Abstract In sensorimotor integration, the brain needs to decide how its predictions should accommodate novel evidence by ‘gating’ sensory data depending on the current context. Here, we examined the oscillatory correlates of this process using magnetoencephalography (MEG). We used virtual reality to decouple visual (virtual) and proprioceptive (real) hand postures during a task requiring matching either modality’s grasping movements to a target oscillation. Thus, we rendered visual information either task-relevant or a (to-be-ignored) distractor. Under visuo-proprioceptive incongruence, occipital beta power decreased relative to congruence when vision was task-relevant but increased when it had to be ignored. Dynamic causal modelling (DCM) revealed that this interaction was best explained by diametrical, task-dependent changes in visual gain. These results suggest a crucial role for beta oscillations in sensorimotor integration; particularly, in the contextual gating (i.e., gain or precision control) of visual vs proprioceptive action feedback, depending on concurrent behavioral demands.
This technical report revisits the analysis of family-wise error rates in statistical parametric mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for parametric procedures. Hum Brain Mapp, 40:2052-2054, 2019. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
Positron emission tomography measurements of regional cerebral blood flow (rCBF) were performed in normal volunteers during a graded auditory-verbal memory task. Subjects were required to remember and then immediately, and freely, recall a series of auditorily presented word lists varying from two to 13 words in length. Significant regional correlations between rCBF and memory load (word list length) were identified using statistical parametric mapping. Increasing memory load correlated with increasing rCBF in the cerebellar vermis and hemispheres, thalamus bilaterally, the superior and middle frontal gyri bilaterally, anterior insular regions bilaterally, anterior cingulate, precuneus and left and right lateral premotor areas. Increasing memory load also correlated with decreasing rCBF in the left and right superior temporal/insular regions, medial frontal gyrus, Brodmann's area 37 bilaterally, cuneus, inferior parietal lobule bilaterally and the mid-portion of the cingulate cortex. The pattern of rCBF change closely resembled that identified in a previously reported study using a cognitive subtraction paradigm and provides further evidence for a widespread neural system subserving auditory-verbal memory. The patterns of rCBF response suggest that the areas identified are associated with limited capacity processes for encoding and retrieval.
PET is potentially the most powerful tool yet available for the direct, in vivo investigation of the biologic basis of psychiatric and neuropsychiatric disorders. The fulfillment of its potential rests on the development of methodologies and study design appropriate to psychiatric disorders. To date, findings in both schizophrenia and affective disorder, using protocols largely based on resting state data acquisition, suggest altered regional metabolism. These approaches need to be extended, particularly by the application of protocols that utilize PET to obtain longitudinal data under controlled experimental situations. In two conditions traditionally ascribed to psychologic causes, OCD and PD, there is intriguing evidence of specific biologic abnormalities, which, if confirmed, would lead to a fundamental revision of their nosologic status. In neuropsychiatric disorders PET findings, although preliminary in nature, offer an alternative paradigm to traditional clinicopathologic correlations by suggesting that clinical impairments relate to physiologic effects at sites distant from structural lesions.
No abstract is provided for this article.
In this treatment of random dynamical systems, we consider the existence—and identification—of conditional independencies at nonequilibrium steady-state. These independencies underwrite a particular partition of states, in which internal states are statistically secluded from external states by blanket states. The existence of such partitions has interesting implications for the information geometry of internal states. In brief, this geometry can be read as a physics of sentience, where internal states look as if they are inferring external states. However, the existence of such partitions—and the functional form of the underlying densities—have yet to be established. Here, using the Lorenz system as the basis of stochastic chaos, we leverage the Helmholtz decomposition—and polynomial expansions—to parameterise the steady-state density in terms of surprisal or self-information. We then show how Markov blankets can be identified—using the accompanying Hessian—to characterise the coupling between internal and external states in terms of a generalised synchrony or synchronisation of chaos. We conclude by suggesting that this kind of synchronisation may provide a mathematical basis for an elemental form of (autonomous or active) sentience in biology.
This dataset includes skin conductance response (SCR) measurements for each of 40 healthy unmedicated participants (20 males and 20 females aged 21.9 +/- 3.8 years) in response to the 16 most arousing negative, and most arousing positive (excluding explicit nude) and 16 least arousing neutral IAPS pictures, presented for 1 s each in 1 block, while listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was 4 s, plus a variable delay of around 0.4 s for image loading.
Background: A better understanding of the neural changes associated with paresis in stroke patients could have important implications for therapeutic approaches.Dynamic Causal Modeling (DCM) for functional magnetic resonance imaging (fMRI) is commonly used for analyzing effective connectivity patterns of brain networks due to its significant property of modeling neural states behind fMRI signals.We applied this technique to analyze the differences between motor networks (MNW) activated by continuous passive movement (CPM) of paretic and non-paretic ankles in subacute stroke patients.This study aimed to identify CPM induced connectivity characteristics of the primary sensory area (S1) and the differences in extrinsic directed connections of the MNW and to explain the hemodynamic differences of brain regions of MNW.Methods: For the network analysis, we used ten stroke patients' task fMRI data collected under CPMs of both ankles.Regions for the MNW, the primary motor cortex (M1), the premotor cortex (PM), the supplementary motor area (SMA) and the S1 were defined in a data-driven way, by independent component analysis.For the network analysis of both CPMs, we compared twelve models organized into two model-families, depending on the S1 connections and input stimulus modelling.Using DCM, we evaluated the extrinsic connectivity strengths and hemodynamic parameters of both stimulations of all patients.Results: After a statistical comparison of the extrinsic connections and their modulations of the "best model", we concluded that three contralateral self-inhibitions (cM1, cS1, and cSMA), one contralateral inter-regional connection (cSMA→cM1), and one interhemispheric connection (cM1→iM1) were significantly different.Our research shows that hemodynamic parameters can be
Distributed linear solutions have frequently been used to solve the source localization problem in EEG. Here we introduce an approach based on the weighted minimum norm (WMN) method that imposes constraints using anatomical and physiological information derived from other imaging modalities. The anatomical constraints are used to reduce the solution space a priori by modeling the spatial source distribution with a set of basis functions. These spatial basis functions are chosen in a principled way using information theory. The reduced problem is then solved with a classical WMN method. Further (functional) constraints can be introduced in the weighting of the solution using fMRI brain responses to augment spatial priors. We used simulated data to explore the behavior of the approach over a range of the model's hyperparameters. To assess the construct validity of our method we compared it with two established approaches to the source localization problem, a simple weighted minimum norm and a maximum smoothness (Loreta-like) solution. This involved simulations, using single and multiple sources that were analyzed under different levels of confidence in the priors.
