Spatiotemporal information transfer pattern differences in motor selection

Analysis of information transfer between variables in brain images is currently a popular topic, e.g. [1]. Such work typically focuses on average information transfer (i.e. transfer entropy [2]), yet the dynamics of transfer from a source to a destination can also be quantified at individual time points using the local transfer entropy (TE) [3]. This local perspective is known to reveal dynamical structure that the average cannot. We present a method to quantify local TE values in time between source and destination regions of variables in brain-imaging data, combining: a. computation of inter-regional transfer between two regions of variables (e.g. voxels) [1], with b. the local perspective of the dynamics of such transfer in time [3]. Transfer is computed over samples from all variables – there is no training in or subset selection of variables to use. We apply this method to a set of fMRI measurements where we could expect to see differences in local information transfer between two conditions at specific time steps. The fMRI data set analyzed (from [4]) contains brain activity recorded from 7 localized regions while 12 subjects (who gave informed written consent) were asked to freely decide whether to pus

Sequential dependencies between trials in free choice tasks

In two previous experiments we investigated the neural precursors of subjects' "free" choices for one of two options (pressing one of two buttons, and choosing between adding and subtracting numbers). In these experiments the distribution of sequence lengths was taken as an approximate indicator of the randomness (or lack of sequential dependency) of the choice sequences. However, this method is limited in its ability to reveal sequential dependencies. Here we present a more detailed individual-subject analysis and conclude that despite of the presence of significant sequential dependencies the subjects' behavior still approximates randomness, as measured by an entropy rate (on pooled data) of 0.940 bit / trial and 0.965 bit / trial in the two experiments. We also provide the raw single-subject behavioral data

The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods

Standard neuroimaging data analysis based on traditional principles of experimental design, modelling, and statistical inference is increasingly complemented by novel analysis methods, driven e.g. by machine learning methods. While these novel approaches provide new insights into neuroimaging data, they often have unexpected properties, generating a growing literature on possible pitfalls. We propose to meet this challenge by adopting a habit of systematic testing of experimental design, analysis procedures, and statistical inference. Specifically, we suggest to apply the analysis method used for experimental data also to aspects of the experimental design, simulated confounds, simulated null data, and control data. We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided. We describe and discuss this Same Analysis Approach in detail, and demonstrate it in two worked examples using multivariate decoding. With these examples, we reveal two sources of error: A mismatch between counterbalancing (crossover designs) and cross-validation which leads to systematic below-chance accuracies, and linear decoding of a nonlinear effect, a difference in variance

Emergence of qualia from brain activity or from an interaction of proto-consciousness with the brain: which one is the weirder? Available evidence and a research agenda

This contribution to the science of consciousness aims at comparing how two different theories can explain the emergence of different qualia experiences, meta-awareness, meta-cognition, the placebo effect, out-of-body experiences, cognitive therapy and meditation-induced brain changes, etc. The first theory postulates that qualia experiences derive from specific neural patterns, the second one, that qualia experiences derive from the interaction of a proto-consciousness with the brain\u2019s neural activity. From this comparison it will be possible to judge which one seems to better explain the different qualia experiences and to offer a more promising research agenda

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Testing Simulation Theory with Cross-Modal Multivariate Classification of fMRI Data

The discovery of mirror neurons has suggested a potential neural basis for simulation and common coding theories of action perception, theories which propose that we understand other people's actions because perceiving their actions activates some of our neurons in much the same way as when we perform the actions. We propose testing this model directly in humans with functional magnetic resonance imaging (fMRI) by means of cross-modal classification. Cross-modal classification evaluates whether a classifier that has learned to separate stimuli in the sensory domain can also separate the stimuli in the motor domain. Successful classification provides support for simulation theories because it means that the fMRI signal, and presumably brain activity, is similar when perceiving and performing actions. In this paper we demonstrate the feasibility of the technique by showing that classifiers which have learned to discriminate whether a participant heard a hand or a mouth action, based on the activity patterns in the premotor cortex, can also determine, without additional training, whether the participant executed a hand or mouth action. This provides direct evidence that, while perceiving others' actions, (1) the pattern of activity in premotor voxels with sensory properties is a significant source of information regarding the nature of these actions, and (2) that this information shares a common code with motor execution

Decoding the neural mechanisms of human tool use.

Sophisticated tool use is a defining characteristic of the primate species but how is it supported by the brain, particularly the human brain? Here we show, using functional MRI and pattern classification methods, that tool use is subserved by multiple distributed action-centred neural representations that are both shared with and distinct from those of the hand. In areas of frontoparietal cortex we found a common representation for planned hand- and tool-related actions. In contrast, in parietal and occipitotemporal regions implicated in hand actions and body perception we found that coding remained selectively linked to upcoming actions of the hand whereas in parietal and occipitotemporal regions implicated in tool-related processing the coding remained selectively linked to upcoming actions of the tool. The highly specialized and hierarchical nature of this coding suggests that hand- and tool-related actions are represented separately at earlier levels of sensorimotor processing before becoming integrated in frontoparietal cortex. DOI:http://dx.doi.org/10.7554/eLife.00425.001

Led into Temptation? Rewarding Brand Logos Bias the Neural Encoding of Incidental Economic Decisions

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness