Investigating the functional role of callosal connections with dynamic causal models

Propagation of BOLD Activity Reveals Task-dependent Directed Interactions Across Human Visual Cortex

It has recently been shown that large-scale propagation of blood-oxygen-level-dependent (BOLD) activity is constrained by anatomical connections and reflects transitions between behavioral states. It remains to be seen, however, if the propagation of BOLD activity can also relate to the brain's anatomical structure at a more local scale. Here, we hypothesized that BOLD propagation reflects structured neuronal activity across early visual field maps. To explore this hypothesis, we characterize the propagation of BOLD activity across V1, V2, and V3 using a modeling approach that aims to disentangle the contributions of local activity and directed interactions in shaping BOLD propagation. It does so by estimating the effective connectivity (EC) and the excitability of a noise-diffusion network to reproduce the spatiotemporal covariance structure of the data. We apply our approach to 7T fMRI recordings acquired during resting state (RS) and visual field mapping (VFM). Our results reveal different EC interactions and changes in cortical excitability in RS and VFM, and point to a reconfiguration of feedforward and feedback interactions across the visual system. We conclude that the propagation of BOLD activity has functional relevance, as it reveals directed interactions and changes in cortical excitability in a task-dependent manner.

Diffusional kurtosis imaging of the corpus callosum in autism

Background

The corpus callosum is implicated in the pathophysiology of autism spectrum disorder (ASD). However, specific structural deficits and underlying mechanisms are yet to be well defined.

Methods

We employed diffusional kurtosis imaging (DKI) metrics to characterize white matter properties within five discrete segments of the corpus callosum in 17 typically developing (TD) adults and 16 age-matched participants with ASD without co-occurring intellectual disability (ID). The DKI metrics included axonal water fraction (f_axon) and intra-axonal diffusivity (D_axon), which reflect axonal density and caliber, and extra-axonal radial (RD_extra) and axial (AD_extra) diffusivities, which reflect myelination and microstructural organization of the extracellular space. The relationships between DKI metrics and processing speed, a cognitive feature known to be impaired in ASD, were also examined.

Results

ASD group had significantly decreased callosal f_axon and D_axon (p = .01 and p = .045), particularly in the midbody, isthmus, and splenium. Regression analysis showed that variation in DKI metrics, primarily in the mid and posterior callosal regions explained up to 70.7% of the variance in processing speed scores for TD (p = .001) but not for ASD (p > .05).

Conclusion

Decreased DKI metrics suggested that ASD may be associated with axonal deficits such as reduced axonal caliber and density in the corpus callosum, especially in the mid and posterior callosal areas. These data suggest that impaired interhemispheric connectivity may contribute to decreased processing speed in ASD participants.

Electronic supplementary material

The online version of this article (10.1186/s13229-018-0245-1) contains supplementary material, which is available to authorized users.

Mechanisms of hemispheric lateralization: A replication study

It has been shown, using functional magnetic resonance imaging (fMRI), that hemispheric lateralization of brain activity depends on the requirements of the cognitive task performed during the processing of a sensory stimulus rather than on the intrinsic characteristics of that stimulus [Stephan et al., 2003, Science 301 (5631): 384-6]. Task-dependent increase in the coupling of the anterior cingulate cortex (ACC), a region involved in cognitive control, and brain areas in the left prefrontal and right parietal cortex, respectively, regions involved in task execution, was proposed as the mechanism underlying this task-dependency of hemispheric lateralization. The aim of the present study was two-fold: First, we aimed for a conceptual replication of these findings in an independent sample of subjects. Second, we investigated the test-retest reliability of the imaging paradigm to assess whether the task can be used to capture reliable measures of inter-individual differences in hemispheric lateralization. We were able to confirm previous findings showing that hemispheric lateralization depends on the nature of the cognitive task rather than on the nature of the processed stimuli. The task-related brain activation patterns were highly reliable across sessions (as indicated by intra-class correlation coefficients - ICCs, ≥.51). We could, however, not replicate previous results proposing task-dependent changes in the coupling between ACC and brain regions for task execution as the mechanism underlying hemispheric lateralization. This re-opens the question which mechanisms could determine the task-dependent functional asymmetries that were observed previously and replicated in this study.

