Estimation Of Effective Connectivity Research Articles

Consistency of EEG source localization and connectivity estimates

As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure.

EEG Resting-State Brain Topological Reorganization as a Function of Age.

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated by combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signals recorded at rest from 71 healthy subjects (age: 20–63 years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80%.

Dependence of inter-neuronal effective connectivity on synchrony dynamics in neuronal network motifs

Effective connectivity, characterized as directional causal influences among neural units, is functionally significant to be reconstructed. Various dynamic regimes have been considered to underlie reshaping of the effective connections. In this work, the impact of zero-lag synchronization on the reconstruction of effective connectivity in neuronal network motifs is investigated. The synchronization analysis and effective connectivity estimation by using Granger causality (GC) method are performed. It is shown that the synchronization of the neurons at zero lag contributes to the reconstruction of reciprocal effective connections without synaptic connections. In addition, delay-induced zero-lag synchronous transition facilitates dynamic transformation of the causal interactions. With the increase of synaptic coupling strength, the causal interplay undergoes the transition to be statistically significant at a critical value. Furthermore, it can be found that multiple effective motifs are extracted from different synchronization states of the underlying structural motifs. GC measures of effective connectivity are proved to be reliable compared with the Information Flow for causal analysis. The obtained results may be helpful to future research about information processes.

Time-varying effective connectivity of the cortical neuroelectric activity associated with behavioural microsleeps

An episode of complete failure to respond during an attentive task accompanied by behavioural signs of sleep is called a behavioural microsleep. We proposed a combination of high-resolution EEG and an advanced method for time-varying effective connectivity estimation for reconstructing the temporal evolution of the causal relations between cortical regions when microsleeps occur during a continuous visuomotor task. We found connectivity patterns involving left–right frontal, left–right parietal, and left-frontal/right-parietal connections commencing in the interval [−500; −250] ms prior to the onset of microsleeps and disappearing at the end of the microsleeps. Our results from global graph indices derived from effective connectivity analysis have revealed EEG-based biomarkers of all stages of microsleeps (preceding, onset, pre-recovery, recovery). In particular, this raises the possibility of being able to predict microsleeps in real-world tasks and initiate a ‘wake-up’ intervention to avert the microsleeps and, hence, prevent injurious and even multi-fatality accidents.

A deconvolution-based approach to identifying large-scale effective connectivity

Rapid, robust computation of effective connectivity between neural regions is an important next step in characterizing the brain’s organization, particularly in the resting state. However, recent work has called into question the value of causal inference computed directly from BOLD, demonstrating that valid inferences require transformation of the BOLD signal into its underlying neural events as necessary for accurate causal inference. In this work we develop an approach for effective connectivity estimation directly from deconvolution-based features and estimates of inter-regional communication lag. We then test, in both simulation as well as whole-brain fMRI BOLD signal, the viability of this approach. Our results show that deconvolution precision and network size play outsized roles in effective connectivity estimation performance. Idealized simulation conditions allow for statistically significant effective connectivity estimation of networks of up to four hundred regions-of-interest (ROIs). Under simulation of realistic recording conditions and deconvolution performance, however, our result indicates that effective connectivity is viable in networks containing up to approximately sixty ROIs. We then validated the ability for the proposed method to reliably detect effective connectivity in whole-brain fMRI signal parcellated into networks of viable size.

Estimation of effective connectivity via data-driven neural modeling

This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used to track the mechanisms involved in seizure initiation and termination.

A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data.

The Independent Component Analysis (ICA)—linear non-Gaussian acyclic model (LiNGAM), an algorithm that can be used to estimate the causal relationship among non-Gaussian distributed data, has the potential value to detect the effective connectivity of human brain areas. Under the assumptions that (a): the data generating process is linear, (b) there are no unobserved confounders, and (c) data have non-Gaussian distributions, LiNGAM can be used to discover the complete causal structure of data. Previous studies reveal that the algorithm could perform well when the data points being analyzed is relatively long. However, there are too few data points in most neuroimaging recordings, especially functional magnetic resonance imaging (fMRI), to allow the algorithm to converge. Smith's study speculates a method by pooling data points across subjects may be useful to address this issue (Smith et al., 2011). Thus, this study focus on validating Smith's proposal of pooling data points across subjects for the use of LiNGAM, and this method is named as pooling-LiNGAM (pLiNGAM). Using both simulated and real fMRI data, our current study demonstrates the feasibility and efficiency of the pLiNGAM on the effective connectivity estimation.

Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment.

In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.

