Terms Of Effective Connectivity Research Articles

Task-related brain activity in upper limb dystonia revealed by simultaneous fNIRS and EEG

ObjectiveThe aim of this study was to explore differences in brain activity and connectivity using simultaneous electroencephalography and near-infrared spectroscopy in patients with focal dystonia during handwriting and finger-tapping tasks. MethodsPatients with idiopathic right upper limb focal dystonia and controls were assessed by simultaneous near-infrared spectroscopy and electroencephalography during the writing and finger-tapping tasks in terms of the mu-alpha, mu-beta, beta and low gamma power and effective connectivity, as well as relative changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin using a channel-wise approach with a mixed-effect model. ResultsPatients exhibited higher oxy-Hb levels in the right and left motor cortex and supplementary motor area during writing, but lower oxy-Hb levels in the left sensorimotor and bilateral somatosensory area during finger-tapping compared to controls. During writing, patients showed increased low gamma power in the bilateral sensorimotor cortex and less mu-beta and beta attenuation compared to controls. Additionally, patients had reduced connectivity between the supplementary motor area and the left sensorimotor cortex during writing. No differences were observed in terms of effective connectivity in either task. Finally, patients failed to attenuate the mu-alpha, mu-beta, and beta rhythms during the finger-tapping task. ConclusionsCortical blood flow and EEG spectral power differ between controls and dystonia patients, depending on the task. Writing increased blood flow and altered connectivity in dystonia patients, and it also decreased slow-band attenuation. Finger-tapping decreased blood flow and slow-band attenuation. SignificanceSimultaneous fNIRS and EEG may show relevant information regarding brain dynamics in movement disorders patients in unconstrained environments.

Investigating altered brain functional hubs and causal connectivity in coronary artery disease with cognitive impairment.

Coronary artery disease (CAD) and cognitive impairment (CI) have become significant global disease and medical burdens. There have been several reports documenting the alterations in regional brain function and their correlation with CI in CAD patients. However, there is limited research on the changes in brain network connectivity in CAD patients. To investigate the resting-state connectivity and further understand the effective connectivity strength and directionality in patients with CAD, we utilized degree centrality (DC) and spectral dynamic causal modeling (spDCM) to detect functional hubs in the whole brain network, followed by an analysis of directional connections. Using the aforementioned approaches, it is possible to investigate the hub regions and aberrant connections underlying the altered brain function in CAD patients, providing neuroimaging evidence for the cognitive decline in patients with coronary artery disease. This study was prospectively conducted involving 24 patients diagnosed with CAD and 24 healthy controls (HC) who were matched in terms of age, gender, and education. Functional MRI (fMRI) scans were utilized to investigate brain activity in these individuals. Neuropsychological examinations were performed on all participants. DC analysis and spDCM were employed to investigate abnormal brain networks in patients with CAD. Additionally, the association between effective connectivity strength and cognitive function in patients with CAD was examined based on the aforementioned results. By assessing cognitive functions, we discovered that patients with CAD exhibited notably lower cognitive function compared to the HC group. By utilizing DC analysis and spDCM, we observed significant reductions in DC values within the left parahippocampal cortex (PHC) and the left medial temporal gyrus (MTG) in CAD patients when compared to the control group. In terms of effective connectivity, we observed the absence of positive connectivity between the right superior frontal gyrus (SFG) and PHC in CAD patients. Moreover, there was an increase in negative connectivity from PHC and MTG to SFG, along with a decrease in the strength of positive connectivity between PHC and MTG. Furthermore, we identified a noteworthy positive correlation (r = 0.491, p = 0.015) between the strength of connectivity between the PHC and the MTG and cognitive function in CAD patients. These research findings suggest that alterations in the connectivity of the brain networks involving SFG, PHC, and MTG in CAD patients may mediate changes in cognitive function.

Dissociation of Connectivity for Syntactic Irregularity and Perceptual Ambiguity in Musical Chord Stimuli.

Musical syntax has been studied mainly in terms of “syntactic irregularity” in harmonic/melodic sequences. However, “perceptual ambiguity” referring to the uncertainty of judgment/classification of presented stimuli can in addition be involved in our musical stimuli using three different chord sequences. The present study addresses how “syntactic irregularity” and “perceptual ambiguity” on musical syntax are dissociated, in terms of effective connectivity between the bilateral inferior frontal gyrus (IFGs) and superior temporal gyrus (STGs) by linearized time-delayed mutual information (LTDMI). Three conditions were of five-chord sequences with endings of dominant to tonic, dominant to submediant, and dominant to supertonic. The dominant to supertonic is most irregular, compared with the regular dominant to tonic. The dominant to submediant of the less irregular condition is the most ambiguous condition. In the LTDMI results, connectivity from the right to the left IFG (IFG-LTDMI) was enhanced for the most irregular condition, whereas that from the right to the left STG (STG-LTDMI) was enhanced for the most ambiguous condition (p = 0.024 in IFG-LTDMI, p < 0.001 in STG-LTDMI, false discovery rate (FDR) corrected). Correct rate was negatively correlated with STG-LTDMI, further reflecting perceptual ambiguity (p = 0.026). We found for the first time that syntactic irregularity and perceptual ambiguity coexist in chord stimulus testing musical syntax and that the two processes are dissociated in interhemispheric connectivities in the IFG and STG, respectively.

