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Tolle HM, Farah JC, Mallaroni P, Mason NL, Ramaekers JG, Amico E. The unique neural signature of your trip: Functional connectome fingerprints of subjective psilocybin experience. Netw Neurosci 2024; 8:203-225. [PMID: 38562294 PMCID: PMC10898784 DOI: 10.1162/netn_a_00349] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/23/2023] [Indexed: 04/04/2024] Open
Abstract
The emerging neuroscientific frontier of brain fingerprinting has recently established that human functional connectomes (FCs) exhibit fingerprint-like idiosyncratic features, which map onto heterogeneously distributed behavioral traits. Here, we harness brain-fingerprinting tools to extract FC features that predict subjective drug experience induced by the psychedelic psilocybin. Specifically, in neuroimaging data of healthy volunteers under the acute influence of psilocybin or a placebo, we show that, post psilocybin administration, FCs become more idiosyncratic owing to greater intersubject dissimilarity. Moreover, whereas in placebo subjects idiosyncratic features are primarily found in the frontoparietal network, in psilocybin subjects they concentrate in the default mode network (DMN). Crucially, isolating the latter revealed an FC pattern that predicts subjective psilocybin experience and is characterized by reduced within-DMN and DMN-limbic connectivity, as well as increased connectivity between the DMN and attentional systems. Overall, these results contribute to bridging the gap between psilocybin-mediated effects on brain and behavior, while demonstrating the value of a brain-fingerprinting approach to pharmacological neuroimaging.
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Affiliation(s)
- Hanna M. Tolle
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Juan Carlos Farah
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Natasha L. Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Johannes G. Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Enrico Amico
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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2
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Mallaroni P, Mason NL, Kloft L, Reckweg JT, van Oorsouw K, Toennes SW, Tolle HM, Amico E, Ramaekers JG. Shared functional connectome fingerprints following ritualistic ayahuasca intake. Neuroimage 2024; 285:120480. [PMID: 38061689 DOI: 10.1016/j.neuroimage.2023.120480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/06/2023] [Accepted: 11/29/2023] [Indexed: 01/13/2024] Open
Abstract
The knowledge that brain functional connectomes are unique and reliable has enabled behaviourally relevant inferences at a subject level. However, whether such "fingerprints" persist under altered states of consciousness is unknown. Ayahuasca is a potent serotonergic psychedelic which produces a widespread dysregulation of functional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel interactions between mental state and functional connectome (FC) idiosyncrasy. Using 7T fMRI, we assessed resting-state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-subject study. Here, connectome fingerprinting revealed FCs showed reduced idiosyncrasy, accompanied by a spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-order FC motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Collectively, our findings offer an example of how individualised connectivity markers can be used to trace a subject's FC across altered states of consciousness.
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Affiliation(s)
- Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands.
| | - Natasha L Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Lilian Kloft
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Johannes T Reckweg
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Kim van Oorsouw
- Department of Forensic Psychology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Stefan W Toennes
- Institute of Legal Medicine, University Hospital, Goethe University, Frankfurt/Main, Germany
| | | | | | - Johannes G Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
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Duong-Tran D, Kaufmann R, Chen J, Wang X, Garai S, Xu F, Bao J, Amico E, Kaplan AD, Petri G, Goni J, Zhao Y, Shen L. Homological landscape of human brain functional sub-circuits. bioRxiv 2023:2023.12.22.573062. [PMID: 38187668 PMCID: PMC10769445 DOI: 10.1101/2023.12.22.573062] [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] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H 1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task H 0 , ii) Motor task H 1 , and iii) Working memory task H 2 . At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject- domain which sheds light to subsequent Investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Ralph Kaufmann
- Department of Mathematics, Purdue University, West Lafayette, IN, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA, USA
| | - Xuan Wang
- Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Enrico Amico
- Neuro-X Institute, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Giovanni Petri
- CENTAI Institute, 10138 Torino, Italy
- NPLab, Network Science Institute, Northeastern University London, London, E1W 1LP, United Kingdom
- Networks Unit, IMT Lucca Institute, 55100 Lucca, Italy
| | - Joaquin Goni
- 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, US
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
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4
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Griffa A, Mach M, Dedelley J, Gutierrez-Barragan D, Gozzi A, Allali G, Grandjean J, Van De Ville D, Amico E. Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice. Nat Commun 2023; 14:8216. [PMID: 38081838 PMCID: PMC10713651 DOI: 10.1038/s41467-023-43971-z] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain's computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapt across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in male mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.
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Affiliation(s)
- Alessandra Griffa
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Mathieu Mach
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Julien Dedelley
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Gilles Allali
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Joanes Grandjean
- Department of Medical Imaging, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 EN, Nijmegen, The Netherlands
| | - Dimitri Van De Ville
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
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5
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Luppi AI, Golkowski D, Ranft A, Ilg R, Jordan D, Bzdok D, Owen AM, Naci L, Stamatakis EA, Amico E, Misic B. General anaesthesia reduces the uniqueness of brain connectivity across individuals and across species. bioRxiv 2023:2023.11.08.566332. [PMID: 38014199 PMCID: PMC10680788 DOI: 10.1101/2023.11.08.566332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The human brain is characterised by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain: both with respect to the brains of other individuals, and the brains of another species. We report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organised: it co-localises with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol, and reversed upon recovery. Providing convergent evidence, we show that under anaesthesia the functional connectivity of the human brain becomes more similar to the macaque brain. Finally, anaesthesia diminishes the match between spontaneous brain activity and meta-analytic brain patterns aggregated from the NeuroSynth engine. Collectively, the present results reveal that anaesthetised human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.
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Abbas K, Liu M, Wang M, Duong-Tran D, Tipnis U, Amico E, Kaplan AD, Dzemidzic M, Kareken D, Ances BM, Harezlak J, Goñi J. Tangent functional connectomes uncover more unique phenotypic traits. iScience 2023; 26:107624. [PMID: 37694156 PMCID: PMC10483051 DOI: 10.1016/j.isci.2023.107624] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the "fingerprint gradient" (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).
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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
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Michael Wang
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Duong-Tran
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Uttara Tipnis
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Alan D. Kaplan
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - Beau M. Ances
- Department of Neurology, Washington University in Saint Louis, School of Medicine, St Louis, MO, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, 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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7
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Bolton TAW, Van De Ville D, Amico E, Preti MG, Liégeois R. The arrow-of-time in neuroimaging time series identifies causal triggers of brain function. Hum Brain Mapp 2023; 44:4077-4087. [PMID: 37209360 PMCID: PMC10258533 DOI: 10.1002/hbm.26331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/07/2023] [Accepted: 04/18/2023] [Indexed: 05/22/2023] Open
Abstract
Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.
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Affiliation(s)
- Thomas A. W. Bolton
- Connectomics Laboratory, Department of RadiologyCentre Hospitalier Universitaire VaudoisLausanneSwitzerland
- Department of Clinical NeurosciencesCentre Hospitalier Universitaire VaudoisLausanneSwitzerland
| | - Dimitri Van De Ville
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Enrico Amico
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Maria G. Preti
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- CIBM Center for Biomedical ImagingVaudSwitzerland
| | - Raphaël Liégeois
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
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8
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Colenbier N, Sareen E, Del-Aguila Puntas T, Griffa A, Pellegrino G, Mantini D, Marinazzo D, Arcara G, Amico E. Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states. Neuroimage 2023; 271:120021. [PMID: 36918139 DOI: 10.1016/j.neuroimage.2023.120021] [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: 11/17/2022] [Revised: 02/21/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.
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Affiliation(s)
| | - Ekansh Sareen
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Tamara Del-Aguila Puntas
- Laboratorio de Psicobiologia, Departmento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Spain
| | - Alessandra Griffa
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | | | - Enrico Amico
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland.
