA, Paltrinieri AL, Piras P, Palomares I Perera C, Pardos Véglia A, Garcia-Algar O, Orlandi S. Multi-modal analysis of infant cry types characterization: Acoustics, body language and brain signals. Comput Biol Med 2023; 167:107626. [PMID: 37918262 DOI: 10.1016/j.compbiomed.2023.107626] [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: 07/04/2023] [Revised: 09/14/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Infant crying is the first attempt babies use to communicate during their initial months of life. A misunderstanding of the cry message can compromise infant care and future neurodevelopmental process. METHODS An exploratory study collecting multimodal data (i.e., crying, electroencephalography (EEG), near-infrared spectroscopy (NIRS), facial expressions, and body movements) from 38 healthy full-term newborns was conducted. Cry types were defined based on different conditions (i.e., hunger, sleepiness, fussiness, need to burp, and distress). Statistical analysis, Machine Learning (ML), and Deep Learning (DL) techniques were used to identify relevant features for cry type classification and to evaluate a robust DL algorithm named Acoustic MultiStage Interpreter (AMSI). RESULTS Significant differences were found across cry types based on acoustics, EEG, NIRS, facial expressions, and body movements. Acoustics and body language were identified as the most relevant ML features to support the cause of crying. The DL AMSI algorithm achieved an accuracy rate of 92%. CONCLUSIONS This study set a precedent for cry analysis research by highlighting the complexity of newborn cry expression and strengthening the potential use of infant cry analysis as an objective, reliable, accessible, and non-invasive tool for cry interpretation, improving the infant-parent relationship and ensuring family well-being.
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Affiliation(s)
| | | | | | - Jonathan Adrián Zegarra-Valdivia
- Global Brain Health Institute, University of California, San Francisco, CA, USA; Achucarro Basque Center for Neuroscience, Leioa, Spain; Universidad Señor de Sipán, Chiclayo, Peru
| | | | | | | | | | - Oscar Garcia-Algar
- Neonatology Unit, Hospital Clínic-Maternitat, ICGON, BCNatal, 08028, Barcelona, Spain; Department de Cirurgia I Especialitats Mèdico-quirúrgiques, Universitat de Barcelona, 08036, Barcelona, Spain
| | - Silvia Orlandi
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi"(DEI), University of Bologna, Bologna, Italy; Health Sciences and Technologies Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
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Laguna A, Pusil S, Acero-Pousa I, Zegarra-Valdivia JA, Paltrinieri AL, Bazán À, Piras P, Palomares i Perera C, Garcia-Algar O, Orlandi S. How can cry acoustics associate newborns' distress levels with neurophysiological and behavioral signals? Front Neurosci 2023; 17:1266873. [PMID: 37799341 PMCID: PMC10547902 DOI: 10.3389/fnins.2023.1266873] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/07/2023] [Indexed: 10/07/2023] Open
Abstract
Introduction Even though infant crying is a common phenomenon in humans' early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different cry distress levels of newborns. Methods Multimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Statistical analysis was conducted on this dataset to identify correlations among variables during three different infant conditions (i.e., resting, cry, and distress). A Deep Learning (DL) algorithm was used to objectively and automatically evaluate the level of cry distress in infants. Results We found correlations between most of the features extracted from the signals depending on the infant's arousal state, among them: fundamental frequency (F0), brain activity (delta, theta, and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension, and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Finally, the DL audio model developed was able to classify the different levels of distress achieving 93% accuracy. Conclusion Our findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians.
