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De la Fuente IM, Carrasco-Pujante J, Camino-Pontes B, Fedetz M, Bringas C, Pérez-Samartín A, Pérez-Yarza G, López JI, Malaina I, Cortes JM. Systemic cellular migration: The forces driving the directed locomotion movement of cells. PNAS Nexus 2024; 3:pgae171. [PMID: 38706727 PMCID: PMC11067954 DOI: 10.1093/pnasnexus/pgae171] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/11/2024] [Indexed: 05/07/2024]
Abstract
Directional motility is an essential property of cells. Despite its enormous relevance in many fundamental physiological and pathological processes, how cells control their locomotion movements remains an unresolved question. Here, we have addressed the systemic processes driving the directed locomotion of cells. Specifically, we have performed an exhaustive study analyzing the trajectories of 700 individual cells belonging to three different species (Amoeba proteus, Metamoeba leningradensis, and Amoeba borokensis) in four different scenarios: in absence of stimuli, under an electric field (galvanotaxis), in a chemotactic gradient (chemotaxis), and under simultaneous galvanotactic and chemotactic stimuli. All movements were analyzed using advanced quantitative tools. The results show that the trajectories are mainly characterized by coherent integrative responses that operate at the global cellular scale. These systemic migratory movements depend on the cooperative nonlinear interaction of most, if not all, molecular components of cells.
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Affiliation(s)
- Ildefonso M De la Fuente
- Department of Mathematics, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, Leioa 48940, Spain
- Department of Nutrition, CEBAS-CSIC Institute, Espinardo University Campus, Murcia 30100, Spain
| | - Jose Carrasco-Pujante
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa 48940, Spain
| | | | - Maria Fedetz
- Department of Cell Biology and Immunology, Institute of Parasitology and Biomedicine “López-Neyra”, CSIC, Granada 18016, Spain
| | - Carlos Bringas
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa 48940, Spain
| | - Alberto Pérez-Samartín
- Department of Neurosciences, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa 48940, Spain
| | - Gorka Pérez-Yarza
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa 48940, Spain
| | - José I López
- Biobizkaia Health Research Institute, Barakaldo 48903, Spain
| | - Iker Malaina
- Department of Mathematics, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, Leioa 48940, Spain
| | - Jesus M Cortes
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa 48940, Spain
- Biobizkaia Health Research Institute, Barakaldo 48903, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao 48009, Spain
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Jimenez-Marin A, Diez I, Erramuzpe A, Stramaglia S, Bonifazi P, Cortes JM. Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain. Sci Data 2024; 11:256. [PMID: 38424112 PMCID: PMC10904384 DOI: 10.1038/s41597-024-03060-2] [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: 08/10/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.
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Affiliation(s)
- Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, United States of America
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, Bari, Italy
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain.
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain.
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
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Ramos-Usuga D, Jimenez-Marin A, Cabrera-Zubizarreta A, Benito-Sanchez I, Rivera D, Martínez-Gutiérrez E, Panera E, Boado V, Labayen F, Cortes JM, Arango-Lasprilla JC. Cognitive and brain connectivity trajectories in critically ill COVID-19 patients. NeuroRehabilitation 2024; 54:359-371. [PMID: 38393927 DOI: 10.3233/nre-230216] [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] [Indexed: 02/25/2024]
Abstract
BACKGROUND Multiple Organ failure (MOF) is one of the main causes of admission to the Intensive Care Unit (ICU) of patients infected with COVID-19 and can cause short- and long-term neurological deficits. OBJECTIVE To compare the cognitive functioning and functional brain connectivity at 6-12 months after discharge in two groups of individuals with MOF, one due to COVID-19 and the other due to another cause (MOF-group), with a group of Healthy Controls (HC). METHODS Thirty-six participants, 12 from each group, underwent a neuropsychological and neuroimaging assessment at both time-points. Functional connectivity of the resting state networks was compared between COVID-19 and HC while controlling for the effect of MOF. The association between functional connectivity and neuropsychological performance was also investigated. RESULTS Compared to the HC, COVID-19 group demonstrated hypoconnectivity between the Default Mode Network and Salience Network. This pattern was associated with worse performance on tests of attention and information processing speed, at both time-points. CONCLUSION The study of the association between cognitive function and brain functional connectivity in COVID-19 allows the understanding of the short- and long-term neurological alterations of this disease and promotes the development of intervention programs to improve the quality of life for this understudied population.
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Affiliation(s)
- Daniela Ramos-Usuga
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Antonio Jimenez-Marin
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | | | - Itziar Benito-Sanchez
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Diego Rivera
- Department of Health Sciences, Public University of Navarre, Pamplona, Spain
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Endika Martínez-Gutiérrez
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Dipartamento Interateneo di Fisica, National Institute for Nuclear Physics - Bari, Bari, Italy
| | - Elena Panera
- Intensive Care Unit, Cruces University Hospital, Barakaldo, Spain
| | - Victoria Boado
- Intensive Care Unit, Cruces University Hospital, Barakaldo, Spain
| | - Fermín Labayen
- Intensive Care Unit, Cruces University Hospital, Barakaldo, Spain
| | - Jesus M Cortes
- Biobizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
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Rasero J, Jimenez-Marin A, Diez I, Toro R, Hasan MT, Cortes JM. The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals. Biol Psychiatry 2023; 94:804-813. [PMID: 37088169 DOI: 10.1016/j.biopsych.2023.04.014] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 03/24/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND There is little consensus and controversial evidence on anatomical alterations in the brains of people with autism spectrum disorder (ASD), due in part to the large heterogeneity present in ASD, which in turn is a major drawback for developing therapies. One strategy to characterize this heterogeneity in ASD is to cluster large-scale functional brain connectivity profiles. METHODS A subtyping approach based on consensus clustering of functional brain connectivity patterns was applied to a population of 657 autistic individuals with quality-assured neuroimaging data. We then used high-resolution gene transcriptomic data to characterize the molecular mechanism behind each subtype by performing enrichment analysis of the set of genes showing a high spatial similarity with the profiles of functional connectivity alterations between each subtype and a group of typically developing control participants. RESULTS Two major stable subtypes were found: subtype 1 exhibited hypoconnectivity (less average connectivity than typically developing control participants) and subtype 2, hyperconnectivity. The 2 subtypes did not differ in structural imaging metrics in any of the analyzed regions (68 cortical and 14 subcortical) or in any of the behavioral scores (including IQ, Autism Diagnostic Interview, and Autism Diagnostic Observation Schedule). Finally, only subtype 2, comprising about 43% of ASD participants, led to significant enrichments after multiple testing corrections. Notably, the dominant enrichment corresponded to excitation/inhibition imbalance, a leading well-known primary mechanism in the pathophysiology of ASD. CONCLUSIONS Our results support a link between excitation/inhibition imbalance and functional connectivity alterations, but only in one ASD subtype, overall characterized by brain hyperconnectivity and major alterations in somatomotor and default mode networks.
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Affiliation(s)
- Javier Rasero
- Cognitive Axon Laboratory, Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania.
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Laboratory, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Biomedical Research Doctorate Program, University of the Basque Country, Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Roberto Toro
- Institut Pasteur, Université de Paris, Département de neuroscience, Paris, France
| | - Mazahir T Hasan
- Laboratory of Brain Circuits Therapeutics, Achucarro Basque Center for Neuroscience, Leioa, Spain; Ikerbasque, The Basque Foundation for Science, Bilbao, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Laboratory, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Ikerbasque, The Basque Foundation for Science, Bilbao, Spain; Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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Garcia Condado J, Cortes JM. NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease. Alzheimers Dement (Amst) 2023; 15:e12493. [PMID: 37908437 PMCID: PMC10614125 DOI: 10.1002/dad2.12493] [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] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 08/21/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
INTRODUCTION BrainAge models based on neuroimaging data have diagnostic classification power but have replicability issues due to site and patient variability. BrainAge models trained on neuropsychological tests could help distinguish stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD). METHODS A linear regressor BrainAge model was trained on healthy controls using neuropsychological tests and neuroimaging features separately. The BrainAge delta, predicted age minus chronological age, was used to distinguish between sMCI and pMCI. RESULTS The cross-validated area under the receiver-operating characteristic (ROC) curve for sMCI versus pMCI was 0.91 for neuropsychological features in contrast to 0.68 for neuroimaging features. The BrainAge delta was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD. DISCUSSION The BrainAge delta from neuropsychological tests is a good biomarker to distinguish between sMCI and pMCI. Other neurological and psychiatric disorders could be studied using this strategy. Highlights BrainAge models based on neuropsychological tests outperform models based on neuroimaging features when distinguishing between stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).The combination of neuropsychological tests with neuroimaging features does not lead to an improvement in sMCI versus pMCI classification compared to using neuropsychological tests on their own.BrainAge delta of both neuroimaging and neuropsychological models was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD.
