1
|
|
2
|
Jiang L, Ma X, Li S, Luo H, Zhang G, Wang Y, Zhang T. Frequency-Dependent Changes in Interhemispheric Functional Connectivity Measured by Resting-State fMRI in Children With Idiopathic Generalized Epilepsy. Front Neurol 2020; 11:645. [PMID: 32903710 PMCID: PMC7438858 DOI: 10.3389/fneur.2020.00645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Accepted: 05/29/2020] [Indexed: 11/13/2022] Open
Abstract
Epilepsy is associated with abnormal spatiotemporal changes in resting-state brain connectivity, but how these changes are characterized in interhemispheric coupling remains unclear. This study aimed to characterize frequency-dependent alterations in voxel-wise mirrored homotopic connectivity (VMHC) measured by resting-state functional magnetic resonance imaging (rs-fMRI) in children with idiopathic generalized epilepsy (IGE). Rs-fMRI data were collected in 21 children with IGE and 22 demographically matched children with typical development. We used three resting-state frequency bands (full, 0.01–0.08 Hz; slow-4, 0.027–0.073 Hz; slow-5, 0.01–0.027 Hz) to compute VMHC and locate the significant foci. Voxel-wise p <0.001 and cluster-level p <0.05 cluster-level family-wise error correction was applied. In between-group comparisons, we identified that the full and higher frequency (slow-4) bands showed similar reductions in VMHC including Rolandic operculum, putamen, superior frontal, lateral parietal, middle cingulate, and precuneus in children with IGE. In the lower frequency band (slow-5), we identified specific reductions in VMHC in orbitofrontal and middle temporal gyri in children with IGE. Further analyses on main effects and interaction between group and frequency band suggested significant frequency-dependent changes in VMHC, and no significant interaction was found. The results were generally similar with global brain signal regression. Additional association analysis showed that VMHC in the putamen within the full and slow-4 bands was significantly positively correlated with chronological age in children with IGE, and the same analysis was non-significant in the controls; VMHC in the medial prefrontal region in the slow-4 band was significantly positively correlated with IQ performance sub-score. Our findings suggest that IGE children show frequency-dependent changes in interhemispheric integration that spans regions and systems involving cortical-subcortical, language, and visuomotor processing. Decreased functional coupling within the dorsal striatum may reflect atypical development in children with IGE.
Collapse
Affiliation(s)
- Lin Jiang
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xuejin Ma
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Shiguang Li
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Hongjian Luo
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Guoming Zhang
- Department of Radiology, Medical Imaging Center of Guizhou Province, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Yanan Wang
- Department of Radiology, Medical Imaging Center of Guizhou Province, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Tijiang Zhang
- Department of Radiology, Medical Imaging Center of Guizhou Province, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Tijiang Zhang
| |
Collapse
|
3
|
Anzellotti S, Coutanche MN. Beyond Functional Connectivity: Investigating Networks of Multivariate Representations. Trends Cogn Sci 2018; 22:258-269. [DOI: 10.1016/j.tics.2017.12.002] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 12/05/2017] [Accepted: 12/07/2017] [Indexed: 11/27/2022]
|
4
|
Hasson U, Iacovacci J, Davis B, Flanagan R, Tagliazucchi E, Laufs H, Lacasa L. A combinatorial framework to quantify peak/pit asymmetries in complex dynamics. Sci Rep 2018; 8:3557. [PMID: 29476077 PMCID: PMC5824940 DOI: 10.1038/s41598-018-21785-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 02/08/2018] [Indexed: 12/05/2022] Open
Abstract
We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We first showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.
Collapse
Affiliation(s)
- Uri Hasson
- Center for Mind and Brain Sciences, University of Trento, Trento, Italy.