Activity in dlPFC and its effective connectivity to vmPFC are associated with temporal discounting

There is widespread interest in identifying computational and neurobiological mechanisms that influence the ability to choose long-term benefits over more proximal and readily available rewards in domains such as dietary and economic choice. We present the results of a human fMRI study that examines how neural activity relates to observed individual differences in the discounting of future rewards during an intertemporal monetary choice task. We found that a region of left dorsolateral prefrontal cortex (dlPFC) BA-46 was more active in trials where subjects chose delayed rewards, after controlling for the subjective value of those rewards. We also found that the connectivity from dlPFC BA-46 to a region of ventromedial prefrontal cortex (vmPFC) widely associated with the computation of stimulus values, increased at the time of choice, and especially during trials in which subjects chose delayed rewards. Finally, we found that estimates of effective connectivity between these two regions played a critical role in predicting out-of-sample, between-subject differences in discount rates. Together with previous findings in dietary choice, these results suggest that a common set of computational and neurobiological mechanisms facilitate choices in favor of long-term reward in both settings.

EPA-0242 – Personality dimensions modulate working memory-related changes in functional brain connectivity

Introduction Personality dimensions have been repeatedly associated with individual differences in brain organisation during cognitive processing, especially working memory (WM). WM consistently engages the lateral prefrontal (DLPFC), parietal (PAR) and the anterior cingulate cortex (ACC). Within this network, Neuroticism has been associated with attenuated DLPFC function and frontoparietal anatomical connectivity. Objectives To test that personality dimensions modulate task-dependent changes in effective connectivity within the WM network. Aims We predicted that WM-dependent changes in effective connectivity, will be either constrained or facilitated by personality traits. Methods We obtained fMRI data from 40 healthy adults while performing the 3-back WM task. Estimates of effective connectivity were obtained using Dynamic Causal Modelling based on a six-region model comprising bidirectional, ipsilateral and contralateral connections between the DLPFC, PAR and ACC. Personality dimensions were measured using the Neuroticism-Extroversion-Openness Personality Inventory-Revised. Results The 3-back memory load (WM modulation) significantly increased the forward connection from the right PAR to the right DLPFC. The strength of the WM modulation on right PAR?DLPFC was associated with improved response time (r=−0.33, p=0.04). Higher Neuroticism scores were associated with reduced WM modulation (r=−0.867, p=0.001), while the opposite was found for Conscientiousness (r=0.746, p=0.001). Conclusions We found that neuroticism and conscientiousness, respectively, constrain and facilitate the functional integration between parietal and frontal regions. Our results demonstrate that personality dimensions impact on WM task-dependent plasticity, as measured by changes in effective connectivity, and suggest that this plasticity may be a central link between individual differences in behaviour, cognitive efficiency and neural function.

Dissecting psychiatric spectrum disorders by generative embedding

This proof-of-concept study examines the feasibility of defining subgroups in psychiatric spectrum disorders by generative embedding, using dynamical system models which infer neuronal circuit mechanisms from neuroimaging data. To this end, we re-analysed an fMRI dataset of 41 patients diagnosed with schizophrenia and 42 healthy controls performing a numerical n-back working-memory task. In our generative-embedding approach, we used parameter estimates from a dynamic causal model (DCM) of a visual–parietal–prefrontal network to define a model-based feature space for the subsequent application of supervised and unsupervised learning techniques. First, using a linear support vector machine for classification, we were able to predict individual diagnostic labels significantly more accurately (78%) from DCM-based effective connectivity estimates than from functional connectivity between (62%) or local activity within the same regions (55%). Second, an unsupervised approach based on variational Bayesian Gaussian mixture modelling provided evidence for two clusters which mapped onto patients and controls with nearly the same accuracy (71%) as the supervised approach. Finally, when restricting the analysis only to the patients, Gaussian mixture modelling suggested the existence of three patient subgroups, each of which was characterised by a different architecture of the visual–parietal–prefrontal working-memory network. Critically, even though this analysis did not have access to information about the patients' clinical symptoms, the three neurophysiologically defined subgroups mapped onto three clinically distinct subgroups, distinguished by significant differences in negative symptom severity, as assessed on the Positive and Negative Syndrome Scale (PANSS). In summary, this study provides a concrete example of how psychiatric spectrum diseases may be split into subgroups that are defined in terms of neurophysiological mechanisms specified by a generative model of network dynamics such as DCM. The results corroborate our previous findings in stroke patients that generative embedding, compared to analyses of more conventional measures such as functional connectivity or regional activity, can significantly enhance both the interpretability and performance of computational approaches to clinical classification.

