Effective Connectivity Measures Research Articles

Functional integration of parietal lobe activity in early Alzheimer disease

Parietal lobe dysfunction is an important characteristic of early Alzheimer disease (AD). Functional studies have shown conflicting parietal activation patterns indicative of either compensatory or dysfunctional mechanisms. This study aimed at examining activation differences in early AD using a visuospatial task. We focused on functional characteristics of the parietal lobe and examined compensation or disconnection mechanisms by combining a fMRI task with effective connectivity measures from Granger causality mapping (GCM). Eighteen male patients with amnestic mild cognitive impairment (aMCI) and 18 male cognitively healthy older individuals were given a mental rotation task with different rotation angles. There were no behavioral group differences on the fMRI task. Separate measurements at each angle revealed widespread activation group differences. More temporal and parietal activation in the higher angle condition was observed in patients with aMCI. The parametric modulation, which identifies regions associated with increasing angle, confirmed these results. The GCM showed increased connectivity within the parietal lobe and between parietal and temporal regions in patients with aMCI. Decreased connectivity was found between the inferior parietal lobule and posterior cingulate gyrus. Connectivity patterns correlated with memory performance scores in patients with aMCI. Our results demonstrate increased effective temporoparietal connectivity in patients with aMCI, while maintaining intact behavioral performance. This might be a compensational mechanism to counteract a parietal-posterior cingulate gyrus disconnection. These findings highlight the importance of connectivity changes in the pathophysiology of AD. In addition, effective connectivity may be a promising method for evaluating interventions aimed at the promotion of compensatory mechanisms.

Enhanced frontocentral EEG connectivity in photosensitive generalized epilepsies: A partial directed coherence study

Photosensitive epilepsy (PSE) is the most common form of reflex epilepsy presenting with electroencephalography (EEG) paroxysms elicited by intermittent photic stimulation (IPS). To investigate whether the neuronal network undergoes dynamic changes before and during the transition to an EEG epileptic discharge, we estimated EEG connectivity patterns in photosensitive (PS) patients with idiopathic generalized epilepsy. EEG signals were evaluated under resting conditions and during 14 Hz IPS, a frequency that consistently induces photoparoxysmal responses (PPRs) in PS patients. Partial directed coherence (PDC), a linear measure of effective connectivity based on multivariate autoregressive models, was used in 10 PS patients and 10 controls. Anterior versus posterior (F3, F4, C3, C4, and P3, P4, O1, O2) and interhemispheric connectivity patterns (F4, C4, P4, O2, and F3, C3, P3, O1) were estimated with focus on beta and gamma band activity. PDC analysis revealed an enhanced connectivity pattern in terms of both the number and strength of outflow connections in the PS patient group. Under resting condition, the greater connectivity in the PS patients occurred in the beta band, whereas it mainly involved the gamma band during IPS (i.e., the frequencies ranging from 40-60 Hz that include the higher harmonics of the stimulus frequency). Both at rest and during IPS, the differences between the PS patients and controls were due primarily to clearly increased connectivity involving the anterior cortical regions. Our findings indicate that PS patients are characterised by abnormal EEG hyperconnectivity, primarily involving the anterior cortical regions under resting conditions and during IPS. This suggests that, even if the occipital cortical regions are the recipient zone of the stimulus and probably hyperexcitable, the anterior cortical areas are prominently involved in generating the hypersynchronization underlying the spike-and wave discharges elicited by IPS.

Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development.

Brain structural and functional development, throughout childhood and into adulthood, underlies the maturation of increasingly sophisticated cognitive abilities. High-level attentional and cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks. The right fronto-insular cortex (rFIC) is a critical component of a salience network (SN) that mediates interactions between large-scale brain networks involved in externally oriented attention [central executive network (CEN)] and internally oriented cognition [default mode network (DMN)]. How these systems reconfigure and mature with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. Using functional and effective connectivity measures applied to fMRI data, we examine interactions within and between the SN, CEN, and DMN. We find that functional coupling between key network nodes is stronger in adults than in children, as are causal links emanating from the rFIC. Specifically, the causal influence of the rFIC on nodes of the SN and CEN was significantly greater in adults compared with children. Notably, these results were entirely replicated on an independent dataset of matched children and adults. Developmental changes in functional and effective connectivity were related to structural connectivity along these links. Diffusion tensor imaging tractography revealed increased structural integrity in adults compared with children along both within- and between-network pathways associated with the rFIC. These results suggest that structural and functional maturation of rFIC pathways is a critical component of the process by which human brain networks mature during development to support complex, flexible cognitive processes in adulthood.