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Affiliation(s)
- Jorge Garcia Condado
- Computational Neuroimaging LaboratoryBiobizkaia Health Research InstituteBarakaldo, BizkaiaSpain
- Biomedical Research Doctorate ProgramUniversity of the Basque CountryLeioa, BizkaiaSpain
| | - Jesus M. Cortes
- Computational Neuroimaging LaboratoryBiobizkaia Health Research InstituteBarakaldo, BizkaiaSpain
- Department of Cell Biology and HistologyUniversity of the Basque CountryLeioa, BizkaiaSpain
- IKERBASQUE Basque Foundation for ScienceBilbaoSpain
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Camino-Pontes B, Gonzalez-Lopez F, Santamaría-Gomez G, Sutil-Jimenez AJ, Sastre-Barrios C, de Pierola IF, Cortes JM. One-year prediction of cognitive decline following cognitive-stimulation from real-world data. J Neuropsychol 2023. [PMID: 36727214 DOI: 10.1111/jnp.12307] [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: 11/26/2021] [Revised: 01/09/2023] [Accepted: 01/17/2023] [Indexed: 02/03/2023]
Abstract
Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.
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Affiliation(s)
| | | | | | | | | | | | - Jesus M Cortes
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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Martinez-Gutierrez E, Jimenez-Marin A, Stramaglia S, Cortes JM. The structure of anticorrelated networks in the human brain. Front Netw Physiol 2022; 2:946380. [PMID: 36926060 PMCID: PMC10012996 DOI: 10.3389/fnetp.2022.946380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 10/24/2022] [Indexed: 06/18/2023]
Abstract
During the performance of a specific task--or at rest--, the activity of different brain regions shares statistical dependencies that reflect functional connections. While these relationships have been studied intensely for positively correlated networks, considerably less attention has been paid to negatively correlated networks, a. k.a. anticorrelated networks (ACNs). Although the most celebrated of all ACNs is the default mode network (DMN), and has even been extensively studied in health and disease, for systematically all ACNs other than DMN, there is no comprehensive study yet. Here, we have addressed this issue by making use of three neuroimaging data sets: one of N = 192 healthy young adults to fully describe ACN, another of N = 40 subjects to compare ACN between two groups of young and old participants, and another of N = 1,000 subjects from the Human Connectome Project to evaluate the association between ACN and cognitive scores. We first provide a comprehensive description of the anatomical composition of all ACNs, each of which participated in distinct resting-state networks (RSNs). In terms of participation ranking, from highest to the lowest, the major anticorrelated brain areas are the precuneus, the anterior supramarginal gyrus and the central opercular cortex. Next, by evaluating a more detailed structure of ACN, we show it is possible to find significant differences in ACN between specific conditions, in particular, by comparing groups of young and old participants. Our main finding is that of increased anticorrelation for cerebellar interactions in older subjects. Finally, in the voxel-level association study with cognitive scores, we show that ACN has multiple clusters of significance, clusters that are different from those obtained from positive correlated networks, indicating a functional cognitive meaning of ACN. Overall, our results give special relevance to ACN and suggest their use to disentangle unknown alterations in certain conditions, as could occur in early-onset neurodegenerative diseases or in some psychiatric conditions.
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Affiliation(s)
- Endika Martinez-Gutierrez
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, Bari, Italy
| | - Antonio Jimenez-Marin
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country, Leioa, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, Bari, Italy
| | - Jesus M. Cortes
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
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Fernandez-Iriondo I, Jimenez-Marin A, Sierra B, Aginako N, Bonifazi P, Cortes JM. Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis. Front Neurosci 2022; 16:889725. [PMID: 35801180 PMCID: PMC9255673 DOI: 10.3389/fnins.2022.889725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization. Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear combination of connectivity features to predict multiple behavioral performance scores. To assess the differences in the predictive power of each modality, we separately applied three different strategies: structural unimodal, functional unimodal, and multimodal, that is, structural in combination with functional features of the brain network. Our results show that the multimodal association outperforms any of the unimodal analyses. Then, to answer which human brain structures were most involved in predicting multiple behavioral scores, we simulated different synthetic scenarios in which in each case we completely deleted a brain structure or a complete resting state network, and recalculated performance in its absence. In deletions, we found critical structures to affect performance when predicting single behavioral domains, but this occurred in a lesser manner for prediction of multi-domain behavior. Overall, our results confirm that although there are synergistic contributions between brain structure and function that enhance behavioral prediction, brain networks may also be mutually redundant in predicting multidomain behavior, such that even after deletion of a structure, the connectivity of the others can compensate for its lack in predicting behavior.
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Affiliation(s)
- Izaro Fernandez-Iriondo
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Doctoral Programme in Informatics Engineering, University of the Basque Country (UPV/EHU), San Sebastian, Spain
- *Correspondence: Izaro Fernandez-Iriondo
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Basilio Sierra
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Naiara Aginako
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
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Safari A, Moretti P, Diez I, Cortes JM, Muñoz MA. Persistence of hierarchical network organization and emergent topologies in models of functional connectivity. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.02.096] [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: 10/21/2022]
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Nunez-Ibero M, Camino-Pontes B, Diez I, Erramuzpe A, Martinez-Gutierrez E, Stramaglia S, Alvarez-Cienfuegos JO, Cortes JM. A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers. IEEE J Biomed Health Inform 2021; 25:2948-2957. [PMID: 33999827 DOI: 10.1109/jbhi.2021.3080935] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE To develop a new device for identifying physiological markers of pain perception by reading the brain's electrical activity and hemodynamic interactions while applying thermoalgesic stimulation. METHODS We designed a compact prototype that generates well-controlled thermal stimuli using a computer-driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females). We first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increased the temperature. Next, we defined the painful condition as the one occurring at temperature equal to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. RESULTS Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals differentiated the condition of pain. In particular, the SE for PPG was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. CONCLUSION Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. SIGNIFICANCE The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.
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He C, Cortes JM, Kang X, Cao J, Chen H, Guo X, Wang R, Kong L, Huang X, Xiao J, Shan X, Feng R, Chen H, Duan X. Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder. Hum Brain Mapp 2021; 42:3282-3294. [PMID: 33934442 PMCID: PMC8193534 DOI: 10.1002/hbm.25434] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 01/01/2023] Open
Abstract
Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.
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Affiliation(s)
- Changchun He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Jesus M. Cortes
- Computational Neuroimaging LaboratoryBiocruces‐Bizkaia Health Research InstituteBarakaldoSpain
- Ikerbasque: The Basque Foundation for ScienceBilbaoSpain
- Department of Cell Biology and HistologyUniversity of the Basque CountryLeioaSpain
| | - Xiaodong Kang
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCMSichuan Bayi Rehabilitation CenterChengduChina
| | - Jing Cao
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCMSichuan Bayi Rehabilitation CenterChengduChina
| | - Heng Chen
- School of MedicineMedical College of Guizhou UniversityGuiyangChina
| | - Xiaonan Guo
- School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
- Hebei Key Laboratory of information transmission and signal processingYanshan UniversityQinhuangdaoChina
| | - Ruishi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and EngineeringSouth China University of TechnologyGuangzhouChina
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Rui Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
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12
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Abstract
Background: Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a "redundancy core" constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available.
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Affiliation(s)
- Marilyn Gatica
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile.,Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom.,Data Science Institute, Imperial College London, London, United Kingdom.,Centre for Complexity Science, Imperial College London, London, United Kingdom
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile.,Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA.,Neurology Department, Harvard Medical School, Boston, Massachusetts, USA.,Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Stephan P Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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13
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Jimenez-Marin A, Diez I, Labayru G, Sistiaga A, Caballero MC, Andres-Benito P, Sepulcre J, Ferrer I, Lopez de Munain A, Cortes JM. Transcriptional signatures of synaptic vesicle genes define myotonic dystrophy type I neurodegeneration. Neuropathol Appl Neurobiol 2021; 47:1092-1108. [PMID: 33955002 PMCID: PMC9292638 DOI: 10.1111/nan.12725] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 08/25/2020] [Revised: 02/08/2021] [Accepted: 04/19/2021] [Indexed: 01/09/2023]
Abstract
Aim To delineate the neurogenetic profiles of brain degeneration patterns in myotonic dystrophy type I (DM1). Methods In two cohorts of DM1 patients, brain maps of volume loss (VL) and neuropsychological deficits (NDs) were intersected to large‐scale transcriptome maps provided by the Allen Human Brain Atlas (AHBA). For validation, neuropathological and RNA analyses were performed in a small series of DM1 brain samples. Results Twofold: (1) From a list of preselected hypothesis‐driven genes, confirmatory analyses found that three genes play a major role in brain degeneration: dystrophin (DMD), alpha‐synuclein (SNCA) and the microtubule‐associated protein tau (MAPT). Neuropathological analyses confirmed a highly heterogeneous Tau‐pathology in DM1, different to the one in Alzheimer's disease. (2) Exploratory analyses revealed gene clusters enriched for key biological processes in the central nervous system, such as synaptic vesicle recycling, localization, endocytosis and exocytosis, and the serotonin and dopamine neurotransmitter pathways. RNA analyses confirmed synaptic vesicle dysfunction. Conclusions The combination of large‐scale transcriptome interactions with brain imaging and cognitive function sheds light on the neurobiological mechanisms of brain degeneration in DM1 that might help define future therapeutic strategies and research into this condition.
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Affiliation(s)
- Antonio Jimenez-Marin
- Computational Neuroimaging Group, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.,Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Garazi Labayru
- Neuroscience Area, Biodonostia Research Institute, San Sebastián, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Institute Carlos III, Madrid, Spain.,Personality, Assessment and Psychological Treatment Department; Psychology Faculty, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Andone Sistiaga
- Neuroscience Area, Biodonostia Research Institute, San Sebastián, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Institute Carlos III, Madrid, Spain.,Personality, Assessment and Psychological Treatment Department; Psychology Faculty, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | | | - Pol Andres-Benito
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Institute Carlos III, Madrid, Spain.,Department of Pathology and Experimental Therapeutics, University of Barcelona, Barcelona, Spain.,Institute of Biomedical Research of Bellvitge (IBIDELL), Hospitalet de Llobregat, Spain
| | - Jorge Sepulcre
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Isidro Ferrer
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Institute Carlos III, Madrid, Spain.,Department of Pathology and Experimental Therapeutics, University of Barcelona, Barcelona, Spain.,Institute of Biomedical Research of Bellvitge (IBIDELL), Hospitalet de Llobregat, Spain.,Institute of Neurosciences, University of Barcelona, Hospitalet de Llobregat, Spain
| | - Adolfo Lopez de Munain
- Neuroscience Area, Biodonostia Research Institute, San Sebastián, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Institute Carlos III, Madrid, Spain.,Neurology Department, Donostia University Hospital, Donostia-San Sebastian, Spain.,Neurosciences Department, University of the Basque Country (UPV/EHU) Donostia-San Sebastian, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Group, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.,Cell Biology and Histology Department, University of the Basque Country (UPV/EHU), Leioa, Spain.,IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
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14
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Labayru G, Jimenez‐Marin A, Fernández E, Villanua J, Zulaica M, Cortes JM, Díez I, Sepulcre J, López de Munain A, Sistiaga A. Neurodegeneration trajectory in pediatric and adult/late DM1: A follow-up MRI study across a decade. Ann Clin Transl Neurol 2020; 7:1802-1815. [PMID: 32881379 PMCID: PMC7545612 DOI: 10.1002/acn3.51163] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 07/24/2020] [Accepted: 07/25/2020] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE To characterize the progression of brain structural abnormalities in adults with pediatric and adult/late onset DM1, as well as to examine the potential predictive markers of such progression. METHODS 21 DM1 patients (pediatric onset: N = 9; adult/late onset: N = 12) and 18 healthy controls (HC) were assessed longitudinally over 9.17 years through brain MRI. Additionally, patients underwent neuropsychological, genetic, and muscular impairment assessment. Inter-group comparisons of total and voxel-level regional brain volume were conducted through Voxel Based Morphometry (VBM); cross-sectionally and longitudinally, analyzing the associations between brain changes and demographic, clinical, and cognitive outcomes. RESULTS The percentage of GM loss did not significantly differ in any of the groups compared with HC and when assessed independently, adult/late DM1 patients and their HC group suffered a significant loss in WM volume. Regional VBM analyses revealed subcortical GM damage in both DM1 groups, evolving to frontal regions in the pediatric onset patients. Muscular impairment and the outcomes of certain neuropsychological tests were significantly associated with follow-up GM damage, while visuoconstruction, attention, and executive function tests showed sensitivity to WM degeneration over time. INTERPRETATION Distinct patterns of brain atrophy and its progression over time in pediatric and adult/late onset DM1 patients are suggested. Results indicate a possible neurodevelopmental origin of the brain abnormalities in DM1, along with the possible existence of an additional neurodegenerative process. Fronto-subcortical networks appear to be involved in the disease progression at young adulthood in pediatric onset DM1 patients. The involvement of a multimodal integration network in DM1 is discussed.
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Affiliation(s)
- Garazi Labayru
- Personality, Assessment and psychological treatment department; Psychology FacultyUniversity of the Basque Country (UPV/EHU)San SebastiánGipuzkoaSpain
- Neuroscience AreaBiodonostia Research Institute, OsakidetzaDonostia‐San SebastiánGipuzkoaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)Institute Carlos IIIMadridSpain
| | - Antonio Jimenez‐Marin
- Biocruces‐Bizkaia Health Research InstituteBarakaldoBizkaiaSpain
- Biomedical Research Doctorate ProgramUniversity of the Basque Country (UPV/EHU)LeioaSpain
| | - Esther Fernández
- OsatekDonostia University HospitalDonostia‐ San SebastiánGipuzkoaSpain
| | - Jorge Villanua
- OsatekDonostia University HospitalDonostia‐ San SebastiánGipuzkoaSpain
| | - Miren Zulaica
- Neuroscience AreaBiodonostia Research Institute, OsakidetzaDonostia‐San SebastiánGipuzkoaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)Institute Carlos IIIMadridSpain
| | - Jesus M. Cortes
- Biocruces‐Bizkaia Health Research InstituteBarakaldoBizkaiaSpain
- Cell Biology and Histology DepartmentUniversity of the Basque Country (UPV/EHU)LeioaSpain
- IKERBASQUEThe Basque Foundation for ScienceBilbaoSpain
| | - Ibai Díez
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Neurotechnology LaboratoryTecnalia Health DepartmentDerioSpain
| | - Jorge Sepulcre
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Adolfo López de Munain
- Neuroscience AreaBiodonostia Research Institute, OsakidetzaDonostia‐San SebastiánGipuzkoaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)Institute Carlos IIIMadridSpain
- Neurology DepartmentDonostia University HospitalDonostia‐ San SebastiánGipuzkoaSpain
- Neuroscience DepartmentUniversity of the Basque Country (UPV/EHU)Donostia‐San SebastiánGipuzkoaSpain
| | - Andone Sistiaga
- Personality, Assessment and psychological treatment department; Psychology FacultyUniversity of the Basque Country (UPV/EHU)San SebastiánGipuzkoaSpain
- Neuroscience AreaBiodonostia Research Institute, OsakidetzaDonostia‐San SebastiánGipuzkoaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)Institute Carlos IIIMadridSpain
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15
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He C, Chen H, Uddin LQ, Erramuzpe A, Bonifazi P, Guo X, Xiao J, Chen H, Huang X, Li L, Sheng W, Liao W, Cortes JM, Duan X. Structure-Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain. Cereb Cortex 2020; 30:5028-5037. [PMID: 32377684 PMCID: PMC7391416 DOI: 10.1093/cercor/bhaa098] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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: 01/30/2020] [Revised: 03/08/2020] [Accepted: 03/25/2020] [Indexed: 12/25/2022] Open
Abstract
Accumulating neuroimaging evidence shows that age estimation obtained from brain connectomics reflects the level of brain maturation along with neural development. It is well known that autism spectrum disorder (ASD) alters neurodevelopmental trajectories of brain connectomics, but the precise relationship between chronological age (ChA) and brain connectome age (BCA) during development in ASD has not been addressed. This study uses neuroimaging data collected from 50 individuals with ASD and 47 age- and gender-matched typically developing controls (TDCs; age range: 5-18 years). Both functional and structural connectomics were assessed using resting-state functional magnetic resonance imaging and diffusion tensor imaging data from the Autism Brain Imaging Data Exchange repository. For each participant, BCA was estimated from structure-function connectomics through linear support vector regression. We found that BCA matched well with ChA in TDC children and adolescents, but not in ASD. In particular, our findings revealed that individuals with ASD exhibited accelerated brain maturation in youth, followed by a delay of brain development starting at preadolescence. Our results highlight the critical role of BCA in understanding aberrant developmental trajectories in ASD and provide the new insights into the pathophysiological mechanisms of this disorder.
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Affiliation(s)
- Changchun He
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Huafu Chen
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Asier Erramuzpe
- Computational Neuroimaging Laboratory, Biocruces-Bizkaia Health Research Institute, Barakaldo 48903, Spain
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91905, Israel
| | - Paolo Bonifazi
- Computational Neuroimaging Laboratory, Biocruces-Bizkaia Health Research Institute, Barakaldo 48903, Spain
- Ikerbasque: The Basque Foundation for Science, Bilbao 48013, Spain
| | - Xiaonan Guo
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jinming Xiao
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Heng Chen
- School of Medicine, Medical College of Guizhou University, Guiyang 550025, China
| | - Xinyue Huang
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lei Li
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Sheng
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Liao
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jesus M Cortes
- Ikerbasque: The Basque Foundation for Science, Bilbao 48013, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| | - Xujun Duan
- Department of Life Science and Technology, The clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
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16
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De Las Heras J, Diez I, Jimenez-Marin A, Cabrera A, Ramos-Usuga D, Diaz-Fernandez MV, Torices L, Nunes-Xavier CE, Pulido R, Arango-Lasprilla JC, Cortes JM. Brain Circuit Alterations and Cognitive Disability in Late-Onset Cobalamin D Disorder. J Clin Med 2020; 9:E990. [PMID: 32252256 PMCID: PMC7231091 DOI: 10.3390/jcm9040990] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/21/2020] [Accepted: 04/01/2020] [Indexed: 12/13/2022] Open
Abstract
Neuroimaging studies describing brain circuits' alterations in cobalamin (vitamin B12)-deficient patients are limited and have not been carried out in patients with inborn errors of cobalamin metabolism. The objective of this study was to assess brain functionality and brain circuit alterations in a patient with an ultra-rare inborn error of cobalamin metabolism, methylmalonic aciduria, and homocystinuria due to cobalamin D disease, as compared with his twin sister as a healthy control (HC). We acquired magnetic resonance imaging (including structural, functional, and diffusion images) to calculate brain circuit abnormalities and combined these results with the scores after a comprehensive neuropsychological evaluation. As compared with HC, the patient had severe patterns of damage, such as a 254% increment of ventricular volume, pronounced subcortical and cortical atrophies (mainly at striatum, cingulate cortex, and precuneus), and connectivity alterations at fronto-striato-thalamic circuit, cerebellum, and corpus callosum. In agreement with brain circuit alterations, cognitive deficits existed in attention, executive function, inhibitory control, and mental flexibility. This is the first study that provides the clinical, genetic, neuroanatomical, neuropsychological, and psychosocial characterization of a patient with the cobalamin D disorder, showing functional alterations in central nervous system motor tracts, thalamus, cerebellum, and basal ganglia, that, as far as we know, have not been reported yet in vitamin B12-related disorders.