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Troisi Lopez E, Minino R, Liparoti M, Polverino A, Romano A, De Micco R, Lucidi F, Tessitore A, Amico E, Sorrentino G, Jirsa V, Sorrentino P. Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment. Hum Brain Mapp 2022; 44:1239-1250. [PMID: 36413043 PMCID: PMC9875937 DOI: 10.1002/hbm.26156] [Citation(s) in RCA: 4] [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: 06/30/2022] [Revised: 10/18/2022] [Accepted: 11/10/2022] [Indexed: 11/23/2022] Open
Abstract
The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source-reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross-validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Roberta Minino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Marianna Liparoti
- Department of Developmental and Social PsychologyUniversity "La Sapienza" of RomeRomeItaly
| | - Arianna Polverino
- Institute for Diagnosis and Treatment Hermitage CapodimonteNaplesItaly
| | - Antonella Romano
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Rosa De Micco
- Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Fabio Lucidi
- Department of Developmental and Social PsychologyUniversity "La Sapienza" of RomeRomeItaly
| | - Alessandro Tessitore
- Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFLGenevaSwitzerland,Department of Radiology and Medical InformaticsUniversity of Geneva (UNIGE)GenevaSwitzerland
| | - Giuseppe Sorrentino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly,Institute for Diagnosis and Treatment Hermitage CapodimonteNaplesItaly,Institute of Applied Sciences and Intelligent Systems, National Research CouncilNaplesItaly
| | - Viktor Jirsa
- Institut de Neurosciences des SystèmesAix‐Marseille UniversitéMarseilleFrance
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10
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Troisi Lopez E, Sorrentino P, Liparoti M, Minino R, Polverino A, Romano A, Carotenuto A, Amico E, Sorrentino G. The kinectome: A comprehensive kinematic map of human motion in health and disease. Ann N Y Acad Sci 2022; 1516:247-261. [PMID: 35838306 PMCID: PMC9796708 DOI: 10.1111/nyas.14860] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body elements. Network science can be a useful approach to describe movement as a whole and to extract features that are relevant to understanding both its complex physiology and the pathophysiology of movement disorders. Here, we propose to represent human movement as a network (that we named the kinectome), where nodes represent body points, and edges are defined as the correlations of the accelerations between each pair of them. We applied this framework to healthy individuals and patients with Parkinson's disease, observing that the patients' kinectomes display less symmetrical patterns as compared to healthy controls. Furthermore, we used the kinectomes to successfully identify both healthy and diseased subjects using short gait recordings. Finally, we highlighted topological features that predict the individual clinical impairment in patients. Our results define a novel approach to study human movement. While deceptively simple, this approach is well-grounded, and represents a powerful tool that may be applied to a wide spectrum of frameworks.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | | | - Marianna Liparoti
- Department of Developmental and Social PsychologyUniversity “La Sapienza” of RomeRomeItaly
| | - Roberta Minino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Arianna Polverino
- Institute for Diagnosis and TreatmentHermitage CapodimonteNaplesItaly
| | - Antonella Romano
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Anna Carotenuto
- Alzheimer Unit and Movement Disorders ClinicDepartment of NeurologyCardarelli HospitalNaplesItaly
| | - Enrico Amico
- Institute of Bioengineering, Center for NeuroprostheticsEPFLGenevaSwitzerland,Department of Radiology and Medical InformaticsUniversity of Geneva (UNIGE)GenevaSwitzerland
| | - Giuseppe Sorrentino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly,Institute for Diagnosis and TreatmentHermitage CapodimonteNaplesItaly,Institute of Applied Sciences and Intelligent SystemsCNRPozzuoliItaly
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11
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Omidvarnia A, Liégeois R, Amico E, Preti MG, Zalesky A, Van De Ville D. On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI. Entropy (Basel) 2022; 24:1148. [PMID: 36010812 PMCID: PMC9407401 DOI: 10.3390/e24081148] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.
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Affiliation(s)
- Amir Omidvarnia
- Applied Machine Learning Group, Institute of Neuroscience and Medicine, Forschungszentrum Juelich, 52428 Juelich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, 40225 Duesseldorf, Germany
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Raphaël Liégeois
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Enrico Amico
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Maria Giulia Preti
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
- CIBM Center for Biomedical Imaging, 1015 Lausanne, Switzerland
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC 3010, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
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12
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Romano A, Trosi Lopez E, Liparoti M, Polverino A, Minino R, Trojsi F, Bonavita S, Mandolesi L, Granata C, Amico E, Sorrentino G, Sorrentino P. The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment. Neuroimage Clin 2022; 35:103095. [PMID: 35764029 PMCID: PMC9241102 DOI: 10.1016/j.nicl.2022.103095] [Citation(s) in RCA: 8] [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: 01/17/2022] [Revised: 05/31/2022] [Accepted: 06/19/2022] [Indexed: 10/25/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprinting represents a reliable approach to assess subject-specific connectivity features within a given population (healthy or diseased). Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each patient was recognisable based on his/her connectome, as compared to healthy controls. The analysis was performed in the five canonical frequency bands. Then, we built a multilinear regression model to test the ability of the "clinical fingerprint" to predict the clinical evolution of the disease, as assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King's disease staging system, and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in the identifiability of patients in the alpha band compared to the healthy controls. Furthermore, the "clinical fingerprint" was predictive of the ALSFRS-r (p = 0.0397; β = 32.8), the King's (p = 0.0001; β = -7.40), and the MiToS (p = 0.0025; β = -4.9) scores. Accordingly, it negatively correlated with the King's (Spearman's rho = -0.6041, p = 0.0003) and MiToS scales (Spearman's rho = -0.4953, p = 0.0040). Our results demonstrated the ability of the CCF approach to predict the individual motor impairment in patients affected by ALS. Given the subject-specificity of our approach, we hope to further exploit it to improve disease management.
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Affiliation(s)
- Antonella Romano
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy
| | - Emahnuel Trosi Lopez
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy
| | - Marianna Liparoti
- Department of Social and Developmental Psychology, University of Rome "Sapienza", Italy
| | - Arianna Polverino
- Institute of Diagnosis and Treatment Hermitage Capodimonte, via Cupa delle Tozzole 2, 80131 Naples, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy
| | - Francesca Trojsi
- Department of Advanced Medical and Surgical Sciences, Division of Neurology, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Simona Bonavita
- Department of Advanced Medical and Surgical Sciences, Division of Neurology, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Laura Mandolesi
- Department of Humanistic Studies, University of Naples Federico II, via Porta di Massa 1, 80133, Naples, Italy
| | - Carmine Granata
- Institute of Applied Sciences and Intelligent Systems, CNR, via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy; Institute of Diagnosis and Treatment Hermitage Capodimonte, via Cupa delle Tozzole 2, 80131 Naples, Italy; Institute of Applied Sciences and Intelligent Systems, CNR, via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy.
| | - Pierpaolo Sorrentino
- Institute of Applied Sciences and Intelligent Systems, CNR, via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy; Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
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13
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Griffa A, Amico E, Liégeois R, Ville DVD, Preti MG. Brain structure-function coupling provides signatures for task decoding and individual fingerprinting. Neuroimage 2022; 250:118970. [DOI: 10.1016/j.neuroimage.2022.118970] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/12/2022] [Accepted: 02/03/2022] [Indexed: 11/17/2022] Open
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15
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Abstract
The importance of implementing new methodologies to study the ever-increasing amount of Covid-19 data is apparent. The aftermath analysis of these data could inform us on how specific political decisions influenced the dynamics of the pandemic outbreak. In this paper we use the Italian outbreak as a case study, to study six different Covid indicators collected in twenty Italian regions. We define a new object, the Covidome, to investigate the network of functional Covid interactions between regions. We analyzed the Italian Covidome over the course of 2020, and found that Covid connectivity between regions follows a sharp North-South community gradient. Furthermore, we explored the Covidome dynamics and individuated differences in regional Covid connectivity between the first and second waves of the pandemic. These differences can be associated to the two different lockdown strategies adopted for the first and the second wave from the Italian government. Finally, we explored to what extent Covid connectivity was associated with the Italian geographical network, and found that Central regions were more tied to the structural constraints than Northern or Southern regions in the spread of the virus. We hope that this approach will be useful in gaining new insights on how political choices shaped Covid dynamics across nations.