Detecting effective connectivity in networks of coupled neuronal oscillators

The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

Preferential Inhibition of Frontal-to-Parietal Feedback Connectivity Is a Neurophysiologic Correlate of General Anesthesia in Surgical Patients

BackgroundThe precise mechanism and optimal measure of anesthetic-induced unconsciousness has yet to be elucidated. Preferential inhibition of feedback connectivity from frontal to parietal brain networks is one potential neurophysiologic correlate, but has only been demonstrated in animals or under limited conditions in healthy volunteers.Methods and FindingsWe recruited eighteen patients presenting for surgery under general anesthesia; electroencephalography of the frontal and parietal regions was acquired during (i) baseline consciousness, (ii) anesthetic induction with propofol or sevoflurane, (iii) general anesthesia, (iv) recovery of consciousness, and (v) post-recovery states. We used two measures of effective connectivity, evolutional map approach and symbolic transfer entropy, to analyze causal interactions of the frontal and parietal regions. The dominant feedback connectivity of the baseline conscious state was inhibited after anesthetic induction and during general anesthesia, resulting in reduced asymmetry of feedback and feedforward connections in the frontoparietal network. Dominant feedback connectivity returned when patients recovered from anesthesia. Both analytic techniques and both classes of anesthetics demonstrated similar results in this heterogeneous population of surgical patients.ConclusionsThe disruption of dominant feedback connectivity in the frontoparietal network is a common neurophysiologic correlate of general anesthesia across two anesthetic classes and two analytic measures. This study represents a key translational step from the underlying cognitive neuroscience of consciousness to more sophisticated monitoring of anesthetic effects in human surgical patients.

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.

A comparison of multivariate causality based measures of effective connectivity

During the past several years a variety of methods have been developed to estimate the effective connectivity of neural networks from measurements of brain activity in an attempt to study causal interactions among distinct brain areas. Understanding the relative strengths and weaknesses of these methods, the assumptions they rely on, the accuracy they provide, and the computation time they require is of paramount importance in selecting the optimal method for a particular experimental task and for interpreting the results obtained. In this paper, the accuracy of the six most commonly used techniques for calculating effective connectivity are compared, namely directed transfer function, partial directed coherence, squared partial directed coherence, full frequency directed transfer function, direct directed transfer function, and Granger causality. These measures are derived from the coefficients and error terms of autoregressive models calculated using the dynamic autoregressive neuromagnetic causal imaging (DANCI) algorithm. These techniques were evaluated using magnetoencephalography recordings as well as several synthetic datasets that simulate neurophysiological signals, which varied on several parameters, including network size, signal-to-noise ratio, and network complexity, etc. The results show that Granger causality is the most accurate method across all experimental conditions explored and suggest that large multisensor data sets can be accurately analyzed using Granger causality with the DANCI algorithm.

Motor callosal disconnection in early relapsing‐remitting multiple sclerosis

In relapsing-remitting multiple sclerosis (RRMS) the corpus callosum (CC) is often and early affected by macroscopic lesions when investigated by conventional MRI. We sought to determine to which extent microstructural and effective disconnection of the CC are already present in RRMS patients at the earliest stages of the disease prior to evidence of macroscopic CC lesion. We compared 16 very early RRMS patients (median expanded disability status scale (EDSS), 1.5; range, 0-2.0) to an age-matched group of healthy controls and focused analysis to the motor CC, i.e. that part of the CC relaying interhemispheric motor information. A combined functional magnetic resonance imaging/diffusion tensor imaging fiber-tracking procedure was applied to identify the callosal motor fibers (CMFs) connecting the hand areas of the primary motor cortices of the two hemispheres. Fractional anisotropy (FA) within the motor CC (FA-CC) assessed the CMF microstructural integrity. Bifocal paired transcranial magnetic stimulation (TMS) tested short-interval interhemispheric inhibition (S-IHI), an established measure of CMF effective connectivity. FA-CC and S-IHI were significantly reduced in early RRMS compared to healthy controls. Furthermore, a significant linear correlation between microstructure (FA-CC) and function (S-IHI) in the controls was broken down in the patients. These abnormalities were obtained in the absence of macroscopic CMF lesion in conventional MRI, and whilst motor hand/arm function in the nine-hole-peg test and corticospinal conduction time were normal. Findings suggest that reductions in FA and S-IHI may serve as surrogate markers of motor callosal disconnection at the earliest stages of RRMS prior to development of macroscopic lesion.

