The descending projections from motor cortex share many features with top-down or backward connections in visual cortex; for example, corticospinal projections originate in infragranular layers, are highly divergent and (along with descending cortico-cortical projections) target cells expressing NMDA receptors. This is somewhat paradoxical because backward modulatory characteristics would not be expected of driving motor command signals. We resolve this apparent paradox using a functional characterisation of the motor system based on Helmholtz's ideas about perception; namely, that perception is inference on the causes of visual sensations. We explain behaviour in terms of inference on the causes of proprioceptive sensations. This explanation appeals to active inference, in which higher cortical levels send descending proprioceptive predictions, rather than motor commands. This process mirrors perceptual inference in sensory cortex, where descending connections convey predictions, while ascending connections convey prediction errors. The anatomical substrate of this recurrent message passing is a hierarchical system consisting of functionally asymmetric driving (ascending) and modulatory (descending) connections: an arrangement that we show is almost exactly recapitulated in the motor system, in terms of its laminar, topographic and physiological characteristics. This perspective casts classical motor reflexes as minimising prediction errors and may provide a principled explanation for why motor cortex is agranular.
This technical report addresses the predictive validity of long-term epidemiological forecasting based upon dynamic causal modeling. It uses complementary prospective and retrospective analyses. The prospective analysis completes a series of (annual) reports comparing predictions with subsequent outcomes (i.e., cases, deaths, hospital admissions and Long COVID) reported a year later. Predictive validity is then addressed retrospectively by examining predictions at various points during the pandemic, in relation to actual outcomes at three, six and 12 months after the predictions were evaluated. This analysis suggests that—with a sufficiently expressive dynamic causal model—three, six and 12 month projections can be remarkably accurate (to within 10% or less of observed outcomes) at certain phases of the epidemic: namely, the initial phase—before the emergence of highly transmissible variants—and toward the end of the pandemic, when slow fluctuations in transmissibility and virulence can be estimated more precisely. However, the predictive accuracy in the intervening periods are compromised, to the extent that some forecasts only remain within their Bayesian credible intervals for 3 months. We provide a quantitative analysis of predictive accuracy for future reference and discuss the implications for epidemiological modeling, and forecasting, of this sort.
This dataset includes skin conductance response (SCR) measurements for each of 22 healthy unmedicated participants (11 males and 11 females aged 24.7+/-4.5 years) in response to white noise stimulation (1s length; 10ms ramp; ~85dB) under 5 experimental conditions: single stimuli, double stimuli with ISI of 2.5s, 5.5s or 9s and triple stimuli with ISIs randomly determined to be 2s, 5.5s or 9s.
Perception entails interactions between activated brain visual areas and the records of previous sensations, allowing for processes like figure–ground segregation and object recognition. The aim of this study was to characterize top-down effects that originate in the visual cortex and that are involved in the generation and perception of form. We performed a functional magnetic resonance imaging experiment, where subjects viewed 3 groups of stimuli comprising oriented lines with different levels of recognizable high-order structure (none, collinearity, and meaning). Our results showed that recognizable stimuli cause larger activations in anterior visual and frontal areas. In contrast, when stimuli are random or unrecognizable, activations are greater in posterior visual areas, following a hierarchical organization where areas V1/V2 were less active with "collinearity" and the middle occipital cortex was less active with "meaning." An effective connectivity analysis using dynamic causal modeling showed that high-order visual form engages higher visual areas that generate top-down signals, from multiple levels of the visual hierarchy. These results are consistent with a model in which if a stimulus has recognizable attributes, such as collinearity and meaning, the areas specialized for processing these attributes send top-down messages to the lower levels to facilitate more efficient encoding of visual form.
The neural correlates of rapid eye movements (REMs) in sleep are extraordinarily robust; including REM-locked multisensory-motor integration and accompanying activation in the retrosplenial cortex, the supplementary eye field and areas encompassing cholinergic basal nucleus (Hong et al., 2009). The phenomenology of REMs speaks to the notion that perceptual experience in both sleep and wakefulness is a constructive process - in which we generate predictions of sensory inputs and then test those predictions through actively sampling the sensorium with eye movements. On this view, REMs during sleep may index an internalized active sampling or 'scanning' of self-generated visual constructs that are released from the constraints of visual input. If this view is correct, it renders REMs an ideal probe to study consciousness as "an exclusively internal affair" (Metzinger, 2009). In other words, REMs offer a probe of active inference - in the sense of predictive coding - when the brain is isolated from the sensorium in virtue of the natural blockade of sensory afferents during REM sleep. Crucially, REMs are temporally precise events that enable powerful inferences based on time series analyses. As a natural, task-free probe, (REMs) could be used in non-compliant subjects, including infants and animals. In short, REMs constitute a promising probe to study the ontogenetic and phylogenetic development of consciousness and perhaps the psychopathology of schizophrenia and autism, which have been considered in terms of aberrant predictive coding.
Since the brain may engage different neuronal systems for a single behavioral goal, activation may show intersubject variability expressed in the systematic recruitment of multiple distinct networks. We apply a principal component analysis (PCA) to activation over task conditions and subjects to reveal cortical networks that may underlie this intersubject variability. Normal subjects were presented with novel meaningless objects, which appeared in personally familiar or unfamiliar places. During a subsequent, event-related functional MRI (fMRI) experiment, each subject was presented with learned or novel objects in isolation and performed a learned/novel judgment. Recollection of places was not essential for the task, and may exhibit large intersubject variations. The right posterodorsal posterior cingulate cortex (pPCC) and left retrosplenial cortex, whose involvement in place-recognition has been previously established, were selected as regions of interest for the PCA. Neural responses to objects associated with familiar relative to unfamiliar places in pPCC, but not in the retrosplenial cortex, were negatively correlated with task-related activation (common over all objects) in the right anterolateral prefrontal cortex and the left intraparietal sulcus. The latter areas have been implicated previously in cognitive control and object recognition, respectively. These results suggest right prefrontal control over neural processes both in the left parietal cortex, related to object-recognition (enhancement), and pPCC, related to nonessential recollection of place-memory (suppression), but not in the retrosplenial cortex, related to the sense of familiarity. This analysis revealed an important aspect of functional anatomy that was not detectable using a conventional analysis of average activations.
