Publications

Abstract Cognitive self-organization rests on activity-dependent plasticity to extend the ontogenetic process of neural differentiation and integration of the cerebral cortex in each act of cognition. This account of neurocognitive growth can be formulated in terms of active inference and learning. The organism regulates synaptic connectivity as it seeks its goals actively, through excitatory, feedforward expectancies that manifest its species-specific affordances. These adaptive expectancies are modified reactively, through inhibitory feedback error-correction to fit and predict environmental encounters. Although adaptive behavior, and concomitant synaptic plasticity, occur during waking, the synaptic architecture requires ongoing consolidation and refinement during sleep. We propose that memory consolidation during sleep is a continuation of the neurodevelopmental process that proceeds through a kind of inversion of waking active inference: NREM sleep first refines the brain’s representations of new, unpredicted experiences during waking, implementing inhibitory mechanisms of long-term depression that both differentiate and stabilize new representations for consolidation. REM sleep then updates the organism’s generative world model in light of this new learning through excitatory long-term potentiation of synaptic plasticity. REM thereby reconsolidates and integrates the organism’s adaptive, feedforward predictions, the Bayesian priors for effective coping.

Substantial evidence indicates that incentive value depends on an anticipation of rewards within a given context. However, the computations underlying this context sensitivity remain unknown. To address this question, we introduce a normative (Bayesian) account of how rewards map to incentive values. This assumes that the brain inverts a model of how rewards are generated. Key features of our account include (i) an influence of prior beliefs about the context in which rewards are delivered (weighted by their reliability in a Bayes-optimal fashion), (ii) the notion that incentive values correspond to precision-weighted prediction errors, (iii) and contextual information unfolding at different hierarchical levels. This formulation implies that incentive value is intrinsically context-dependent. We provide empirical support for this model by showing that incentive value is influenced by context variability and by hierarchically nested contexts. The perspective we introduce generates new empirical predictions that might help explaining psychopathologies, such as addiction.

Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive coding, where prediction errors are weighted by precision associated with attentional modulation. Here, we tested the predictive coding account of attention and expectation using magnetoencephalography and modeling. Temporal attention and sensory expectation were orthogonally manipulated in an auditory mismatch paradigm, revealing opposing effects on evoked response amplitude. Mismatch negativity (MMN) was enhanced by attention, speaking against its supposedly pre-attentive nature. This interaction effect was modeled in a canonical microcircuit using dynamic causal modeling, comparing models with modulation of extrinsic and intrinsic connectivity at different levels of the auditory hierarchy. While MMN was explained by recursive interplay of sensory predictions and prediction errors, attention was linked to the gain of inhibitory interneurons, consistent with its modulation of sensory precision.

This article has been written in response to Dr. Fred L. Bookstein’s article entitled ‘“Voxel-Based Morphometry” Should Not Be Used with Imperfectly Registered Images’ in this issue of NeuroImage. We will address three main issues: (i) Dr. Bookstein appears to have misunderstood the objective of voxel-based morphometry (VBM) and the nature of the continuum we referred to. (ii) We agree with him when he states that findings from VBM can pertain to systematic registration errors during spatial normalization. (iii) His argument about voxelwise tests on smooth data holds in the absence of error variance, but is of no consequence when using actual data. We first review the tenets of VBM, paying particular attention to the relationship between VBM and tensor-based morphometry. The last two sections of this response deal with the specific concerns raised by Dr. Bookstein. © 2001 Academic Press

We recently described a dynamic causal model of a COVID-19 outbreak within a single region Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium

This study demonstrates the neural system potentially involved in the representation of, and choice between, stimulus classifications in an ambiguous, novel, decision-making task. This difficult choice behaviour is taken as an example of a basic executive processing task. Subjects heard sounds that were consonant-vowel combinations that had been distorted and were required to categorize each stimulus as speech-like or not-speech-like. Cerebral activity was measured with positron emission tomography. A neural system (thalamic and medial prefrontal cortical regions) was demonstrated; there was greater activity involved in assigning the sound to the larger class of not-speech-like sounds than to the more restricted category of speech-like sounds. We interpret this activity as reflecting process and representation in a simple central executive task.

Positron emission tomographic (PET) images of regional cerebral blood flow (rCBF) from 30 normal, resting volunteers aged 30 to 85 years were analysed to identify areas where rCBF fell with age. Images were anatomically normalised, and a pixel-by-pixel linear regression was performed to remove differences in global CBF between subjects. Pixels at which rCBF then showed a significant ( p < 0.01) negative correlation with age were identified. They were displayed as a statistical parametric map (SPM) of correlations. We demonstrate an age-related decrease in adjusted rCBF in the cingulate, parahippocampal, superior temporal, medial frontal, and posterior parietal cortices bilaterally, and in the left insular and left posterior prefrontal cortices (omnibus significance, χ 2 = 2,291, p < 0.0001, df = 1). Decreases in rCBF suggest a regionally specific loss of cerebral function with age. The affected areas were all limbic, or association, cortices. Therefore, these decreases may constitute the cerebral substrate of the cognitive changes that occur during normal aging.

Traumatic spinal cord injury is often disabling and recovery of function is limited. As a consequence of damage, both spinal cord and brain undergo anatomical and functional changes. Besides clinical measures of recovery, biomarkers that can detect early anatomical and functional changes might be useful in determining clinical outcome-during the course of rehabilitation and recovery-as well as furnishing a tool to evaluate novel treatment interventions and their mechanisms of action. Recent evidence suggests an interesting three-way relationship between neurological deficit and changes in the spinal cord and of the brain and that, importantly, noninvasive magnetic resonance imaging techniques, both structural and functional, provide a sensitive tool to lay out these interactions. This review describes recent findings from multimodal imaging studies of remote anatomical changes (i.e., beyond the lesion site), cortical reorganization, and their relationship to clinical disability. These developments in this field may improve our understanding of effects on the nervous system that are attributable to the injury itself and will allow their distinction from changes that result from rehabilitation (i.e., functional retraining) and from interventions affecting the nervous system directly (i.e., neuroprotection or regeneration).