迁移性安慰剂效应的多模态机制 ：从痛觉到情绪的行为、ERP和fMRI研究

Although studies on placebo effect proved the placebo expectation established by pain-alleviating treatment could significantly alleviate later pain perception, or the placebo expectation established by anxiety-reducing treatment could significantly reduce the intensity of induced negative feelings, it is still unclear whether or not the placebo effect can occur in a transferable manner. That is, we still don’t know if the placebo expectation derived from pain-alleviating can significantly reduce later negative emotional arousal or not. Experiment 1: We compared the effect of the verbal expectation (purely verbal induction and without pain-alleviating reinforcement) with the reinforced expectation (building the belief in the placebo’s ataractic efficiency on unpleasant picture processing by secret reduction of the intensity of the pain-evoking stimulus) on the negative emotion. The results showed that the expectation, which was reinforced by actual analgesia, was transferable and could produce significant placebo effect on negative emotional arousal. However, the expectation that was merely induced by verbal instruction did not have such power. Experiment 2 both examined the direct analgesic effect of the placebo on the sensory pain (how strong is the pain stimulus) and emotional pain (how disturbing is the pain stimulus) and the transferable ataractic effect of the placebo on the negative emotion (how disturbing is the emotional picture stimulus), and further proved that the placebo expectation that was established from pain-reducing reinforcement not only induced significant placebo effect on pain, but also significant placebo effect on unpleasant feeling. These results support the viewpoint that the reduction of affective pain based on the conditioning mechanism plays an important role in the placebo analgesia, but can’t explain the transferred placebo effect on visual unpleasantness. Experiment 3 continued to use the paradigm of the reinforced expectation group and recorded the EEG activities, the data showed that the transferable placebo treatment was accompanied with decreased P2 amplitude and increased N2 distributed, and significant differences between the transferable placebo condition and the control condition (i.e., P2 and N2) were observed within the first 150-300 ms, a duration brief enough to rule out the possibility that differences between the two conditions merely reflect a bias “to try to please the investigator. In Experiment 4, we selected the placebo responders in the pre-experiment and let them to go through the formal fMRI scan. The results found that the transferable placebo treatment reduced the negative emotional response, emotion-responsive regions such as the amygdala, insula, anterior cingulate cortex and the thalamus showed an attenuated activation. And in the placebo condition, there was an enhanced activation in the subcollosal gyrus, which may be involved in emotional regulation. In conclusion, the transferable placebo treatment induced the reliable placebo effect on the behavior, EEG activity and bold signal, and we attempted to discuss the pychophysiological mechanism based on the positive expectancy.

Reti complesse e analisi del segnale elettroencefalografico

The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analyzed using a phase synchronization based measure, minimum spanning tree and k-core decomposition. The analysis was performed for each classical brain rhythm separately. Furthermore, we aim to provide a network approach insensitive to the effects that epoch length has on functional connectivity (FC) and network reconstruction. Two different measures, the phase lag index (PLI) and the Amplitude Envelope Correlation (AEC), were applied to EEG resting-state recordings for a group of eighteen healthy volunteers. Weighted clustering coefficient (CCw), weighted characteristic path length (Lw) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Results about distinctive functional core, show highest classification rates from k-core decomposition in gamma (EER=0.130, AUC=0.943) and high beta (EER=0.172, AUC=0.905) frequency bands. Results from scalp analysis concerning the influence of epoch length, show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 seconds for PLI and 6 seconds for AEC. Moreover, CCw and Lw show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 seconds versus 4-8 seconds for AEC). At the source-level the results were even more reliable, with stability already at 1 second duration for PLI-based MSTs. Our results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. Regarding epoch length, the present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.

Early biomarkers to predict grade of encephalopathy following hypoxic ischaemic injury

The standard early markers for identifying and grading HIE severity, are not sufficient to ensure all children who would benefit from treatment are identified in a timely fashion. The aim of this thesis was to explore potential early biomarkers of HIE. Methods: To achieve this a cohort of infants with perinatal depression was prospectively recruited. All infants had cord blood samples drawn and biobanked, and were assessed with standardised neurological examination, and early continuous multi-channel EEG. Cord samples from a control cohort of healthy infants were used for comparison. Biomarkers studied included; multiple inflammatory proteins using multiplex assay; the metabolomics profile using LC/MS; and the miRNA profile using microarray. Results: Eighty five infants with perinatal depression were recruited. Analysis of inflammatory proteins consisted of exploratory analysis of 37 analytes conducted in a sub-population, followed by validation of all significantly altered analytes in the remaining population. IL-6 and IL-6 differed significantly in infants with a moderate/severely abnormal vs. a normal-mildly abnormal EEG in both cohorts (Exploratory: p=0.016, p=0.005: Validation: p=0.024, p=0.039; respectively). Metabolomic analysis demonstrated a perturbation in 29 metabolites. A Cross- validated Partial Least Square Discriminant Analysis model was developed, which accurately predicted HIE with an AUC of 0.92 (95% CI: 0.84-0.97). Analysis of the miRNA profile found 70 miRNA significantly altered between moderate/severely encephalopathic infants and controls. miRNA target prediction databases identified potential targets for the altered miRNA in pathways involved in cellular metabolism, cell cycle and apoptosis, cell signaling, and the inflammatory cascade. Conclusion: This thesis has demonstrated that the recruitment of a large cohortof asphyxiated infants, with cord blood carefully biobanked, and detailed early neurophysiological and clinical assessment recorded, is feasible. Additionally the results described, provide potential alternate and novel blood based biomarkers for the identification and assessment of HIE.