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Affiliation(s)
- Enrico Amico
- Institute of Bioengineering/Center for Neuroprosthetics EPFL—Ecole Polytechnique, Institute of Bioengineering Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Iulia Martina Bulai
- Institute of Bioengineering/Center for Neuroprosthetics EPFL—Ecole Polytechnique, Institute of Bioengineering Fédérale de Lausanne, Lausanne, Switzerland
- Department of Mathematics, Computer Science and Economics, University of Basilicata, Potenza, Italy
- * E-mail:
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16
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Stampacchia S, Asadi S, Ribaldi F, Tomczyk S, Altomare D, Pievani M, Frisoni G, Amico E, Garibotto V. Towards fingerprinting and identifiability within the Alzheimer’s continuum using resting‐state functional connectivity. Alzheimers Dement 2021. [DOI: 10.1002/alz.057724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Sara Stampacchia
- Department of Radiology and Medical Informatics University of Geneva Geneva Switzerland
| | | | | | | | - Daniele Altomare
- University of Geneva Geneva Switzerland
- University Hospital of Geneva Geneva Switzerland
| | - Michela Pievani
- Laboratory Alzheimer's Neuroimaging & Epidemiology IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy
| | - Giovanni Frisoni
- University Hospitals and University of Geneva Geneva Switzerland
| | - Enrico Amico
- Department of Radiology and Medical Informatics University of Geneva Geneva Switzerland
- EPFL‐Ecole Polytechnique Fédérale de Lausanne Geneva Switzerland
| | - Valentina Garibotto
- Department of Radiology and Medical Informatics University of Geneva Geneva Switzerland
- Geneva University Hospitals Geneva Switzerland
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17
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Van De Ville D, Farouj Y, Preti MG, Liégeois R, Amico E. When makes you unique: Temporality of the human brain fingerprint. Sci Adv 2021; 7:eabj0751. [PMID: 34652937 PMCID: PMC8519575 DOI: 10.1126/sciadv.abj0751] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/20/2021] [Indexed: 05/30/2023]
Abstract
The extraction of “fingerprints” from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability are still largely unexplored. We here investigate the dynamics of brain fingerprints along two complementary axes: (i) What is the optimal time scale at which brain fingerprints integrate information and (ii) when best identification happens. Using dynamic identifiability, we show that the best identification emerges at longer time scales; however, short transient “bursts of identifiability,” associated with neuronal activity, persist even when looking at shorter functional interactions. Furthermore, we report evidence that different parts of connectome fingerprints relate to different time scales, i.e., more visual-somatomotor at short temporal windows and more frontoparietal-DMN driven at increasing temporal windows. Last, different cognitive functions appear to be meta-analytically implicated in dynamic fingerprints across time scales. We hope that this investigation will advance our understanding of what makes our brains unique.
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Affiliation(s)
- Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
| | - Younes Farouj
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Maria Giulia Preti
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
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18
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Amico E. Exploring MEG Brain Fingerprints: Evaluation, Interpretations and Clinical Potential. Int J Psychophysiol 2021. [DOI: 10.1016/j.ijpsycho.2021.07.110] [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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19
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Duong-Tran D, Abbas K, Amico E, Corominas-Murtra B, Dzemidzic M, Kareken D, Ventresca M, Goñi J. A morphospace of functional configuration to assess configural breadth based on brain functional networks. Netw Neurosci 2021; 5:666-688. [PMID: 34746622 PMCID: PMC8567831 DOI: 10.1162/netn_a_00193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 01/02/2020] [Accepted: 03/17/2021] [Indexed: 11/07/2022] Open
Abstract
The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.
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Affiliation(s)
- Duy Duong-Tran
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Kausar Abbas
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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20
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Amico E, Abbas K, Duong-Tran DA, Tipnis U, Rajapandian M, Chumin E, Ventresca M, Harezlak J, Goñi J. Toward an information theoretical description of communication in brain networks. Netw Neurosci 2021; 5:646-665. [PMID: 34746621 PMCID: PMC8567835 DOI: 10.1162/netn_a_00185] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 11/06/2019] [Accepted: 01/18/2021] [Indexed: 11/21/2022] Open
Abstract
Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: path processing score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); path broadcasting strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main "communication regimes" of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); and transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; visual and somatomotor cortices act as multichannel transducted broadcasters. This work paves the way toward the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.
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Affiliation(s)
- Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Kausar Abbas
- 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
- School of Industrial Engineering, Purdue University, West Lafayette, 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
| | | | - Evgeny Chumin
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, 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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21
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Svaldi DO, Goñi J, Abbas K, Amico E, Clark DG, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2021; 42:3500-3516. [PMID: 33949732 PMCID: PMC8249900 DOI: 10.1002/hbm.25448] [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: 12/11/2019] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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Affiliation(s)
| | - Joaquín Goñi
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Kausar Abbas
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - Enrico Amico
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - David G. Clark
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - John D. West
- Indiana University School of MedicineIndianapolisIndianaUSA
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22
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Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 2021; 240:118331. [PMID: 34237444 DOI: 10.1016/j.neuroimage.2021.118331] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [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: 02/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 12/16/2022] Open
Abstract
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.
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Affiliation(s)
- Ekansh Sareen
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Sélima Zahar
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anubha Gupta
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Alessandra Griffa
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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23
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Sorrentino P, Rucco R, Lardone A, Liparoti M, Troisi Lopez E, Cavaliere C, Soricelli A, Jirsa V, Sorrentino G, Amico E. Clinical connectome fingerprints of cognitive decline. Neuroimage 2021; 238:118253. [PMID: 34116156 DOI: 10.1016/j.neuroimage.2021.118253] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [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: 02/04/2021] [Revised: 05/29/2021] [Accepted: 06/07/2021] [Indexed: 12/29/2022] Open
Abstract
Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that "clinical fingerprints" can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.
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Affiliation(s)
- Pierpaolo Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - Rosaria Rucco
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy
| | - Anna Lardone
- Department of Social and Developmental Psychology, University of Rome "Sapienza, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy
| | | | | | - Andrea Soricelli
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy; IRCCS SDN, Naples, Italy
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - Giuseppe Sorrentino
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy; Hermitage Capodimonte Clinic, Naples, Italy.
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 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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25
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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26
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Di Pardo A, Pepe G, Capocci L, Marracino F, Amico E, Del Vecchio L, Giova S, Jeong SK, Park BM, Park BD, Maglione V. Treatment with K6PC-5, a selective stimulator of SPHK1, ameliorates intestinal homeostasis in an animal model of Huntington's disease. Neurobiol Dis 2020; 143:105009. [PMID: 32634578 DOI: 10.1016/j.nbd.2020.105009] [Citation(s) in RCA: 6] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 06/10/2020] [Accepted: 06/29/2020] [Indexed: 02/08/2023] Open
Abstract
Emerging evidence indicates that Huntington's disease (HD) may be described as multi-organ pathology. In this context, we and others have contributed to demonstrate that the disease is characterized by an impairment of the homeostasis of gastro-intestinal (GI) tract. Sphingolipids represent a class of molecules involved in the regulation and maintenance of different tissues and organs including GI system. In this study, we investigated whether the alteration of Sphingosine-1-phosphate (S1P) metabolism, previously described in human HD brains and animal models, is also detectable peripherally in R6/2 HD mice. Our findings indicate, for the first time, that sphingolipid metabolism is perturbed early in the disease in the intestinal tract of HD mice and, its modulation by K6PC-5, a selective activator of S1P synthesis, preserved intestinal integrity and homeostasis. These results further support the evidence that modulation of sphingolipid pathways may represent a potential therapeutic option in HD and suggest that it has also the potential to counteract the peripheral disturbances which may usually complicate the management of the disease and affect patient's quality of life.