Quantitative measures of cortical functional connectivity: A state-of-the-art brief survey

The modern concepts of quantitative measurement of the strength of functional and effective cortical connectivity in neurocognitive networks (large-scale distributed brain systems of interacting neuronal populations that are believed to underlie cognitive processing) are reviewed. The two main classes of the methods of connectivity assessment (linear and nonlinear) are discussed. In the class of linear methods, in addition to coherence routinely used to measure the strength of functional links, the vector autoregressive modeling of multichannel EEG is discussed in detail. The latter technique allows the estimation of both functional and effective connectivity, i.e., the measures such as directed transfer function (DTF) and direct partial coherence (PDC) extensively used in cognitive neuroscience. The impact of volume conduction on different estimates of connectivity is considered and the imaginary part of complex-valued coherence as a way to reduce the artificial influence of volume conduction is discussed. Independent component analysis (ICA) and transfer entropy as a method of estimating direct influence are reviewed in the class of nonlinear methods.

State-dependent differences between functional and effective connectivity of the human cortical motor system

Neural processing is based on interactions between functionally specialized areas that can be described in terms of functional or effective connectivity. Functional connectivity is often assessed by task-free, resting-state functional magnetic resonance imaging (fMRI), whereas effective connectivity is usually estimated from task-based fMRI time-series. To investigate whether different connectivity approaches assess similar network topologies in the same subjects, we scanned 36 right-handed volunteers with resting-state fMRI followed by active-state fMRI involving a hand movement task. Time-series information was extracted from identical locations defined from individual activation maxima derived from the motor task. Dynamic causal modeling (DCM) was applied to the motor task time-series to estimate endogenous and context-dependent effective connectivity. In addition, functional connectivity was computed for both the rest and the motor task condition by means of inter-regional time-series correlations. At the group-level, we found strong interactions between the motor areas of interest in all three connectivity analyses. However, although the sample size warranted 90% power to detect correlations of medium effect size, resting-state functional connectivity was only weakly correlated with both task-based functional and task-based effective connectivity estimates for corresponding region-pairs. By contrast, task-based functional connectivity showed strong positive correlations with DCM effective connectivity parameters. In conclusion, resting-state and task-based connectivity reflect different components of functional integration that particularly depend on the functional state in which the subject is being scanned. Therefore, resting-state fMRI and DCM should be used as complementary measures when assessing functional brain networks.

The applicability of effective connectivity measures to time series of neuronal oscillators

The use of time series analysis techniques such as Granger causality (GC), partial directed coherence (PDC) and phase dynamics modeling (PDM) to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining the effective connectivity structure of brain networks [1,2], the results they produce can be incorrect when the recording channels have a high degree of coherence [3] or a large bias in their degree of noise [4]. Nevertheless, it is standard practice to apply these techniques to multichannel datasets with little or no regard for whether the channels fall into one of these problematic categories. In this work, we address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method based on decision tree classifiers to assess the ability of GC, PDC, generalized PDC, and PDM to accurately determine effective connectivity in a neuronal network. We begin by generating an ensemble of time series datasets by systematically varying the parameters of a simple network of two coupled Morris-Lecar (ML) neurons. The varied parameters include the coupling type (linear diffusive, nonlinear fast threshold, nonlinear synaptic), coupling strength, intrinsic firing rates and noise levels. Next, we characterize each dataset of the ensemble using the following time series features: i) phase coherence, ii) difference in the coefficient of variation of interspike intervals, iii) mean coefficient of variation of interspike intervals, and iv) difference in firing frequency. We then apply four effective connectivity measures to the datasets in the ensemble to obtain estimates for the direction of interactions in the network. These results are used to label the datasets as “correct” (+1) or “incorrect” (0) depending on whether the measure correctly or incorrectly predicts the known connectivity structure of the underlying ML network. Finally, we use the calculated time series features and the labeled ML network datasets to develop a decision tree classifier. We apply the decision tree classifiers to larger networks of three neuronal oscillators by considering time series features for all pairwise interactions in the network and determining the conditions under which the effective connectivity measures can successfully detect directionality of coupling in these systems. We find that if a majority of pairwise interactions in the network are classified as 0 for a given measure, then that measure should not be applied since it will most likely (probability >50%) result in failure. Extending our observations to multichannel datasets with greater than three nodes, we conclude that if all pairwise interactions in a network are classified as 0 by our decision tree classifier, then a given measure should not be applied to the data since effective connectivity estimates are likely to be incorrect.