The effects of sleep hypoxia on coagulant factors and hepatic inflammation in emphysematous rats.

OBJECTIVES: To develop a sleep hypoxia (SH) in emphysema (SHE) rat model and to explore whether SHE results in more severe hepatic inflammation than emphysema alone and whether the inflammation changes levels of coagulant/anticoagulant factors synthesized in the liver. METHODS: Seventy-five rats were put into 5 groups: SH control (SHCtrl), treated with sham smoke exposure (16 weeks) and SH exposure (12.5% O(2), 3 h/d, latter 8 weeks); emphysema control (ECtrl), smoke exposure and sham SH exposure (21% O(2)); short SHE (SHEShort), smoke exposure and short SH exposure (1.5 h/d); mild SHE (SHEMild), smoke exposure and mild SH exposure (15% O(2)); standard SHE (SHEStand), smoke exposure and SH exposure. Therefore, ECtrl, SHEShort, SHEMild and SHEStand group were among emphysematous groups. Arterial blood gas (ABG) data was obtained during preliminary tests. After exposure, hepatic inflammation (interleukin -6 [IL-6] mRNA and protein, tumor necrosis factor α [TNFα] mRNA and protein) and liver coagulant/anticoagulant factors (antithrombin [AT], fibrinogen [FIB] and Factor VIII [F VIII]) were evaluated. SPSS 11.5 software was used for statistical analysis. RESULTS: Characteristics of emphysema were obvious in emphysematous groups and ABGs reached SH criteria on hypoxia exposure. Hepatic inflammation parameters and coagulant factors are the lowest in SHCtrl and the highest in SHEStand while AT is the highest in SHCtrl and the lowest in SHEStand. Inflammatory cytokines of liver correlate well with coagulant factors positively and with AT negatively. CONCLUSIONS: When SH is combined with emphysema, hepatic inflammation and coagulability enhance each other synergistically and produce a more significant liver-derivative inflammatory and prothrombotic status.

Attentional Biases in Value-Based Decision-Making

Humans make decisions in highly complex physical, economic and social environments. In order to adaptively choose, the human brain has to learn about- and attend to- sensory cues that provide information about the potential outcome of different courses of action. Here I present three event-related potential (ERP) studies, in which I evaluated the role of the interactions between attention and reward learning in economic decision-making. I focused my analyses on three ERP components (Chap. 1): (1) the N2pc, an early lateralized ERP response reflecting the lateralized focus of visual; (2) the feedback-related negativity (FRN), which reflects the process by which the brain extracts utility from feedback; and (3) the P300 (P3), which reflects the amount of attention devoted to feedback-processing. I found that learned stimulus-reward associations can influence the rapid allocation of attention (N2pc) towards outcome-predicting cues, and that differences in this attention allocation process are associated with individual differences in economic decision performance (Chap. 2). Such individual differences were also linked to differences in neural responses reflecting the amount of attention devoted to processing monetary outcomes (P3) (Chap. 3). Finally, the relative amount of attention devoted to processing rewards for oneself versus others (as reflected by the P3) predicted both charitable giving and self-reported engagement in real-life altruistic behaviors across individuals (Chap. 4). Overall, these findings indicate that attention and reward processing interact and can influence each other in the brain. Moreover, they indicate that individual differences in economic choice behavior are associated both with biases in the manner in which attention is drawn towards sensory cues that inform subsequent choices, and with biases in the way that attention is allocated to learn from the outcomes of recent choices.

Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

The incidence of unacceptable movement with motor evoked potentials during craniotomy for aneurysm clipping.

OBJECTIVE: To review the experience at a single institution with motor evoked potential (MEP) monitoring during intracranial aneurysm surgery to determine the incidence of unacceptable movement. METHODS: Neurophysiology event logs and anesthetic records from 220 craniotomies for aneurysm clipping were reviewed for unacceptable patient movement or reason for cessation of MEPs. Muscle relaxants were not given after intubation. Transcranial MEPs were recorded from bilateral abductor hallucis and abductor pollicis muscles. MEP stimulus intensity was increased up to 500 V until evoked potential responses were detectable. RESULTS: Out of 220 patients, 7 (3.2%) exhibited unacceptable movement with MEP stimulation-2 had nociception-induced movement and 5 had excessive field movement. In all but one case, MEP monitoring could be resumed, yielding a 99.5% monitoring rate. CONCLUSIONS: With the anesthetic and monitoring regimen, the authors were able to record MEPs of the upper and lower extremities in all patients and found only 3.2% demonstrated unacceptable movement. With a suitable anesthetic technique, MEP monitoring in the upper and lower extremities appears to be feasible in most patients and should not be withheld because of concern for movement during neurovascular surgery.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.