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Affiliation(s)
- Javier De Las Heras
- Division of Pediatric Metabolism, Cruces University Hospital, 48903 Barakaldo, Spain; (J.D.L.H.); (M.V.D.-F.)
- Inborn Errors of Metabolism Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Pediatrics, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA;
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
- Neurotechnology Laboratory, Tecnalia Health Department, 48160 Derio, Spain
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain;
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain;
| | | | - Daniela Ramos-Usuga
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain;
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | | | - Leire Torices
- Biomarkers in Cancer Unit, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain; (L.T.); (C.E.N.-X.); (R.P.)
| | - Caroline E. Nunes-Xavier
- Biomarkers in Cancer Unit, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain; (L.T.); (C.E.N.-X.); (R.P.)
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0424 Oslo, Norway
| | - Rafael Pulido
- Biomarkers in Cancer Unit, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain; (L.T.); (C.E.N.-X.); (R.P.)
- IKERBASQUE. Basque Foundation for Science, 48013 Bilbao, Spain
| | - Juan Carlos Arango-Lasprilla
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- IKERBASQUE. Basque Foundation for Science, 48013 Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain;
- IKERBASQUE. Basque Foundation for Science, 48013 Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
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17
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Jimenez-Marin A, Rivera D, Boado V, Diez I, Labayen F, Garrido I, Ramos-Usuga D, Benito-Sánchez I, Rasero J, Cabrera-Zubizarreta A, Gabilondo I, Stramaglia S, Arango-Lasprilla JC, Cortes JM. Brain connectivity and cognitive functioning in individuals six months after multiorgan failure. Neuroimage Clin 2019; 25:102137. [PMID: 31931402 PMCID: PMC6957787 DOI: 10.1016/j.nicl.2019.102137] [Citation(s) in RCA: 5] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 12/03/2019] [Accepted: 12/21/2019] [Indexed: 01/05/2023]
Abstract
Multiorgan failure (MOF) is a life-threating condition that affects two or more systems of organs not involved in the disorder that motivates admission to an Intensive Care Unit (ICU). Patients who survive MOF frequently present long-term functional, neurological, cognitive, and psychiatric sequelae. However, the changes to the brain that explain such symptoms remain unclear. OBJECTIVE To determine brain connectivity and cognitive functioning differences between a group of MOF patients six months after ICU discharge and healthy controls (HC). METHODS 22 MOF patients and 22 HC matched by age, sex, and years of education were recruited. Both groups were administered a 3T magnetic resonance imaging (MRI), including structural T1 and functional BOLD, as well as a comprehensive neuropsychological evaluation that included tests of learning and memory, speed of information processing and attention, executive function, visual constructional abilities, and language. Voxel-based morphometry was used to analyses T1 images. For the functional data at rest, functional connectivity (FC) analyses were performed. RESULTS There were no significant differences in structural imaging and neuropsychological performance between groups, even though patients with MOF performed worse in all the cognitive tests. Functional neuroimaging in the default mode network (DMN) showed hyper-connectivity towards sensory-motor, cerebellum, and visual networks. DMN connectivity had a significant association with the severity of MOF during ICU stay and with the neuropsychological scores in tests of attention and visual constructional abilities. CONCLUSIONS In MOF patients without structural brain injury, DMN connectivity six months after ICU discharge is associated with MOF severity and neuropsychological impairment, which supports the use of resting-state functional MRI as a potential tool to predict the onset of long-term cognitive deficits in these patients. Similar to what occurs at the onset of other pathologies, the observed hyper-connectivity might suggest network re-adaptation following MOF.
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Affiliation(s)
- Antonio Jimenez-Marin
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Diego Rivera
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Victoria Boado
- Intensive Care Unit. Cruces University Hospital, Barakaldo, Spain
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Fermin Labayen
- Intensive Care Unit. Cruces University Hospital, Barakaldo, Spain
| | - Irati Garrido
- Intensive Care Unit. Cruces University Hospital, Barakaldo, Spain
| | - Daniela Ramos-Usuga
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Itziar Benito-Sánchez
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Javier Rasero
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | | | - Iñigo Gabilondo
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita di Bari, and INFN, Sezione di Bari, Italy
| | - Juan Carlos Arango-Lasprilla
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain; Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Jesus M Cortes
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain; Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
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18
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De la Fuente IM, Bringas C, Malaina I, Regner B, Pérez-Samartín A, Boyano MD, Fedetz M, López JI, Pérez-Yarza G, Cortes JM, Sejnowski T. The nucleus does not significantly affect the migratory trajectories of amoeba in two-dimensional environments. Sci Rep 2019; 9:16369. [PMID: 31704992 PMCID: PMC6841717 DOI: 10.1038/s41598-019-52716-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 10/21/2019] [Indexed: 12/11/2022] Open
Abstract
For a wide range of cells, from bacteria to mammals, locomotion movements are a crucial systemic behavior for cellular life. Despite its importance in a plethora of fundamental physiological processes and human pathologies, how unicellular organisms efficiently regulate their locomotion system is an unresolved question. Here, to understand the dynamic characteristics of the locomotion movements and to quantitatively study the role of the nucleus in the migration of Amoeba proteus we have analyzed the movement trajectories of enucleated and non-enucleated amoebas on flat two-dimensional (2D) surfaces using advanced non-linear physical-mathematical tools and computational methods. Our analysis shows that both non-enucleated and enucleated amoebas display the same kind of dynamic migration structure characterized by highly organized data sequences, super-diffusion, non-trivial long-range positive correlations, persistent dynamics with trend-reinforcing behavior, and move-step fluctuations with scale invariant properties. Our results suggest that the presence of the nucleus does not significantly affect the locomotion of amoeba in 2D environments.
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Affiliation(s)
- Ildefonso M De la Fuente
- Department of Nutrition, CEBAS-CSIC Institute, Espinardo University Campus, Murcia, 30100, Spain.
- Department of Mathematics, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, Leioa, 48940, Spain.
| | - Carlos Bringas
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa, 48940, Spain
| | - Iker Malaina
- Department of Applied Mathematics, Statistics and Operational Research, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, Leioa, 48940, Spain
| | | | - Alberto Pérez-Samartín
- Department of Neurosciences, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa, 48940, Spain
| | - María Dolores Boyano
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa, 48940, Spain
| | - María Fedetz
- Department of Cellular Biology and Immunology, Institute of Parasitology and Biomedicine "López-Neyra", CSIC, Granada, 18100, Spain
| | - José I López
- Department of Pathology, Cruces University Hospital, Biocruces-Bizkaia Health Research Institute, University of the Basque Country, UPV/EHU, Barakaldo, 48903, Spain
| | - Gorka Pérez-Yarza
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa, 48940, Spain
| | - Jesus M Cortes
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, Leioa, 48940, Spain
- Biocruces-Bizkaia Health Research Institute, Cruces University Hospital, Barakaldo, 48903, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, 48013, Spain
| | - Terrence Sejnowski
- Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, 92037, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, California, 92093, USA
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19
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Arango-Lasprilla JC, Jiménez-Marín A, Rivera D, Diez I, Labayen F, Garrido I, Ramos-Usuga D, Rasero J, Cabrera A, Cortes JM. Hyperconnectivity of the Default Mode Network is Linked to Cognitive Disability in Multiorgan Dysfunction Syndrome. Arch Phys Med Rehabil 2019. [DOI: 10.1016/j.apmr.2019.08.241] [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: 11/29/2022]
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20
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beim Graben P, Jimenez-Marin A, Diez I, Cortes JM, Desroches M, Rodrigues S. Metastable Resting State Brain Dynamics. Front Comput Neurosci 2019; 13:62. [PMID: 31551744 PMCID: PMC6743347 DOI: 10.3389/fncom.2019.00062] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [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: 05/29/2019] [Accepted: 08/23/2019] [Indexed: 12/26/2022] Open
Abstract
Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation-BOLD-signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.
