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Zanin M, Aktürk T, Yıldırım E, Yerlikaya D, Yener G, Güntekin B. Reconstructing brain functional networks through identifiability and deep learning. Netw Neurosci 2024; 8:241-259. [PMID: 38562295 PMCID: PMC10923503 DOI: 10.1162/netn_a_00353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/17/2023] [Indexed: 04/04/2024] Open
Abstract
We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer's and Parkinson's disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting-state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson's disease patients.
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Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain
| | - Tuba Aktürk
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Ebru Yıldırım
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Görsev Yener
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
- School of Medicine, Izmir University of Economics, Izmir, Turkey
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
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2
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Fitzgerald B, Bari S, Vike N, Lee TA, Lycke RJ, Auger JD, Leverenz LJ, Nauman E, Goñi J, Talavage TM. Longitudinal changes in resting state fMRI brain self-similarity of asymptomatic high school American football athletes. Sci Rep 2024; 14:1747. [PMID: 38243048 PMCID: PMC10799081 DOI: 10.1038/s41598-024-51688-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024] Open
Abstract
American football has become the focus of numerous studies highlighting a growing concern that cumulative exposure to repetitive, sports-related head acceleration events (HAEs) may have negative consequences for brain health, even in the absence of a diagnosed concussion. In this longitudinal study, brain functional connectivity was analyzed in a cohort of high school American football athletes over a single play season and compared against participants in non-collision high school sports. Football athletes underwent four resting-state functional magnetic resonance imaging sessions: once before (pre-season), twice during (in-season), and once 34-80 days after the contact activities play season ended (post-season). For each imaging session, functional connectomes (FCs) were computed for each athlete and compared across sessions using a metric reflecting the (self) similarity between two FCs. HAEs were monitored during all practices and games throughout the season using head-mounted sensors. Relative to the pre-season scan session, football athletes exhibited decreased FC self-similarity at the later in-season session, with apparent recovery of self-similarity by the time of the post-season session. In addition, both within and post-season self-similarity was correlated with cumulative exposure to head acceleration events. These results suggest that repetitive exposure to HAEs produces alterations in functional brain connectivity and highlight the necessity of collision-free recovery periods for football athletes.
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Affiliation(s)
- Bradley Fitzgerald
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.
| | - Sumra Bari
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
| | - Nicole Vike
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, USA
| | - Taylor A Lee
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Roy J Lycke
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Joshua D Auger
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Larry J Leverenz
- Department of Health and Kinesiology, Purdue University, West Lafayette, IN, USA
| | - Eric Nauman
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, USA
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Joaquín Goñi
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Thomas M Talavage
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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3
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Sasse L, Larabi DI, Omidvarnia A, Jung K, Hoffstaedter F, Jocham G, Eickhoff SB, Patil KR. Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity. Commun Biol 2023; 6:705. [PMID: 37429937 DOI: 10.1038/s42003-023-05073-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/26/2023] [Indexed: 07/12/2023] Open
Abstract
Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.
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Affiliation(s)
- Leonard Sasse
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstrasse 1a, Leipzig, Germany
| | - Daouia I Larabi
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Amir Omidvarnia
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Gerhard Jocham
- Institute for Experimental Psychology, Faculty of Mathematics and Natural Sciences, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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4
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Jiang Z, Dong L, Wu L, Liu Y. Quantifying navigation complexity in transportation networks. PNAS NEXUS 2022; 1:pgac126. [PMID: 36741457 PMCID: PMC9896943 DOI: 10.1093/pnasnexus/pgac126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 07/20/2022] [Indexed: 02/07/2023]
Abstract
The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that (1) people conserve a small number of repeatedly used routes and (2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.
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Affiliation(s)
- Zhuojun Jiang
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Lei Dong
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Lun Wu
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Yu Liu
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
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5
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Chiêm B, Abbas K, Amico E, Duong-Tran DA, Crevecoeur F, Goñi J. Improving Functional Connectome Fingerprinting with Degree-Normalization. Brain Connect 2022; 12:180-192. [PMID: 34015966 PMCID: PMC8978572 DOI: 10.1089/brain.2020.0968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine. Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks. Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space. Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected subnetworks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.
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Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.,Institute of Neurosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Duy Anh Duong-Tran
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.,Institute of Neurosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.,Address correspondence to: Joaquín Goñi, Purdue Institute for Integrative Neuroscience, Purdue University, 315 North Grant Street, West Lafayette, IN 47907, USA
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6
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Billings J, Tivadar R, Murray MM, Franceschiello B, Petri G. Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing. Brain Topogr 2022; 35:79-95. [PMID: 35001322 DOI: 10.1007/s10548-021-00882-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 11/05/2021] [Indexed: 11/30/2022]
Abstract
Electroencephalography (EEG) is among the most widely diffused, inexpensive, and adopted neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analyses. It is therefore valuable to obtain quantities that are minimally affected by reference and pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, was neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individual's brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects' space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional-topological-level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines.