Computational neuroimaging strategies for single patient predictions

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

Handedness is related to neural mechanisms underlying hemispheric lateralization of face processing

While the right-hemispheric lateralization of the face perception network is well established, recent evidence suggests that handedness affects the cerebral lateralization of face processing at the hierarchical level of the fusiform face area (FFA). However, the neural mechanisms underlying differential hemispheric lateralization of face perception in right- and left-handers are largely unknown. Using dynamic causal modeling (DCM) for fMRI, we aimed to unravel the putative processes that mediate handedness-related differences by investigating the effective connectivity in the bilateral core face perception network. Our results reveal an enhanced recruitment of the left FFA in left-handers compared to right-handers, as evidenced by more pronounced face-specific modulatory influences on both intra- and interhemispheric connections. As structural and physiological correlates of handedness-related differences in face processing, right- and left-handers varied with regard to their gray matter volume in the left fusiform gyrus and their pupil responses to face stimuli. Overall, these results describe how handedness is related to the lateralization of the core face perception network, and point to different neural mechanisms underlying face processing in right- and left-handers. In a wider context, this demonstrates the entanglement of structurally and functionally remote brain networks, suggesting a broader underlying process regulating brain lateralization.

What do patients with epilepsy tell us about language dynamics? A review of fMRI studies

The objective of this review is to resume major neuroimaging findings on language organization and plasticity in patients with focal and refractory epilepsy, to discuss the effect of modulatory variables that should be considered alongside patterns of reorganization, and to propose possible models of language reorganization. The focal and refractory epilepsy provides a real opportunity to investigate various types of language reorganization in different conditions. The 'chronic' condition (induced by the epileptogenic zone or EZ) is associated with either recruitment of homologous regions of the opposite hemisphere or recruitment of intrahemispheric, nonlinguistic regions. In the 'acute' condition (neurosurgery and EZ resection), the initial interhemispheric shift (induced by the chronic EZ) could follow a reverse direction, back to the initial hemisphere. These different patterns depend on several modulatory factors and are associated with various levels of language performance. As a neuroimaging tool, functional magnetic resonance imaging enables the detailed investigation of both hemispheres simultaneously and allows for comparison with healthy controls, potentially creating a more comprehensive and more realistic picture of brain-language relations. Importantly, functional neuroimaging approaches demonstrate a good degree of concordance on a theoretical level, but also a considerable degree of individual variability, attesting to the clinical importance with these methods to establish, empirically, language localization in individual patients. Overall, the unique features of epilepsy, combined with ongoing advances in technology, promise further improvement in understanding of language substrate.

Differential contribution of bilateral supplementary motor area to the effective connectivity networks induced by task conditions using dynamic causal modeling

Functional imaging studies have indicated hemispheric asymmetry of activation in bilateral supplementary motor area (SMA) during unimanual motor tasks. However, the hemispherically special roles of bilateral SMAs on primary motor cortex (M1) in the effective connectivity networks (ECN) during lateralized tasks remain unclear. Aiming to study the differential contribution of bilateral SMAs during the motor execution and motor imagery tasks, and the hemispherically asymmetric patterns of ECN among regions involved, the present study used dynamic causal modeling to analyze the functional magnetic resonance imaging data of the unimanual motor execution/imagery tasks in 12 right-handed subjects. Our results demonstrated that distributions of network parameters underlying motor execution and motor imagery were significantly different. The variation was mainly induced by task condition modulations of intrinsic coupling. Particularly, regardless of the performing hand, the task input modulations of intrinsic coupling from the contralateral SMA to contralateral M1 were positive during motor execution, while varied to be negative during motor imagery. The results suggested that the inhibitive modulation suppressed the overt movement during motor imagery. In addition, the left SMA also helped accomplishing left hand tasks through task input modulation of left SMA→right SMA connection, implying that hemispheric recruitment occurred when performing nondominant hand tasks. The results specified differential and altered contributions of bilateral SMAs to the ECN during unimanual motor execution and motor imagery, and highlighted the contributions induced by the task input of motor execution/imagery.