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Affiliation(s)
| | - G Pepe
- IRCCS Neuromed, Pozzilli, Italy
| | | | | | - E Amico
- IRCCS Neuromed, Pozzilli, Italy
| | - L Del Vecchio
- IRCCS Neuromed, Pozzilli, Italy; Unità complessa di radiodiagnostica (U.O.C.) POS, University of Foggia, Foggia, Italy
| | - S Giova
- IRCCS Neuromed, Pozzilli, Italy
| | - S K Jeong
- Department of Cosmetic Science, Seowon University, Cheongju, Republic of Korea
| | - B M Park
- NeoPharm USA Inc., Engelwood Cliffs, NJ, USA
| | - B D Park
- Dr. Raymond Laboratories, Inc, Englewood cliffs, NJ, USA
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27
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Rajapandian M, Amico E, Abbas K, Ventresca M, Goñi J. Uncovering differential identifiability in network properties of human brain functional connectomes. Netw Neurosci 2020; 4:698-713. [PMID: 32885122 PMCID: PMC7462422 DOI: 10.1162/netn_a_00140] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [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: 11/21/2019] [Accepted: 03/30/2020] [Indexed: 01/05/2023] Open
Abstract
The identifiability framework (𝕀f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.
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Affiliation(s)
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Integrative Neuroscience, West Lafayette, IN, USA
| | - Kausar Abbas
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Integrative Neuroscience, West Lafayette, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Integrative Neuroscience, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, West Lafayette, IN, USA
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28
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Wirsich J, Amico E, Giraud AL, Goñi J, Sadaghiani S. Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition. Netw Neurosci 2020; 4:658-677. [PMID: 32885120 PMCID: PMC7462430 DOI: 10.1162/netn_a_00135] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 10/15/2019] [Accepted: 02/27/2020] [Indexed: 01/02/2023] Open
Abstract
Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FCEEG to second range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals. Functional connectivity is governed by a whole-brain organization measurable over multiple timescales by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The relationship across the whole-brain organization captured at the different timescales of EEG and fMRI is largely unknown. Using concurrent EEG-fMRI, we identified spatially independent components consisting of brain connectivity patterns that co-occur in EEG and fMRI over subjects. We observed a component with similar connectivity organization across EEG and fMRI as well as a component with divergent connectivity. The former component governed all EEG frequencies while the latter was modulated by frequency. These findings show that part of functional connectivity organizes in a common spatial layout over several timescales, while a spatially independent part is modulated by frequency-specific information.
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Affiliation(s)
- Jonathan Wirsich
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Geneva, Switzerland
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Sepideh Sadaghiani
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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29
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Elifani F, Amico E, Pepe G, Capocci L, Castaldo S, Rosa P, Montano E, Pollice A, Madonna M, Filosa S, Calogero A, Maglione V, Crispi S, Di Pardo A. Curcumin dietary supplementation ameliorates disease phenotype in an animal model of Huntington's disease. Hum Mol Genet 2020; 28:4012-4021. [PMID: 31630202 DOI: 10.1093/hmg/ddz247] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 10/01/2019] [Accepted: 10/02/2019] [Indexed: 11/14/2022] Open
Abstract
Huntington's disease (HD) has traditionally been described as a disorder purely of the brain; however, evidence indicates that peripheral abnormalities are also commonly seen. Among others, severe unintended body weight loss represents a prevalent and often debilitating feature of HD pathology, with no therapies available. It correlates with disease progression and significantly affects the quality of life of HD patients. Curcumin, a naturally occurring polyphenol with multiple therapeutic properties, has been validated to exert important beneficial effects under health conditions as well as in different pathological settings, including neurodegenerative and gastrointestinal (GI) disorders. Here, we investigated the potential therapeutic action that curcumin-supplemented diet may exert on central and peripheral dysfunctions in R6/2 mice, a well-characterized HD animal model which recapitulates some features of human pathology. Maintenance of normal motor function, protection from neuropathology and from GI dysfunction and preservation of GI emptying and conserved intestinal contractility, proved the beneficial role of life-long dietary curcumin in HD and corroborated the potential of the compound to be exploited to alleviate very debilitating symptoms associated with the disease.
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Affiliation(s)
- F Elifani
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
| | - E Amico
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
| | - G Pepe
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
| | - L Capocci
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
| | - S Castaldo
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
| | - P Rosa
- Department of Medical Surgical Sciences and Biotechnology, Sapienza University of Rome Polo di Latina, Latina, Italy
| | - E Montano
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy.,Department of Biology, Università degli Studi di Napoli - Federico II, Napoli, Italy
| | - A Pollice
- Department of Biology, Università degli Studi di Napoli - Federico II, Napoli, Italy
| | - M Madonna
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
| | - S Filosa
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy.,IBBR-CNR, Institute of Biosciences and Bioresources Napoli, Italy
| | - A Calogero
- Department of Medical Surgical Sciences and Biotechnology, Sapienza University of Rome Polo di Latina, Latina, Italy
| | - V Maglione
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
| | - S Crispi
- IBBR-CNR, Institute of Biosciences and Bioresources Napoli, Italy
| | - A Di Pardo
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed, Pozzilli (IS), Italy
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30
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Amico E, Dzemidzic M, Oberlin BG, Carron CR, Harezlak J, Goñi J, Kareken DA. The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk. Neuroimage 2020; 209:116515. [PMID: 31904492 PMCID: PMC8496455 DOI: 10.1016/j.neuroimage.2020.116515] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/01/2020] [Indexed: 10/25/2022] Open
Abstract
Human functional brain connectivity is usually measured either at "rest" or during cognitive tasks, ignoring life's moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.
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Affiliation(s)
- Enrico Amico
- Purdue Institute for Integrative Neuroscience, Purdue University, USA; School of Industrial Engineering, Purdue University, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA
| | - Brandon G Oberlin
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA; Department of Psychiatry, Indiana University School of Medicine, USA
| | - Claire R Carron
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, USA; School of Industrial Engineering, Purdue University, USA; Weldon School of Biomedical Engineering, Purdue University, USA.
| | - David A Kareken
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA.
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31
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Yan J, Raja V V, Huang Z, Amico E, Nho K, Fang S, Sporns O, Wu YC, Saykin A, Goni J, Shen L. Brain-wide structural connectivity alterations under the control of Alzheimer risk genes. Int J Comput Biol Drug Des 2020; 13:58-70. [PMID: 32095160 DOI: 10.1504/ijcbdd.2020.10026789] [Citation(s) in RCA: 3] [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] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Background Alzheimer's disease is the most common form of brain dementia characterized by gradual loss of memory followed by further deterioration of other cognitive function. Large-scale genome-wide association studies have identified and validated more than 20 AD risk genes. However, how these genes are related to the brain-wide breakdown of structural connectivity in AD patients remains unknown. Methods We used the genotype and DTI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructing the brain network for each subject, we extracted three types of link measures, including fiber anisotropy, fiber length and density. We then performed a targeted genetic association analysis of brain-wide connectivity measures using general linear regression models. Age at scan and gender were included in the regression model as covariates. For fair comparison of the genetic effect on different measures, fiber anisotropy, fiber length and density were all normalized with mean as 0 and standard deviation as one.We aim to discover the abnormal brain-wide network alterations under the control of 34 AD risk SNPs identified in previous large-scale genome-wide association studies. Results After enforcing the stringent Bonferroni correction, rs10498633 in SLC24A4 were found to significantly associated with anisotropy, total number and length of fibers, including some connecting brain hemispheres. With a lower level of significance at 5e-6, we observed significant genetic effect of SNPs in APOE, ABCA7, EPHA1 and CASS4 on various brain connectivity measures.
