1
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Tashakori-Sabzevar F, Munn RG, Bilkey DK, Ward RD. Basal forebrain and prelimbic cortex connectivity is related to behavioral response in an attention task. iScience 2024; 27:109266. [PMID: 38439980 PMCID: PMC10910283 DOI: 10.1016/j.isci.2024.109266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/01/2024] [Accepted: 02/14/2024] [Indexed: 03/06/2024] Open
Abstract
The basal forebrain (BF) is critical for the motivational recruitment of attention in response to reward-related cues. This finding is consistent with a role for the BF in encoding and transmitting motivational salience and readying prefrontal circuits for further attentional processing. We recorded local field potentials to determine connectivity between prelimbic cortex (PrL) and BF during the modulation of attention by reward-related cues. We find that theta and gamma power are robustly associated with behavior. Power in both bands is significantly lower during trials in which an incorrect behavioral response is made. We find strong coherence during responses that are significantly stronger when a correct response is made. We show that information flow is largely monodirectional from BF to and is strongest when correct responses are made. These experiments demonstrate that connectivity between BF and the PrL increases during periods of increased motivational recruitment of attentional resources.
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Affiliation(s)
| | - Robert G.K. Munn
- Department of Anatomy, University of Otago, Dunedin 9054, New Zealand
| | - David K. Bilkey
- Department of Psychology, University of Otago, Dunedin 9054, New Zealand
| | - Ryan D. Ward
- Department of Psychology, University of Otago, Dunedin 9054, New Zealand
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2
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Gallos IK, Lehmberg D, Dietrich F, Siettos C. Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator. CHAOS (WOODBURY, N.Y.) 2024; 34:013151. [PMID: 38285718 DOI: 10.1063/5.0157881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/22/2023] [Indexed: 01/31/2024]
Abstract
We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI time series. Our approach is a three stage one. First, we exploit manifold learning and, in particular, diffusion maps (DMs) to discover a set of variables that parametrize the latent space on which the emergent high-dimensional fMRI time series evolve. Then, we construct ROMs on the embedded manifold via two techniques: Feedforward Neural Networks (FNNs) and the Koopman operator. Finally, for predicting the out-of-sample long-term dynamics of brain activity in the ambient fMRI space, we solve the pre-image problem, i.e., the construction of a map from the low-dimensional manifold to the original high-dimensional (ambient) space by coupling DMs with Geometric Harmonics (GH) when using FNNs and the Koopman modes per se. For our illustrations, we have assessed the performance of the two proposed schemes using two benchmark fMRI time series: (i) a simplistic five-dimensional model of stochastic discrete-time equations used just for a "transparent" illustration of the approach, thus knowing a priori what one expects to get, and (ii) a real fMRI dataset with recordings during a visuomotor task. We show that the proposed Koopman operator approach provides, for any practical purposes, equivalent results to the FNN-GH approach, thus bypassing the need to train a non-linear map and to use GH to extrapolate predictions in the ambient space; one can use instead the low-frequency truncation of the DMs function space of L2-integrable functions to predict the entire list of coordinate functions in the ambient space and to solve the pre-image problem.
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Affiliation(s)
- Ioannis K Gallos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece
| | - Daniel Lehmberg
- School of Computation, Information and Technology, Technical University of Munich, Munich 80333, Germany
| | - Felix Dietrich
- School of Computation, Information and Technology, Technical University of Munich, Munich 80333, Germany
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli," Universitá degli Studi di Napoli Federico II, Naples 80125, Italy
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3
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Pugh ZH, Huang J, Leshin J, Lindquist KA, Nam CS. Culture and gender modulate dlPFC integration in the emotional brain: evidence from dynamic causal modeling. Cogn Neurodyn 2023; 17:153-168. [PMID: 36704624 PMCID: PMC9871122 DOI: 10.1007/s11571-022-09805-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/12/2022] [Accepted: 03/26/2022] [Indexed: 01/29/2023] Open
Abstract
Past research has recognized culture and gender variation in the experience of emotion, yet this has not been examined on a level of effective connectivity. To determine culture and gender differences in effective connectivity during emotional experiences, we applied dynamic causal modeling (DCM) to electroencephalography (EEG) measures of brain activity obtained from Chinese and American participants while they watched emotion-evoking images. Relative to US participants, Chinese participants favored a model bearing a more integrated dorsolateral prefrontal cortex (dlPFC) during fear v. neutral experiences. Meanwhile, relative to males, females favored a model bearing a less integrated dlPFC during fear v. neutral experiences. A culture-gender interaction for winning models was also observed; only US participants showed an effect of gender, with US females favoring a model bearing a less integrated dlPFC compared to the other groups. These findings suggest that emotion and its neural correlates depend in part on the cultural background and gender of an individual. To our knowledge, this is also the first study to apply both DCM and EEG measures in examining culture-gender interaction and emotion.