Behavioral evidence for inter-hemispheric cooperation during a lexical decision task: a divided visual field experiment

HIGHLIGHTS

The redundant bilateral visual presentation of verbal stimuli decreases asymmetry and increases the cooperation between the two hemispheres.

The increased cooperation between the hemispheres is related to semantic information during lexical processing.

The inter-hemispheric interaction is represented by both inhibition and cooperation.

This study explores inter-hemispheric interaction (IHI) during a lexical decision task by using a behavioral approach, the bilateral presentation of stimuli within a divided visual field experiment. Previous studies have shown that compared to unilateral presentation, the bilateral redundant (BR) presentation decreases the inter-hemispheric asymmetry and facilitates the cooperation between hemispheres. However, it is still poorly understood which type of information facilitates this cooperation. In the present study, verbal stimuli were presented unilaterally (left or right visual hemi-field successively) and bilaterally (left and right visual hemi-field simultaneously). Moreover, during the bilateral presentation of stimuli, we manipulated the relationship between target and distractors in order to specify the type of information which modulates the IHI. Thus, three types of information were manipulated: perceptual, semantic, and decisional, respectively named pre-lexical, lexical and post-lexical processing. Our results revealed left hemisphere (LH) lateralization during the lexical decision task. In terms of inter-hemisphere interaction, the perceptual and decision-making information increased the inter-hemispheric asymmetry, suggesting the inhibition of one hemisphere upon the other. In contrast, semantic information decreased the inter-hemispheric asymmetry, suggesting cooperation between the hemispheres. We discussed our results according to current models of IHI and concluded that cerebral hemispheres interact and communicate according to various excitatory and inhibitory mechanisms, all which depend on specific processes and various levels of word processing.

Dynamic network structure of interhemispheric coordination

Fifty years ago Gazzaniga and coworkers published a seminal article that discussed the separate roles of the cerebral hemispheres in humans. Today, the study of interhemispheric communication is facilitated by a battery of novel data analysis techniques drawn from across disciplinary boundaries, including dynamic systems theory and network theory. These techniques enable the characterization of dynamic changes in the brain's functional connectivity, thereby providing an unprecedented means of decoding interhemispheric communication. Here, we illustrate the use of these techniques to examine interhemispheric coordination in healthy human participants performing a split visual field experiment in which they process lexical stimuli. We find that interhemispheric coordination is greater when lexical information is introduced to the right hemisphere and must subsequently be transferred to the left hemisphere for language processing than when it is directly introduced to the language-dominant (left) hemisphere. Further, we find that putative functional modules defined by coherent interhemispheric coordination come online in a transient manner, highlighting the underlying dynamic nature of brain communication. Our work illustrates that recently developed dynamic, network-based analysis techniques can provide novel and previously unapproachable insights into the role of interhemispheric coordination in cognition.

Corpus callosum in aging and neurodegenerative diseases

SUMMARY The corpus callosum (CC) is a major white matter bundle that connects primarily homologous areas of the cortex. The structure may be involved in interhemispheric communication and enable the lateralization of certain cerebral functions. Despite its possible role as the main conduit for interhemispheric communication, interest from researchers has, at times, been sparse. Renewed interest has led to research that has shown that the CC may play a role in both cognitive aging and neurodegenerative diseases including Alzheimer´s disease and frontotemporal dementia. Studies employing structural MRI and diffusion-weighted MRI have found distinct subregional patterns of callosal atrophy in aging, Alzheimer´s disease and frontotemporal dementia. Furthermore, imaging studies may help to elucidate the underlying pathological mechanisms of callosal atrophy. The present review aims to provide an overview of the current knowledge of the structure and function of the CC and its role in aging and neurodegenerative disease.

fMRI and corpus callosum relationships in monozygotic twins discordant for handedness

To further investigate brain structure and function in 26 handedness discordant monozygotic twin pairs (MzHd), MRI and behavioural assessments were carried out. These showed significant correlation between language-specific functional laterality in inferior and middle frontal gyri, and anterior corpus callosum. Previous studies of handedness discordant monozygotic twins failed to resolve the issue concerning handedness and hemispheric laterality for language due to methodological disparities. The results would be relevant to genetic theories as well as to brain structure:function explanations. MzHd twins underwent MRI and fMRI scanning as well as behavioural assessment of motor performance and cognition. There were significant differences on MRI and fMRI laterality measures, as well as a significant correlation between anterior callosal widths and functional laterality. LH twins showed higher frequencies of atypical functional laterality. There was no significant within-twin pair correlation on fMRI verbal laterality, nor did results show within-twin pair differences on verbal fluency or IQ. Implications for the field of laterality research pertain to frontal hemispheric equipotentiality for verbal processes in healthy individuals. In particular, there can be an apparent lack of cognitive 'cost' to atypical laterality. An fMRI verbal laterality index correlated significantly with corpus callosum widths near Broca's area.