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Affiliation(s)
- Peter beim Graben
- Communication Engineering, Institute of Electrical Engineering and Information Science, Brandenburg University of Technology Cottbus – Senftenberg, Cottbus, Germany
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
- Neurology Department, Harvard Medical School, Boston, MA, United States
- Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Ikerbasque - the Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| | - Mathieu Desroches
- MathNeuro Team, Inria Sophia Antipolis Méditerranée, Valbonne, France
- Université Côte d'Azur, Nice, France
| | - Serafim Rodrigues
- Ikerbasque - the Basque Foundation for Science, Bilbao, Spain
- Mathematical, Computational and Experimental Neuroscience, Basque Center for Applied Mathematics, Bilbao, Spain
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21
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Rasero J, Diez I, Cortes JM, Marinazzo D, Stramaglia S. Connectome sorting by consensus clustering increases separability in group neuroimaging studies. Netw Neurosci 2019; 3:325-343. [PMID: 30793085 PMCID: PMC6370473 DOI: 10.1162/netn_a_00074] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 11/09/2018] [Indexed: 01/27/2023] Open
Abstract
A fundamental challenge in preprocessing pipelines for neuroimaging datasets is to increase the signal-to-noise ratio for subsequent analyses. In the same line, we suggest here that the application of the consensus clustering approach to brain connectivity matrices can be a valid additional step for connectome processing to find subgroups of subjects with reduced intragroup variability and therefore increasing the separability of the distinct subgroups when connectomes are used as a biomarker. Moreover, by partitioning the data with consensus clustering before any group comparison (for instance, between a healthy population vs. a pathological one), we demonstrate that unique regions within each cluster arise and bring new information that could be relevant from a clinical point of view.
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Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute, Hospital Universitario de Cruces, Barakaldo, Spain
| | - Ibai Diez
- Functional Neurology Research Group, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Gordon Center, Department of Nuclear Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Jesus M. Cortes
- Biocruces Health Research Institute, Hospital Universitario de Cruces, Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
- Ikerbasque, The Basque Foundation for Science, Bilbao, Spain
| | - Daniele Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi “Aldo Moro” Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Universitá degli Studi “Aldo Moro” Bari, Italy
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22
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Rasero J, Aerts H, Ontivero Ortega M, Cortes JM, Stramaglia S, Marinazzo D. Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data. PLoS One 2018; 13:e0207385. [PMID: 30419063 PMCID: PMC6231684 DOI: 10.1371/journal.pone.0207385] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 10/29/2018] [Indexed: 11/23/2022] Open
Abstract
Intrinsic Connectivity Networks, patterns of correlated activity emerging from “resting-state” BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.
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Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Dipartimento di Fisica, Universitá degli Studi “Aldo Moro” Bari, Italy
| | - Hannelore Aerts
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium
| | - Marlis Ontivero Ortega
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium
- Neuroinformatics Department, Cuban Center for Neuroscience (CNeuro), La Habana, Cuba
| | - Jesus M. Cortes
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Ikerbasque, The Basque Foundation for Science, E-48011, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi “Aldo Moro” Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- * E-mail:
| | - Daniele Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium
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23
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Camino-Pontes B, Diez I, Jimenez-Marin A, Rasero J, Erramuzpe A, Bonifazi P, Stramaglia S, Swinnen S, Cortes JM. Interaction Information Along Lifespan of the Resting Brain Dynamics Reveals a Major Redundant Role of the Default Mode Network. Entropy (Basel) 2018; 20:e20100742. [PMID: 33265831 PMCID: PMC7512305 DOI: 10.3390/e20100742] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [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: 08/01/2018] [Revised: 09/07/2018] [Accepted: 09/24/2018] [Indexed: 01/06/2023]
Abstract
Interaction Information (II) generalizes the univariate Shannon entropy to triplets of variables, allowing the detection of redundant (R) or synergetic (S) interactions in dynamical networks. Here, we calculated II from functional magnetic resonance imaging data and asked whether R or S vary across brain regions and along lifespan. Preserved along lifespan, we found high overlapping between the pattern of high R and the default mode network, whereas high values of S were overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, we have found a robust balance between R and S among different age intervals, indicating informational compensatory mechanisms in brain networks.
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Affiliation(s)
- Borja Camino-Pontes
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Ibai Diez
- Functional Neurology Research Group, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Gordon Center, Department of Nuclear Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Neurotechnology Laboratory, Tecnalia Health Department, 48160 Derio, Spain
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Javier Rasero
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, 48013 Bilbao, Spain
| | | | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium
- Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, 48013 Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, 48940 Leioa, Spain
- Correspondence: ; Tel.: +34-94600600 (ext. 5199)
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24
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Bonifazi P, Erramuzpe A, Diez I, Gabilondo I, Boisgontier MP, Pauwels L, Stramaglia S, Swinnen SP, Cortes JM. Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging. Hum Brain Mapp 2018; 39:4663-4677. [PMID: 30004604 DOI: 10.1002/hbm.24312] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [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: 03/09/2018] [Revised: 06/27/2018] [Accepted: 06/28/2018] [Indexed: 12/15/2022] Open
Abstract
Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.
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Affiliation(s)
- Paolo Bonifazi
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | | | - Ibai Diez
- Biocruces Health Research Institute, Barakaldo, Spain
| | | | - Matthieu P Boisgontier
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Lisa Pauwels
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universita di Bari, and INFN, Sezione di Bari, Italy
| | - Stephan P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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25
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Lissek T, Adams M, Adelman J, Ahissar E, Akaaboune M, Akil H, al’Absi M, Arain F, Arango-Lasprilla JC, Atasoy D, Avila J, Badawi A, Bading H, Baig AM, Baleriola J, Belmonte C, Bertocchi I, Betz H, Blakemore C, Blanke O, Boehm-Sturm P, Bonhoeffer T, Bonifazi P, Brose N, Campolongo P, Celikel T, Chang CC, Chang TY, Citri A, Cline HT, Cortes JM, Cullen K, Dean K, Delgado-Garcia JM, Desroches M, Disterhoft JF, Dowling JE, Draguhn A, El-Khamisy SF, El Manira A, Enam SA, Encinas JM, Erramuzpe A, Esteban JA, Fariñas I, Fischer E, Fukunaga I, Gabilondo I, Ganten D, Gidon A, Gomez-Esteban JC, Greengard P, Grinevich V, Gruart A, Guillemin R, Hariri AR, Hassan B, Häusser M, Hayashi Y, Hussain NK, Jabbar AA, Jaber M, Jahn R, Janahi EM, Kabbaj M, Kettenmann H, Kindt M, Knafo S, Köhr G, Komai S, Krugers H, Kuhn B, Ghazal NL, Larkum ME, London M, Lutz B, Matute C, Martinez-Millan L, Maroun M, McGaugh J, Moustafa AA, Nasim A, Nave KA, Neher E, Nikolich K, Outeiro T, Palmer LM, Penagarikano O, Perez-Otano I, Pfaff DW, Poucet B, Rahman AU, Ramos-Cabrer P, Rashidy-Pour A, Roberts RJ, Rodrigues S, Sanes JR, Schaefer AT, Segal M, Segev I, Shafqat S, Siddiqui NA, Soreq H, Soriano-García E, Spanagel R, Sprengel R, Stuart G, Südhof TC, Tønnesen J, Treviño M, Uthman BM, Venter JC, Verkhratsky A, Weiss C, Wiesel TN, Yaksi E, Yizhar O, Young LJ, Young P, Zawia NH, Zugaza JL, Hasan MT. Building Bridges through Science. Neuron 2017; 96:730-735. [DOI: 10.1016/j.neuron.2017.09.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 09/16/2017] [Accepted: 09/19/2017] [Indexed: 10/18/2022]
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Kroos JM, Marinelli I, Diez I, Cortes JM, Stramaglia S, Gerardo-Giorda L. Patient-specific computational modeling of cortical spreading depression via diffusion tensor imaging. Int J Numer Method Biomed Eng 2017; 33:e2874. [PMID: 28226410 DOI: 10.1002/cnm.2874] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 02/15/2017] [Accepted: 02/19/2017] [Indexed: 06/06/2023]
Abstract
Cortical spreading depression, a depolarization wave originating in the visual cortex and traveling towards the frontal lobe, is commonly accepted as a correlate of migraine visual aura. As of today, little is known about the mechanisms that can trigger or stop such phenomenon. However, the complex and highly individual characteristics of the brain cortex suggest that the geometry might have a significant impact in supporting or contrasting the propagation of cortical spreading depression. Accurate patient-specific computational models are fundamental to cope with the high variability in cortical geometries among individuals, but also with the conduction anisotropy induced in a given cortex by the complex neuronal organisation in the grey matter. In this paper, we integrate a distributed model for extracellular potassium concentration with patient-specific diffusivity tensors derived locally from diffusion tensor imaging data.
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Affiliation(s)
- Julia M Kroos
- Basque Center for Applied Mathematics, Bilbao, Spain
| | | | - Ibai Diez
- Comp. Neuroimaging Lab, BioCruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain
| | - Jesus M Cortes
- Comp. Neuroimaging Lab, BioCruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain
- Ikerbasque: The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Sebastiano Stramaglia
- Basque Center for Applied Mathematics, Bilbao, Spain
- Dipartimento di Fisica, Universita di Bari, Italy
- INFN, Sezione di Bari, Italy
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27
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Abstract
A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (a) define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node in all pairs of subjects; (b) cluster the distance matrix for each node; (c) build the consensus network from the corresponding partitions; and (d) extract groups of subjects by finding the communities of the consensus network thus obtained. Different from the previous implementations of consensus clustering, we thus propose to use the consensus strategy to combine the information arising from the connectivity patterns of each node. The proposed approach may be seen either as an exploratory technique or as an unsupervised pretraining step to help the subsequent construction of a supervised classifier. Applications on a toy model and two real datasets show the effectiveness of the proposed methodology, which represents heterogeneity of a set of subjects in terms of a weighted network, the consensus matrix.