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Affiliation(s)
- Zachary H. Pugh
- Department of Psychology, North Carolina State University, Raleigh, NC USA
| | - Jiali Huang
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC USA
| | - Joseph Leshin
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapell Hill, NC USA
| | - Kristen A. Lindquist
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapell Hill, NC USA
| | - Chang S. Nam
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC USA
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4
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Gao X, Huang W, Liu Y, Zhang Y, Zhang J, Li C, Chelangat Bore J, Wang Z, Si Y, Tian Y, Li P. A novel robust Student’s t-based Granger causality for EEG based brain network analysis. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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5
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Pugh ZH, Choo S, Leshin JC, Lindquist KA, Nam CS. Emotion Depends on Context, Culture, and Their Interaction: Evidence from Effective Connectivity. Soc Cogn Affect Neurosci 2021; 17:206-217. [PMID: 34282842 PMCID: PMC8847905 DOI: 10.1093/scan/nsab092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 06/21/2021] [Accepted: 07/19/2021] [Indexed: 11/12/2022] Open
Abstract
Situated models of emotion hypothesize that emotions are optimized for the context at hand, but most neuroimaging approaches ignore context. For the first time, we applied Granger causality (GC) analysis to determine how an emotion is affected by a person's cultural background and situation. Electroencephalographic (EEG) recordings were taken from mainland Chinese and US participants as they viewed and rated fearful and neutral images displaying either social or non-social contexts. Independent components analysis (ICA) and GC analysis was applied to determine the epoch of peak effect for each condition and to identify sources and sinks among brain regions of interest. We found that source-sink couplings differed across culture, situation, and culture x situation. Mainland Chinese participants alone showed preference for an early-onset source-sink pairing with the supramarginal gyrus as a causal source, suggesting that, relative to US participants, Chinese participants more strongly prioritized a scene's social aspects in their response to fearful scenes. Our findings suggest that the neural representation of fear indeed varies according to both culture, situation, and their interaction in ways that are consistent with norms instilled by cultural background.
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Affiliation(s)
- Zachary H Pugh
- Department of Psychology, North Carolina State University, United States
| | - Sanghyun Choo
- Department of Industrial and Systems Engineering, North Carolina State University, United States
| | - Joseph C Leshin
- Department of Psychology, University of North Carolina at Chapel Hill, United States
| | - Kristen A Lindquist
- Department of Psychology, University of North Carolina at Chapel Hill, United States
| | - Chang S Nam
- Department of Industrial and Systems Engineering, North Carolina State University, United States
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6
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Galaris E, Gallos I, Myatchin I, Lagae L, Siettos C. Electroencephalography source localization analysis in epileptic children during a visual working-memory task. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3404. [PMID: 33029905 DOI: 10.1002/cnm.3404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 06/15/2020] [Accepted: 09/28/2020] [Indexed: 06/11/2023]
Abstract
We localize the sources of brain activity of children with epilepsy based on electroencephalograph (EEG) recordings acquired during a visual discrimination working memory task. For the numerical solution of the inverse problem, with the aid of age-specific MRI scans processed from a publicly available database, we use and compare three regularization numerical methods, namely the standardized low resolution brain electromagnetic tomography (sLORETA), the weighted minimum norm estimation (wMNE) and the dynamic statistical parametric mapping (dSPM). We show that all three methods provide the same spatio-temporal patterns of differences between the groups of epileptic and control children. In particular, our analysis reveals statistically significant differences between the two groups in regions of the parietal cortex indicating that these may serve as "biomarkers" for diagnostic purposes and ultimately localized treatment.