Disordered corticolimbic interactions during affective processing in children and adolescents at risk for schizophrenia revealed by functional magnetic resonance imaging and dynamic causal modeling

CONTEXT

Disordered functional architecture of brain networks may contribute to the well-documented increased risk for psychiatric disorders in offspring of patients with schizophrenia.

OBJECTIVE

To investigate aberrant interactions between regions associated with affective processing in children and adolescent offspring of patients with schizophrenia (HR-SCZ group) and healthy control subjects using dynamic causal modeling of functional magnetic resonance imaging data.

DESIGN

Subjects participated in a continuous affective processing task during which positive, negative, and neutral valenced faces were presented. Interactions between regions in the brain's face- and emotion-processing network were modeled using dynamic causal modeling. Multiple competing models were evaluated by a combinatorial approach and distinguished at the second level using Bayesian model selection before parameter inference.

SETTING

Participants were recruited from the community.

PARTICIPANTS

Twenty-four controls with no family history of psychosis (to the second degree) and 19 children and adolescent offspring of a parent with schizophrenia (age range, 8 to 20 years).

RESULTS

Bayesian model selection revealed a winning model, the architecture of which revealed bidirectional frontolimbic connections that were modulated by valence. Analyses of parameter estimates revealed that HR-SCZ group members were characterized by (1) decreased driving inputs to the visual cortex; (2) decreased intrinsic coupling, most robustly between frontolimbic regions; and (3) increased modulatory inhibition by negative valence of frontolimbic connections (all P < .01, Bonferroni corrected).

CONCLUSIONS

These results are the first demonstration of network analyses techniques for functional magnetic resonance imaging data in children and adolescents at risk for schizophrenia. Dysfunctional interactions within the emotional processing network provide evidence of latent vulnerabilities that may confer risk for disordered adolescent development and eventually the emergence of the manifest disorder.

Tracing the route to path analysis in neuroimaging

This article provides a personal perspective of the adoption of path analysis (structural equation modeling) to neuroimaging. The paper covers the motivation stemming from the need to merge functional measures with neuroanatomy and early innovations in its application. The use of path analysis as a means to test directional hypotheses about networks is presented along with the development of the complementary method, partial least squares. A method is useful when it provides insights that were previously inaccessible, and reflecting this, the paper concludes with a synopsis of the theoretical developments that arose for the routine use of methods like path analysis.

Top-down modulations in the visual form pathway revealed with dynamic causal modeling

Perception entails interactions between activated brain visual areas and the records of previous sensations, allowing for processes like figure-ground segregation and object recognition. The aim of this study was to characterize top-down effects that originate in the visual cortex and that are involved in the generation and perception of form. We performed a functional magnetic resonance imaging experiment, where subjects viewed 3 groups of stimuli comprising oriented lines with different levels of recognizable high-order structure (none, collinearity, and meaning). Our results showed that recognizable stimuli cause larger activations in anterior visual and frontal areas. In contrast, when stimuli are random or unrecognizable, activations are greater in posterior visual areas, following a hierarchical organization where areas V1/V2 were less active with "collinearity" and the middle occipital cortex was less active with "meaning." An effective connectivity analysis using dynamic causal modeling showed that high-order visual form engages higher visual areas that generate top-down signals, from multiple levels of the visual hierarchy. These results are consistent with a model in which if a stimulus has recognizable attributes, such as collinearity and meaning, the areas specialized for processing these attributes send top-down messages to the lower levels to facilitate more efficient encoding of visual form.

