Interregional Effective Connectivity Research Articles

Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex.

Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.

Targeted Modulation of Human Brain Interregional Effective Connectivity With Spike-Timing Dependent Plasticity

ObjectiveThe ability to selectively up- or downregulate interregional brain connectivity would be useful for research and clinical purposes. Toward this aim, cortico-cortical paired associative stimulation (ccPAS) protocols have been developed in which two areas are repeatedly stimulated with a millisecond-level asynchrony. However, ccPAS results in humans using bifocal transcranial magnetic stimulation (TMS) have been variable, and the mechanisms remain unproven. In this study, our goal was to test whether ccPAS mechanism is spike-timing–dependent plasticity (STDP). Materials and MethodsEleven healthy participants received ccPAS to the left primary motor cortex (M1) → right M1 with three different asynchronies (5 milliseconds shorter, equal to, or 5 milliseconds longer than the 9-millisecond transcallosal conduction delay) in separate sessions. To observe the neurophysiological effects, single-pulse TMS was delivered to the left M1 before and after ccPAS while cortico-cortical evoked responses were extracted from the contralateral M1 using source-resolved electroencephalography. ResultsConsistent with STDP mechanisms, the effects on synaptic strengths flipped depending on the asynchrony. Further implicating STDP, control experiments suggested that the effects were unidirectional and selective to the targeted connection. ConclusionThe results support the idea that ccPAS induces STDP and may selectively up- or downregulate effective connectivity between targeted regions in the human brain.

Treadmill training in Parkinson’s disease is underpinned by the interregional connectivity in cortical-subcortical network

Treadmill training (TT) has been extensively used as an intervention to improve gait and mobility in patients with Parkinson’s disease (PD). Regional and global effects on brain activity could be induced through TT. Training effects can lead to a beneficial shift of interregional connectivity towards a physiological range. The current work investigates the effects of TT on brain activity and connectivity during walking and at rest by using both functional near-infrared spectroscopy and functional magnetic resonance imaging. Nineteen PD patients (74.0 ± 6.59 years, 13 males, disease duration 10.45 ± 6.83 years) before and after 6 weeks of TT, along with 19 age-matched healthy controls were assessed. Interregional effective connectivity (EC) between cortical and subcortical regions were assessed and its interrelation to prefrontal cortex (PFC) activity. Support vector regression (SVR) on the resting-state ECs was used to predict prefrontal connectivity. In response to TT, EC analysis indicated modifications in the patients with PD towards the level of healthy controls during walking and at rest. SVR revealed cerebellum related connectivity patterns that were associated with the training effect on PFC. These findings suggest that the potential therapeutic effect of training on brain activity may be facilitated via changes in compensatory modulation of the cerebellar interregional connectivity.

On the intersection between data quality and dynamical modelling of large-scale fMRI signals

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

Concurrent TMS-fMRI for causal network perturbation and proof of target engagement

The experimental manipulation of neural activity by neurostimulation techniques overcomes the inherent limitations of correlative recordings, enabling the researcher to investigate causal brain-behavior relationships. But only when stimulation and recordings are combined, the direct impact of the stimulation on neural activity can be evaluated. In humans, this can be achieved non-invasively through the concurrent combination of transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging (fMRI). Concurrent TMS-fMRI allows the assessment of the neurovascular responses evoked by TMS with excellent spatial resolution and full-brain coverage. This enables the functional mapping of both local and remote network effects of TMS in cortical as well as deep subcortical structures, offering unique opportunities for basic research and clinical applications. The purpose of this review is to introduce the reader to this powerful tool. We will introduce the technical challenges and state-of-the art solutions and provide a comprehensive overview of the existing literature and the available experimental approaches. We will highlight the unique insights that can be gained from concurrent TMS-fMRI, including the state-dependent assessment of neural responsiveness and inter-regional effective connectivity, the demonstration of functional target engagement, and the systematic evaluation of stimulation parameters. We will also discuss how concurrent TMS-fMRI during a behavioral task can help to link behavioral TMS effects to changes in neural network activity and to identify peripheral co-stimulation confounds. Finally, we will review the use of concurrent TMS-fMRI for developing TMS treatments of psychiatric and neurological disorders and suggest future improvements for further advancing the application of concurrent TMS-fMRI.