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Affiliation(s)
- Jingwen Yan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 719 Indiana Ave, Indianapolis, USA
| | - Vinesh Raja V
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 719 Indiana Ave, Indianapolis, USA
| | - Zhi Huang
- Electrical and Computing Engineering, Indiana University Purdue University Indianapolis, 46202 Indianapolis, USA
| | - Enrico Amico
- Industrial Engineering, Purdue University, 47096 West Lafayette, USA
| | - Kwangsik Nho
- Radiology and Imaging Sciences, Indiana University School of Medicine, 46202 Indianapolis, USA
| | - Shiaofeng Fang
- Computer Science, Indiana University Purdue University Indianapolis, 46202 Indianapolis, USA
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana University, 47405 Bloomington, USA
| | - Yu-Chien Wu
- Radiology and Imaging Sciences, Indiana University School of Medicine, 46202 Indianapolis, USA
| | - Andrew Saykin
- Radiology and Imaging Sciences, Indiana University School of Medicine, 46202 Indianapolis, USA
| | - Joaquin Goni
- Industrial Engineering, Purdue University, 47096 West Lafayette, USA
| | - Li Shen
- Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 19104 Philadelphia, USA
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Svaldi DO, Goñi J, Amico E, Abbas K, Charanya M, West JD, Dzemidzic M, Risacher SL, Saykin AJ, Apostolova LG. IC-P-032: IMPROVING PREDICTION OF COGNITIVE OUTCOMES FROM FUNCTIONAL CONNECTIVITY IN ALZHEIMER'S DISEASE. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.4194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
| | | | | | | | | | - John D. West
- Indiana University School of Medicine; Indianapolis IN USA
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Svaldi DO, Goñi J, Amico E, Abbas K, Charanya M, Dzemidzic M, Risacher SL, Saykin AJ, Apostolova LG. P4-334: IMPROVING PREDICTION OF COGNITIVE OUTCOMES FROM FUNCTIONAL CONNECTIVITY IN ALZHEIMER'S DISEASE. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.4004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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34
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Svaldi DO, Goñi J, Amico E, Risacher SL, Stage E, Muralidharan C, West JD, Dzemidzic M, Saykin AJ, Apostolova LG. IC-P-033: COVARYING PATTERNS OF FUNCTIONAL CONNECTIVITY WITH AMYLOID AND TAU DEPOSITION IN EARLY STAGE ALZHEIMER'S DISEASE. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.4195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | | | | | - Eddie Stage
- Indiana University School of Medicine; Indianapolis IN USA
| | | | - John D. West
- Indiana University School of Medicine; Indianapolis IN USA
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35
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Di Pardo A, Pepe G, Castaldo S, Marracino F, Capocci L, Amico E, Madonna M, Giova S, Jeong SK, Park BM, Park BD, Maglione V. Stimulation of Sphingosine Kinase 1 (SPHK1) Is Beneficial in a Huntington's Disease Pre-clinical Model. Front Mol Neurosci 2019; 12:100. [PMID: 31068790 PMCID: PMC6491579 DOI: 10.3389/fnmol.2019.00100] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 04/03/2019] [Indexed: 12/19/2022] Open
Abstract
Although several agents have been identified to provide therapeutic benefits in Huntington disease (HD), the number of conventionally used treatments remains limited and only symptomatic. Thus, it is plausible that the need to identify new therapeutic targets for the development of alternative and more effective treatments is becoming increasingly urgent. Recently, the sphingosine-1-phosphate (S1P) axis has been reported to be a valid potential novel molecular target for therapy development in HD. Modulation of aberrant metabolism of S1P in HD has been proved to exert neuroprotective action in vitro settings including human HD iPSC-derived neurons. In this study, we investigated whether promoting S1P production by stimulating Sphingosine Kinase 1 (SPHK1) by the selective activator, K6PC-5, may have therapeutic benefit in vivo in R6/2 HD mouse model. Our findings indicate that chronic administration of 0.05 mg/kg K6PC-5 exerted an overall beneficial effect in R6/2 mice. It significantly slowed down the progressive motor deficit associated with disease progression, modulated S1P metabolism, evoked the activation of pro-survival pathways and markedly reduced the toxic mutant huntingtin (mHtt) aggregation. These results suggest that K6PC-5 may represent a future therapeutic option in HD and may potentially counteract the perturbed brain function induced by deregulated S1P pathways.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Se Kyoo Jeong
- Department of Cosmetic Science, Seowon University, Cheongju, South Korea
| | - Bu-Mahn Park
- NeoPharm USA Inc., Engelwood Cliffs, NJ, United States
| | - Byeong Deog Park
- Dr. Raymond Laboratories, Inc., Englewood Cliffs, NJ, United States
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36
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Di Pardo A, Castaldo S, Amico E, Pepe G, Marracino F, Capocci L, Giovannelli A, Madonna M, van Bergeijk J, Buttari F, van der Kam E, Maglione V. Stimulation of S1PR5 with A-971432, a selective agonist, preserves blood-brain barrier integrity and exerts therapeutic effect in an animal model of Huntington's disease. Hum Mol Genet 2019; 27:2490-2501. [PMID: 29688337 DOI: 10.1093/hmg/ddy153] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 04/19/2018] [Indexed: 12/21/2022] Open
Abstract
Huntington's disease (HD) is the most common neurodegenerative disorder for which no effective cure is yet available. Although several agents have been identified to provide benefits so far, the number of therapeutic options remains limited with only symptomatic treatment available. Over the past few years, we have demonstrated that sphingolipid-based approaches may open the door to new and more targeted treatments for the disease. In this study, we investigated the therapeutic potential of stimulating sphingosine-1-phosphate (S1P) receptor 5 by the new selective agonist A-971432 (provided by AbbVie) in R6/2 mice, a widely used HD animal model. Chronic administration of low-dose (0.1 mg/kg) A-971432 slowed down the progression of the disease and significantly prolonged lifespan in symptomatic R6/2 mice. Such beneficial effects were associated with activation of pro-survival pathways (BDNF, AKT and ERK) and with reduction of mutant huntingtin aggregation. A-971432 also protected blood-brain barrier (BBB) homeostasis in the same mice. Interestingly, when administered early in the disease, before any overt symptoms, A-971432 completely protected HD mice from the classic progressive motor deficit and preserved BBB integrity. Beside representing a promising strategy to take into consideration for the development of alternative therapeutic options for HD, selective stimulation of S1P receptor 5 may be also seen as an effective approach to target brain vasculature defects in the disease.
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Affiliation(s)
- Alba Di Pardo
- IRCCS Neuromed, Localitá Camerelle, Pozzilli (IS), Italy
| | | | - Enrico Amico
- IRCCS Neuromed, Localitá Camerelle, Pozzilli (IS), Italy
| | - Giuseppe Pepe
- IRCCS Neuromed, Localitá Camerelle, Pozzilli (IS), Italy
| | | | - Luca Capocci
- IRCCS Neuromed, Localitá Camerelle, Pozzilli (IS), Italy
| | | | | | | | - Fabio Buttari
- IRCCS Neuromed, Localitá Camerelle, Pozzilli (IS), Italy
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37
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Abstract
A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks.
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Affiliation(s)
- Enrico Amico
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
| | - Alex Arenas
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West-Lafayette, IN, USA
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38
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Xie L, Amico E, Salama P, Wu YC, Fang S, Sporns O, Saykin AJ, Goñi J, Yan J, Shen L. Heritability Estimation of Reliable Connectomic Features. Connect Neuroimaging (2018) 2018; 11083:58-66. [PMID: 30906933 DOI: 10.1007/978-3-030-00755-3_7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.
