The analgesic effect of electroacupuncture on acute thermal pain perception-a central neural correlate study with fMRI

Abstract Background Electrical acupuncture (EA) has been utilized in acute pain management. However, the neuronal mechanisms that lead to the analgesic effect are still not well defined. The current study assessed the intensity [optimal EA (OI-EA) vs. minimal EA (MI-EA)] effect of non-noxious EA on supraspinal regions related to noxious heat pain (HP) stimulation utilizing an EA treatment protocol for acute pain and functional magnetic resonance imaging (fMRI) with correlation in behavioral changes. Subjects underwent five fMRI scanning paradigms: one with heat pain (HP), two with OI-EA and MI-EA, and two with OI-EA and HP, and MI-EA and HP. Results While HP resulted in activations (excitatory effect) in supraspinal areas known for pain processing and perception, EA paradigms primarily resulted in deactivations (suppressive effect) in most of these corresponding areas. In addition, OI-EA resulted in a more robust supraspinal sedative effect in comparison to MI-EA. As a result, OI-EA is more effective than MI-EA in suppressing the excitatory effect of HP in supraspinal areas related to both pain processing and perception. Conclusion Intensities of EA plays an important role in modulating central pain perception

Inference with interference between units in an fMRI experiment of motor inhibition

An experimental unit is an opportunity to randomly apply or withhold a treatment. There is interference between units if the application of the treatment to one unit may also affect other units. In cognitive neuroscience, a common form of experiment presents a sequence of stimuli or requests for cognitive activity at random to each experimental subject and measures biological aspects of brain activity that follow these requests. Each subject is then many experimental units, and interference between units within an experimental subject is likely, in part because the stimuli follow one another quickly and in part because human subjects learn or become experienced or primed or bored as the experiment proceeds. We use a recent fMRI experiment concerned with the inhibition of motor activity to illustrate and further develop recently proposed methodology for inference in the presence of interference. A simulation evaluates the power of competing procedures.Comment: Published by Journal of the American Statistical Association at http://www.tandfonline.com/doi/full/10.1080/01621459.2012.655954 . R package cin (Causal Inference for Neuroscience) implementing the proposed method is freely available on CRAN at https://CRAN.R-project.org/package=ci

A novel method for classifying cortical state to identify the accompanying changes in cerebral hemodynamics

Background: Many brain imaging techniques interpret the haemodynamic response as an indirect indicator of underlying neural activity. However, a challenge when interpreting this blood based signal is how changes in brain state may affect both baseline and stimulus evoked haemodynamics. New method: We developed an Automatic Brain State Classifier (ABSC), validated on data from anaesthetised rodents. It uses vectorised information obtained from the windowed spectral frequency power of the Local Field Potential. Current state is then classified by comparing this vectorised information against that calculated from state specific training datasets. Results: The ABSC identified two user defined brain states (synchronised and desynchronised), with high accuracy (~90%). Baseline haemodynamics were found to be significantly different in the two identified states. During state defined periods of elevated baseline haemodynamics we found significant decreases in evoked haemodynamic responses to somatosensory stimuli. Comparison to existing methods: State classification - The ABSC (~90%) demonstrated greater accuracy than clustering (~66%) or 'power threshold' (~64%) methods of comparison.Haemodynamic averaging - Our novel approach of selectively averaging stimulus evoked haemodynamic trials by brain state yields higher quality data than creating a single average from all trials. Conclusions: The ABSC can account for some of the commonly observed trial-to-trial variability in haemodynamic responses which arises from changes in cortical state. This variability might otherwise be incorrectly attributed to alternative interpretations. A greater understanding of the effects of cortical state on haemodynamic changes could be used to inform techniques such as general linear modelling (GLM), commonly used in fMRI

On the Role of the Striatum in Response Inhibition

BACKGROUND: Stopping a manual response requires suppression of the primary motor cortex (M1) and has been linked to activation of the striatum. Here, we test three hypotheses regarding the role of the striatum in stopping: striatum activation during successful stopping may reflect suppression of M1, anticipation of a stop-signal occurring, or a slower response build-up. METHODOLOGY/PRINCIPAL FINDINGS: Twenty-four healthy volunteers underwent functional magnetic resonance imaging (fMRI) while performing a stop-signal paradigm, in which anticipation of stopping was manipulated using a visual cue indicating stop-signal probability, with their right hand. We observed activation of the striatum and deactivation of left M1 during successful versus unsuccessful stopping. In addition, striatum activation was proportional to the degree of left M1 deactivation during successful stopping, implicating the striatum in response suppression. Furthermore, striatum activation increased as a function of stop-signal probability and was to linked to activation in the supplementary motor complex (SMC) and right inferior frontal cortex (rIFC) during successful stopping, suggesting a role in anticipation of stopping. Finally, trial-to-trial variations in response time did not affect striatum activation. CONCLUSIONS/SIGNIFICANCE: The results identify the striatum as a critical node in the neural network associated with stopping motor responses. As striatum activation was related to both suppression of M1 and anticipation of a stop-signal occurring, these findings suggest that the striatum is involved in proactive inhibitory control over M1, most likely in interaction with SMC and rIFC

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Driving and Driven Architectures of Directed Small-World Human Brain Functional Networks

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome