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376
Human Brain Function
, 1997
"... Dynamic representations and generative models of ..."
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Cited by 192 (15 self)
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Dynamic representations and generative models of
Resolving emotional conflict: A role for the rostral anterior cingulated cortex in modulating activity in the amygdala
- Neuron
, 2006
"... Effective mental functioning requires that cognition be protected from emotional conflict from interference by task-irrelevant emotionally salient stimuli. The neural mechanisms by which the brain detects and resolves emotional conflict are still largely unknown, however. Drawing on the classic Stro ..."
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Cited by 160 (9 self)
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Effective mental functioning requires that cognition be protected from emotional conflict from interference by task-irrelevant emotionally salient stimuli. The neural mechanisms by which the brain detects and resolves emotional conflict are still largely unknown, however. Drawing on the classic Stroop conflict task, we developed a protocol that allowed us to dissociate the generation and monitoring of emotional conflict from its resolution. Using functional magnetic resonance imaging (fMRI), we find that activity in the amygdala and dorsomedial and dorsolateral prefrontal cortices reflects the amount of emotional conflict. By contrast, the resolution of emotional conflict is associated with activation of the rostral anterior cingulate cortex. Activation of the rostral cingulate is predicted by the amount of previous-trial conflict-related neural activity and is accompanied by a simultaneous and correlated reduction of amygdalar activity. These data suggest that emotional conflict is resolved through top-down inhibition of amygdalar activity by the rostral cingulate cortex.
Mapping directed influence over the brain using Granger causality and fMRI
- NEUROIMAGE. 25:230--242
, 2005
"... We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguish ..."
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Cited by 120 (4 self)
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We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.
Attending to the present: Mindfulness meditation reveals distinct modes of self-reference
- Social Cognitive and Affective Neuroscience
, 2007
"... It has long been theorised that there are two temporally distinct forms of self-reference: extended self-reference linking experiences across time, and momentary self-reference centred on the present. To characterise these two aspects of awareness, we used functional magnetic resonance imaging (fMRI ..."
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Cited by 88 (2 self)
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It has long been theorised that there are two temporally distinct forms of self-reference: extended self-reference linking experiences across time, and momentary self-reference centred on the present. To characterise these two aspects of awareness, we used functional magnetic resonance imaging (fMRI) to examine monitoring of enduring traits (’narrative ’ focus, NF) or momentary experience (’experiential ’ focus, EF) in both novice participants and those having attended an 8 week course in mindfulness meditation, a program that trains individuals to develop focused attention on the present. In novices, EF yielded focal reductions in self-referential cortical midline regions (medial prefrontal cortex, mPFC) associated with NF. In trained participants, EF resulted in more marked and pervasive reductions in the mPFC, and increased engagement of a right lateralised network, comprising the lateral PFC and viscerosomatic areas such as the insula, secondary somatosensory cortex and inferior parietal lobule. Functional connectivity analyses further demonstrated a strong coupling between the right insula and the mPFC in novices that was uncoupled in the mindfulness group. These results suggest a fundamental neural dissociation between two distinct forms of self-awareness that are habitually integrated but can be dissociated through attentional training: the self across time and in the present moment.
A neural mass model for MEG/EEG: coupling and neuronal dynamics
- NeuroImage
, 2003
"... Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequency-specific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their func ..."
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Cited by 81 (21 self)
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Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequency-specific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their functional significance remains a matter of debate. Some of the mechanisms that generate these signals are known at the cellular level and rest on a balance of excitatory and inhibitory interactions within and between populations of neurons. The kinetics of the ensuing population dynamics determine the frequency of oscillations. In this work we extended the classical nonlinear lumped-parameter model of alpha rhythms, initially developed by Lopes da Silva and colleagues [Kybernetik 15 (1974) 27], to generate more complex dynamics. We show that the whole spectrum of MEG/EEG signals can be reproduced within the oscillatory regime of this model by simply changing the population kinetics. We used the model to examine the influence of coupling strength and propagation delay on the rhythms generated by coupled cortical areas. The main findings were that (1) coupling induces phase-locked activity, with a phase shift of 0 or π when the coupling is bidirectional, and (2) both coupling and propagation delay are critical determinants of the MEG/EEG spectrum. In forthcoming articles, we will use this model to (1) estimate how neuronal interactions are expressed in MEG/EEG oscillations and establish the construct validity of various indices of nonlinear coupling, and (2) generate event-related transients to derive physiologically informed basis functions for statistical modelling of average evoked responses.
Unified univariate and multivariate random field theory. Neuroimage 23
- Suppl
, 2004
"... We report new random field theory P-values for peaks of canonical correlation SPMs for detecting multiple contrasts in a linear model for multivariate image data. This completes results for all types of univariate and multivariate image data analysis. All other known univariate and multivariate rand ..."