Assessing Connectivity in stroke patients: the relationship between effective connectivity in the motor system and electrophysiological excitability of the cortico-spinal system in chronic stroke patients

Background: Recovery of motor function after stroke varies considerably between patients. Techniques such as transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) are frequently used to identify predictors of clinical outcome. After stroke ipsilesional motor thresholds are often elevated and tend to decrease over time concurrent to recovery. Likewise, fMRI measures of effective connectivity among motor areas demonstrated pathological coupling to ipsilesional M1. In this study the relationship between the parameters of these two different brain-mapping tools was investigated in chronic stroke patients.

A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM. To compare the effectiveness of our approach with traditional pair-wise GCM models, we applied a well-established conditional GCM to preselected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis of an fMRI data set in the temporal domain. Data sets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM-detected brain activation regions in the emotion-related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state data set, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network that can be characterized as both afferent and efferent influences on the medial prefrontal cortex and posterior cingulate cortex. These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy in detecting network connectivity than the widely used pair-wise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.

FMRI based Granger causality as a measure of effective connectivity in macaque visual cortex

Event Abstract Back to Event fMRI based Granger causality as a measure of effective connectivity in macaque visual cortex Vesna Vuksanovic1, 2, 3*, Mario Bartolo3, Dave Hunter3, Li Sun3 and Alex Thiele3 1 Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Philippstraße 13, 10115 Berlin, Germany, Germany 2 Institut für Theoretische Physik, Technische Universität Berlin, Germany 3 Institute of Neuroscience, Newcastle university, United Kingdom Granger causality (GC) analysis of fMRI blood oxygen level dependent (BOLD) time series has been proposed as a statistical tool to analyse directional influences between remote brain regions. Ongoing BOLD activity is treated as a set of stochastic time series (STS) generated by stochastic processes whose properties can be captured by autoregressive models. GC quantifies the improvement in predicting one brain region STS by inclusion of that from another region. Here, we show how GC can be tested using the ensemble of single-voxel time series in a region of interest (ROI). Granger causality was tested on F statistics derived from linear regression models using BOLD time series. Ongoing BOLD activity was extracted from four ROIs: three in V1 and one in MT/V5 area of the macaque visual cortex. fMRI scans were made under three stimulus conditions, whereby (1) a single (‘centre’) grating was presented, (2) the single grating was flanked by 2 additional gratings, and (3) only the flanking gratings were shown. ROIs were not spatially homogenous in their influence on each other; we found a variable number of voxel pairs with significant F statistics. GC analysis showed that the V1 ROI which represented the ‘centre’ grating was influenced by the MT/V5 ROI when centre-flanker stimuli were shown, but not by the V1 ROIs representing the flanking gratings. It thus appears that centre-flanker interactions (at distances >3 deg of visual angle) arise mainly through feedback, not through lateral intra-areal connections. Keywords: dynamical systems, effective connectivity, fMRI, networks, Neurons, Visual Cortex Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords) Citation: Vuksanovic V, Bartolo M, Hunter D, Sun L and Thiele A (2011). fMRI based Granger causality as a measure of effective connectivity in macaque visual cortex. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00200 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: 19 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Dr. Vesna Vuksanovic, Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Philippstraße 13, 10115 Berlin, Germany, Berlin, Germany, 10115, Germany, vesna.vuksanovic@bccn-berlin.de 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 Vesna Vuksanovic Mario Bartolo Dave Hunter Li Sun Alex Thiele Google Vesna Vuksanovic Mario Bartolo Dave Hunter Li Sun Alex Thiele Google Scholar Vesna Vuksanovic Mario Bartolo Dave Hunter Li Sun Alex Thiele PubMed Vesna Vuksanovic Mario Bartolo Dave Hunter Li Sun Alex Thiele 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.

TMS/EEG measures of cortical effective connectivity during wakefulness, NREM sleep, dreaming, anesthesia and coma.

