Publications

Functional neuroimaging was used to investigate how lesions to the Broca's area impair neuronal responses in remote undamaged cortical regions. Four patients with speech output problems, but relatively preserved comprehension, were scanned while viewing words relative to consonant letter strings. In normal subjects, this results in left lateralized activation in the posterior inferior frontal, middle temporal, and posterior inferior temporal cortices. Each patient activated normally in the middle temporal region but abnormally in the damaged posterior inferior frontal cortex and the undamaged posterior inferior temporal cortex. In the damaged frontal region, activity was insensitive to the presence of words but in the undamaged posterior inferior temporal region, activity decreased in the presence of words rather than increasing as it did in the normal individuals. The reversal of responses in the left posterior inferior temporal region illustrate the context-sensitive nature of the abnormality and that failure to activate the left posterior temporal region could not simply be accounted for by insufficient demands on the underlying function. We propose that, in normal individuals, visual word presentation changes the effective connectivity among reading areas and, in patients, posterior temporal responses are abnormal when they depend upon inputs from the damaged inferior frontal cortex. Our results serve to introduce the concept of dynamic diaschisis; the anatomically remote and context-sensitive effects of focal brain lesions. Dynamic diaschisis reveals abnormalities of functional integration that may have profound implications for neuropsychological inference, functional anatomy and, vicariously, cognitive rehabilitation.

Predictive coding frameworks suggest that neural computations rely on hierarchical error minimization, where sensory signals are evaluated against internal model predictions. However, the neural implementation of this inference process remains unclear. We propose that cross-frequency coupling (CFC) furnishes a fundamental mechanism for this form of inference. We first demonstrate that our previously described laminar neural mass model (LaNMM) supports two key forms of CFC: (i) Signal-Envelope Coupling (SEC), where low-frequency rhythms modulate the amplitude envelope of higher-frequency oscillations, and (ii) Envelope-Envelope Coupling (EEC), where the envelopes of slower oscillations modulate the envelopes of higher-frequency rhythms. Then, we propose that - by encoding information in signals and their envelopes - these processes instantiate a hierarchical ``Comparator'' mechanism at the columnar level. Specifically, SEC generates fast prediction-error signals by subtracting top-down predictions from bottom-up oscillatory envelopes, while EEC operates at slower timescales to instantiate gating - a critical computational mechanism for precision-weighting and selective information routing. To establish the face validity - and clinical implications of - this proposal, we model perturbations of these CFC mechanisms to investigate their roles in pathophysiological and altered neuronal function. We illustrate how, in disorders such as Alzheimer's disease, disruptions in gamma oscillations following dysfunction in fast-spiking inhibitory interneurons impact Comparator function with an aberrant amplification of prediction errors in the early stages and a drastic attenuation in late phases of the disease. In contrast, by increasing excitatory gain, serotonergic psychedelics diminish the modulatory effect of predictions, resulting in a failure to attenuate prediction error signals (c.f., a failure of sensory attenuation). Together, these results establish cross-frequency coupling - across temporal scales - as candidate computational processes underlying hierarchical predictive coding in health and disease.

In this work, we show that electrophysiological responses during pitch perception are best explained by distributed activity in a hierarchy of cortical sources and, crucially, that the effective connectivity between these sources is modulated with pitch strength. Local field potentials were recorded in two subjects from primary auditory cortex and adjacent auditory cortical areas along the axis of Heschl's gyrus (HG) while they listened to stimuli of varying pitch strength. Dynamic causal modeling was used to compare system architectures that might explain the recorded activity. The data show that representation of pitch requires an interaction between nonprimary and primary auditory cortex along HG that is consistent with the principle of predictive coding.

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and 'social forces' such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modelling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically-observed collective phenomena, including cohesion, milling and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference -- without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal non-trivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.

The localised PET cerebral correlates of the painful experience in the normal human brain have previously been demonstrated. This study examined whether these responses are different in patients with chronic atypical facial pain. The regional cerebral responses to non-painful and painful thermal stimuli in six female patients with atypical facial pain and six matched female controls were studied by taking serial measurements of regional blood flow by PET. Both groups displayed highly significant differences in responses to painful heat compared with non-painful heat in the thalamus, anterior cingulate cortex (area 24), lentiform nucleus, insula, and prefrontal cortex. These structures are closely related to the "medial pain system". The atypical facial pain group had increased blood flow in the anterior cingulate cortex and decreased blood flow in the prefrontal cortex. These findings show the importance of the anterior cingulate cortex and the reciprocal (possibly inhibitory) connections with the prefrontal cortex in the processing of pain in patients with this disorder. A hypothesis is proposed to explain the mechanisms of cognitive and pharmacological manipulation of these pain processes.

Abstract The prefrontal cortex is vital for a range of cognitive processes, including working memory, attention, and decision-making. Notably, its absence impairs the performance of tasks requiring the maintenance of information through a delay period. In this paper, we formulate a rodent task—which requires maintenance of delay-period activity—as a Markov decision process and treat optimal task performance as an (active) inference problem. We simulate the behavior of a Bayes optimal mouse presented with 1 of 2 cues that instructs the selection of concurrent visual and auditory targets on a trial-by-trial basis. Formulating inference as message passing, we reproduce features of neuronal coupling within and between prefrontal regions engaged by this task. We focus on the micro-circuitry that underwrites delay-period activity and relate it to functional specialization within the prefrontal cortex in primates. Finally, we simulate the electrophysiological correlates of inference and demonstrate the consequences of lesions to each part of our in silico prefrontal cortex. In brief, this formulation suggests that recurrent excitatory connections—which support persistent neuronal activity—encode beliefs about transition probabilities over time. We argue that attentional modulation can be understood as the contextualization of sensory input by these persistent beliefs.