Identifying brain effective connectivity patterns from EEG: performance of Granger Causality, DTF, PDC and PSI on simulated data

Background High temporal resolution and relatively low cost make electroencephalography (EEG) the most suitable noninvasive tool for studying brain dynamics. One of the major challenges is the determination of effective connectivity, i.e., directed (causal) information flow between brain areas. The common definition of effective connectivity is based on Granger’s argument, that “the cause must precede the effect” [1], which is implemented by the original Granger Causality (GC) score [1], the Directed Transfer Function (DTF) [3], Partial Directed Coherence (PDC) [2] and the Phase-slope Index (PSI) [4], all of which have been applied to EEG data previously. However, due to volume conduction in the head, the original causallyrelated sources are mixed into EEG channels. We conducted a realistic simulation study to investigate how volume conduction affects EEG sensor-space effective connectivity estimation.

Effective connectivity analysis of fMRI and MEG data collected under identical paradigms

Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.

Generalised filtering and stochastic DCM for fMRI

This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states. We compare and contrast deterministic and stochastic DCMs, which do and do not ignore random fluctuations or noise on hidden states. We then compare stochastic DCMs, which do and do not ignore conditional dependence between hidden states and model parameters (generalised filtering and dynamic expectation maximisation, respectively). We first characterise state-noise by comparing the log evidence of models with different a priori assumptions about its amplitude, form and smoothness. Face validity of the inversion scheme is then established using data simulated with and without state-noise to ensure that DCM can identify the parameters and model that generated the data. Finally, we address construct validity using real data from an fMRI study of internet addiction. Our analyses suggest the following. (i) The inversion of stochastic causal models is feasible, given typical fMRI data. (ii) State-noise has nontrivial amplitude and smoothness. (iii) Stochastic DCM has face validity, in the sense that Bayesian model comparison can distinguish between data that have been generated with high and low levels of physiological noise and model inversion provides veridical estimates of effective connectivity. (iv) Relaxing conditional independence assumptions can have greater construct validity, in terms of revealing group differences not disclosed by variational schemes. Finally, we note that the ability to model endogenous or random fluctuations on hidden neuronal (and physiological) states provides a new and possibly more plausible perspective on how regionally specific signals in fMRI are generated.

Dynamic causal modeling with genetic algorithms

In the last years, dynamic causal modeling has gained increased popularity in the neuroimaging community as an approach for the estimation of effective connectivity from functional magnetic resonance imaging (fMRI) data. The algorithm calls for an a priori defined model, whose parameter estimates are subsequently computed upon the given data. As the number of possible models increases exponentially with additional areas, it rapidly becomes inefficient to compute parameter estimates for all models in order to reveal the family of models with the highest posterior probability. In the present study, we developed a genetic algorithm for dynamic causal models and investigated whether this evolutionary approach can accelerate the model search. In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function. Using crossover and mutation, populations of models are created and compared with each other. The most probable ones survive the current generation and serve as a source for the next generation of models. Tests with artificially created data sets show that the genetic algorithm approximates the most plausible models faster than a random-driven brute-force search. The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques.

Estimation of effective connectivity between cortical areas using near-infrared spectroscopy (NIRS)

