Publications

Positron emission tomography was used to investigate changes in regional cerebral blood flow (rCBF) in neurologically normal subjects during word reading and word repetition. The blood flow in these conditions was compared with control conditions where subjects were presented with stimuli of comparable auditory and visual complexity to real words and said the same word on presentation of each stimulus. The control condition for word repetition (hearing spoken words presented backwards) resulted in bilateral activation of the superior temporal gyrus. Word repetition caused a significant increase in rCBF over this control condition in the left superior and middle temporal gyri. The control condition for word reading (seeing stimuli written in 'false fonts', i. e. non-existent letter-like forms) resulted in significant changes in rCBF bilaterally in the striate and extrastriate cortex. Word reading caused a significant increase in blood flow relative to this control in the posterior part of the left middle temporal gyrus. The implications of these results are discussed, and it is argued that they are consistent with localization of a lexicon for spoken word recognition in the middle part of the left superior and middle temporal gyri, and a lexicon for written word recognition in the posterior part of the left middle temporal gyrus.

This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both concerning the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

BackgroundDiminished synaptic gain—the sensitivity of postsynaptic responses to neural inputs—may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms.MethodsPeople with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms—resting, mismatch negativity, and 40-Hz auditory steady-state response—and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits.ResultsClassic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all best explained by models with greater self-inhibition (decreased synaptic gain) in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in PScz in 3 paradigms.ConclusionsFirst, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation.

A fundamental challenge for humans—and other animals—is to survive in response to a natural diversity of dangers. Here, we provide a new perspective called Adaptive Threat Coding (ATC), where human defensive behavior is constructed in response to sensory, inferential, internal and subjective states that facilitate survival in a multidimensional landscape of danger. In humans, these states are guided by culture and social norms that influence the defensive response (e.g., avoidance of shame). The ATC model proposes that two contiguous survival strategies emerge: generalization, which is associated with representations that have high dimensionality and which furnish flexible cognitive heuristics (including efficient perceptual and conceptual representations, relations between threats, higher-order imagination, memory) and which converge through the construction of defensive states and via transfer learning. These representations support generalization and ‘explorative’ behaviors including information seeking and curiosity, where the goal is to increase situational awareness that can be used to mitigate current and future threat. Conversely, specialization entails lower dimensional representations, which underpin specialized, ‘exploitative’ behaviors. This specialization facilitates the economic execution of defensive behaviors, that are generated through a combination of learning and innate configuration. We finally propose that systems implicated in specialization and generalization coalesce through active inference and learning—and that this captures a central adaptive feature of human defensive systems; namely, self-preservation in response to a mélange of threats across diverse ecologies.

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches – Bayesian model selection and generative embedding – which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

Abstract The target article “Thinking Through Other Minds” (TTOM) offered an account of the distinctively human capacity to acquire cultural knowledge, norms, and practices. To this end, we leveraged recent ideas from theoretical neurobiology to understand the human mind in social and cultural contexts. Our aim was both synthetic – building an integrative model adequate to account for key features of cultural learning and adaptation; and prescriptive – showing how the tools developed to explain brain dynamics can be applied to the emergence of social and cultural ecologies of mind. In this reply to commentators, we address key issues, including: (1) refining the concept of culture to show how TTOM and the free-energy principle (FEP) can capture essential elements of human adaptation and functioning; (2) addressing cognition as an embodied, enactive, affective process involving cultural affordances; (3) clarifying the significance of the FEP formalism related to entropy minimization, Bayesian inference, Markov blankets, and enactivist views; (4) developing empirical tests and applications of the TTOM model; (5) incorporating cultural diversity and context at the level of intra-cultural variation, individual differences, and the transition to digital niches; and (6) considering some implications for psychiatry. The commentators’ critiques and suggestions point to useful refinements and applications of the model. In ongoing collaborations, we are exploring how to augment the theory with affective valence, take into account individual differences and historicity, and apply the model to specific domains including epistemic bias.

Even after realignment there is residual movement-related variance present in fMRI time-series, causing loss of sensitivity and, potentially, also specificity. One cause is the differential deformation of the sampling matrix, by field inhomogeneities, at different object positions, i.e., a movement-by-inhomogeneity interaction. This has been addressed previously by using empirical field measurements. In the present paper we suggest a forward model of how data is affected by an inhomogeneous field at different object positions. From this model we derive a method to solve the inverse problem of estimating the field inhomogeneities and their derivatives with respect to object position, directly from the EPI data and estimated realignment parameters. The field is modeled as a linear combination of cosine basis fields, which facilitates a fast way of implementing the necessary matrix operations. Simulations suggest that the solution is tractable and that the fields are estimable given the deformed images and knowledge of the relative positions at which they have been acquired. An experiment on a subject performing voluntary movements in the scanner yielded plausible estimates of the deformation fields and their application to “unwarp” the time series significantly reduced movement-related variance.

Abstract Both classic and contemporary models of auditory word repetition involve at least four left hemisphere regions: primary auditory cortex for processing sounds; pSTS (within Wernicke’s area) for processing auditory images of speech; pOp (within Broca’s area) for processing motor images of speech; and primary motor cortex for overt speech articulation. Previous functional-MRI (fMRI) studies confirm that auditory repetition activates these regions, in addition to many others. Crucially, however, contemporary models do not specify how regions interact and drive each other during auditory repetition. Here, we used dynamic causal modelling, to test the functional interplay among the four core brain regions during single auditory word and pseudoword repetition. Our analysis is grounded in the principle of degeneracy—i.e., many-to-one structure-function relationships—where multiple neural pathways can execute the same function. Contrary to expectation, we found that, for both word and pseudoword repetition, (i) the effective connectivity between pSTS and pOp was predominantly bidirectional and inhibitory; (ii) activity in the motor cortex could be driven by either pSTS or pOp; and (iii) the latter varied both within and between individuals. These results suggest that different neural pathways can support auditory speech repetition. This degeneracy may explain resilience to functional loss after brain damage.