Analyzing effective connectivity with functional magnetic resonance imaging

Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context-dependent changes in connection strengths. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Effective Connectivity of the Fronto-parietal Network during Attentional Control

The ACC, the dorsolateral prefrontal cortex (DLPFC), and the parietal cortex near/along the intraparietal sulcus (IPS) are members of a network subserving attentional control. Our recent study revealed that these regions participate in both response anticipation and conflict processing. However, little is known about the relative contribution of these regions in attentional control and how the dynamic interactions among these regions are modulated by detection of predicted versus unpredicted targets and conflict processing. Here, we examined effective connectivity using dynamic causal modeling among these three regions during a flanker task with or without a target onset cue. We compared various models in which different connections among ACC, DLPFC, and IPS were modulated by bottom–up stimulus-driven surprise and top–down conflict processing using Bayesian model selection procedures. The most optimal of these models incorporated contextual modulation that allowed processing of unexpected (surprising) targets to mediate the influence of the IPS over ACC and DLPFC and conflict processing to mediate the influence of ACC and DLPFC over the IPS. This result suggests that the IPS plays an initiative role in this network in the processing of surprise targets, whereas ACC and DLPFC interact with each other to resolve conflict through attentional modulation implemented via the IPS.

Multi-subject analyses with dynamic causal modeling

Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects' parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects' posterior densities according to Bayes' theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far. Based on simulated data generated from a two-region network we examined the effects of signal-to-noise ratio (SNR) and population heterogeneity on group-level parameter estimates. Data sets were simulated assuming either a homogeneous large population (N=60) with constant connectivities across subjects or a heterogeneous population with varying parameters. TA showed advantages at lower SNR but is limited in its applicability. Because BPA and PVWA take into account posterior (co)variance structure, they can yield non-intuitive results when only considering posterior means. This problem is relevant for high SNR data, pronounced parameter interdependencies and when FFX assumptions are violated (i.e. inhomogeneous groups). It diminishes with decreasing SNR and is absent for models with independent parameters or when FFX assumptions are appropriate. Group results obtained with these FFX approaches should therefore be interpreted carefully by considering estimates of dependencies among model parameters.

Transcallosal transfer of information and functional asymmetry of the human brain

The corpus callosum is the largest commissure in the brain and acts as a "bridge" of nerve fibres connecting the two cerebral hemispheres. It plays a crucial role in interhemispheric integration and is responsible for normal communication and cooperation between the two hemispheres. Evolutionary pressures guiding brain size are accompanied by reduced interhemispheric and enhanced intrahemispheric connectivity. Some lines of evidence suggest that the speed of transcallosal conduction is limited in large brains (e.g., in humans), thus favouring intrahemispheric processing and brain lateralisation. Patterns of directional symmetry/asymmetry of transcallosal transfer time may be related to the degree of brain lateralisation. Neural network modelling and electrophysiological studies on interhemispheric transmission provide data supporting this supposition.

Inter-hemispheric functional coupling of eyes-closed resting EEG rhythms in adolescents with Down syndrome

OBJECTIVE

We tested the hypothesis that inter-hemispheric directional functional coupling of eyes-closed resting EEG rhythms is abnormal in adolescents with Down syndrome (DS).

METHODS

Eyes-closed resting EEG data were recorded in 38 DS adolescents (18.7 years +/-0.67 SE, IQ=49+/-1.9 SE) and in 17 matched normal control subjects (NYoung=19.1 years +/-0.39 SE). The EEG data were recorded from 8 electrodes (Fp1, Fp2, C3, C4, T3, T4, O1, O2) referenced to vertex. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Power of EEG rhythms was evaluated by FFT for control purposes, whereas inter-hemispheric directional EEG functional coupling was computed by directed transfer function (DTF).

RESULTS

As expected, alpha, beta, and gamma power was widely higher in NYoung than DS subjects, whereas the opposite was true for delta power. As a novelty, DTF (directionality) values globally prevailed from right to left occipital areas in NYoung subjects and in the opposite direction in DS patients. A control experiment showed that this DTF difference could not be observed in the comparison between DS adults with mild cognitive impairment and normal age-matched adults.

CONCLUSIONS

These results indicate a peculiar abnormal directional inter-hemispheric interplay in visual occipital areas of DS adolescents.