- Center for Practical Wisdom, The University of Chicago, Chicago, USA.
| | - Jacopo Iacovacci
- Department of Surgery and Cancer, Division of Computational and Systems Medicine, Imperial College London, London, SW7 2AZ, United Kingdom
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
| | - Ben Davis
- Center for Mind and Brain Sciences, University of Trento, Trento, Italy
| | - Ryan Flanagan
- School of Mathematical Sciences, Queen Mary University of London, E14NS, London, United Kingdom
| | - Enzo Tagliazucchi
- Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam-Zuidoost, The Netherlands
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Frankfurt, Germany
- Department of Neurology, University Hospital Kiel, Kiel, Germany
| | - Lucas Lacasa
- School of Mathematical Sciences, Queen Mary University of London, E14NS, London, United Kingdom.
| |
Collapse
|
5
|
Tagliazucchi E, Siniatchkin M, Laufs H, Chialvo DR. The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process. Front Neurosci 2016; 10:381. [PMID: 27601975 PMCID: PMC4994538 DOI: 10.3389/fnins.2016.00381] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Accepted: 08/04/2016] [Indexed: 11/13/2022] Open
Abstract
Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this "functional connectome" based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series consist of all-or-none temporally localized events, analogous to the avalanches of neural activity observed in recordings of local field potentials (LFP), an observation of potentially high neurobiological relevance.
Collapse
Affiliation(s)
- Enzo Tagliazucchi
- Institute for Medical Psychology, Christian-Albrechts UniversityKiel, Germany; Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am MainGermany; Department of Sleep and Cognition, Netherlands Institute for NeuroscienceAmsterdam, Netherlands
| | - Michael Siniatchkin
- Institute for Medical Psychology, Christian-Albrechts University Kiel, Germany
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am MainGermany; Department of Neurology, University Hospital Schleswig-Holstein, Christian-Albrechts-University KielKiel, Germany
| | - Dante R Chialvo
- Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET)Buenos Aires, Argentina; Center for Multidisciplinary Complex Systems Studies and Brain Sciences (CEMSC3), Escuela de Ciencia y Tecnología, Universidad Nacional de San MartínBuenos Aires, Argentina
| |
Collapse
|
6
|
Davis B, Tagliazucchi E, Jovicich J, Laufs H, Hasson U. Progression to deep sleep is characterized by changes to BOLD dynamics in sensory cortices. Neuroimage 2016; 130:293-305. [PMID: 26724779 PMCID: PMC4819724 DOI: 10.1016/j.neuroimage.2015.12.034] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 11/26/2015] [Accepted: 12/18/2015] [Indexed: 11/17/2022] Open
Abstract
Sleep has been shown to subtly disrupt the spatial organization of functional connectivity networks in the brain, but in a way that largely preserves the connectivity within sensory cortices. Here we evaluated the hypothesis that sleep does impact sensory cortices, but through alteration of activity dynamics. We therefore examined the impact of sleep on hemodynamics using a method for quantifying non-random, high frequency signatures of the blood-oxygen-level dependent (BOLD) signal (amplitude variance asymmetry; AVA). We found that sleep was associated with the elimination of these dynamics in a manner that is restricted to auditory, motor and visual cortices. This elimination was concurrent with increased variance of activity in these regions. Functional connectivity between regions showing AVA during wakefulness maintained a relatively consistent hierarchical structure during wakefulness and N1 and N2 sleep, despite a gradual reduction of connectivity strength as sleep progressed. Thus, sleep is related to elimination of high frequency non-random activity signatures in sensory cortices that are robust during wakefulness. The elimination of these AVA signatures conjointly with preservation of the structure of functional connectivity patterns may be linked to the need to suppress sensory inputs during sleep while still maintaining the capacity to react quickly to complex multimodal inputs.
Collapse
Affiliation(s)
- Ben Davis
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy
| | - Enzo Tagliazucchi
- Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy
| | - Helmut Laufs
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany; Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Uri Hasson
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy.
| |
Collapse
|
7
|
Minati L. Experimental dynamical characterization of five autonomous chaotic oscillators with tunable series resistance. CHAOS (WOODBURY, N.Y.) 2014; 24:033110. [PMID: 25273190 DOI: 10.1063/1.4890530] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
In this paper, an experimental characterization of the dynamical properties of five autonomous chaotic oscillators, based on bipolar-junction transistors and obtained de-novo through a genetic algorithm in a previous study, is presented. In these circuits, a variable resistor connected in series to the DC voltage source acts as control parameter, for a range of which the largest Lyapunov exponent, correlation dimension, approximate entropy, and amplitude variance asymmetry are calculated, alongside bifurcation diagrams and spectrograms. Numerical simulations are compared to experimental measurements. The oscillators can generate a considerable variety of regular and chaotic sine-like and spike-like signals.