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Cited by 62 (4 self)
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We report new random field theory P-values for peaks of canonical correlation SPMs for detecting multiple contrasts in a linear model for multivariate image data. This completes results for all types of univariate and multivariate image data analysis. All other known univariate and multivariate random field theory results are now special cases, so these new results present a true unification of all currently known results. As an illustration, we use these results in a deformation based morphometry (DBM) analysis to look for regions of the brain where vector deformations of non-missile trauma patients are related to several verbal memory scores, to detect regions of changes in anatomical effective connectivity between the trauma patients and a group of age and sex matched controls, and to look for anatomical connectivity in cortical thickness. 1
The neural correlates and functional integration of cognitive control in a Stroop task
- NeuroImage
, 2005
"... It is well known that performance on a given trial of a cognitive task is affected by the nature of previous trials. For example, conflict effects on interference tasks, such as the Stroop task, are reduced subsequent to high-conflict trials relative to low-conflict trials. This interaction effect b ..."
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Cited by 55 (7 self)
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It is well known that performance on a given trial of a cognitive task is affected by the nature of previous trials. For example, conflict effects on interference tasks, such as the Stroop task, are reduced subsequent to high-conflict trials relative to low-conflict trials. This interaction effect between previous and current trial types is called bconflict adaptationQ and thought to be due to processing adjustments in cognitive control. The current study aimed to identify the neural substrates of cognitive control during conflict adaptation by isolating neural correlates of reduced conflict from those of increased cognitive control. We expected cognitive control to be implemented by prefrontal cortex through context-specific modulation of posterior regions involved in sensory and motor aspects of task performance. We collected event-related fMRI data on a color-word naming Stroop task and found distinct fronto-parietal networks of current trial conflict detection and conflict adaptation through cognitive control. Conflict adaptation was associated with increased activity in left middle frontal gyrus (GFm) and superior frontal gyrus (GFs), consistent with increased cognitive control, and with decreased activity in bilateral prefrontal and parietal cortices, consistent with reduced response conflict. Psychophysiological interaction analysis (PPI) revealed that cognitive control activation in GFs and GFm was accompanied by increased functional integration with bilateral inferior frontal, right temporal and parietal areas, and the anterior cerebellum. These data suggest that cognitive control is implemented by medial and lateral prefrontal cortices that bias processes in regions that have been implicated in high-level perceptual and motor processes.
Multivariate autoregressive modeling of fmri time series. NeuroImage
, 1477
"... We propose the use of Multivariate Autoregressive (MAR) models of fMRI time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterise inter-regional dependence. We extend lin ..."
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Cited by 54 (9 self)
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We propose the use of Multivariate Autoregressive (MAR) models of fMRI time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterise inter-regional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented using Bayesian methods. 2 1
Modelling functional integration: a comparison of structural equation and dynamic causal models
- NeuroImage
, 2004
"... The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper we review two different approaches to modelling ef ..."
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Cited by 44 (2 self)
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The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper we review two different approaches to modelling effective connectivity from fMRI data, Structural Equation Models (SEMs) and Dynamic Causal Models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM a distinction is made between the ‘neuronal level ’ and the ‘hemodynamic level’. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics which in turn cause changes in the observed hemodynamics. In SEM changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.
Implicit multisensory associations influence voice recognition
, 2006
"... Natural objects provide partially redundant information to the brain through different sensory modalities. For example, voices and faces both give information about the speech content, age, and gender of a person. Thanks to this redundancy, multimodal recognition is fast, robust, and automatic. In u ..."
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Cited by 42 (1 self)
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Natural objects provide partially redundant information to the brain through different sensory modalities. For example, voices and faces both give information about the speech content, age, and gender of a person. Thanks to this redundancy, multimodal recognition is fast, robust, and automatic. In unimodal perception, however, only part of the information about an object is available. Here, we addressed whether, even under conditions of unimodal sensory input, crossmodal neural circuits that have been shaped by previous associative learning become activated and underpin a performance benefit. We measured brain activity with functional magnetic resonance imaging before, while, and after participants learned to associate either sensory redundant stimuli, i.e. voices and faces, or arbitrary multimodal combinations, i.e. voices and written names, ring tones, and cell phones or brand names of these cell phones. After learning, participants were better at recognizing unimodal auditory voices that had been paired with faces than those paired with written names, and association of voices with faces resulted in an increased functional coupling between voice and face areas. No such effects were observed for ring tones that had been paired with cell phones or names. These findings demonstrate that brief exposure to ecologically valid and sensory redundant stimulus pairs, such as voices and faces, induces specific multisensory associations. Consistent with predictive coding theories, associative representations become thereafter available for unimodal perception and facilitate object recognition. These data suggest that for natural objects effective predictive signals can be generated across sensory systems and proceed by optimization of functional connectivity between specialized cortical sensory modules.