Dynamic causal modeling of hippocampal activity measured via mesoscopic voltage-sensitive dye imaging

The aim of this paper is to present a dynamic causal modeling (DCM) framework for hippocampal activity measured via voltage-sensitive dye imaging (VSDI). We propose a DCM model of the hippocampus that summarizes interactions between the hilus, CA3 and CA1 regions. The activity of each region is governed via a neuronal mass model with two inhibitory and one/two excitatory neuronal populations, which can be linked to measurement VSDI by scaling neuronal activity. To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a mesoscopic hippocampus model; and Bayesian model reduction to determine the parameterization of neural properties, and to test and include potential connections (morphologically inferred without direct evidence yet) in the model by evaluating group-level model evidence. The proposed method was applied to model spatiotemporal patterns of accumulative responses to consecutive stimuli in separate groups of wild-type mice and epileptic aristaless-related homeobox gene (Arx) conditional knock-out mutant mice (Arx−/+;Dlx5/6CRE−IRES-GFP) in order to identify group differences in the effective connectivity within the hippocampus. The causal role of each group-differing connectivity in generating mutant-like responses was further tested. The group-level analysis identified altered intra- and inter-regional effective connectivity, some of which are crucial for explaining mutant-like responses. The modelling results for the hippocampal activity suggest the plausibility of the proposed mesoscopic hippocampus model and the usefulness of utilizing the Bayesian framework for model construction in the mesoscale modeling of neural interactions using DCM.

Interregional compensatory mechanisms of motor functioning in progressing preclinical neurodegeneration

Understanding brain reserve in preclinical stages of neurodegenerative disorders allows determination of which brain regions contribute to normal functioning despite accelerated neuronal loss. Besides the recruitment of additional regions, a reorganisation and shift of relevance between normally engaged regions are a suggested key mechanism. Thus, network analysis methods seem critical for investigation of changes in directed causal interactions between such candidate brain regions. To identify core compensatory regions, fifteen preclinical patients carrying the genetic mutation leading to Huntington's disease and twelve controls underwent fMRI scanning. They accomplished an auditory paced finger sequence tapping task, which challenged cognitive as well as executive aspects of motor functioning by varying speed and complexity of movements. To investigate causal interactions among brain regions a single Dynamic Causal Model (DCM) was constructed and fitted to the data from each subject. The DCM parameters were analysed using statistical methods to assess group differences in connectivity, and the relationship between connectivity patterns and predicted years to clinical onset was assessed in gene carriers.In preclinical patients, we found indications for neural reserve mechanisms predominantly driven by bilateral dorsal premotor cortex, which increasingly activated superior parietal cortices the closer individuals were to estimated clinical onset. This compensatory mechanism was restricted to complex movements characterised by high cognitive demand. Additionally, we identified task-induced connectivity changes in both groups of subjects towards pre- and caudal supplementary motor areas, which were linked to either faster or more complex task conditions. Interestingly, coupling of dorsal premotor cortex and supplementary motor area was more negative in controls compared to gene mutation carriers. Furthermore, changes in the connectivity pattern of gene carriers allowed prediction of the years to estimated disease onset in individuals.Our study characterises the connectivity pattern of core cortical regions maintaining motor function in relation to varying task demand. We identified connections of bilateral dorsal premotor cortex as critical for compensation as well as task-dependent recruitment of pre- and caudal supplementary motor area. The latter finding nicely mirrors a previously published general linear model-based analysis of the same data. Such knowledge about disease specific inter-regional effective connectivity may help identify foci for interventions based on transcranial magnetic stimulation designed to stimulate functioning and also to predict their impact on other regions in motor-associated networks.

Multivariate granger causality analysis of acupuncture effects in mild cognitive impairment patients: an FMRI study.

Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments.

A role for neural modeling in the study of brain disorders.

A role for neural modeling in the study of brain disorders.

Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems.

Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a “node” in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an “instantaneous” connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis.

Noradrenergic enhancement improves motor network connectivity in stroke patients

Both animal and human data suggest that noradrenergic stimulation may enhance motor performance after brain damage. We conducted a placebo-controlled, double-blind and crossover design study to investigate the effects of noradrenergic stimulation on the cortical motor system in hemiparetic stroke patients. Stroke patients (n = 11) in the subacute or chronic stage with mild-to-moderate hand paresis received a single oral dose of 6 mg reboxetine (RBX), a selective noradrenaline reuptake inhibitor. We used functional magnetic resonance imaging and dynamic causal modeling to assess changes in neural activity and interregional effective connectivity while patients moved their paretic hand. RBX stimulation significantly increased maximum grip power and index finger-tapping frequency of the paretic hand. Enhanced motor performance was associated with a reduction of cortical "hyperactivity" toward physiological levels as observed in healthy control subjects, especially in the ipsilesional ventral premotor cortex (vPMC) and supplementary motor area (SMA), but also in the temporoparietal junction and prefrontal cortex. Connectivity analyses revealed that in stroke patients neural coupling with SMA or vPMC was significantly reduced compared with healthy controls. This "hypoconnectivity" was partially normalized when patients received RBX, especially for the coupling of ipsilesional SMA with primary motor cortex. The data suggest that noradrenergic stimulation by RBX may help to modulate the pathologically altered motor network architecture in stroke patients, resulting in increased coupling of ipsilesional motor areas and thereby improved motor function.

