The immediacy and directness of our subjective visual experience belies the complexity of the neural mechanisms involved, which remain incompletely understood. This review focuses on how the ...subjective contents of human visual awareness are encoded in neural activity. Empirical evidence to date suggests that no single brain area is both necessary and sufficient for consciousness. Instead, necessary and sufficient conditions appear to involve both activation of a distributed representation of the visual scene in primary visual cortex and ventral visual areas, plus parietal and frontal activity. The key empirical focus is now on characterizing qualitative differences in the type of neural activity in these areas underlying conscious and unconscious processing. To this end, recent progress in developing novel approaches to accurately decoding the contents of consciousness from brief samples of neural activity show great promise.
Theoretically, autism should be underpinned by aberrant brain dynamics. However, how brain activity changes over time in individuals with autism spectrum disorder (ASD) remains unknown. Here we ...characterize brain dynamics in autism using an energy-landscape analysis applied to resting-state fMRI data. Whereas neurotypical brain activity frequently transits between two major brain states via an intermediate state, high-functioning adults with ASD show fewer neural transitions due to an unstable intermediate state, and these infrequent transitions predict the severity of autism. Moreover, in contrast to the controls whose IQ is correlated with the neural transition frequency, IQ scores of individuals with ASD are instead predicted by the stability of their brain dynamics. Finally, such brain-behaviour associations are related to functional segregation between brain networks. These findings suggest that atypical functional coordination in the brains of adults with ASD underpins overly stable neural dynamics, which supports both their ASD symptoms and cognitive abilities.
Neural correlates of consciousness Rees, Geraint
Annals of the New York Academy of Sciences,
August 2013, Letnik:
1296, Številka:
1
Journal Article
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Jon Driver's scientific work was characterized by an innovative combination of new methods for studying mental processes in the human brain in an integrative manner. In our collaborative work, he ...applied this approach to the study of attention and awareness, and their relationship to neural activity in the human brain. Here I review Jon's scientific work that relates to the neural basis of human consciousness, relating our collaborative work to a broader scientific context. I seek to show how his insights led to a deeper understanding of the causal connections between distant brain structures that are now believed to characterize the neural underpinnings of human consciousness.
Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain ...reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.
Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of the literature uses functional connectivity to examine these intrinsic brain ...networks. Functional connectivity has well documented limitations because of its inherent inability to identify causal interactions. Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems — known as effective connectivity. This technical note addresses the validity of a recently proposed DCM for resting state fMRI – as measured in terms of their complex cross spectral density – referred to as spectral DCM. Spectral DCM differs from (the alternative) stochastic DCM by parameterising neuronal fluctuations using scale free (i.e., power law) forms, rendering the stochastic model of neuronal activity deterministic. Spectral DCM not only furnishes an efficient estimation of model parameters but also enables the detection of group differences in effective connectivity, the form and amplitude of the neuronal fluctuations or both. We compare and contrast spectral and stochastic DCM models with endogenous fluctuations or state noise on hidden states. We used simulated data to first establish the face validity of both schemes and show that they can recover the model (and its parameters) that generated the data. We then used Monte Carlo simulations to assess the accuracy of both schemes in terms of their root mean square error. We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation using real resting state fMRI data (from an open access resource) to study the functional integration within default mode network using spectral and stochastic DCMs.
•This paper provides construct validation of spectral DCM against stochastic DCM.•Spectral DCM is shown to be more accurate than stochastic DCM in terms of root mean square error.•Spectral DCM is shown to be more sensitive at identifying group differences.
Humans mind-wander quite intensely. Mind wandering is markedly different from other cognitive behaviors because it is spontaneous, self-generated, and inwardly directed (inner thoughts). However, can ...such an internal and intimate mental function also be modulated externally by means of brain stimulation? Addressing this question could also help identify the neural correlates of mind wandering in a causal manner, in contrast to the correlational methods used previously (primarily functional MRI). In our study, participants performed a monotonous task while we periodically sampled their thoughts to assess mind wandering. Concurrently, we applied transcranial direct current stimulation (tDCS). We found that stimulation of the frontal lobes anode electrode at the left dorsolateral prefrontal cortex (DLPFC), cathode electrode at the right supraorbital area, but not of the occipital cortex or sham stimulation, increased the propensity to mind-wander. These results demonstrate for the first time, to our knowledge, that mind wandering can be enhanced externally using brain stimulation, and that the frontal lobes play a causal role in mind-wandering behavior. These results also suggest that the executive control network associated with the DLPFC might be an integral part of mind-wandering neural machinery.
Significance Mind wandering is a spontaneous and self-generated behavior believed to be important for many mental functions, including creativity and future planning. Can the propensity to mind-wander be modulated externally? If so, this observation would mean that directly modifying spontaneous neural activity can change internally directed thought. To answer this question, we used noninvasive transcranial direct current stimulation (tDCS) to stimulate the prefrontal cortex. Our results showed, for the first time to our knowledge, that mind wandering, probably the most omnipresent internal cognitive function, can be enhanced by external stimulation. In addition, we showed that the frontal lobes play a causal role in mind wandering. We furthermore suggest that the executive control system, and specifically the dorsolateral prefrontal cortex, might play an important role in mind-wandering behavior.
How long neural information is stored in a local brain area reflects functions of that region and is often estimated by the magnitude of the autocorrelation of intrinsic neural signals in the area. ...Here, we investigated such intrinsic neural timescales in high-functioning adults with autism and examined whether local brain dynamics reflected their atypical behaviours. By analysing resting-state fMRI data, we identified shorter neural timescales in the sensory/visual cortices and a longer timescale in the right caudate in autism. The shorter intrinsic timescales in the sensory/visual areas were correlated with the severity of autism, whereas the longer timescale in the caudate was associated with cognitive rigidity. These observations were confirmed from neurodevelopmental perspectives and replicated in two independent cross-sectional datasets. Moreover, the intrinsic timescale was correlated with local grey matter volume. This study shows that functional and structural atypicality in local brain areas is linked to higher-order cognitive symptoms in autism.
When visual input has conflicting interpretations, conscious perception can alternate spontaneously between competing interpretations 1. There is a large amount of unexplained variability between ...individuals in the rate of such spontaneous alternations in perception 2–5. We hypothesized that variability in perceptual rivalry might be reflected in individual differences in brain structure, because brain structure can exhibit systematic relationships with an individual's cognitive experiences and skills 6–9. To test this notion, we examined in a large group of individuals how cortical thickness, local gray-matter density, and local white-matter integrity correlate with individuals' alternation rate for a bistable, rotating structure-from-motion stimulus 10. All of these macroscopic measures of brain structure consistently revealed that the structure of bilateral superior parietal lobes (SPL) could account for interindividual variability in perceptual alternation rate. Furthermore, we examined whether the bilateral SPL regions play a causal role in the rate of perceptual alternations by using transcranial magnetic stimulation (TMS) and found that transient disruption of these areas indeed decreases the rate of perceptual alternations. These findings demonstrate a direct relationship between structure of SPL and individuals' perceptual switch rate.
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► Structure of superior parietal lobe (SPL) predicts switch rate in perceptual rivalry ► White-matter integrity in SPL correlates with individuals' switch rate ► Deactivation of SPL with transcranial magnetic stimulation slows perceptual rivalry
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this ...paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying ...two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.
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