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Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute. Hospital Universitario de Cruces, Barakaldo, Spain
- Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
| | - Mario Pellicoro
- Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, Italy
| | - Leonardo Angelini
- Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro Bari, Italy
| | - Jesus M. Cortes
- Biocruces Health Research Institute. Hospital Universitario de Cruces, Barakaldo, Spain
- Ikerbasque, the Basque Foundation for Science, Bilbao, Spain
| | - Daniele Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro Bari, Italy
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Rasero J, Alonso-Montes C, Diez I, Olabarrieta-Landa L, Remaki L, Escudero I, Mateos B, Bonifazi P, Fernandez M, Arango-Lasprilla JC, Stramaglia S, Cortes JM. Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer's Disease. Front Aging Neurosci 2017; 9:215. [PMID: 28736521 PMCID: PMC5500648 DOI: 10.3389/fnagi.2017.00215] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [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: 02/13/2017] [Accepted: 06/20/2017] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1 = 36, healthy control subjects, Control), G2 (N2 = 36, early mild cognitive impairment, EMCI), G3 (N3 = 36, late mild cognitive impairment, LMCI) and G4 (N4 = 36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I (Control vs. EMCI), stage II (Control vs. LMCI) and stage III (Control vs. AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were threefold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks, including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.
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Affiliation(s)
- Javier Rasero
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | - Ibai Diez
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | | | - Iñaki Escudero
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Beatriz Mateos
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Paolo Bonifazi
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
| | - Manuel Fernandez
- Biocruces Health Research InstituteBarakaldo, Spain
- Neurology Service, Cruces University HospitalBarakaldo, Spain
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Basque Center for Applied MathematicsBilbao, Spain
| | - Jesus M. Cortes
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
- Department of Cell Biology and Histology, University of the Basque CountryLeioa, Spain
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29
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Diez I, Drijkoningen D, Stramaglia S, Bonifazi P, Marinazzo D, Gooijers J, Swinnen SP, Cortes JM. Enhanced prefrontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population. Netw Neurosci 2017; 1:116-142. [PMID: 29911675 PMCID: PMC5988395 DOI: 10.1162/netn_a_00007] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [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: 09/15/2016] [Accepted: 01/28/2017] [Indexed: 11/04/2022] Open
Abstract
Traumatic brain injury (TBI) affects structural connectivity, triggering the reorganization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain network reorganization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severe TBI, comparing them to young, typically developing control participants. TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: (1) a subcortical network, including parts of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulate gyrus, and precuneus; and (2) a task-positive network, involving regions of the dorsal attention system, together with dorsolateral and ventrolateral prefrontal regions. We also found that the increased prefrontal connectivity in TBI patients was correlated with some postural control indices, such as the amount of body sway, whereby patients with worse balance increased their connectivity in frontal regions more strongly. The increased prefrontal connectivity found in TBI patients may provide the structural scaffolding for stronger cognitive control of certain behavioral functions, consistent with the observations that various motor tasks are performed less automatically following TBI and that more cognitive control is associated with such actions.
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Affiliation(s)
- Ibai Diez
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain
| | - David Drijkoningen
- KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, Belgium
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universita degli Studi di Bari and INFN, Bari, Italy.,Basque Center for Applied Mathematics (BCAM), Bilbao, Spain
| | - Paolo Bonifazi
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain.,Ikerbasque: The Basque Foundation for Science, Bilbao, Spain
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychological and Pedagogical Sciences, University of Ghent, Ghent, Belgium
| | - Jolien Gooijers
- KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, Belgium
| | - Stephan P Swinnen
- KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, Belgium.,KU Leuven, Leuven Research Institute for Neuroscience & Disease (LIND), Leuven, Belgium
| | - Jesus M Cortes
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain.,Ikerbasque: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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30
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M De la Fuente I, Malaina I, Pérez-Samartín A, Boyano MD, Pérez-Yarza G, Bringas C, Villarroel Á, Fedetz M, Arellano R, Cortes JM, Martínez L. Dynamic properties of calcium-activated chloride currents in Xenopus laevis oocytes. Sci Rep 2017; 7:41791. [PMID: 28198817 PMCID: PMC5304176 DOI: 10.1038/srep41791] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [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: 07/22/2016] [Accepted: 12/30/2016] [Indexed: 11/18/2022] Open
Abstract
Chloride is the most abundant permeable anion in the cell, and numerous studies in the last two decades highlight the great importance and broad physiological role of chloride currents mediated anion transport. They participate in a multiplicity of key processes, as for instance, the regulation of electrical excitability, apoptosis, cell cycle, epithelial secretion and neuronal excitability. In addition, dysfunction of Cl− channels is involved in a variety of human diseases such as epilepsy, osteoporosis and different cancer types. Historically, chloride channels have been of less interest than the cation channels. In fact, there seems to be practically no quantitative studies of the dynamics of chloride currents. Here, for the first time, we have quantitatively studied experimental calcium-activated chloride fluxes belonging to Xenopus laevis oocytes, and the main results show that the experimental Cl− currents present an informational structure characterized by highly organized data sequences, long-term memory properties and inherent “crossover” dynamics in which persistent correlations arise at short time intervals, while anti-persistent behaviors become dominant in long time intervals. Our work sheds some light on the understanding of the informational properties of ion currents, a key element to elucidate the physiological functional coupling with the integrative dynamics of metabolic processes.
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Affiliation(s)
- Ildefonso M De la Fuente
- Department of Nutrition, CEBAS-CSIC Institute, Espinardo University Campus, Murcia, Spain.,Department of Mathematics, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Iker Malaina
- Department of Mathematics, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Alberto Pérez-Samartín
- Department of Neurosciences, Faculty of Medicine and Dentistry, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - María Dolores Boyano
- Department of Cell Biology and Histology, Faculty of Medicine and Dentistry, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Gorka Pérez-Yarza
- Department of Cell Biology and Histology, Faculty of Medicine and Dentistry, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Carlos Bringas
- Department of Cell Biology and Histology, Faculty of Medicine and Dentistry, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Álvaro Villarroel
- Biophysics Unit, CSIC, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - María Fedetz
- Department of Biochemistry and Pharmacology, Institute of Parasitology and Biomedicine "López-Neyra", CSIC, Granada, Spain
| | - Rogelio Arellano
- Laboratory of Cellular Neurophysiology, Neurobiology Institute, UNAM, Querétaro, México
| | - Jesus M Cortes
- Department of Cell Biology and Histology, Faculty of Medicine and Dentistry, University of the Basque Country, UPV/EHU, Leioa, Spain.,BioCruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | - Luis Martínez
- Department of Mathematics, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, Leioa, Spain
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31
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Stramaglia S, Angelini L, Cortes JM, Marinazzo D. Synergy, redundancy and unnormalized Granger causality. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:4037-40. [PMID: 26737180 DOI: 10.1109/embc.2015.7319280] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. We show that maximization of the total Granger causality to a given target, over all the possible partitions of the set of driving variables, puts in evidence redundant multiplets of variables influencing the target, provided that an unnormalized definition of Granger causality is adopted. Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target; thus, this definition differs from previous definitions of synergy.
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Abstract
We recently showed that in order to detect intra-tumor heterogeneity a Divide-and-Conquer (DAC) strategy of tumor sampling outperforms current routine protocols. This paper is a continuation of this work, but here we focus on DAC implementation in the Pathology Laboratory. In particular, we describe a new simple method that makes use of a cutting grid device and is applied to clear cell renal cell carcinomas for DAC implementation. This method assures a thorough sampling of large surgical specimens, facilitates the demonstration of intratumor heterogeneity, and saves time to pathologists in the daily practice. The method involves the following steps: 1. Thin slicing of the tumor (by hand or machine), 2. Application of a cutting grid to the slices ( e.g., a French fry cutter), resulting in multiple tissue cubes with fixed position within the slice, 3. Selection of tissue cubes for analysis, and finally, 4. Inclusion of selected cubes into a cassette for histological processing (with about eight tissue fragments within each cassette). Thus, using our approach in a 10 cm in-diameter-tumor we generate 80 tumor tissue fragments placed in 10 cassettes and, notably, in a tenth of time. Eighty samples obtained across all the regions of the tumor will assure a much higher performance in detecting intratumor heterogeneity, as proved recently with synthetic data.
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Affiliation(s)
- Jose I. Lopez
- Department of Pathology, Cruces University Hospital, Barakaldo, Spain
- Biomarkers in Cancer Unit, Biocruces Research Institute, Barakaldo, Spain
- University of the Basque Country, Leioa, Spain
| | - Jesus M. Cortes
- Quantitative Biomedicine Unit, Biocruces Research Institute, Barakaldo, Spain
- Ikerbasque: The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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33
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Abstract
We recently showed that in order to detect intra-tumor heterogeneity a Divide-and-Conquer (DAC) strategy of tumor sampling outperforms current routine protocols. This paper is a continuation of this work, but here we focus on DAC implementation in the Pathology Laboratory. In particular, we describe a new simple method that makes use of a cutting grid device and is applied to clear cell renal cell carcinomas for DAC implementation. This method assures a thorough sampling of large surgical specimens, facilitates the demonstration of intratumor heterogeneity, and saves time to pathologists in the daily practice. The method involves the following steps: 1. Thin slicing of the tumor (by hand or machine), 2. Application of a cutting grid to the slices ( e.g., a French fry cutter), resulting in multiple tissue cubes with fixed position within the slice, 3. Selection of tissue cubes for analysis, and finally, 4. Inclusion of selected cubes into a cassette for histological processing (with about eight tissue fragments within each cassette). Thus, using our approach in a 10 cm in-diameter-tumor we generate 80 tumor tissue fragments placed in 10 cassettes and, notably, in a tenth of time. Eighty samples obtained across all the regions of the tumor will assure a much higher performance in detecting intratumor heterogeneity, as proved recently with synthetic data.