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.

The integration of large-scale neural network modeling and functional brain imaging in speech motor control

Speech production demands a number of integrated processing stages. The system must encode the speech motor programs that command movement trajectories of the articulators and monitor transient spatiotemporal variations in auditory and somatosensory feedback. Early models of this system proposed that independent neural regions perform specialized speech processes. As technology advanced, neuroimaging data revealed that the dynamic sensorimotor processes of speech require a distributed set of interacting neural regions. The DIVA (Directions into Velocities of Articulators) neurocomputational model elaborates on early theories, integrating existing data and contemporary ideologies, to provide a mechanistic account of acoustic, kinematic, and functional magnetic resonance imaging (fMRI) data on speech acquisition and production. This large-scale neural network model is composed of several interconnected components whose cell activities and synaptic weight strengths are governed by differential equations. Cells in the model are associated with neuroanatomical substrates and have been mapped to locations in Montreal Neurological Institute stereotactic space, providing a means to compare simulated and empirical fMRI data. The DIVA model also provides a computational and neurophysiological framework within which to interpret and organize research on speech acquisition and production in fluent and dysfluent child and adult speakers. The purpose of this review article is to demonstrate how the DIVA model is used to motivate and guide functional imaging studies. We describe how model predictions are evaluated using voxel-based, region-of-interest-based parametric analyses and inter-regional effective connectivity modeling of fMRI data.

How well does structural equation modeling reveal abnormal brain anatomical connections? An fMRI simulation study

Many brain disorders result from alterations in the strength of anatomical connectivity between different brain regions. This study investigates whether such alterations can be revealed by examining differences in interregional effective connectivity between patient and normal subjects. We applied one prominent effective connectivity method – Structural Equation Modeling (SEM) – to simulated functional MRI (fMRI) timeseries from a neurobiologically realistic network model in which the anatomical connectivity is known and can be manipulated. These timeseries were simulated for two task conditions, a delayed match-to-sample (DMS) task and passive-viewing, and for “normal subjects” and “patients” who had one weakened anatomical connection in the neural network model. SEM results were compared between task conditions as well as between groups. A significantly reduced effective connectivity corresponding to the weakened anatomical connection during the DMS task was found. We also obtained a significantly reduced set of effective connections in the patient networks for anatomical connections “downstream” from the weakened linkage. However, some “upstream” effective connections were significantly larger in the patient group relative to normals. Finally, we found that of the SEM model measures we examined, the total error variance was the best at distinguishing a patient network from a normal network. These results suggest that caution is necessary in applying effective connectivity methods to fMRI data obtained from non-normal populations, and emphasize that functional interactions among network elements can appear as abnormal even if only part of a network is damaged.

Combining Path Analysis with Time-resolved Functional Magnetic Resonance Imaging: The Neurocognitive Network Underlying Mental Rotation

There is strong evidence to suggest that the complex cognitive process underlying mental rotation does not have a discrete neural correlate, but is represented as a distributed neural system. Although the neuroanatomical nodes of this so-called rotation network are well established, there is as yet little empirical evidence to indicate how these nodes interact during task performance. Using an optimized, event-related paradigm, this study aimed to test a previously proposed hypothetical neurocognitive network for mental rotation in female subjects with path analysis, and to examine changes in effective connections across different levels of task difficulty. Path analysis was carried out in combination with a time-resolved functional magnetic resonance imaging (fMRI) analysis in order to relate the observed changes on the network level to changes in specific temporal characteristics of the hemodynamic response function on the level of individual neuroanatomical nodes. Overall, it was found that the investigated sequential model did not provide an adequate fit to the data and that a model with parallel information processing was superior to the serial model. This finding challenges traditional cognitive models describing the complex cognitive process underlying mental rotation by a set of sequentially organized, functionally distinct processing stages. It was further demonstrated that the observed in interregional effective connectivity changes with the level of task demand. These changes were directly related to the time course of the experimental paradigm. The results of path analysis in fMRI should therefore only be interpreted in the light of a specific experimental design and should not be considered as general indicators of effective connections.