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Affiliation(s)
- Linhui Xie
- Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shiaofen Fang
- Department of Computer Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Science, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
| | - Li Shen
- Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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39
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Abstract
One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework, to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common "hybrid" connectivity patterns that represent the connectivity fingerprint of a subject. We test this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.
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Affiliation(s)
- Enrico Amico
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA.,Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA.,Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA.,Weldon School of Biomedical Engineering, Purdue University, West-Lafayette, IN, USA
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40
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Svaldi DO, Goñi J, Amico E, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. P2‐435: SEPARATION OF FUNCTIONAL CONNECTOMES ACROSS THE AD SPECTRUM BASED ON DISEASE SENSITIVE PRINCIPAL COMPONENTS. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.1127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | | | | | | | - John D. West
- Indiana Alzheimer Disease CenterIndianapolisINUSA
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41
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Svaldi DO, Goñi J, Amico E, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. IC‐P‐044: SEPARATION OF FUNCTIONAL CONNECTOMES ACROSS THE AD SPECTRUM BASED ON DISEASE‐SENSITIVE PRINCIPAL COMPONENTS. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.2108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
| | | | | | | | | | - John D. West
- Indiana Alzheimer Disease CenterIndianapolisINUSA
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42
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Rosenthal G, Váša F, Griffa A, Hagmann P, Amico E, Goñi J, Avidan G, Sporns O. Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes. Nat Commun 2018; 9:2178. [PMID: 29872218 PMCID: PMC5988787 DOI: 10.1038/s41467-018-04614-w] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [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: 07/22/2017] [Accepted: 04/18/2018] [Indexed: 01/01/2023] Open
Abstract
Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function.
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Affiliation(s)
- Gideon Rosenthal
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel
- The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel
| | - František Váša
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Alessandra Griffa
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), 1011, Lausanne, Switzerland
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), 1011, Lausanne, Switzerland
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West-Lafayette, 47907, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, 47907, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West-Lafayette, 47907, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, 47907, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West-Lafayette, 47907, IN, USA
| | - Galia Avidan
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel
- The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel
- Department of Psychology, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
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43
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Yan J, Liu K, Li H, Amico E, Risacher SL, Wu YC, Fang S, Sporns O, Saykin AJ, Goñi J, Shen L. JOINT EXPLORATION AND MINING OF MEMORY-RELEVANT BRAIN ANATOMIC AND CONNECTOMIC PATTERNS VIA A THREE-WAY ASSOCIATION MODEL. Proc IEEE Int Symp Biomed Imaging 2018; 2018:6-9. [PMID: 30271529 DOI: 10.1109/isbi.2018.8363511] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Early change in memory performance is a key symptom of many brain diseases, but its underlying mechanism remains largely unknown. While structural MRI has been playing an essential role in revealing potentially relevant brain regions, increasing availability of diffusion MRI data (e.g., Human Connectome Project (HCP)) provides excellent opportunities for exploration of their complex coordination. Given the complementary information held in these two imaging modalities, we hypothesize that studying them as a whole, rather than individually, and exploring their association will provide us valuable insights of the memory mechanism. However, many existing association methods, such as sparse canonical correlation analysis (SCCA), only manage to handle two-way association and thus cannot guarantee the selection of biomarkers and associations to be memory relevant. To overcome this limitation, we propose a new outcome-relevant SCCA model (OSCCA) together with a new algorithm to enable the three-way associations among brain connectivity, anatomic structure and episodic memory performance. In comparison with traditional SCCA, we demonstrate the effectiveness of our model with both synthetic and real data from the HCP cohort.
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Affiliation(s)
- Jingwen Yan
- BioHealth Informatics, Indiana University Indianapolis, IN, USA
| | - Kefei Liu
- Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA
| | - Huang Li
- Computer and Information Science, Purdue University Indianapolis, IN, USA
| | - Enrico Amico
- Industrial Engineering, Purdue University West-Lafayette, IN, USA
| | - Shannon L Risacher
- Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA
| | - Yu-Chien Wu
- Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA
| | - Shiaofen Fang
- Computer and Information Science, Purdue University Indianapolis, IN, USA
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana University Bloomington, IN, USA
| | - Andrew J Saykin
- Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA
| | - Joaquín Goñi
- Industrial Engineering, Purdue University West-Lafayette, IN, USA
| | - Li Shen
- Biostatistics, Epidemiology and Informatics, University of Pennsylvania, PA, USA
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44
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Annen J, Heine L, Ziegler E, Frasso G, Bahri M, Di Perri C, Stender J, Martial C, Wannez S, D'ostilio K, Amico E, Antonopoulos G, Bernard C, Tshibanda F, Hustinx R, Laureys S. Function-structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET. Hum Brain Mapp 2018; 37:3707-3720. [PMID: 27273334 DOI: 10.1002/hbm.23269] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 04/12/2016] [Accepted: 05/16/2016] [Indexed: 02/05/2023] Open
Abstract
A vast body of literature exists showing functional and structural dysfunction within the brains of patients with disorders of consciousness. However, the function (fluorodeoxyglucose FDG-PET metabolism)-structure (MRI-diffusion-weighted images; DWI) relationship and how it is affected in severely brain injured patients remains ill-defined. FDG-PET and MRI-DWI in 25 severely brain injured patients (19 Disorders of Consciousness of which 7 unresponsive wakefulness syndrome, 12 minimally conscious; 6 emergence from minimally conscious state) and 25 healthy control subjects were acquired here. Default mode network (DMN) function-structure connectivity was assessed by fractional anisotropy (FA) and metabolic standardized uptake value (SUV). As expected, a profound decline in regional metabolism and white matter integrity was found in patients as compared with healthy subjects. Furthermore, a function-structure relationship was present in brain-damaged patients between functional metabolism of inferior-parietal, precuneus, and frontal regions and structural integrity of the frontal-inferiorparietal, precuneus-inferiorparietal, thalamo-inferioparietal, and thalamofrontal tracts. When focusing on patients, a stronger relationship between structural integrity of thalamo-inferiorparietal tracts and thalamic metabolism in patients who have emerged from the minimally conscious state as compared with patients with disorders of consciousness was found. The latter finding was in line with the mesocircuit hypothesis for the emergence of consciousness. The findings showed a positive function-structure relationship within most regions of the DMN. Hum Brain Mapp 37:3707-3720, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- J Annen
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - L Heine
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - E Ziegler
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - G Frasso
- Faculty of Social Sciences, Quantitative Methods for Social Sciences, University of Liège, Liège, Belgium
| | - M Bahri
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - C Di Perri
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - J Stender
- University of Copenhagen, Copenhagen, Denmark
| | - C Martial
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - S Wannez
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.,University Hospital of Liège, Liège, Belgium
| | - K D'ostilio
- Headache Research Unit, University of Liège, Liège, Belgium
| | - E Amico
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - G Antonopoulos
- Cyclotron Research Centre, University of Liège, Liège, Belgium.,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - C Bernard
- University Hospital of Liège, Liège, Belgium
| | - F Tshibanda
- University Hospital of Liège, Liège, Belgium
| | - R Hustinx
- University Hospital of Liège, Liège, Belgium
| | - S Laureys
- Cyclotron Research Centre, University of Liège, Liège, Belgium. .,Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium. .,University Hospital of Liège, Liège, Belgium.