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Affiliation(s)
- Jose I. Lopez
- Department of Pathology, Cruces University Hospital, Barakaldo, Spain
- Biomarkers in Cancer Unit, Biocruces Research Institute, Barakaldo, Spain
- University of the Basque Country, Leioa, Spain
| | - Jesus M. Cortes
- Quantitative Biomedicine Unit, Biocruces Research Institute, Barakaldo, Spain
- Ikerbasque: The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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34
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Kroos JM, Diez I, Cortes JM, Stramaglia S, Gerardo-Giorda L. Geometry Shapes Propagation: Assessing the Presence and Absence of Cortical Symmetries through a Computational Model of Cortical Spreading Depression. Front Comput Neurosci 2016; 10:6. [PMID: 26869913 PMCID: PMC4735361 DOI: 10.3389/fncom.2016.00006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [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: 07/14/2015] [Accepted: 01/12/2016] [Indexed: 01/27/2023] Open
Abstract
Cortical spreading depression (CSD), a depolarization wave which originates in the visual cortex and travels toward the frontal lobe, has been suggested to be one neural correlate of aura migraine. To the date, little is known about the mechanisms which can trigger or stop aura migraine. Here, to shed some light on this problem and, under the hypothesis that CSD might mediate aura migraine, we aim to study different aspects favoring or disfavoring the propagation of CSD. In particular, by using a computational neuronal model distributed throughout a realistic cortical mesh, we study the role that the geometry has in shaping CSD. Our results are two-fold: first, we found significant differences in the propagation traveling patterns of CSD, both intra and inter-hemispherically, revealing important asymmetries in the propagation profile. Second, we developed methods able to identify brain regions featuring a peculiar behavior during CSD propagation. Our study reveals dynamical aspects of CSD, which, if applied to subject-specific cortical geometry, might shed some light on how to differentiate between healthy subjects and those suffering migraine.
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Affiliation(s)
- Julia M. Kroos
- BCAM – Basque Center for Applied MathematicsBilbao, Spain,*Correspondence: Julia M. Kroos
| | - Ibai Diez
- Computational Neuroimaging Group, Quantitative Biomedicine Unit, Biocruces Health Research Institute, Cruces University HospitalBarakaldo, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Group, Quantitative Biomedicine Unit, Biocruces Health Research Institute, Cruces University HospitalBarakaldo, Spain,Ikerbasque, The Basque Foundation for ScienceBilbao, Spain,Department of Cell Biology and Histology, University of the Basque CountryLeioa, Spain
| | - Sebastiano Stramaglia
- BCAM – Basque Center for Applied MathematicsBilbao, Spain,Dipartimento di Fisica, Center of Innovative Technologies for Signal Detection and Processing, Istituto Nazionale di Fisica Nucleare Sezione di Bari, Università di BariBari, Italy
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35
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Erramuzpe A, Ortega GJ, Pastor J, Sola RGD, Marinazzo D, Stramaglia S, Cortes JM. Identification of redundant and synergetic circuits in triplets of electrophysiological data. J Neural Eng 2015; 12:066007. [DOI: 10.1088/1741-2560/12/6/066007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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36
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Alonso-Montes C, Diez I, Remaki L, Escudero I, Mateos B, Rosseel Y, Marinazzo D, Stramaglia S, Cortes JM. Lagged and instantaneous dynamical influences related to brain structural connectivity. Front Psychol 2015; 6:1024. [PMID: 26257682 PMCID: PMC4508482 DOI: 10.3389/fpsyg.2015.01024] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [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: 03/12/2015] [Accepted: 07/06/2015] [Indexed: 11/13/2022] Open
Abstract
Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC). Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.
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Affiliation(s)
| | - Ibai Diez
- Biocruces Health Research Institute, Cruces University Hospital Barakaldo, Spain
| | | | - Iñaki Escudero
- Biocruces Health Research Institute, Cruces University Hospital Barakaldo, Spain ; Radiology Service, Cruces University Hospital Barakaldo, Spain
| | - Beatriz Mateos
- Biocruces Health Research Institute, Cruces University Hospital Barakaldo, Spain ; Radiology Service, Cruces University Hospital Barakaldo, Spain
| | - Yves Rosseel
- Department of Data Analysis, Faculty of Psychological and Pedagogical Sciences, Ghent University Ghent, Belgium
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychological and Pedagogical Sciences, Ghent University Ghent, Belgium
| | - Sebastiano Stramaglia
- Basque Center for Applied Mathematics Bilbao, Spain ; Dipartimento di Fisica, Universitá degli Studi di Bari and INFN Bari, Italy
| | - Jesus M Cortes
- Biocruces Health Research Institute, Cruces University Hospital Barakaldo, Spain ; Ikerbasque, The Basque Foundation for Science Bilbao, Spain ; Department of Cell Biology and Histology, University of the Basque Country Leioa, Spain
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37
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Erramuzpe A, Encinas JM, Sierra A, Maletic-Savatic M, Brewster AL, Anderson AE, Stramaglia S, Cortes JM. Longitudinal variations of brain functional connectivity: A case report study based on a mouse model of epilepsy. F1000Res 2015; 4:144. [PMID: 26167275 PMCID: PMC4482210 DOI: 10.12688/f1000research.6570.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/15/2015] [Indexed: 11/20/2022] Open
Abstract
Brain Functional Connectivity (FC) quantifies statistical dependencies between areas of the brain. FC has been widely used to address altered function of brain circuits in control conditions compared to different pathological states, including epilepsy, a major neurological disorder. However, FC also has the as yet unexplored potential to help us understand the pathological transformation of the brain circuitry. Our hypothesis is that FC can differentiate global brain interactions across a time-scale of days. To this end, we present a case report study based on a mouse model for epilepsy and analyze longitudinal intracranial electroencephalography data of epilepsy to calculate FC changes from the initial insult (status epilepticus) and over the latent period, when epileptogenic networks emerge, and at chronic epilepsy, when unprovoked seizures occur as spontaneous events. We found that the overall network FC at low frequency bands decreased immediately after status epilepticus was provoked, and increased monotonously later on during the latent period. Overall, our results demonstrate the capacity of FC to address longitudinal variations of brain connectivity across the establishment of pathological states.
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Affiliation(s)
- A Erramuzpe
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, 48903, Spain
| | - J M Encinas
- Achucarro Basque Center for Neuroscience, Zamudio, 48170, Spain.,University of the Basque Country (UPV/EHU), Leioa, 48940, Spain.,Ikerbasque: The Basque Foundation for Science, Bilbao, 48013, Spain
| | - A Sierra
- Achucarro Basque Center for Neuroscience, Zamudio, 48170, Spain.,University of the Basque Country (UPV/EHU), Leioa, 48940, Spain.,Ikerbasque: The Basque Foundation for Science, Bilbao, 48013, Spain
| | - M Maletic-Savatic
- Neurological Research Institute, Baylor College of Medicine, Houston, Texas, 77030, USA
| | - A L Brewster
- Neurological Research Institute, Baylor College of Medicine, Houston, Texas, 77030, USA
| | - Anne E Anderson
- Neurological Research Institute, Baylor College of Medicine, Houston, Texas, 77030, USA
| | - S Stramaglia
- Dipartimento di Fisica, Universita degla Studi di Bari and INFN, Bari, 70125, Italy.,BCAM, Basque Center for Applied Mathematics, Bilbao, 48009, Spain
| | - Jesus M Cortes
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, 48903, Spain.,University of the Basque Country (UPV/EHU), Leioa, 48940, Spain.,Ikerbasque: The Basque Foundation for Science, Bilbao, 48013, Spain
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38
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Cortes JM, Marinazzo D, Muñoz MA. Editorial for the research topic: information-based methods for neuroimaging: analyzing structure, function and dynamics. Front Neuroinform 2015; 8:86. [PMID: 25566050 PMCID: PMC4271603 DOI: 10.3389/fninf.2014.00086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Accepted: 12/03/2014] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jesus M Cortes
- Biocruces Health Research Institute, Hospital Universitario Cruces Barakaldo, Spain ; Ikerbasque: The Basque Foundation for Science Bilbao, Spain
| | | | - Miguel A Muñoz
- Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, University of Granada Granada, Spain
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Mäki-Marttunen V, Diez I, Cortes JM, Chialvo DR, Villarreal M. Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness. Front Neuroinform 2013; 7:24. [PMID: 24312048 PMCID: PMC3826091 DOI: 10.3389/fninf.2013.00024] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [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: 08/23/2013] [Accepted: 10/10/2013] [Indexed: 11/19/2022] Open
Abstract
Severe traumatic brain injury can lead to disorders of consciousness (DOC) characterized by deficit in conscious awareness and cognitive impairment including coma, vegetative state, minimally consciousness, and lock-in syndrome. Of crucial importance is to find objective markers that can account for the large-scale disturbances of brain function to help the diagnosis and prognosis of DOC patients and eventually the prediction of the coma outcome. Following recent studies suggesting that the functional organization of brain networks can be altered in comatose patients, this work analyzes brain functional connectivity (FC) networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI). Two approaches are used to estimate the FC: the Partial Correlation (PC) and the Transfer Entropy (TE). Both the PC and the TE show significant statistical differences between the group of patients and control subjects; in brief, the inter-hemispheric PC and the intra-hemispheric TE account for such differences. Overall, these results suggest two possible rs-fMRI markers useful to design new strategies for the management and neuropsychological rehabilitation of DOC patients.