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Affiliation(s)
- Jacob Billings
- ISI Foundation, Turin, Italy
- Department of Complex Systems, Institute for Computer Science, Czech Academy of Science, Prague, Czechia
| | - Ruxandra Tivadar
- Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Ophthalmology, Fondation Asile des aveugles and University of Lausanne, Lausanne, Switzerland
- Cognitive Computational Neuroscience Group, Institute for Computer Science, University of Bern, Bern, Switzerland
| | - Micah M Murray
- Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Ophthalmology, Fondation Asile des aveugles and University of Lausanne, Lausanne, Switzerland
- EEG CHUV-UNIL Section, CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA
| | - Benedetta Franceschiello
- Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Ophthalmology, Fondation Asile des aveugles and University of Lausanne, Lausanne, Switzerland
- EEG CHUV-UNIL Section, CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Giovanni Petri
- ISI Foundation, Turin, Italy.
- ISI Global Science Foundation, New York, NY, USA.
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7
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Jo Y, Faskowitz J, Esfahlani FZ, Sporns O, Betzel RF. Subject identification using edge-centric functional connectivity. Neuroimage 2021; 238:118204. [PMID: 34087363 DOI: 10.1016/j.neuroimage.2021.118204] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 05/23/2021] [Accepted: 05/24/2021] [Indexed: 12/11/2022] Open
Abstract
Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.
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Affiliation(s)
- Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
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8
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Abbas K, Liu M, Venkatesh M, Amico E, Kaplan AD, Ventresca M, Pessoa L, Harezlak J, Goñi J. Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints. Brain Connect 2021; 11:333-348. [PMID: 33470164 PMCID: PMC8215418 DOI: 10.1089/brain.2020.0881] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual level. Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing FCs, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the functional magnetic resonance imaging scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is data set-dependent and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each data set to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Manasij Venkatesh
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | | | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Luiz Pessoa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
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9
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On the Variability of Functional Connectivity and Network Measures in Source-Reconstructed EEG Time-Series. ENTROPY 2020; 23:e23010005. [PMID: 33375007 PMCID: PMC7822028 DOI: 10.3390/e23010005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/17/2020] [Accepted: 12/21/2020] [Indexed: 12/19/2022]
Abstract
The idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea has developed beyond its initial stages, its practical application is still far away from being widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying statistical interdependence among the (interacting) units. This issue has probably contributed to hindering comparisons among different studies. Not only do all these approaches go under the same name (functional connectivity), but they have often been tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often arbitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects the ambiguity and concern that should always be discussed when reporting findings based on any connectivity metric.
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10
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Vera-Ávila VP, Sevilla-Escoboza R, Goñi J, Rivera-Durón RR, Buldú JM. Identifiability of structural networks of nonlinear electronic oscillators. Sci Rep 2020; 10:14668. [PMID: 32887920 PMCID: PMC7474090 DOI: 10.1038/s41598-020-71373-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 08/13/2020] [Indexed: 11/09/2022] Open
Abstract
The interplay between structure and function is critical in the understanding of complex systems, their dynamics and their behavior. We investigated the interplay between structural and functional networks by means of the differential identifiability framework, which here quantifies the ability of identifying a particular network structure based on (1) the observation of its functional network and (2) the comparison with a prior observation under different initial conditions. We carried out an experiment consisting of the construction of [Formula: see text] different structural networks composed of [Formula: see text] nonlinear electronic circuits and studied the regions where network structures are identifiable. Specifically, we analyzed how differential identifiability is related to the coupling strength between dynamical units (modifying the level of synchronization) and what are the consequences of increasing the amount of noise existing in the functional networks. We observed that differential identifiability reaches its highest value for low to intermediate coupling strengths. Furthermore, it is possible to increase the identifiability parameter by including a principal component analysis in the comparison of functional networks, being especially beneficial for scenarios where noise reaches intermediate levels. Finally, we showed that the regime of the parameter space where differential identifiability is the highest is highly overlapped with the region where structural and functional networks correlate the most.
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Affiliation(s)
- V P Vera-Ávila
- Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de Leon, Paseos de la Montaña, 47460, Lagos de Moreno, Jalisco, Mexico
| | - R Sevilla-Escoboza
- Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de Leon, Paseos de la Montaña, 47460, Lagos de Moreno, Jalisco, Mexico
| | - J Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West-Lafayette, IN, USA
| | - R R Rivera-Durón
- Unmanned Systems Research Institute, Northwestern Polytechnical University, Xi'an, 710072, China
| | - J M Buldú
- Unmanned Systems Research Institute, Northwestern Polytechnical University, Xi'an, 710072, China.
- Complex Systems Group and GISC, Universidad Rey Juan Carlos, Madrid, Spain.
- Laboratory of Biological Networks, Center for Biomedical Technology, UPM, Pozuelo de Alarcón, 28223, Madrid, Spain.
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11
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Abbas K, Amico E, Svaldi DO, Tipnis U, Duong-Tran DA, Liu M, Rajapandian M, Harezlak J, Ances BM, Goñi J. GEFF: Graph embedding for functional fingerprinting. Neuroimage 2020; 221:117181. [PMID: 32702487 DOI: 10.1016/j.neuroimage.2020.117181] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/23/2020] [Accepted: 07/16/2020] [Indexed: 11/16/2022] Open
Abstract
It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Diana Otero Svaldi
- Indiana University School of Medicine, Indiana University, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University, Indianapolis, IN, USA
| | - Uttara Tipnis
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Anh Duong-Tran
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Meenusree Rajapandian
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, IN, USA
| | - Beau M Ances
- Washington University School of Medicine, Washington University, St Louis, MO, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
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