SIGNIFICANCE

Direction of inter-hemispheric EEG functional coupling unveils a new abnormal brain network feature in DS adolescents.

Mapping causal interregional influences with concurrent TMS-fMRI

Transcranial magnetic stimulation (TMS) produces a direct causal effect on brain activity that can now be studied by new approaches that simultaneously combine TMS with neuroimaging methods, such as functional magnetic resonance imaging (fMRI). In this review we highlight recent concurrent TMS-fMRI studies that illustrate how this novel combined technique may provide unique insights into causal interactions among brain regions in humans. We show how fMRI can detect the spatial topography of local and remote TMS effects and how these may vary with psychological factors such as task-state. Concurrent TMS-fMRI may furthermore reveal how the brain adapts to so-called virtual lesions induced by TMS, and the distributed activity changes that may underlie the behavioural consequences often observed during cortical stimulation with TMS. We argue that combining TMS with neuroimaging techniques allows a further step in understanding the physiological underpinnings of TMS, as well as the neural correlated of TMS-evoked consequences on perception and behaviour. This can provide powerful new insights about causal interactions among brain regions in both health and disease that may ultimately lead to developing more efficient protocols for basic research and therapeutic TMS applications.

Dynamic causal models of steady-state responses

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.

fMRI and its interpretations: an illustration on directional selectivity in area V5/MT

fMRI is a tool to study brain function noninvasively that can reliably identify sites of neural involvement for a given task. However, to what extent can fMRI signals be related to measures obtained in electrophysiology? Can the blood-oxygen-level-dependent signal be interpreted as spatially pooled spiking activity? Here we combine knowledge from neurovascular coupling, functional imaging and neurophysiology to discuss whether fMRI has succeeded in demonstrating one of the most established functional properties in the visual brain, namely directional selectivity in the motion-processing region V5/MT+. We also discuss differences of fMRI and electrophysiology in their sensitivity to distinct physiological processes. We conclude that fMRI constitutes a complement, not a poor-resolution substitute, to invasive techniques, and that it deserves interpretations that acknowledge its stand as a separate signal.

Dynamic causal models and autopoietic systems

Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models) for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated.

The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling

Although motor imagery is widely used for motor learning in rehabilitation and sports training, the underlying mechanisms are still poorly understood. Based on fMRI data sets acquired with very high temporal resolution (300 ms) under motor execution and imagery conditions, we utilized Dynamic Causal Modeling (DCM) to determine effective connectivity measures between supplementary motor area (SMA) and primary motor cortex (M1). A set of 28 models was tested in a Bayesian framework and the by-far best-performing model revealed a strong suppressive influence of the motor imagery condition on the forward connection between SMA and M1. Our results clearly indicate that the lack of activation in M1 during motor imagery is caused by suppression from the SMA. These results highlight the importance of the SMA not only for the preparation and execution of intended movements, but also for suppressing movements that are represented in the motor system but not to be performed.

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 modelling for fMRI: a two-state model

Dynamical causal modelling (DCM) for functional magnetic resonance imaging (fMRI) is a technique to infer directed connectivity among brain regions. These models distinguish between a neuronal level, which models neuronal interactions among regions, and an observation level, which models the hemodynamic responses each region. The original DCM formulation considered only one neuronal state per region. In this work, we adopt a more plausible and less constrained neuronal model, using two neuronal states (populations) per region. Critically, this gives us an explicit model of intrinsic (between-population) connectivity within a region. In addition, by using positivity constraints, the model conforms to the organization of real cortical hierarchies, whose extrinsic connections are excitatory (glutamatergic). By incorporating two populations within each region we can model selective changes in both extrinsic and intrinsic connectivity. Using synthetic data, we show that the two-state model is internal consistent and identifiable. We then apply the model to real data, explicitly modelling intrinsic connections. Using model comparison, we found that the two-state model is better than the single-state model. Furthermore, using the two-state model we find that it is possible to disambiguate between subtle changes in coupling; we were able to show that attentional gain, in the context of visual motion processing, is accounted for sufficiently by an increased sensitivity of excitatory populations of neurons in V5, to forward afferents from earlier visual areas.

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.