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Affiliation(s)
- Evangelos Galaris
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Universita' degli Studi di Napoli Federico II, Napoli, Italy
| | - Ioannis Gallos
- School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - Ivan Myatchin
- Department of Anesthesiology, Sint-Trudo Regional Hospital, Sint-Truiden, Belgium
| | - Lieven Lagae
- Department of Development and Regeneration, Section Paediatric Neurology, KU Leuven, Leuven, Belgium
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Universita' degli Studi di Napoli Federico II, Napoli, Italy
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7
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Almpanis E, Siettos C. Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach. AIMS Neurosci 2020; 7:66-88. [PMID: 32607412 PMCID: PMC7321769 DOI: 10.3934/neuroscience.2020005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/25/2020] [Indexed: 11/29/2022] Open
Abstract
We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. The particular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies.
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Affiliation(s)
- Evangelos Almpanis
- Section of Condensed Matter Physics, National and Kapodistrian University of Athens, Greece.,Institute of Nanoscience and Nanotechnology, NCSR "Demokritos," Athens, Greece
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Università degli Studi di Napoli Federico II, Italy
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8
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Liu S, Poh JH, Koh HL, Ng KK, Loke YM, Lim JKW, Chong JSX, Zhou J. Carrying the past to the future: Distinct brain networks underlie individual differences in human spatial working memory capacity. Neuroimage 2018; 176:1-10. [DOI: 10.1016/j.neuroimage.2018.04.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 03/07/2018] [Accepted: 04/08/2018] [Indexed: 10/17/2022] Open
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9
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Weber I, Florin E, von Papen M, Timmermann L. The influence of filtering and downsampling on the estimation of transfer entropy. PLoS One 2017; 12:e0188210. [PMID: 29149201 PMCID: PMC5693301 DOI: 10.1371/journal.pone.0188210] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Accepted: 11/02/2017] [Indexed: 01/24/2023] Open
Abstract
Transfer entropy (TE) provides a generalized and model-free framework to study Wiener-Granger causality between brain regions. Because of its nonparametric character, TE can infer directed information flow also from nonlinear systems. Despite its increasing number of applications in neuroscience, not much is known regarding the influence of common electrophysiological preprocessing on its estimation. We test the influence of filtering and downsampling on a recently proposed nearest neighborhood based TE estimator. Different filter settings and downsampling factors were tested in a simulation framework using a model with a linear coupling function and two nonlinear models with sigmoid and logistic coupling functions. For nonlinear coupling and progressively lower low-pass filter cut-off frequencies up to 72% false negative direct connections and up to 26% false positive connections were identified. In contrast, for the linear model, a monotonic increase was only observed for missed indirect connections (up to 86%). High-pass filtering (1 Hz, 2 Hz) had no impact on TE estimation. After low-pass filtering interaction delays were significantly underestimated. Downsampling the data by a factor greater than the assumed interaction delay erased most of the transmitted information and thus led to a very high percentage (67–100%) of false negative direct connections. Low-pass filtering increases the number of missed connections depending on the filters cut-off frequency. Downsampling should only be done if the sampling factor is smaller than the smallest assumed interaction delay of the analyzed network.
