Sebo Uithol

Many studies have identified networks in parietal and prefrontal cortex that are involved in intentional action. Yet, our understanding of the way these networks are involved in intentions is still very limited. In this study, we investigate two characteristics of these processes: context- and reason-dependence of the neural states associated with intentions. We ask whether these states depend on the context a person is in and the reasons they have for choosing an action. We used a combination of functional magnetic resonance imaging (fMRI) and multivariate decoding to directly assess the context- and reason-dependency of the neural states underlying intentions. We show that action intentions can be decoded from fMRI data based on a classifier trained in the same context and with the same reason, in line with previous decoding studies. Furthermore, we found that intentions can be decoded across different reasons for choosing an action. However, decoding across different contexts was not successful. We found anecdotal to moderate evidence against context-invariant information in all regions of interest and for all conditions but one. These results suggest that the neural states associated with intentions are modulated by the context of the action.

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss the ways to infer causality in fMRI research. We also formulate recommendations for the future directions in this area. 1 What is causality? Although inferring causal relations is a fundamental aspect of scientific research, the notion of causation itself can be notoriously difficult to define. The basic idea is straightforward: When process A is the cause of process B, A is necessarily in the past from B, and without A, B would not occur. But in practice, and in dynamic systems su...

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss the ways to infer causality in fMRI research. We also formulate recommendations for the future directions in this area. 1 What is causality? Although inferring causal relations is a fundamental aspect of scientific research, the notion of causation itself can be notoriously difficult to define. The basic idea is straightforward: When process A is the cause of process B, A is necessarily in the past from B, and without A, B would not occur. But in practice, and in dynamic systems such as the brain in particular, the picture is far less clear. First, for any event a large number of (potential) causes can be identified. The efficacy of certain neuronal process in producing behavior is dependent on the state of many other (neuronal) processes, but also on the availability of glucose and oxygen in the brain, etc. In a neuroscientific context, we are generally not interested in most of these causes, but only in a cause that stands out in such a way that it is deemed to provide a substantial part of the explanation, for instance causes that vary with the experimental conditions. However, the contrast between relevant and irrelevant causes (in terms of explanatory power) is arbitrary and strongly dependent on experimental setup, contextual factors, etc. For instance, respiratory movement is typically considered a confound in fMRI experiments, unless the research question concerns the influence of respiration speed on the dynamics of the neuronal networks. In dynamic systems, causal processes are unlikely to be part of a unidirectional chain of events, but rather a causal web, with often mutual influences between process A and B. As a result, it is hard to maintain the temporal ordering of cause and effect and, indeed, a clear separation between them [119]. Furthermore, causation can never be established directly, just correlation [61]. When a correlation is highly stable, we are inclined to infer a causal link. Additional information is then needed to assess the direction of the assumed causal link, as correlation indicates 1 ar X iv :1 70 8. 04 02 0v 1 [ qbi o. Q M ] 1 4 A ug 2 01 7 for association and not for causation [1]. For example, the motor cortex is always active when a movement is made, so we assume a causal link between the two phenomena. The anatomical and physiological properties of the motor cortex, and the timing of the two phenomena provide clues about the direction of causality (i.e. cortical activity causes the movement, and not the other way around). However, only intervention studies, such as delivering Transcranial Magnetic Simulation (TMS, [75]) pulses over the motor cortex or lesion studies, can confirm the causal link between the activity in the motor cortex and behavior. Causal studies in fMRI are based on three types of correlations: correlating neuronal activity to 1) mental and behavioral phenomena; 2) physiological state (such as neurotransmitters, hormones, etc.), and 3) neuronal activity in other parts of the brain. In this review we will focus on the last field of research: establishing causal connections between two or more brain areas. fMRI studies currently use a variety of algorithms to infer causal links [35, 131]. All these methods have different assumptions, advantages and disadvantages (see for instance [141, 136]). In a seminal study by Smith et al., popular approaches to causal processes were compared using synthetic data created with a Dynamic Causal Modeling (DCM, see below) generative model [42]. Surprisingly, most of the methods struggled to perform above chance level, even though the test networks were sparse and the noise levels introduced to the model were low compared to what one would expect in real recordings. This raises the question: given the characteristics of fMRI data (low temporal resolution, slow haemodynamics, low signal-to-noise ratio; see Section 2) and the fact that causal webs in the brain are likely dense and dynamic, is it in principle possible to investigate causality in the brain using MRI? In this review, we discuss this question. First, we identify seven characteristics of models used to study causality. Then, we compare and contrast the popular approaches to the causal research in fMRI according to these criteria. Our list of features of causality is as follows: 1. Sign of connections: Can the algorithm distinguish between excitatory and inhibitory causal relations? In this context, we do not mean synaptic effects, but rather an overall…

Humans have a remarkable capacity to arrange and rearrange perceptual input according to different categorizations. This begs the question whether the categorization is exclusively a higher visual or amodal process, or whether categorization processes influence early visual areas as well. To investigate this we scanned healthy participants in a magnetic resonance imaging scanner during a conceptual decision task in which participants had to answer questions about upcoming images of animals. Early visual cortices (V1 and V2) contained information about the current visual input, about the granularity of the forthcoming categorical decision, as well as perceptual expectations about the upcoming visual stimulus. The middle temporal gyrus, the anterior temporal lobe, and the inferior frontal gyrus were also involved in the categorization process, constituting an attention and control network that modulates perceptual processing. These findings provide further evidence that early visual p...

Representations in cognitive neuroscienceExplanations in terms of representations are ubiquitous in cognitive neuroscience. In this paper I will show that the question of who is using the representation is of crucial importance, but not often explicitly answered. Two possible users, the scientist and the cognitive system are theoretically strictly distinct, but the distinction is in practice often blurred. It is tempting to jump from ‘representations to the scientist’ to ‘representations to the system’. This step, however, is unwarranted. I will show that representations to the scientist are not in themselves problematic, and can even be useful, but can lead to wrong conclusions. The problems with representations for the system are more fundamental.

Many studies have identified networks in parietal and prefrontal cortex that are involved in intentional action. Yet, knowledge about what these networks exactly encoded is still scarce. In this study we look into the content of those processes. We ask whether the neural representations of intentions are context- and reason-invariant, or whether these processes depend on the context we are in, and the reasons we have for choosing an action. We use a combination of functional magnetic resonance imaging and multivariate decoding to directly assess the context- and reason-dependency of the processes underlying intentional action. We were able to decode action decisions in the same context and for the same reasons from the fMRI data, in line with previous decoding studies. Furthermore, we could decode action decisions across different reasons for choosing an action. Importantly, though, decoding decisions across different contexts was at chance level. These results suggest that for volu...

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel’s Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

In this review, we discuss the actual and active dependence of social cognitive processes on the body, i.e., that part of the organism beyond the central nervous system. In particular, we will discuss mirror mechanisms, and assess the extent to which the body is recruited during these processes. We show that for emotion mirroring, this dependency is well-documented, but for action mirroring far less so. By reviewing these mechanisms and processes while contrasting body from brain, and social from general cognition, we show that both contrasts are arbitrary and problematic and that any study of cognitive processes, both social and general, should take the body into account.

In this review, we discuss the actual and active dependence of social cognitive processes on the body, i.e., that part of the organism beyond the central nervous system. In particular, we will discuss mirror mechanisms, and assess the extent to which the body is recruited during these processes. We show that for emotion mirroring, this dependency is well-documented, but for action mirroring far less so. By reviewing these mechanisms and processes while contrasting body from brain, and social from general cognition, we show that both contrasts are arbitrary and problematic and that any study of cognitive processes, both social and general, should take the body into account. WIREs Cogn Sci 2015, 6:453-460. doi: 10.1002/wcs.1357 For further resources related to this article, please visit the WIREs website.

In analyses of the motor system, two hierarchies are often posited: The first—the action hierarchy—is a decomposition of an action into subactions and sub-subactions. The second—the control hierarchy—is a postulated hierarchy in the neural control processes that are supposed to bring about the action. A general assumption in cognitive neuroscience is that these two hierarchies are internally consistent and provide complementary descriptions of neuronal control processes. In this article, we suggest that neither offers a complete explanation and that they cannot be reconciled in a logical or conceptually coherent way. Furthermore, neither pays proper attention to the dynamics and temporal aspects of neural control processes. We will explore an alternative hierarchical organization in which causality is inherent in the dynamics over time. Specifically, high levels of the hierarchy encode more stable (goal-related) representations, whereas lower levels represent more transient (actions...

The discovery of mirror neurons in the 1990s has led to much excitement in the cognitive neurosciences. After the initial discovery more and more abilities have been attributed to these neurons. As mirror neurons are commonly viewed as vehicles of representation, we analyze the increasingly wider representational role mirror neurons play and argue for a principled distinction between mirror and non-mirror neurons.