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Di Pardo A, Basit A, Armirotti A, Amico E, Castaldo S, Pepe G, Marracino F, Buttari F, Digilio AF, Maglione V. De novo Synthesis of Sphingolipids Is Defective in Experimental Models of Huntington's Disease. Front Neurosci 2017; 11:698. [PMID: 29311779 PMCID: PMC5742211 DOI: 10.3389/fnins.2017.00698] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [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: 09/01/2017] [Accepted: 11/27/2017] [Indexed: 11/23/2022] Open
Abstract
Alterations of lipid metabolism have been frequently associated with Huntington's disease (HD) over the past years. HD is the most common neurodegenerative disorder, with a complex pathogenic profile, typically characterized by progressive striatal and cortical degeneration and associated motor, cognitive and behavioral disturbances. Previous findings from our group support the idea that disturbed sphingolipid metabolism could represent an additional hallmark of the disease. Although such a defect represents a common biological denominator among multiple disease models ranging from cells to humans through mouse models, more efforts are needed to clearly define its clinical significance and the role it may play in the progression of the disease. In this study, we provided the first evidence of a defective de novo biosynthetic pathway of sphingolipids in multiple HD pre-clinical models. qPCR analysis revealed perturbed gene expression of sphingolipid-metabolizing enzymes in both early and late stage of the disease. In particular, reduction in the levels of sptlc1 and cerS1 mRNA in the brain tissues from manifest HD mice resulted in a significant decrease in the content of dihydroSphingosine, dihydroSphingosine-1-phospahte and dihydroCeramide [C18:0] as assessed by mass spectrometry. Moreover, in vitro studies highlighted the relevant role that aberrant sphingolipid metabolism may have in the HD cellular homeostasis. With this study, we consolidate the evidence of disturbed sphingolipid metabolism in HD and demonstrate for the first time that the de novo biosynthesis pathway is also significantly affected in the disease. This finding further supports the hypothesis that perturbed sphingolipid metabolism may represent a crucial factor accounting for the high susceptibility to disease in HD.
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Affiliation(s)
| | - Abdul Basit
- Department of Drug Discovery and Development, Fondazione Istituto Italiano di Tecnologia, Genova, Italy
| | - Andrea Armirotti
- Department of Drug Discovery and Development, Fondazione Istituto Italiano di Tecnologia, Genova, Italy
| | | | | | | | | | | | - Anna F Digilio
- Institute of Biosciences and Bioresources, National Research Council, Naples, Italy
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Di Pardo A, Castaldo S, Capocci L, Amico E, Vittorio M. Assessment of Blood-brain Barrier Permeability by Intravenous Infusion of FITC-labeled Albumin in a Mouse Model of Neurodegenerative Disease. J Vis Exp 2017. [PMID: 29155766 DOI: 10.3791/56389] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Disruption of blood-brain barrier (BBB) integrity is a common feature for different neurological and neurodegenerative diseases. Although the interplay between perturbed BBB homeostasis and the pathogenesis of brain disorders needs further investigation, the development and validation of a reliable procedure to accurately detect BBB alterations may be crucial and represent a useful tool for potentially predicting disease progression and developing targeted therapeutic strategies. Here, we present an easy and efficient procedure for evaluating BBB leakage in a neurodegenerative condition like that occurring in a preclinical mouse model of Huntington disease, in which defects in the permeability of BBB are clearly detectable precociously in the disease. Specifically, the high molecular weight fluorescein isothiocyanate labelled (FITC)-albumin, which is able to cross the BBB only when the latter is impaired, is acutely infused into a mouse jugular vein and its distribution in the vascular or parenchymal districts is then determined by fluorescence microscopy. Accumulation of green fluorescent-albumin in the brain parenchyma functions as an index of aberrant BBB permeability and, when quantitated by using Image J processing software, is reported as Green Fluorescence Intensity.
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Affiliation(s)
- Alba Di Pardo
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed;
| | | | - Luca Capocci
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed
| | - Enrico Amico
- Centre for Neurogenetics and Rare Diseases, IRCCS Neuromed
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Grippi C, Izzi B, Gianfagna F, Noro F, Falcinelli E, Di Pardo A, Amico E, Donati M, de Gaetano G, Iacoviello L, Hoylaerts M, Cerletti C. Neuromedin U potentiates ADP- and epinephrine-induced human platelet activation. Thromb Res 2017; 159:100-108. [DOI: 10.1016/j.thromres.2017.09.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 09/12/2017] [Accepted: 09/27/2017] [Indexed: 10/18/2022]
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Di Perri C, Amico E, Heine L, Annen J, Martial C, Larroque SK, Soddu A, Marinazzo D, Laureys S. Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns. Hum Brain Mapp 2017; 39:89-103. [PMID: 29024197 DOI: 10.1002/hbm.23826] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 08/11/2017] [Accepted: 09/18/2017] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness. METHODS Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames. RESULTS Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of "pathological" within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) "pathological" increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames CONCLUSION: CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Carol Di Perri
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium.,Centre for Clinical Brain Sciences, Centre for Dementia Prevention, UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Enrico Amico
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium.,Department of Data-analysis, University of Ghent, Ghent, B9000, Belgium.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana
| | - Lizette Heine
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
| | | | - Andrea Soddu
- Brain and Mind Institute, Physics & Astronomy Department, Western University, London, Ontario, Canada
| | - Daniele Marinazzo
- Department of Data-analysis, University of Ghent, Ghent, B9000, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