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Affiliation(s)
- Immo Weber
- Department of Neurology, University Hospital Giessen & Marburg, Marburg, Germany
- * E-mail: (IW); (LT)
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Michael von Papen
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
- Institute of Geophysics & Meteorology, University of Cologne, Cologne, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital Giessen & Marburg, Marburg, Germany
- * E-mail: (IW); (LT)
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10
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Yao Q, Zhu D, Li F, Xiao C, Lin X, Huang Q, Shi J. Altered Functional and Causal Connectivity of Cerebello-Cortical Circuits between Multiple System Atrophy (Parkinsonian Type) and Parkinson's Disease. Front Aging Neurosci 2017; 9:266. [PMID: 28848423 PMCID: PMC5554370 DOI: 10.3389/fnagi.2017.00266] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 07/26/2017] [Indexed: 01/28/2023] Open
Abstract
Lesions of the cerebellum lead to motor and non-motor deficits by influencing cerebral cortex activity via cerebello-cortical circuits. It remains unknown whether the cerebello-cortical “disconnection” underlies motor and non-motor impairments both in the parkinsonian variant of multiple system atrophy (MSA-P) and Parkinson’s disease (PD). In this study, we investigated both the functional and effective connectivity of the cerebello-cortical circuits from resting-state functional magnetic resonance imaging (rs-fMRI) data of three groups (26 MSA-P patients, 31 PD patients, and 30 controls). Correlation analysis was performed between the causal connectivity and clinical scores. PD patients showed a weakened cerebellar dentate nucleus (DN) functional coupling in the posterior cingulate cortex (PCC) and inferior parietal lobe compared with MSA-P or controls. MSA-P patients exhibited significantly enhanced effective connectivity from the DN to PCC compared with PD patients or controls, as well as declined causal connectivity from the left precentral gyrus to right DN compared with the controls, and this value is significantly correlated with the motor symptom scores. Our findings demonstrated a crucial role for the cerebello-cortical networks in both MSA-P and PD patients in addition to striatal-thalamo-cortical (STC) networks and indicated that different patterns of cerebello-cortical loop degeneration are involved in the development of the diseases.
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Affiliation(s)
- Qun Yao
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical UniversityNanjing, China
| | - Donglin Zhu
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical UniversityNanjing, China
| | - Feng Li
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical UniversityNanjing, China
| | - Chaoyong Xiao
- Department of Radiology, Affiliated Brain Hospital of Nanjing Medical UniversityNanjing, China
| | - Xingjian Lin
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical UniversityNanjing, China
| | - Qingling Huang
- Department of Radiology, Affiliated Brain Hospital of Nanjing Medical UniversityNanjing, China
| | - Jingping Shi
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical UniversityNanjing, China
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11
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Siettos C, Starke J. Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2016; 8:438-58. [PMID: 27340949 DOI: 10.1002/wsbm.1348] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Revised: 05/01/2016] [Accepted: 05/14/2016] [Indexed: 11/09/2022]
Abstract
The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Constantinos Siettos
- School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - Jens Starke
- School of Mathematical Sciences, Queen Mary University of London, London, UK
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12
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Faes L, Marinazzo D, Nollo G, Porta A. An Information-Theoretic Framework to Map the Spatiotemporal Dynamics of the Scalp Electroencephalogram. IEEE Trans Biomed Eng 2016; 63:2488-2496. [PMID: 27214886 DOI: 10.1109/tbme.2016.2569823] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We present the first application of the emerging framework of information dynamics to the characterization of the electroencephalography (EEG) activity. The framework provides entropy-based measures of information storage (self entropy, SE) and information transfer (joint transfer entropy (TE) and partial TE), which are applied here to detect complex dynamics of individual EEG sensors and causal interactions between different sensors. The measures are implemented according to a model-free and fully multivariate formulation of the framework, allowing the detection of nonlinear dynamics and direct links. Moreover, to deal with the issue of volume conduction, a compensation for instantaneous effects is introduced in the computation of joint and partial TE. The framework is applied to resting state EEG measured from healthy subjects in the eyes open (EO) and eyes closed (EC) conditions, evidencing condition-dependent patterns indicative of how information is distributed in the EEG sensor space. The SE was uniformly low during EO and significantly higher in the posterior areas during EC. The joint and partial TE were saturated by instantaneous effects, documenting how volume conduction blurs the detection of information flow in the EEG. However, the use of compensated TE measures led us to evidence meaningful patterns like the presence of local sinks of information flow and propagation motifs, and the emergence of prevalent front-to-back EEG propagation during EC. These findings support the feasibility of our information-theoretic approach to assess the spatiotemporal dynamics of the scalp EEG in different conditions.