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Affiliation(s)
- Verónica Mäki-Marttunen
- Department of Cognitive Neuroscience, Institute for Neurological Research, FLENI Buenos Aires, Argentina ; CONICET Buenos Aires, Argentina
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40
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Abstract
BACKGROUND The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. METHODOLOGY/PRINCIPAL FINDINGS In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. CONCLUSIONS/SIGNIFICANCE We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed.
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Affiliation(s)
- Ildefonso M De la Fuente
- Quantitative Biomedicine Unit, BioCruces Health Research Institute, Barakaldo, Basque Country, Spain.
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41
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Abstract
The understanding of the effective functionality that governs the enzymatic self-organized processes in cellular conditions is a crucial topic in the post-genomic era. In recent studies, Transfer Entropy has been proposed as a rigorous, robust and self-consistent method for the causal quantification of the functional information flow among nonlinear processes. Here, in order to quantify the functional connectivity for the glycolytic enzymes in dissipative conditions we have analyzed different catalytic patterns using the technique of Transfer Entropy. The data were obtained by means of a yeast glycolytic model formed by three delay differential equations where the enzymatic rate equations of the irreversible stages have been explicitly considered. These enzymatic activity functions were previously modeled and tested experimentally by other different groups. The results show the emergence of a new kind of dynamical functional structure, characterized by changing connectivity flows and a metabolic invariant that constrains the activity of the irreversible enzymes. In addition to the classical topological structure characterized by the specific location of enzymes, substrates, products and feedback-regulatory metabolites, an effective functional structure emerges in the modeled glycolytic system, which is dynamical and characterized by notable variations of the functional interactions. The dynamical structure also exhibits a metabolic invariant which constrains the functional attributes of the enzymes. Finally, in accordance with the classical biochemical studies, our numerical analysis reveals in a quantitative manner that the enzyme phosphofructokinase is the key-core of the metabolic system, behaving for all conditions as the main source of the effective causal flows in yeast glycolysis.
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Fuente IMDL, Cortes JM, Perez-Pinilla MB, Ruiz-Rodriguez V, Veguillas J. The metabolic core and catalytic switches are fundamental elements in the self-regulation of the systemic metabolic structure of cells. PLoS One 2011; 6:e27224. [PMID: 22125607 PMCID: PMC3220688 DOI: 10.1371/journal.pone.0027224] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2011] [Accepted: 10/12/2011] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms. METHODOLOGY/PRINCIPAL FINDINGS In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson's correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows. CONCLUSIONS/SIGNIFICANCE We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub--with a high degree of effective connectivity--exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure.
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Cortes JM, Marinazzo D, Series P, Oram MW, Sejnowski TJ, van Rossum MCW. The effect of neural adaptation on population coding accuracy. J Comput Neurosci 2011; 32:387-402. [PMID: 21915690 PMCID: PMC3367001 DOI: 10.1007/s10827-011-0358-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2011] [Revised: 07/07/2011] [Accepted: 08/05/2011] [Indexed: 11/30/2022]
Abstract
Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.
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Affiliation(s)
- Jesus M Cortes
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
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Abstract
When presented with an item or a face, one might have a sense of recognition without the ability to recall when or where the stimulus has been encountered before. This sense of recognition is called familiarity memory. Following previous computational studies of familiarity memory, we investigate the dynamical properties of familiarity discrimination and contrast two different familiarity discriminators: one based on the energy of the neural network and the other based on the time derivative of the energy. We show how the familiarity signal decays rapidly after stimulus presentation. For both discriminators, we calculate the capacity using mean field analysis. Compared to recall capacity (the classical associative memory in Hopfield nets), both the energy and the slope discriminators have bigger capacity, yet the energy-based discriminator has a higher capacity than one based on its time derivative. Finally, both discriminators are found to have a different noise dependence.
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Torres JJ, Marro J, Cortes JM, Wemmenhove B. Instabilities in attractor networks with fast synaptic fluctuations and partial updating of the neurons activity. Neural Netw 2008; 21:1272-7. [PMID: 18701255 DOI: 10.1016/j.neunet.2008.07.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2007] [Revised: 07/15/2008] [Accepted: 07/19/2008] [Indexed: 11/24/2022]
Abstract
We present and study a probabilistic neural automaton in which the fraction of simultaneously-updated neurons is a parameter, rhoin(0,1). For small rho, there is relaxation towards one of the attractors and a great sensibility to external stimuli and, for rho > or = rho(c), itinerancy among attractors. Tuning rho in this regime, oscillations may abruptly change from regular to chaotic and vice versa, which allows one to control the efficiency of the searching process. We argue on the similarity of the model behavior with recent observations, and on the possible role of chaos in neurobiology.
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Affiliation(s)
- J J Torres
- Institute "Carlos I" for Theoretical and Computational Physics, and Departamento de Electromagnetismo y Física de la Materia, Universidad de Granada, E-18071, Granada, Spain.
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Abstract
We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behaviors, including associative memory and switching of activity between different attractors. We conclude that synaptic facilitation enhances the attractor instability in a way that (1) intensifies the system adaptability to external stimuli, which is in agreement with experiments, and (2) favors the retrieval of information with less error during short time intervals.
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Affiliation(s)
- J J Torres
- Institute Carlos I for Theoretical and Computational Physics, and Department of Electromagnetism and Matter Physics, University of Granada, Granada E-18071, Spain.
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Kusters JMAM, Cortes JM, van Meerwijk WPM, Ypey DL, Theuvenet APR, Gielen CCAM. Hysteresis and bistability in a realistic cell model for calcium oscillations and action potential firing. Phys Rev Lett 2007; 98:098107. [PMID: 17359204 DOI: 10.1103/physrevlett.98.098107] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2006] [Indexed: 05/14/2023]
Abstract
Many cells reveal oscillatory behavior. Some cells reveal action-potential firing resulting from Hodgkin-Huxley (HH) type dynamics of ion channels in the cell membrane. Another type of oscillation relates to periodic inositol triphospate (IP3)-mediated calcium transients in the cytosol. In this study we present a bifurcation analysis of a cell with an excitable membrane and an IP3-mediated intracellular calcium oscillator. With IP3 concentration as a control parameter the model reveals a complex, rich spectrum of both stable and unstable solutions with hysteresis corresponding to experimental data. Our results reveal the emergence of complex behavior due to interactions between subcomponents with a relatively simple dynamical behavior.
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Affiliation(s)
- J M A M Kusters
- Department of Biophysics, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
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Abstract
We study neural automata - or neurobiologically inspired cellular automata - which exhibits chaotic itinerancy among the different stored patterns or memories. This is a consequence of activity-dependent synaptic fluctuations, which continuously destabilize the attractor and induce irregular hopping to other possible attractors. The nature of these irregularities depends on the dynamic details, namely, on the intensity of the synaptic noise and the number of sites of the network, which are synchronously updated at each time step. Varying these factors, different regimes occur, ranging from regular to chaotic dynamics. As a result, and in absence of external agents, the chaotic behavior may turn regular after tuning the noise intensity. It is argued that a similar mechanism might be on the basis of self-controlling chaos in natural systems.
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Affiliation(s)
- J M Cortes
- Institute Carlos I for Theoretical and Computational Physics, and Departamento de Electromagnetismo y Fisica de la Materia, University of Granada, E-18071 Granada, Spain.
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Abstract
We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short timescale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason is that, in general, presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily escape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.
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Affiliation(s)
- J M Cortes
- Institute Carlos I for Theoretical and Computational Physics and Department of Electromagnetism and Physics of Matter, University of Granada, 18071 Granada, Spain.
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Torres JJ, Marro J, Garrido PL, Cortes JM, Ramos F, Muñoz MA. Effects of static and dynamic disorder on the performance of neural automata. Biophys Chem 2005; 115:285-8. [PMID: 15752619 DOI: 10.1016/j.bpc.2004.12.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2004] [Revised: 10/28/2004] [Accepted: 12/10/2004] [Indexed: 11/30/2022]
Abstract
We report on both analytical and numerical results concerning stochastic Hopfield-like neural automata exhibiting the following (biologically inspired) features: (1) Neurons and synapses evolve in time as in contact with respective baths at different temperatures; (2) the connectivity between neurons may be tuned from full connection to high random dilution, or to the case of networks with the small-world property and/or scale-free architecture; and (3) there is synaptic kinetics simulating repeated scanning of the stored patterns. Although these features may apparently result in additional disorder, the model exhibits, for a wide range of parameter values, an extraordinary computational performance, and some of the qualitative behaviors observed in natural systems. In particular, we illustrate here very efficient and robust associative memory, and jumping between pattern attractors.
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Affiliation(s)
- J J Torres
- Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada 18071, Spain.
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