Event Abstract Back to Event Estimation of effective connectivity between cortical areas using near-infrared spectroscopy (NIRS) Young-Jin Jung1, Lee Seungduk2, Koh Dalkwon2, Kim Do-Won1, Kim Beop-Min3 and Chang-Hwan Im1* 1 Yonsei University, Department of Biomedical Engineering, South Korea 2 Korea University, Department of Biomicrosystem Engineering, South Korea 3 Korea University, Department of Biomedical Engineering, South Korea This paper investigated the feasibility of measuring effectivity connectivity between cortical areas with near infrared spectroscopy (NIRS). We recorded cerebral hemodynamic responses at primary somatosensory cortex (S1), secondary somatosensory cortex (S2), and primary motor cortex (M1) regions in rat barrel cortex during electrical stimulation of rat whiskers. Deoxyhemoglobin concentration changes were calculated from the frequency-domain NIRS recordings and the Granger causality based on the multivariate autoregressive (MVAR) model was used to estimate the effective causal connectivity among S1, S2, and M1. Most results showed statistically significant Granger causality values at S1 -> M1 and S2 -> M1 connections when we used p = 0.05 to assess the statistical significance. Most rats did not show statistically significant causality values at the opposite directional connections (M1 -> S1 and M1 -> S2), except for only one case. The estimated causality patterns of seven rats showed consistent unidirectional coupling between the somatosensory areas and the motor areas (S1 and S2 -> M1), which coincides well with our expectation because the rats’ motor function was completely anesthetized. Our preliminary results suggest that the cortico-cortical effectivity connectivity can be successfully investigated with NIRS. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: Instrumentation and Multi-modal Integrations: MEG, Low-field MRI,EEG, fMRI,TMS,NIRS Citation: Jung Y, Seungduk L, Dalkwon K, Do-Won K, Beop-Min K and Im C (2010). Estimation of effective connectivity between cortical areas using near-infrared spectroscopy (NIRS). Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00435 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 12 Apr 2010; Published Online: 12 Apr 2010. * Correspondence: Chang-Hwan Im, Yonsei University, Department of Biomedical Engineering, Wonju, South Korea, ich@yonsei.ac.kr Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Young-Jin Jung Lee Seungduk Koh Dalkwon Kim Do-Won Kim Beop-Min Chang-Hwan Im Google Young-Jin Jung Lee Seungduk Koh Dalkwon Kim Do-Won Kim Beop-Min Chang-Hwan Im Google Scholar Young-Jin Jung Lee Seungduk Koh Dalkwon Kim Do-Won Kim Beop-Min Chang-Hwan Im PubMed Young-Jin Jung Lee Seungduk Koh Dalkwon Kim Do-Won Kim Beop-Min Chang-Hwan Im Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Object familiarity modulates effective connectivity during haptic shape perception.

Event Abstract Back to Event Object familiarity modulates effective connectivity during haptic shape perception. Simon Lacey1, Gopikrishna Deshpande2, Randall Stilla1, Xiaoping Hu2 and K. Sathian1, 3, 4, 5* 1 Emory University, Department of Neurology, United States 2 Emory University Georgia Institute of Technology, Coulter Department of Biomedical Engineering, United States 3 Atlanta VAMC, United States 4 Emory University, Department of Rehabilitation Medicine, United States 5 Emory University, Department of Psychology, United States Visual cortical areas are routinely active in normally sighted individuals during haptic shape perception but there is debate about whether this is due to visual imagery, mediated by top-down pathways from prefrontal areas, or engagement of multisensory representations via bottom-up pathways from somatosensory areas. In two functional magnetic resonance imaging experiments, participants haptically discriminated the shape of unfamiliar (Experiment 1) or familiar (Experiment 2) objects (HS task). In separate sessions, the same participants in each experiment made shape judgments on visual images of objects represented by words they heard (VI task). Regardless of familiarity, VI- and HS-related activations overlapped in the lateral occipital complex (LOC) bilaterally, left ventral premotor cortex, left ventral intraparietal sulcus, and left pulvinar. For familiar objects, there were additional overlap zones in left prefrontal and posterior parietal cortex. We then performed multivariate Granger causality analyses of effective connectivity on task-specific time series data from these experiments, using a novel method that eliminates zero-lag correlations (correlation-purged Granger causality) thus yielding purer estimates of effective connectivity. These analyses showed that the VI and familiar HS tasks activated similar networks involving top-down paths into the LOC, consistent with the use of visual imagery during haptic perception of the shape of familiar objects. However, the unfamiliar HS task activated a different network characterized by bottom-up, somatosensory cortical inputs into the LOC. We conclude that LOC activation during haptic shape perception reflects visual imagery, but that this is mediated by object familiarity. Thus, shape representations in the LOC are flexibly accessible, either top-down or bottom-up, according to task demands. Conference: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences, Atlanta , United States, 5 Mar - 7 Mar, 2010. Presentation Type: Poster Presentation Topic: Posters Citation: Lacey S, Deshpande G, Stilla R, Hu X and Sathian K (2010). Object familiarity modulates effective connectivity during haptic shape perception.. Front. Neurosci. Conference Abstract: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences. doi: 10.3389/conf.fnins.2010.04.00055 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Mar 2010; Published Online: 17 Mar 2010. * Correspondence: K. Sathian, Emory University, Department of Neurology, Atlanta, United States, ksathia@emory.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Simon Lacey Gopikrishna Deshpande Randall Stilla Xiaoping Hu K. Sathian Google Simon Lacey Gopikrishna Deshpande Randall Stilla Xiaoping Hu K. Sathian Google Scholar Simon Lacey Gopikrishna Deshpande Randall Stilla Xiaoping Hu K. Sathian PubMed Simon Lacey Gopikrishna Deshpande Randall Stilla Xiaoping Hu K. Sathian Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.