Robin L. Carhart-Harris

Bio: Robin L. Carhart-Harris is an academic researcher from Imperial College London. The author has contributed to research in topics: Psilocybin & Medicine. The author has an hindex of 52, co-authored 160 publications receiving 9404 citations. Previous affiliations of Robin L. Carhart-Harris include South London and Maudsley NHS Foundation Trust & University of California, San Francisco.

Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin

Abstract: Psychedelic drugs have a long history of use in healing ceremonies, but despite renewed interest in their therapeutic potential, we continue to know very little about how they work in the brain. Here we used psilocybin, a classic psychedelic found in magic mushrooms, and a task-free functional MRI (fMRI) protocol designed to capture the transition from normal waking consciousness to the psychedelic state. Arterial spin labeling perfusion and blood-oxygen level-dependent (BOLD) fMRI were used to map cerebral blood flow and changes in venous oxygenation before and after intravenous infusions of placebo and psilocybin. Fifteen healthy volunteers were scanned with arterial spin labeling and a separate 15 with BOLD. As predicted, profound changes in consciousness were observed after psilocybin, but surprisingly, only decreases in cerebral blood flow and BOLD signal were seen, and these were maximal in hub regions, such as the thalamus and anterior and posterior cingulate cortex (ACC and PCC). Decreased activity in the ACC/medial prefrontal cortex (mPFC) was a consistent finding and the magnitude of this decrease predicted the intensity of the subjective effects. Based on these results, a seed-based pharmaco-physiological interaction/functional connectivity analysis was performed using a medial prefrontal seed. Psilocybin caused a significant decrease in the positive coupling between the mPFC and PCC. These results strongly imply that the subjective effects of psychedelic drugs are caused by decreased activity and connectivity in the brain's key connector hubs, enabling a state of unconstrained cognition.

The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs

Abstract: Entropy is a dimensionless quantity that is used for measuring uncertainty about the state of a system but it can also imply physical qualities, where high entropy is synonymous with high disorder. Entropy is applied here in the context of states of consciousness and their associated neural dynamics, with a particular focus on the psychedelic state. The psychedelic state is considered an exemplar of a primitive or primary state of consciousness that preceded the development of modern, adult, human, normal waking consciousness. Based on neuroimaging data with psilocybin, a classic psychedelic drug, it is argued that the defining feature of ‘primary states’ is elevated entropy in certain aspects of brain function, such as the repertoire of functional connectivity motifs that form and fragment across time. It is noted that elevated entropy in this sense, is a characteristic of systems exhibiting ‘self-organised criticality’, i.e., a property of systems that gravitate towards a ‘critical’ point in a transition zone between order and disorder in which certain phenomena such as power-law scaling appear. This implies that entropy is suppressed in normal waking consciousness, meaning that the brain operates just below criticality. It is argued that this entropy suppression furnishes consciousness with a constrained quality and associated metacognitive functions, including reality-testing and self-awareness. It is also proposed that entry into primary states depends on a collapse of the normally highly organised activity within the default-mode network (DMN) and a decoupling between the DMN and the medial temporal lobes (which are normally significantly coupled). These hypotheses can be tested by examining brain activity and associated cognition in other candidate primary states such as REM sleep and early psychosis and comparing these with non-primary states such as normal waking consciousness and the anaesthetised state.

Neural correlates of the LSD experience revealed by multimodal neuroimaging

Abstract: Lysergic acid diethylamide (LSD) is the prototypical psychedelic drug, but its effects on the human brain have never been studied before with modern neuroimaging. Here, three complementary neuroimaging techniques: arterial spin labeling (ASL), blood oxygen level-dependent (BOLD) measures, and magnetoencephalography (MEG), implemented during resting state conditions, revealed marked changes in brain activity after LSD that correlated strongly with its characteristic psychological effects. Increased visual cortex cerebral blood flow (CBF), decreased visual cortex alpha power, and a greatly expanded primary visual cortex (V1) functional connectivity profile correlated strongly with ratings of visual hallucinations, implying that intrinsic brain activity exerts greater influence on visual processing in the psychedelic state, thereby defining its hallucinatory quality. LSD’s marked effects on the visual cortex did not significantly correlate with the drug’s other characteristic effects on consciousness, however. Rather, decreased connectivity between the parahippocampus and retrosplenial cortex (RSC) correlated strongly with ratings of “ego-dissolution” and “altered meaning,” implying the importance of this particular circuit for the maintenance of “self” or “ego” and its processing of “meaning.” Strong relationships were also found between the different imaging metrics, enabling firmer inferences to be made about their functional significance. This uniquely comprehensive examination of the LSD state represents an important advance in scientific research with psychedelic drugs at a time of growing interest in their scientific and therapeutic value. The present results contribute important new insights into the characteristic hallucinatory and consciousness-altering properties of psychedelics that inform on how they can model certain pathological states and potentially treat others.

Homological scaffolds of brain functional networks

Abstract: Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. ...

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Flow The Psychology Of Optimal Experience

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The role of the posterior cingulate cortex in cognition and disease.

Abstract: The posterior cingulate cortex is a highly connected and metabolically active brain region. Recent studies suggest it has an important cognitive role, although there is no consensus about what this is. The region is typically discussed as having a unitary function because of a common pattern of relative deactivation observed during attentionally demanding tasks. One influential hypothesis is that the posterior cingulate cortex has a central role in supporting internally-directed cognition. It is a key node in the default mode network and shows increased activity when individuals retrieve autobiographical memories or plan for the future, as well as during unconstrained ‘rest’ when activity in the brain is ‘free-wheeling’. However, other evidence suggests that the region is highly heterogeneous and may play a direct role in regulating the focus of attention. In addition, its activity varies with arousal state and its interactions with other brain networks may be important for conscious awareness. Understanding posterior cingulate cortex function is likely to be of clinical importance. It is well protected against ischaemic stroke, and so there is relatively little neuropsychological data about the consequences of focal lesions. However, in other conditions abnormalities in the region are clearly linked to disease. For example, amyloid deposition and reduced metabolism is seen early in Alzheimer’s disease. Functional neuroimaging studies show abnormalities in a range of neurological and psychiatric disorders including Alzheimer’s disease, schizophrenia, autism, depression and attention deficit hyperactivity disorder, as well as ageing. Our own work has consistently shown abnormal posterior cingulate cortex function following traumatic brain injury, which predicts attentional impairments. Here we review the anatomy and physiology of the region and how it is affected in a range of clinical conditions, before discussing its proposed functions. We synthesize key findings into a novel model of the region’s function (the ‘Arousal, Balance and Breadth of Attention’ model). Dorsal and ventral subcomponents are functionally separated and differences in regional activity are explained by considering: (i) arousal state; (ii) whether attention is focused internally or externally; and (iii) the breadth of attentional focus. The predictions of the model can be tested within the framework of complex dynamic systems theory, and we propose that the dorsal posterior cingulate cortex influences attentional focus by ‘tuning’ whole-brain metastability and so adjusts how stable brain network activity is over time.

Nonlinear Time Series Analysis.

Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)