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Affiliation(s)
| | | | | | - Jonathan Adrián Zegarra-Valdivia
- Facultad de Medicina, Universidad Señor de Sipán, Chiclayo, Peru
- Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
- Achucarro Basque Center for Neuroscience, Leioa, Spain
| | - Anna Lucia Paltrinieri
- Neonatology Department, Barcelona Center for Maternal-Fetal and Neonatal Medicine (BCNatal), Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | | | | | - Clàudia Palomares i Perera
- Neonatology Department, Barcelona Center for Maternal-Fetal and Neonatal Medicine (BCNatal), Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Oscar Garcia-Algar
- Neonatology Department, Barcelona Center for Maternal-Fetal and Neonatal Medicine (BCNatal), Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
- Department de Cirurgia I Especialitats Mèdico-Quirúrgiques, Universitat de Barcelona, Barcelona, Spain
| | - Silvia Orlandi
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Bologna, Italy
- Health Sciences and Technologies Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
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Pusil S, Zegarra-Valdivia J, Cuesta P, Laohathai C, Cebolla AM, Haueisen J, Fiedler P, Funke M, Maestú F, Cheron G. Effects of spaceflight on the EEG alpha power and functional connectivity. Sci Rep 2023; 13:9489. [PMID: 37303002 DOI: 10.1038/s41598-023-34744-1] [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: 12/28/2022] [Accepted: 05/06/2023] [Indexed: 06/13/2023] Open
Abstract
Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN's alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space.
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Affiliation(s)
- Sandra Pusil
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Jonathan Zegarra-Valdivia
- Achucarro Basque Center for Neuroscience, Leioa, Spain
- Global Brain Health Institute (GBHI), University of California, San Francisco (UCSF), San Francisco, CA, USA
- Universidad Señor de Sipán, Chiclayo, Peru
| | - Pablo Cuesta
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Radiology, Rehabilitation, and Physiotherapy, Universidad Complutense de Madrid, Madrid, Spain
| | | | - Ana Maria Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Instituto de Investigación Sanitario, Hospital Clínico San Carlos, Madrid, Spain
| | - Guy Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.
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Amoruso L, Pusil S, García AM, Ibañez A. Decoding motor expertise from fine-tuned oscillatory network organization. Hum Brain Mapp 2022; 43:2817-2832. [PMID: 35274804 PMCID: PMC9120567 DOI: 10.1002/hbm.25818] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/26/2022] [Accepted: 02/15/2022] [Indexed: 01/21/2023] Open
Abstract
Can motor expertise be robustly predicted by the organization of frequency-specific oscillatory brain networks? To answer this question, we recorded high-density electroencephalography (EEG) in expert Tango dancers and naïves while viewing and judging the correctness of Tango-specific movements and during resting. We calculated task-related and resting-state connectivity at different frequency-bands capturing task performance (delta [δ], 1.5-4 Hz), error monitoring (theta [θ], 4-8 Hz), and sensorimotor experience (mu [μ], 8-13 Hz), and derived topographical features using graph analysis. These features, together with canonical expertise measures (i.e., performance in action discrimination, time spent dancing Tango), were fed into a data-driven computational learning analysis to test whether behavioral and brain signatures robustly classified individuals depending on their expertise level. Unsurprisingly, behavioral measures showed optimal classification (100%) between dancers and naïves. When considering brain models, the task-based classification performed well (~73%), with maximal discrimination afforded by theta-band connectivity, a hallmark signature of error processing. Interestingly, mu connectivity during rest outperformed (100%) the task-based approach, matching the optimal classification of behavioral measures and thus emerging as a potential trait-like marker of sensorimotor network tuning by intense training. Overall, our findings underscore the power of fine-tuned oscillatory network signatures for capturing expertise-related differences and their potential value in the neuroprognosis of learning outcomes.
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Affiliation(s)
- Lucia Amoruso
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain.,IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Sandra Pusil
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Adolfo Martín García
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina.,National University of Cuyo (UNCuyo), Mendoza, Argentina.,Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, California, USA.,Trinity College Dublin (TCD), Dublin, Ireland
| | - Agustín Ibañez
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina.,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, California, USA.,Trinity College Dublin (TCD), Dublin, Ireland.,Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
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Pusil S, Torres-Simon L, Chino B, López ME, Canuet L, Bilbao Á, Maestú F, Paúl N. Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke. Front Neurol 2022; 13:838170. [PMID: 35280290 PMCID: PMC8914082 DOI: 10.3389/fneur.2022.838170] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/03/2022] [Indexed: 11/29/2022] Open
Abstract
Background Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal. Methods For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level. Findings After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design).
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Affiliation(s)
- Sandra Pusil
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Lucía Torres-Simon
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Brenda Chino
- Institute of Neuroscience, Autonomous University of Barcelona, Barcelona, Spain
| | - María Eugenia López
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Leonides Canuet
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Álvaro Bilbao
- National Centre for Brain Injury Treatment, Centro de Referencia Estatal de Atención Al Daño Cerebral (CEADAC), Madrid, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Nuria Paúl
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
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Pusil S, López ME, Cuesta P, Bruña R, Pereda E, Maestú F. Hypersynchronization in mild cognitive impairment: the ‘X’ model. Brain 2019; 142:3936-3950. [DOI: 10.1093/brain/awz320] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 08/06/2019] [Accepted: 08/13/2019] [Indexed: 12/21/2022] Open
Abstract
Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.
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Affiliation(s)
- Sandra Pusil
- Laboratory of Neuropsychology, University of the Balearic Islands, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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Pusil S, Dimitriadis SI, López ME, Pereda E, Maestú F. Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease. Neuroimage Clin 2019; 24:101972. [PMID: 31522127 PMCID: PMC6745514 DOI: 10.1016/j.nicl.2019.101972] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [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: 05/07/2019] [Revised: 07/19/2019] [Accepted: 08/03/2019] [Indexed: 11/15/2022]
Abstract
Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD. In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network. Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease. 49 features in the five frequency bands discriminated the two groups with 100% accuracy. The analysis revealed most of the links were part of important brain networks as the DMN. The alteration of these links, usually affected in AD, is related to synaptic loss. Our multi-frequency approach provided a MEG connectome biomarker to predict the conversion to AD.
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Affiliation(s)
- Sandra Pusil
- Laboratory of Neuropsychology, University of the Balearic Islands, Spain; Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.
| | - Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom; School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain; Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering, IUNE Universidad de La Laguna, Tenerife, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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López ME, Pusil S, Pereda E, Maestú F, Barceló F. Dynamic low frequency EEG phase synchronization patterns during proactive control of task switching. Neuroimage 2018; 186:70-82. [PMID: 30394328 DOI: 10.1016/j.neuroimage.2018.10.068] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [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: 04/14/2018] [Revised: 10/04/2018] [Accepted: 10/26/2018] [Indexed: 10/28/2022] Open
Abstract
Cognitive flexibility is critical for humans living in complex societies with ever-growing multitasking demands. Yet the low-frequency neural dynamics of distinct task-specific and domain-general mechanisms sub-serving mental flexibility are still ill-defined. Here we estimated phase electroencephalogram synchronization by using inter-trial phase coherence (ITPC) at the source space while twenty six young participants were intermittently cued to switch or repeat their perceptual categorization rule of Gabor gratings varying in color and thickness (switch task). Therefore, the aim of this study was to examine whether a proactive control is associated with connectivity only in the frontoparietal theta network, or also involves distinct neural connectivity within the delta band, as distinct neural signatures while preparing to switch or repeat a task set, respectively. To this end, we focused the analysis on late-latencies (from 500 to 800 msec post-cue onset), since they are known to be associated with top-down cognitive control processes. We confirmed that proactive control during a task switch was associated with frontoparietal theta connectivity. But importantly, we also found a distinct role of delta band oscillatory synchronization in proactive control, engaging more posterior frontotemporal regions as opposed to frontoparietal theta connectivity. Additionally, we built a regression model by using the ITPC results in delta and theta bands as predictors, and the behavioral accuracy in the switch task as the criterion, obtaining significant results for both frequency bands. All these findings support the existence of distinct proactive cognitive control processes related to functionally distinct though highly complementary theta and delta frontoparietal and temporoparietal oscillatory networks at late-latency temporal scales.
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Affiliation(s)
- María Eugenia López
- Department of Experimental Psychology, Psychological Processes and Speech Therapy, Universidad Complutense of Madrid, Spain; Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Sandra Pusil
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Madrid, Spain; Laboratory of Neuropsychology, University of the Balearic Islands, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Madrid, Spain; Electrical Engineering and Bioengineering Group, Department of Industrial Engineering & IUNE, Universidad de La Laguna, Tenerife, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Psychological Processes and Speech Therapy, Universidad Complutense of Madrid, Spain; Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Francisco Barceló
- Laboratory of Neuropsychology, University of the Balearic Islands, Spain.
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