TMS/EEG measures of cortical effective connectivity during wakefulness, NREM sleep, dreaming, anesthesia and coma.

Hippocampus and prefrontal cortex support contextual fear conditioning in different ways: Insights from a novel cue-array paradigm

Hippocampus and prefrontal cortex support contextual fear conditioning in different ways: Insights from a novel cue-array paradigm

Prolonged high-altitude residence impacts verbal working memory: an fMRI study

Oxygen is critical to normal brain functioning and development. In high altitude where the oxygen concentration and pressure are very low, human cognitive capability such as working memory has been found to be jeopardized. Such effect might persist with long-term high-altitude residence. The current study investigated the verbal working memory of 28 high-altitude residents with blood level oxygen dependent (BOLD) functional magnetic resonance imaging (fMRI), in contrast with that of the 30 sea level residents. All of the subjects were healthy college students, matched on their age, gender ratio and social-economic status; they also did not show any difference on their hemoglobin level. The high-altitude subjects showed longer reaction time and decreased response accuracy in behavioral performance. Both groups showed activation in the typical regions associated with the 2-back verbal working memory task, and the behavioral performance of both groups showed significant correlations with the BOLD signal change amplitude and Granger causality values (as a measure of the interregional effective connectivity) between these regions. With group comparison statistics, the high-altitude subjects showed decreased activation at the inferior and middle frontal gyrus, the middle occipital and the lingual gyrus, the pyramis of vermis, as well as the thalamus. In conclusion, the current study revealed impairment in verbal working memory among high-altitude residents, which might be associated with the impact of prolonged chronic hypoxia exposure on the brain functionality.

Dynamics of directed interactions between brain regions during interburst–burst EEG patterns in quiet sleep of full-term neonates

The study investigates time-variant directed interactions between brain regions during the interburst–burst EEG pattern (tracé alternant) characteristic of quiet sleep in healthy neonates. The transition from interburst to burst is of particular interest as the generation of the EEG characteristics at burst onset reflects timing and time-variant interplay between the cortical and the thalamo-cortical brain structures. To study the dynamics of the interactions, time-variant partial directed coherence (PDC), a measure of effective connectivity, was used which allows analysis in the time–frequency range. The main results of the grand mean PDC analysis are: (1) PDC time–frequency patterns are frequently associated with phase-locked oscillations. (2) Interhemispheric interactions are dominant between frontal, central and occipital electrodes and intrahemispheric interactions are much less substantial. (3) An interaction breakdown for the frequency ranges 1–4Hz (Fp1⇒Fp2) and 0.5–3Hz (Fp2⇒Fp1) exists which lasts about 2.5s and which is located at about burst onset. (4) Strong interactions in the high-frequency range 3.5–4.5Hz between the frontal electrodes can be observed for both directions at the burst onset. It can be concluded that the evolution of strong interactions in the high-frequency range, which starts shortly before or at the burst onset from frontal regions to anteroposterior directions as well as the frontal interhemispheric interactions, are associated with the burst onset generation. Additionally, the collapsing of the interactions before burst onset and after the burst are indicative of neuronal reorganisation processes.

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction.

Coordination Dynamics of Large-scale Neural Circuitry Underlying Rhythmic Sensorimotor Behavior

In coordination dynamics, rate is a nonspecific control parameter that alters the stability of behavioral patterns and leads to spontaneous pattern switching. We used fMRI in conjunction with measures of effective connectivity to investigate the neural basis of behavioral dynamics by examining two coordination patterns known to be differentially stable (synchronization and syncopation) across a range of rates (0.75 to 1.75 Hz). Activity in primary auditory and motor cortices increased linearly with rate, independent of coordination pattern. On the contrary, activity in a premotor-cerebellar circuit varied directly with the stability of the collective variable (relative phase) that specifies coordinated behavioral patterns. Connectivity between premotor and motor cortices was also modulated by the stability of the behavioral pattern indicative of greater reliance on sensorimotor integration as action becomes more variable. By establishing a critical connection between behavioral and large scale brain dynamics, these findings reveal a basic principle for the neural organization underlying coordinated action.