Collapse
Affiliation(s)
- Ludovico Minati
- MR-Lab, Center for Mind/Brain Science, University of Trento, Trento, Italy and Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| |
Collapse
|
8
|
Tagliazucchi E, Carhart-Harris R, Leech R, Nutt D, Chialvo DR. Enhanced repertoire of brain dynamical states during the psychedelic experience. Hum Brain Mapp 2014; 35:5442-56. [PMID: 24989126 DOI: 10.1002/hbm.22562] [Citation(s) in RCA: 198] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2014] [Revised: 05/21/2014] [Accepted: 05/23/2014] [Indexed: 01/01/2023] Open
Abstract
The study of rapid changes in brain dynamics and functional connectivity (FC) is of increasing interest in neuroimaging. Brain states departing from normal waking consciousness are expected to be accompanied by alterations in the aforementioned dynamics. In particular, the psychedelic experience produced by psilocybin (a substance found in "magic mushrooms") is characterized by unconstrained cognition and profound alterations in the perception of time, space and selfhood. Considering the spontaneous and subjective manifestation of these effects, we hypothesize that neural correlates of the psychedelic experience can be found in the dynamics and variability of spontaneous brain activity fluctuations and connectivity, measurable with functional Magnetic Resonance Imaging (fMRI). Fifteen healthy subjects were scanned before, during and after intravenous infusion of psilocybin and an inert placebo. Blood-Oxygen Level Dependent (BOLD) temporal variability was assessed computing the variance and total spectral power, resulting in increased signal variability bilaterally in the hippocampi and anterior cingulate cortex. Changes in BOLD signal spectral behavior (including spectral scaling exponents) affected exclusively higher brain systems such as the default mode, executive control, and dorsal attention networks. A novel framework enabled us to track different connectivity states explored by the brain during rest. This approach revealed a wider repertoire of connectivity states post-psilocybin than during control conditions. Together, the present results provide a comprehensive account of the effects of psilocybin on dynamical behavior in the human brain at a macroscopic level and may have implications for our understanding of the unconstrained, hyper-associative quality of consciousness in the psychedelic state.
Collapse
Affiliation(s)
- Enzo Tagliazucchi
- Neurology Department and Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | | | | | | | | |
Collapse
|
9
|
Mapping the voxel-wise effective connectome in resting state FMRI. PLoS One 2013; 8:e73670. [PMID: 24069220 PMCID: PMC3771991 DOI: 10.1371/journal.pone.0073670] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Accepted: 07/20/2013] [Indexed: 11/19/2022] Open
Abstract
A network approach to brain and dynamics opens new perspectives towards understanding of its function. The functional connectivity from functional MRI recordings in humans is widely explored at large scale, and recently also at the voxel level. The networks of dynamical directed connections are far less investigated, in particular at the voxel level. To reconstruct full brain effective connectivity network and study its topological organization, we present a novel approach to multivariate Granger causality which integrates information theory and the architecture of the dynamical network to efficiently select a limited number of variables. The proposed method aggregates conditional information sets according to community organization, allowing to perform Granger causality analysis avoiding redundancy and overfitting even for high-dimensional and short datasets, such as time series from individual voxels in fMRI. We for the first time depicted the voxel-wise hubs of incoming and outgoing information, called Granger causality density (GCD), as a complement to previous repertoire of functional and anatomical connectomes. Analogies with these networks have been presented in most part of default mode network; while differences suggested differences in the specific measure of centrality. Our findings could open the way to a new description of global organization and information influence of brain function. With this approach is thus feasible to study the architecture of directed networks at the voxel level and individuating hubs by investigation of degree, betweenness and clustering coefficient.
Collapse
|
10
|
Boubela RN, Kalcher K, Huf W, Kronnerwetter C, Filzmoser P, Moser E. Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest. Front Hum Neurosci 2013; 7:168. [PMID: 23641208 PMCID: PMC3640215 DOI: 10.3389/fnhum.2013.00168] [Citation(s) in RCA: 113] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2013] [Accepted: 04/16/2013] [Indexed: 01/24/2023] Open
Abstract
Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal – be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz). While the standard model of convolving neuronal activity with a hemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 min of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA) was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heart-beat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high-frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.
Collapse
Affiliation(s)
- Roland N Boubela
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna Vienna, Austria ; MR Centre of Excellence, Medical University of Vienna Vienna, Austria ; Department of Statistics and Probability Theory, Vienna University of Technology Vienna, Austria
| | | | | | | | | | | |
Collapse
|