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Newton AJH, Seidenstein AH, McDougal RA, Pérez-Cervera A, Huguet G, M-Seara T, Haimerl C, Angulo-Garcia D, Torcini A, Cossart R, Malvache A, Skiker K, Maouene M, Ragognetti G, Lorusso L, Viggiano A, Marcelli A, Senatore R, Parziale A, Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés J, Laureys S, Marinazzo D, Stramaglia S, Bassez I, Faes L, Almgren H, Razi A, Van de Steen F, Krebs R, Aerts H, Kanari L, Dlotko P, Scolamiero M, Levi R, Shillcock J, de Kock CP, Hess K, Markram H, Ly C, Marsat G, Gillespie T, Sandström M, Abrams M, Grethe JS, Martone M, De Gernier R, Solinas S, Rössert C, Haelterman M, Massar S, Pasquale V, Pastore VP, Martinoia S, Massobrio P, Capone C, Tort-Colet N, Sanchez-Vives MV, Mattia M, Almasi A, Cloherty SL, Grayden DB, Wong YT, Ibbotson MR, Meffin H, Prince LY, Tsaneva-Atanasova K, Mellor JR, Mazzoni A, Rosa M, Carpaneto J, Romito LM, Priori A, Micera S, Migliore R, Lupascu CA, Franchina F, Bologna LL, Romani A, Saray S, Van Geit W, Káli S, Thomson A, Mercer A, Lange S, Falck J, Muller E, Schürmann F, Todorov D, Capps R, Barnett W, Molkov Y, Devalle F, Pazó D, Montbrió E, Mochol G, Azab H, Hayden BY, Moreno-Bote R, Balasubramani PP, Chakravarthy SV, Muddapu VR, Gheorghiu MD, Mimica B, Withlock J, Mureșan RC, Zick JL, Schultz K, Blackman RK, Chafee MV, Netoff TI, Roberts N, Nagaraj V, Lamperski A, Netoff TI, Grado LL, Johnson MD, Darrow DP, Lonardoni D, Amin H, Di Marco S, Maccione A, Berdondini L, Nieus T, Stimberg M, Goodman DFM, Nowotny T, Koren V, Dragoi V, Obermayer K, Castro S, Fernandez M, El-Deredy W, Xu K, Maidana JP, Orio P, Chen W, Hepburn I, Casalegno F, Devresse A, Ovcharenko A, Pereira F, Delalondre F, De Schutter E, Bratby P, Gallimore AR, Klingbeil G, Zamora C, Zang Y, Crotty P, Palmerduca E, Antonietti A, Casellato C, Erö C, D’Angelo E, Gewaltig MO, Pedrocchi A, Bytschok I, Dold D, Schemmel J, Meier K, Petrovici MA, Shen HA, Surace SC, Pfister JP, Lefebvre B, Marre O, Yger P, Papoutsi A, Park J, Ash R, Smirnakis S, Poirazi P, Felix RA, Dimitrov AG, Portfors C, Daun S, Toth TI, Jędrzejewska-Szmek J, Kabbani N, Blackwel KT, Moezzi B, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Iannella N, Ridding MC, Triesch J, Maex R, Safaryan K, Steuber V, Tang R, Tang YY, Verveyko DV, Brazhe AR, Verisokin AY, Postnov DE, Günay C, Panuccio G, Giugliano M, Prinz AA, Varona P, Rabinovich MI, Denham J, Ranner T, Cohen N, Reva M, Rebola N, Kirizs T, Nusser Z, DiGregorio D, Mavritsaki E, Rentzelas P, Ukani NH, Tomkins A, Yeh CH, Bruning W, Fenichel AL, Zhou Y, Huang YC, Florescu D, Ortiz CL, Richmond P, Lo CC, Coca D, Chiang AS, Lazar AA, Moezzi B, Creaser JL, Lin C, Ashwin P, Brown JT, Ridler T, Levenstein D, Watson BO, Buzsáki G, Rinzel J, Curtu R, Nguyen A, Assadzadeh S, Robinson PA, Sanz-Leon P, Forlim CG, de Almeida LOB, Pinto RD, Rodríguez FB, Lareo Á, Forlim CG, Rodríguez FB, Montero A, Mosqueiro T, Huerta R, Rodriguez FB, Changoluisa V, Rodriguez FB, Cordeiro VL, Ceballos CC, Kamiji NL, Roque AC, Lytton WW, Knox A, Rosenthal JJC, Daun S, Popovych S, Liu L, Wang BA, Tóth TI, Grefkes C, Fink GR, Rosjat N, Perez-Trujillo A, Espinal A, Sotelo-Figueroa MA, Cruz-Aceves I, Rostro-Gonzalez H, Zapotocky M, Hoskovcová M, Kopecká J, Ulmanová O, Růžička E, Gärtner M, Duvarci S, Roeper J, Schneider G, Albert S, Schmack K, Remme M, Schreiber S, Migliore M, Lupascu CA, Bologna LL, Antonel SM, Courcol JD, Schürmann F, Çelikok SU, Navarro-López EM, Şengör NS, Elibol R, Sengor NS, Özdemir MY, Li T, Arleo A, Sheynikhovich D, Nakamura A, Shimono M, Song Y, Park S, Choi I, Jeong J, Shin HS, Sadeh S, Gleeson P, Angus Silver R, Chatzikalymniou AP, Skinner FK, Sanchez-Rodriguez LM, Sotero RC, Hertäg L, Mackwood O, Sprekeler H, Puhlmann S, Weber SN, Higgins D, Naumann LB, Weber SN, Iyer R, Mihalas S, Ticcinelli V, Stankovski T, McClintock PVE, Stefanovska A, Janjić P, Solev D, Seifert G, Kocarev L, Steinhäuser C, Salmasi M, Glasauer S, Stemmler M, Zhang D, Zhang C, Stepanyants A, Goncharenko J, Kros L, Davey N, de Zeeuw C, Hoebeek F, Sinha A, Adams R, Schmuker M, Psarrou M, Schilstra M, Torben-Nielsen B, Metzner C, Schweikard A, Mäki-Marttunen T, Zurowski B, Marinazzo D, Faes L, Stramaglia S, Jordan HOC, Stringer SM, Gajewska-Dendek E, Suffczyński P, Tam N, Zouridakis G, Pollonini L, Tang YY, Asl MM, Valizadeh A, Tass PA, Nold A, Fan W, Konrad S, Endle H, Vogt J, Tchumatchenko T, Herpich J, Tetzlaff C, Luboeinski J, Nachstedt T, Ciba M, Bahmer A, Thielemann C, Kuebler ES, Tauskela JS, Thivierge JP, Bakker R, García-Amado M, Evangelio M, Clascá F, Tiesinga P, Buckley CL, Toyoizumi T, Dubreuil AM, Monasson R, Treves A, Spalla D, Rosay S, Kleberg FI, Wong W, de Oliveira Floriano B, Matsuo T, Uchida T, Dibenedetto D, Uludağ K, Goodarzinick A, Schmidt M, Hilgetag CC, Diesmann M, van Albada SJ, Fauth M, van Rossum M, Reyes-Sánchez M, Amaducci R, Muñiz C, Varona P, Elices I, Arroyo D, Levi R, Cohen B, Chow C, Vattikuti S, Bertolotti E, Burioni R, di Volo M, Vezzani A, Menzat B, Vogels TP, Wagatsuma N, Saha S, Kapoor R, Kerr R, Wagner J, del Molino LCG, Yang GR, Mejias JF, Wang XJ, Song H, Goodliffe J, Luebke J, Weaver CM, Thomas J, Sinha N, Shaju N, Maszczyk T, Jin J, Cash SS, Dauwels J, Brandon Westover M, Karimian M, Moerel M, De Weerd P, Burwick T, Westra RL, Abeysuriya R, Hadida J, Sotiropoulos S, Jbabdi S, Woolrich M, Bensmail C, Wrobel B, Zhou X, Ji Z, Liu X, Xia Y, Wu S, Wang X, Zhang M, Wu S, Ofer N, Shefi O, Yaari G, Carnevale T, Majumdar A, Sivagnanam S, Yoshimoto K, Smirnova EY, Amakhin DV, Malkin SL, Zaitsev AV, Chizhov AV, Zaleshina M, Zaleshin A, Barranca VJ, Zhu G, Skilling QM, Maruyama D, Ognjanovski N, Aton SJ, Zochowski M, Wu J, Aton S, Rich S, Booth V, Budak M, Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Felton MA, Yu AB, Boothe DL, Oie KS, Franaszczuk PJ, Shuvaev SA, Başerdem B, Zador A, Koulakov AA, López-Madrona VJ, Pereda E, Mirasso CR, Canals S, Masoli S, Rongala UB, Mazzoni A, Spanne A, Jorntell H, Oddo CM, Vartanov AV, Neklyudova AK, Kozlovskiy SA, Kiselnikov AA, Marakshina JA, Teleńczuk M, Teleńczuk B, Destexhe A, Kuokkanen PT, Kraemer A, McColgan T, Carr CE, Kempter R. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3. BMC Neurosci 2017. [PMCID: PMC5592441 DOI: 10.1186/s12868-017-0372-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés JM, Laureys S, Marinazzo D. Ising model with conserved magnetization on the human connectome: Implications on the relation structure-function in wakefulness and anesthesia. Chaos 2017; 27:047407. [PMID: 28456159 DOI: 10.1063/1.4978999] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Dynamical models implemented on the large scale architecture of the human brain may shed light on how a function arises from the underlying structure. This is the case notably for simple abstract models, such as the Ising model. We compare the spin correlations of the Ising model and the empirical functional brain correlations, both at the single link level and at the modular level, and show that their match increases at the modular level in anesthesia, in line with recent results and theories. Moreover, we show that at the peak of the specific heat (the critical state), the spin correlations are minimally shaped by the underlying structural network, explaining how the best match between the structure and function is obtained at the onset of criticality, as previously observed. These findings confirm that brain dynamics under anesthesia shows a departure from criticality and could open the way to novel perspectives when the conserved magnetization is interpreted in terms of a homeostatic principle imposed to neural activity.
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Affiliation(s)
- S Stramaglia
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - M Pellicoro
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - L Angelini
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - E Amico
- Coma Science Group, University of Liège, Liège, Belgium
| | - H Aerts
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
| | - J M Cortés
- Ikerbasque, The Basque Foundation for Science, E-48011 Bilbao, Spain
| | - S Laureys
- Coma Science Group, University of Liège, Liège, Belgium
| | - D Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
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