Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex

When humans perceive a sensation, their brains integrate inputs from sensory receptors and process them based on their expectations. The mechanisms of this predictive coding in the human somatosensory system are not fully understood. We fill a basic gap in our understanding of the predictive processing of somatosensation by examining the layer-specific activity in sensory input and predictive feedback in the human primary somatosensory cortex (S1). We acquired submillimeter functional magnetic resonance imaging data at 7T (n = 10) during a task of perceived, predictable, and unpredictable touching sequences. We demonstrate that the sensory input from thalamic projects preferentially activates the middle layer, while the superficial and deep layers in S1 are more engaged for cortico-cortical predictive feedback input. These findings are pivotal to understanding the mechanisms of tactile prediction processing in the human somatosensory cortex

High-resolution CBV-fMRI allows mapping of laminar activity and connectivity of cortical input and output in human M1

Layer-dependent fMRI allows measurements of information flow in cortical circuits, as afferent and efferent connections terminate in different cortical layers. However, it is unknown to what level human fMRI is specific and sensitive enough to reveal directional functional activity across layers. To answer this question, we developed acquisition and analysis methods for blood-oxygen-level-dependent (BOLD) and cerebral-blood-volume (CBV)-based laminar fMRI and used these to discriminate four different tasks in the human motor cortex (M1). In agreement with anatomical data from animal studies, we found evidence for somatosensory and premotor input in superficial layers of M1 and for cortico-spinal motor output in deep layers. Laminar resting-state fMRI showed directional functional connectivity of M1 with somatosensory and premotor areas. Our findings demonstrate that CBV-fMRI can be used to investigate cortical activity in humans with unprecedented detail, allowing investigations of information flow between brain regions and outperforming conventional BOLD results that are often buried under vascular biases

Cortex-Wide Neural Dynamics Predict Behavioral States and Provide a Neural Basis for Resting-State Dynamic Functional Connectivity

Although resting-state functional magnetic resonance imaging (fMRI) studies have observed dynamically changing brain-wide networks of correlated activity, fMRI\u27s dependence on hemodynamic signals makes results challenging to interpret. Meanwhile, emerging techniques for real-time recording of large populations of neurons have revealed compelling fluctuations in neuronal activity across the brain that are obscured by traditional trial averaging. To reconcile these observations, we use wide-field optical mapping to simultaneously record pan-cortical neuronal and hemodynamic activity in awake, spontaneously behaving mice. Some components of observed neuronal activity clearly represent sensory and motor function. However, particularly during quiet rest, strongly fluctuating patterns of activity across diverse brain regions contribute greatly to interregional correlations. Dynamic changes in these correlations coincide with changes in arousal state. Simultaneously acquired hemodynamics depict similar brain-state-dependent correlation shifts. These results support a neural basis for dynamic resting-state fMRI, while highlighting the importance of brain-wide neuronal fluctuations in the study of brain state

Computer Simulation of Final-Stage Sintering: I, Model Kinetics, and Microstructure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65565/1/j.1151-2916.1990.tb06686.x.pd

Risk perception of women during high risk pregnancy: A systematic review

Risk perception in women with high risk pregnancies can affect their attitude to medical care and therefore influence the wellbeing of mother and baby. This article reviews quantitative measures of risk perception in women with high risk pregnancies. A systematic search of eight electronic databases was conducted. Additional articles were obtained through searching references of identified articles. Seven studies were identified that reported quantitative measures of risk perception in relation to high risk pregnancy. The main findings were that women with high risk pregnancies perceive themselves and the pregnancies to be at risk. However, mean risk scores consistently fall below the midpoint on risk perception measures suggesting women do not perceive this risk as extreme. Women with high risk pregnancies consistently rated their risk as being greater than that of women with low risk pregnancies. Results were inconsistent for the association between women's risk perception and that of healthcare professionals. Women with higher socio-economic status were more likely to be concerned about risk, although lower socio-economic status is associated with increased risk in pregnancy. There was a consistent association between high risk pregnancy and higher levels of anxiety. This review indicates that women at high risk during pregnancy do not perceive this risk to be extreme and that there is poor agreement between women's and healthcare professionals’ perceptions of risk. This is likely to have implications for medical care and pregnancy outcomes

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

Orienting Attention Modulates Pain Perception: An ERP Study

2011-2012 > Academic research: refereed > Publication in refereed journalpublished_fina