The neural dynamics of stimulus and response conflict processing as a function of response complexity and task demands.

Both stimulus and response conflict can disrupt behavior by slowing response times and decreasing accuracy. Although several neural activations have been associated with conflict processing, it is unclear how specific any of these are to the type of stimulus conflict or the amount of response conflict. Here, we recorded electrical brain activity, while manipulating the type of stimulus conflict in the task (spatial [Flanker] versus semantic [Stroop]) and the amount of response conflict (two versus four response choices). Behaviorally, responses were slower to incongruent versus congruent stimuli across all task and response types, along with overall slowing for higher response-mapping complexity. The earliest incongruency-related neural effect was a short-duration frontally-distributed negativity at ~200 ms that was only present in the Flanker spatial-conflict task. At longer latencies, the classic fronto-central incongruency-related negativity 'N(inc)' was observed for all conditions, but was larger and ~100 ms longer in duration with more response options. Further, the onset of the motor-related lateralized readiness potential (LRP) was earlier for the two vs. four response sets, indicating that smaller response sets enabled faster motor-response preparation. The late positive complex (LPC) was present in all conditions except the two-response Stroop task, suggesting this late conflict-related activity is not specifically related to task type or response-mapping complexity. Importantly, across tasks and conditions, the LRP onset at or before the conflict-related N(inc), indicating that motor preparation is a rapid, automatic process that interacts with the conflict-detection processes after it has begun. Together, these data highlight how different conflict-related processes operate in parallel and depend on both the cognitive demands of the task and the number of response options.

The temporal dynamics of metacognition: Dissociating task-related activity from later metacognitive processes

In recent years, neuroscience research spent much effort in revealing brain activity related to metacognition. Despite this endeavor, it remains unclear exactly when metacognitive experiences develop during task performance. To investigate this, the current study used EEG to temporally and spatially dissociate task-related activity from metacognitive activity. In a masked priming paradigm, metacognitive experiences of difficulty were induced by manipulating congruency between prime and target. As expected, participants more frequently rated incongruent trials as difficult and congruent trials as easy, while being completely unable to perceive the masked primes. Results showed that both the N2 and the P3 ERP components were modulated by congruency, but that only the P3 modulation interacted with metacognitive experiences. Single-trial analysis additionally showed that the magnitude of the P3 modulation by congruency accurately predicted the metacognitive response. Source localization indicated that the N2 task-related activity originated in the ACC, whereas the P3-interplay between task-related activation and metacognitive experiences originated from the precuneus. We conclude that task-related activity can be dissociated from later metacognitive processing.

Music for Sleeping & Waking Minds

Music for Sleeping & Waking Minds is a 8-hour composition intended for overnight listening. It features 4 performers who wear custom-designed EEG sensors. The performers rest and fall asleep as they naturally would. Over the course of one night, their brainwave activity generates a spatial audio environment. Audiences are invited to sleep or listen as they wish. Composition & concept by Gascia Ouzounian. Physiological interface and interaction design by R. Benjamin Knapp. Audio interface and interaction design by Eric Lyon.

Assessing pain in preterm infants in the neonatal intensive care unit:moving to a 'brain-oriented' approach

Preterm infants in the neonatal intensive care unit undergo repeated exposure to procedural and ongoing pain. Early and long-term changes in pain processing, stress-response systems and development may result from cumulative early pain exposure. So that appropriate treatment can be given, accurate assessment of pain is vital, but is also complex because these infants' responses may differ from those of full-term infants. A variety of uni- and multidimensional assessment tools are available; however, many have incomplete psychometric testing and may not incorporate developmentally important cues. Near-infrared spectroscopy and/or EEG techniques that measure neonatal pain responses at a cortical level offer new opportunities to validate neonatal pain assessment tools.

The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results. © 1964-2012 IEEE.

Intercepting the First Pass:Rapid Categorization is Suppressed for Unseen Stimuli

The operations and processes that the human brain employs to achieve fast visual categorization remain a matter of debate. A first issue concerns the timing and place of rapid visual categorization and to what extent it can be performed with an early feed-forward pass of information through the visual system. A second issue involves the categorization of stimuli that do not reach visual awareness. There is disagreement over the degree to which these stimuli activate the same early mechanisms as stimuli that are consciously perceived. We employed continuous flash suppression (CFS), EEG recordings, and machine learning techniques to study visual categorization of seen and unseen stimuli. Our classifiers were able to predict from the EEG recordings the category of stimuli on seen trials but not on unseen trials. Rapid categorization of conscious images could be detected around 100?ms on the occipital electrodes, consistent with a fast, feed-forward mechanism of target detection. For the invisible stimuli, however, CFS eliminated all traces of early processing. Our results support the idea of a fast mechanism of categorization and suggest that this early categorization process plays an important role in later, more subtle categorizations, and perceptual processes.