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13
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James GA, Kearney-Ramos TE, Young JA, Kilts CD, Gess JL, Fausett JS. Functional independence in resting-state connectivity facilitates higher-order cognition. Brain Cogn 2016; 105:78-87. [PMID: 27105037 DOI: 10.1016/j.bandc.2016.03.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 03/23/2016] [Accepted: 03/25/2016] [Indexed: 01/13/2023]
Abstract
Growing evidence suggests that intrinsic functional connectivity (i.e. highly structured patterns of communication between brain regions during wakeful rest) may encode cognitive ability. However, the generalizability of these findings is limited by between-study differences in statistical methodology and cognitive domains evaluated. To address this barrier, we evaluated resting-state neural representations of multiple cognitive domains within a relatively large normative adult sample. Forty-four participants (mean(sd) age=31(10) years; 18 male and 26 female) completed a resting-state functional MRI scan and neuropsychological assessments spanning motor, visuospatial, language, learning, memory, attention, working memory, and executive function performance. Robust linear regression related cognitive performance to resting-state connectivity among 200 a priori determined functional regions of interest (ROIs). Only higher-order cognitions (such as learning and executive function) demonstrated significant relationships between brain function and behavior. Additionally, all significant relationships were negative - characterized by moderately positive correlations among low performers and weak to moderately negative correlations among high performers. These findings suggest that functional independence among brain regions at rest facilitates cognitive performance. Our interpretation is consistent with graph theoretic analyses which represent the brain as independent functional nodes that undergo dynamic reorganization with task demand. Future work will build upon these findings by evaluating domain-specific variance in resting-state neural representations of cognitive impairment among patient populations.
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Affiliation(s)
- G Andrew James
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, United States.
| | | | - Jonathan A Young
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, United States
| | - Clinton D Kilts
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, United States
| | - Jennifer L Gess
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, United States
| | - Jennifer S Fausett
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, United States
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14
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Protopapa F, Siettos CI, Myatchin I, Lagae L. Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task. Cogn Neurodyn 2016; 10:99-111. [PMID: 27066148 PMCID: PMC4805687 DOI: 10.1007/s11571-015-9373-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 11/30/2015] [Accepted: 12/24/2015] [Indexed: 12/14/2022] Open
Abstract
Using spectral Granger causality (GC) we identified distinct spatio-temporal causal connectivity (CC) patterns in groups of control and epileptic children during the execution of a one-back matching visual discrimination working memory task. Differences between control and epileptic groups were determined for both GO and NOGO conditions. The analysis was performed on a set of 19-channel EEG cortical activity signals. We show that for the GO task, the highest brain activity in terms of the density of the CC networks is observed in α band for the control group while for the epileptic group the CC network seems disrupted as reflected by the small number of connections. For the NOGO task, the denser CC network was observed in θ band for the control group while widespread differences between the control and the epileptic group were located bilaterally at the left temporal-midline and parietal areas. In order to test the discriminative power of our analysis, we performed a pattern analysis approach based on fuzzy classification techniques. The performance of the classification scheme was evaluated using permutation tests. The analysis demonstrated that, on average, 87.6 % of the subjects were correctly classified in control and epileptic. Thus, our findings may provide a helpful insight on the mechanisms pertaining to the cognitive response of children with well controlled epilepsy and could potentially serve as "functional" biomarkers for early diagnosis.
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Affiliation(s)
- Foteini Protopapa
- />School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - Constantinos I. Siettos
- />School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - Ivan Myatchin
- />Department of Woman and Child, Section Paediatric Neurology, K.U. Leuven, Louvain, Belgium
| | - Lieven Lagae
- />Department of Woman and Child, Section Paediatric Neurology, K.U. Leuven, Louvain, Belgium
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15
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Ritter P, Jirsa VK, McIntosh AR, Breakspear M. Editorial: State-dependent brain computation. Front Comput Neurosci 2015; 9:77. [PMID: 26157384 PMCID: PMC4477138 DOI: 10.3389/fncom.2015.00077] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Accepted: 06/10/2015] [Indexed: 12/16/2022] Open
Affiliation(s)
- Petra Ritter
- Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Deparment of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany ; Berlin School of Mind and Brain and Mind and Brain Institute, Humboldt University Berlin, Germany
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine Marseille, France
| | - Anthony R McIntosh
- Rotman Research Institute of Baycrest Centre, University of Toronto Toronto, ON, Canada
| | - Michael Breakspear
- Systems Neuroscience Group, QIMR Berghofer Brisbane, QLD, Australia ; The Royal Brisbane and Woman's Hospital Brisbane, QLD, Australia
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