Dopaminergic modulation of motor network compensatory mechanisms in Parkinson's disease.

The dopaminergic system has a unique gating function in the initiation and execution of movements. When the interhemispheric imbalance of dopamine inherent to the healthy brain is disrupted, as in Parkinson's disease (PD), compensatory mechanisms act to stave off behavioral changes. It has been proposed that two such compensatory mechanisms may be (a) a decrease in motor lateralization, observed in drug-naïve PD patients and (b) reduced inhibition - increased facilitation. Seeking to investigate the differential effect of dopamine depletion and subsequent substitution on compensatory mechanisms in non-drug-naïve PD, we studied 10 PD patients and 16 healthy controls, with patients undergoing two test sessions - "ON" and "OFF" medication. Using a simple visually-cued motor response task and fMRI, we investigated cortical motor activation - in terms of laterality, contra- and ipsilateral percent BOLD signal change and effective connectivity in the parametric empirical Bayes framework. We found that decreased motor lateralization persists in non-drug-naïve PD and is concurrent with decreased contralateral activation in the cortical motor network. Normal lateralization is not reinstated by dopamine substitution. In terms of effective connectivity, disease-related changes primarily affect ipsilaterally-lateralized homotopic cortical motor connections, while medication-related changes affect contralaterally-lateralized homotopic connections. Our findings suggest that, in non-drug-naïve PD, decreased lateralization is no longer an adaptive cortical mechanism, but rather the result of maladaptive changes, related to disease progression and long-term dopamine replacement. These findings highlight the need for the development of noninvasive therapies, which would promote the adaptive mechanisms of the PD brain.

Repetition priming effects for famous faces through dynamic causal modelling of latency-corrected event-related brain potentials.

Repetition priming, that is, the repeated processing of a stimulus, facilitates performance. However, the neural underpinnings of repetition priming for famous faces in terms of effective connectivity are not known. Here we investigated this problem using dynamic causal modelling of latency-corrected event-related brain potentials (RERPs). Source waveforms of RERP-derived sources in the Occipital Lobe, Fusiform Gyrus, Mediotemporal Lobe, Prefrontal Cortex and Anterotemporal Lobe of each hemisphere entered into models with only forward (F) or also with backward (FB) connections. Based on the framework of predictive coding formulated for repetition suppression, modulations of F and FB connections were expected for sources that displayed priming effects in their source waveforms. Hence, neural sources in each hemisphere were fitted with either F or FB connections. Inter-hemispheric connections were considered between homologous areas and were allowed to be modulated in an incremental manner resulting in a model space that comprised of 24 models. Bayesian model averaging across models revealed effective bidirectional connectivity between the Fusiform Gyrus (face perception) and Prefrontal Cortex (decision-making) in both hemispheres to be modulated by priming. In the left hemisphere, there is also a substantial involvement from the Mediotemporal Lobe, indicating the facilitation of automatic retrieval of the famous person's name. Furthermore, there is evidence that the priming is supported by connections from the right to the left Fusiform Gyri possibly in the service of inter-hemispheric cooperation. Altogether, the study indicates that along with top-down modulations, efficient processing within and across the two hemispheres is crucial for famous face priming.

A validation of dynamic causal modelling for 7T fMRI

BackgroundThere is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM). New methodIn this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain). ResultsUsing empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity – as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates; suggesting the 7T data are more informative – and furnish more efficient estimates. Compared with existing methodsIn the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization. ConclusionsIn this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently.

Prefrontal-parietal effective connectivity during working memory in older adults

Theoretical models and preceding studies have described age-related alterations in neuronal activation of frontoparietal regions in a working memory (WM) load-dependent manner. However, to date, underlying neuronal mechanisms of these WM load-dependent activation changes in aging remain poorly understood. The aim of this study was to investigate these mechanisms in terms of effective connectivity by application of dynamic causal modeling with Bayesian Model Selection. Eighteen healthy younger (age: 20–32 years) and 32 older (60–75 years) participants performed an n-back task with 3 WM load levels during functional magnetic resonance imaging (fMRI). Behavioral and conventional fMRI results replicated age group by WM load interactions. Importantly, the analysis of effective connectivity derived from dynamic causal modeling, indicated an age- and performance-related reduction in WM load-dependent modulation of connectivity from dorsolateral prefrontal cortex to inferior parietal lobule. This finding provides evidence for the proposal that age-related WM decline manifests as deficient WM load-dependent modulation of neuronal top-down control and can integrate implications from theoretical models and previous studies of functional changes in the aging brain.