Scaling errors in measures of brain activity cause erroneous estimates of effective connectivity

Effective connectivity (EC) is the collective term for various measures of the interaction between the nodes in a network of neurons or neural populations during a certain experimental condition. Here, I investigated three types of EC that differ with respect to signal normalization, and therefore measure different aspects of neural interactions. Unnormalized EC measures pure connection strength. Amplitude-scaled EC measures the combined influence of signal amplitude and connection strength on neural activity. Finally, normalized EC measures the influence of one node on the activity of another relative to all influences on that node. With a theoretical analysis, I investigated the sensitivity of EC to signal scaling (the ratio of the amplitude of the measured signal and the underlying neural activity) and found that scaling affects the conclusions of the analysis of unnormalized EC severely, whereas normalized EC is not affected by the scaling problem. In an analysis of previously published hemodynamic response functions (Handwerker, D. A., Ollinger, J. M., D'Esposito, M., 2004. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 21, 1639–1651), I tested the predictions of the theoretical analysis. The empirical analysis indicated that signal scaling contributes to a large extent to measurement errors of unnormalized EC, although hemodynamic response function shape variability also contributed. Normalized EC, on the other hand, was only affected by shape differences and not by scaling. In addition to being more accurate, normalized EC is also an appropriate type of measure of neural interactivity if one is interested in the relative influence of one node on another, rather than absolute connection strengths per se.

Understanding why patients with schizophrenia do not perceive the hollow-mask illusion using dynamic causal modelling

Patients suffering from schizophrenia are less susceptible to various visual illusions. For example, healthy participants perceive a hollow mask as a normal face, presumably due to the strength of constraining top-down influences, while patients with schizophrenia do not (Schneider, U., Leweke, F.M., Sternemann, U., Weber, M.M., Emrich, H.M., 1996. Visual 3D illusion: a systems-theoretical approach to psychosis. Eur. Arch. Psychiatry Clin. Neurosci. 246, 256–260; Scheider, U., Borsutzky, M., Seifert, J., Leweke, F.M., Huber, T.J., Rollnik J.D., Emrich, H.M., 2002. Reduced binocular depth inversion in schizophrenic patients. Schizophrenia Research 53, 101–108.; Emrich, H.M., Leweke, F.M., Schneider, U., 1997. Towards a cannabinoid hypothesis of schizophrenia: cognitive impairments due to a dysregulation of the endogenous cannabinoid system. Pharmacol. Biochem. Behav. 56, 803–807). However the neural mechanisms underpinning this effect remain poorly understood. We used functional magnetic resonance imaging to investigate the hollow-mask illusion in schizophrenic patients and healthy controls. The primary aim of this study was to use measures of effective connectivity arising from dynamic causal modelling (DCM) to explain differences in both the perception of the hollow-mask illusion and associated differences in neural responses between patients with schizophrenia and controls, which we hypothesised would be associated with difference in the influences of top-down and bottom-up processes between the groups. Consistent with this explanation, we identified differences between the two groups in effective connectivity. In particular, there was a strengthening of bottom-up processes, and weakening of top-down ones, during the presentation of ‘hollow’ faces for the patients. In contrast, the controls exhibited a strengthening of top-down processes when perceiving the same stimuli. These findings suggest that schizophrenic patients rely on stimulus-driven processing and are less able to employ conceptually-driven top-down strategies during perception, where incoming sensory data are constrained with reference to a generative model that entails stored information from past experience.

Empirical Comparison of Sources of Variation for FMRI Connectivity Analysis

BackgroundIn neuroimaging, connectivity refers to the correlations between signals in different brain regions. Although fMRI measures of connectivity have been widely explored, the methods used have varied. This complicates the interpretation of existing literature in cases when different techniques have been used with fMRI data to measure the single concept of “connectivity.” Additionally the optimum choice of method for future analyses is often unclear.Methodology/Principal FindingsIn this study, measures of functional and effective connectivity in the motor system were calculated based on three sources of variation: inter-subject variation in task activation level; within-subject variation in task-related responses; and within-subject residual variation after removal of task effects. Two task conditions were compared. The methods yielded different inter-regional correlation coefficients. However, all three approaches produced similar results, qualitatively and sometimes quantitatively, for condition differences in connectivity.Conclusions/SignificanceWhile these results are specific to the motor regions studied, they do suggest that within-subject and across-subject results may be usefully compared. Also, the presence of task-specific correlations in residual time series supports arguments that residuals may not substitute for resting-state data, but rather may reflect the same underlying variations present during steady-state performance.