Hierarchical processing of auditory objects in humans

This work examines the computational architecture used by the brain during the analysis of the spectral envelope of sounds, an important acoustic feature for defining auditory objects. Dynamic causal modelling and Bayesian model selection were used to evaluate a family of 16 network models explaining functional magnetic resonance imaging responses in the right temporal lobe during spectral envelope analysis. The models encode different hypotheses about the effective connectivity between Heschl's Gyrus (HG), containing the primary auditory cortex, planum temporale (PT), and superior temporal sulcus (STS), and the modulation of that coupling during spectral envelope analysis. In particular, we aimed to determine whether information processing during spectral envelope analysis takes place in a serial or parallel fashion. The analysis provides strong support for a serial architecture with connections from HG to PT and from PT to STS and an increase of the HG to PT connection during spectral envelope analysis. The work supports a computational model of auditory object processing, based on the abstraction of spectro-temporal “templates” in the PT before further analysis of the abstracted form in anterior temporal lobe areas.

The past decade has seen a phenomenal rise in applications of functional magnetic resonance imaging for both research and clinical applications. Most of the applications, however, concentrate on finding the regions of the brain that mediate the processing of a cognitive/motor task without determining the interaction between the identified regions. It is, however, the interactions between the different regions that accomplish a given task. In this study, we have examined the interactions between three regions—Heshl's gyrus (HG), planum temporale (PT), and superior temporal sulcus (STS)—that have been implicated in processing the spectral envelope of sounds. The spectral envelope is one of the dimensions of timbre that determine the identity of two sounds that have the same pitch, duration, and intensity. The interaction between the regions is examined using a system-based mathematical modelling technique called dynamic causal modelling (DCM). It is found that flow of information is serial, with HG sending information to PT and then to STS with the connectivity between HG to PT being effectively increased by the extraction of spectral envelope. The study provides evidence for an earlier hypothesis that PT is a computational hub.

Distinct and convergent visual processing of high and low spatial frequency information in faces

We tested for differential brain response to distinct spatial frequency (SF) components in faces. During a functional magnetic resonance imaging experiment, participants were presented with "hybrid" faces containing superimposed low and high SF information from different identities. We used a repetition paradigm where faces at either SF range were independently repeated or changed across consecutive trials. In addition, we manipulated which SF band was attended. Our results suggest that repetition and attention affected partly overlapping occipitotemporal regions but did not interact. Changes of high SF faces increased responses of the right inferior occipital gyrus (IOG) and left inferior temporal gyrus (ITG), with the latter response being also modulated additively by attention. In contrast, the bilateral middle occipital gyrus (MOG) responded to repetition and attention manipulations of low SF. A common effect of high and low SF repetition was observed in the right fusiform gyrus (FFG). Follow-up connectivity analyses suggested direct influence of the MOG (low SF), IOG, and ITG (high SF) on the FFG responses. Our results reveal that different regions within occipitotemporal cortex extract distinct visual cues at different SF ranges in faces and that the outputs from these separate processes project forward to the right FFG, where the different visual cues may converge.

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.

Mechanisms of hemispheric specialization: insights from analyses of connectivity

Traditionally, anatomical and physiological descriptions of hemispheric specialization have focused on hemispheric asymmetries of local brain structure or local functional properties, respectively. This article reviews the current state of an alternative approach that aims at unraveling the causes and functional principles of hemispheric specialization in terms of asymmetries in connectivity. Starting with an overview of the historical origins of the concept of lateralization, we briefly review recent evidence from anatomical and developmental studies that asymmetries in structural connectivity may be a critical factor shaping hemispheric specialization. These differences in anatomical connectivity, which are found both at the intra- and inter-regional level, are likely to form the structural substrate of different functional principles of information processing in the two hemispheres. The main goal of this article is to describe how these functional principles can be characterized using functional neuroimaging in combination with models of functional and effective connectivity. We discuss the methodology of established models of connectivity which are applicable to data from positron emission tomography and functional magnetic resonance imaging and review published studies that have applied these approaches to characterize asymmetries of connectivity during lateralized tasks. Adopting a model-based approach enables functional imaging to proceed from mere descriptions of asymmetric activation patterns to mechanistic accounts of how these asymmetries are caused.