Frontoparietal Cortex Mediates Perceptual Transitions in Bistable Perception

During bistable vision, perception oscillates between two mutually exclusive percepts despite constant sensory input. Greater BOLD responses in frontoparietal cortex have been shown to be associated with endogenous perceptual transitions compared with "replay" transitions designed to closely match bistability in both perceptual quality and timing. It has remained controversial, however, whether this enhanced activity reflects causal influences of these regions on processing at the sensory level or, alternatively, an effect of stimulus differences that result in, for example, longer durations of perceptual transitions in bistable perception compared with replay conditions. Using a rotating Lissajous figure in an fMRI experiment on 15 human participants, we controlled for potential confounds of differences in transition duration and confirmed previous findings of greater activity in frontoparietal areas for transitions during bistable perception. In addition, we applied dynamic causal modeling to identify the neural model that best explains the observed BOLD signals in terms of effective connectivity. We found that enhanced activity for perceptual transitions is associated with a modulation of top-down connectivity from frontal to visual cortex, thus arguing for a crucial role of frontoparietal cortex in perceptual transitions during bistable perception.

Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study

The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed.

Interhemispheric Integration of Visual Processing during Task-Driven Lateralization

The mechanisms underlying interhemispheric integration (IHI) remain poorly understood, particularly for lateralized cognitive processes. To test competing theories of IHI, we constructed and fitted dynamic causal models to functional magnetic resonance data from two visual tasks that operated on identical stimuli but showed opposite hemispheric dominance. Using a systematic Bayesian model selection procedure, we found that, in the ventral visual stream, which was activated by letter judgments, interhemispheric connections mediated asymmetric information transfer from the nonspecialized right to the specialized left hemisphere when the latter did not have direct access to stimulus information. Notably, this form of IHI did not engage all areas activated by the task but was specific for areas in the lingual and fusiform gyri. In the dorsal stream, activated by spatial judgments, it did not matter which hemisphere received the stimulus: interhemispheric coupling increased bidirectionally, reflecting recruitment of the nonspecialized left hemisphere. Again, not all areas activated by the task were involved in this form of IHI; instead, it was restricted to interactions between areas in the superior parietal gyrus. Overall, our results provide direct neurophysiological evidence, in terms of effective connectivity, for the existence of context-dependent mechanisms of IHI that are implemented by specific visual areas during task-driven lateralization.

Dynamic causal models of neural system dynamics: current state and future extensions

Complex processes resulting from interaction of multiple elements can rarely be understood by analytical scientific approaches alone; additional, mathematical models of system dynamics are required. This insight, which disciplines like physics have embraced for a long time already, is gradually gaining importance in the study of cognitive processes by functional neuroimaging. In this field, causal mechanisms in neural systems are described in terms of effective connectivity. Recently, dynamic causal modelling (DCM) was introduced as a generic method to estimate effective connectivity from neuroimaging data in a Bayesian fashion. One of the key advantages of DCM over previous methods is that it distinguishes between neural state equations and modality-specific forward models that translate neural activity into a measured signal. Another strength is its natural relation to Bayesian model selection (BMS) procedures. In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing the application of BMS in the context of DCM, we conclude with an outlook to future extensions of DCM. These extensions are guided by the long-term goal of using dynamic system models for pharmacological and clinical applications, particularly with regard to synaptic plasticity.

Dynamic causal modeling: A generative model of slice timing in fMRI

Dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data allows one to make inferences about the architecture of distributed networks in the brain, in terms of effective connectivity. fMRI data are usually acquired using echo planar imaging (EPI). EPI sequences typically acquire slices at different times over a few seconds. DCM, in its original inception, was not informed about these slice timings and assumed that all slices were acquired simultaneously. It has been shown that DCM can cope with slice timing differences of up to 1 s. However, many fMRI studies employ a repetition time (TR) of 3 to 5 s, which precludes a straightforward DCM of these data. We show that this limitation can be overcome easily by including slice timing in the DCM. Using synthetic data we show that the extended DCM furnishes veridical posterior means, even if there are large slice-timing differences. Model comparisons show that, in general, the extended DCM out-performs the original model. We contrast the modeling of slice timing, in the context of DCM, with the less effective approach of 'slice-timing correction', prior to modeling. We apply our procedure to real data and show that slice timings are important parameters. We conclude that, generally, one should use DCM with slice timing.

Beyond phrenology: what can neuroimaging tell us about distributed circuitry?

Unsupervised models of how the brain identifies and categorizes the causes of its sensory input can be divided into two classes: those that minimize the mutual information (i.e., redundancy) among evoked responses and those that minimize the prediction error. Although these models have the same goal, the way that goal is attained, and the functional architectures required, are fundamentally different. This review describes the differences, in the functional anatomy of sensory cortical hierarchies, implied by the two models. We then consider how neuroimaging can be used to disambiguate between them. The key distinction reduces to whether backward connections are employed by the brain to generate a prediction of sensory inputs. To ascertain whether backward influences are evident empirically requires a characterization of functional integration among brain systems. This review summarizes the approaches to measuring functional integration in terms of effective connectivity and proceeds to address the question posed by the theoretical considerations. In short, it will be shown that the conjoint manipulation of bottom-up and top-down inputs to an area can be used to test for interactions between them, in elaborating cortical responses. The conclusion, from these sorts of neuroimaging studies, points to the prevalence of top-down influences and the plausibility of generative models of sensory brain function.