Publications

Identifying prodromal biomarkers for schizophrenia and bipolar disorder using magnetoencephalography

Introduction Schizophrenia (SZ) and bipolar disorder (BD) are severe mental illnesses with large overlapping heritability. Both disorders are associated with altered neurophysiological responses, as measured with magnetoencephalography (MEG) or electroencephalography (EEG), particularly reduced mismatch negativity (MMN) and 40 Hz auditory steady-state responses (ASSR). These deficits could potentially both serve as early markers of illness and provide insight into the underlying pathophysiology as endophenotypes. First-degree relatives to patients with SZ and BD also show some neurophysiological deficits, however whether these deficits can be detected in adolescent offspring of patients is undetermined. Objectives We aim to investigate whether adolescents at familial high risk of schizophrenia and bipolar disorder show aberrant MMN and ASSR compared to population-based controls. Methods We will investigate MMN and 40 Hz ASSR in 15 year old adolescents (n ≈ 175) born to parents diagnosed with either SZ (FHR-SZ), BD (FHR-BD), or neither SZ or BD (population-based controls, PBC) using MEG. We will first perform sensor-level analyses and subsequently apply dynamic causal modeling (DCM) to investigate effective connectivity and make inferences about the underlying neurobiological mechanisms. We will investigate whether current psychopathology, cognitive status, and genetic risk for SZ and BD predict neurophysiological responses in the adolescents. Investigations are part of The Danish High Risk and Resilience Study - VIA (VIA15), a population-based longitudinal cohort study integrating social, psychological and biological risk factors and outcomes for SZ and BD. Results Final results are expected in 2024 Conclusions The VIA15 study will allow for unprecedented insight into the neurobiological development of schizophrenia and bipolar disorder. Disclosure No significant relationships.

Characterizing modulatory interactions between areas V1 and V2 in human cortex: A new treatment of functional MRI data

Abstract Reversible cooling experiments in monkey visual cortex have demonstrated that visually driven neuronal activity in V2 depends on feedforward projections from V1, whereas neuronal activity in V1 is modulated by feedback, or reentrant, projections from V2. We present evidence for a homologous asymmetry in reciprocal connections between V1 and V2 in human cortex using physiological measurements obtained with functional MRI. The analysis was based on a nonlinear model of effective connectivity that partitioned the influence that one region exerted over another into an obligatory effect (an effect that depended only on the input) and a modulatory effect (an effect that represented an interaction between input and activity intrinsic to the target region). Using estimates of the modulatory effect we tested two related hypotheses: (1) that V2 would be a major source of modulatory influences on V1; and (2) that the modulatory effects of V2 on V1 would be greater than those of V1 on V2. The first constitutes a hypothesis about the regional or topographic organization of (modulatory) effective connectivity and the second hypothesis directly addresses the functional asymmetry suggested by reversible cooling experiments. The results confirmed that the origins of feedback modulatory effects on V1 were regionally specific and most pronounced in V2. In contrast, feedforward modulatory influences on V1 on V2 were negligible. This apparent asymmetry between feedforward and feedback modulatory interactions was evident in both hemispheres and appears to be a fairly robust feature of nonlinear interactions between striate and extrastriate cortex. © 1995 Wiley‐Liss, Inc.

Fully three-dimensional nonlinear spatial normalisation: A new approach

EEG-fMRI INTEGRATION: A CRITICAL REVIEW OF BIOPHYSICAL MODELING AND DATA ANALYSIS APPROACHES

The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.

Neural representation in active inference: Using generative models to interact with—and understand—the lived world

Abstract This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action‐perception cycles to those that underwrite planning and imagination; namely, from explicit models that entail variables for predicting concurrent sensations, like objects, faces, or people—to action‐oriented models that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities—and the gradual transition from pragmatic to detached neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation.

The Functional Neuroanatomy of Temporal Discrimination

Two identical stimuli, such as a pair of electrical shocks to the skin, are readily perceived as two separate events in time provided the interval between them is sufficiently long. However, as they are presented progressively closer together, there comes a point when the two separate stimuli are perceived as a single stimulus. Damage to posterior parietal cortex, peri-supplementary motor area (peri-SMA), and basal ganglia can disturb this form of temporal discrimination. Our aim was to establish, in healthy subjects, the brain areas that are involved in this process. During functional magnetic resonance imaging scanning, paired electrical pulses, separated by variable inter-stimulus intervals (5-110 msec), were delivered to different sites on one forearm (8-64 mm from the midline). Subjects were required to simply detect the stimulus (control task) or to identify a stimulus property. For temporal discrimination (TD), subjects reported whether they felt one or two stimuli. For spatial discrimination, they reported whether the stimuli were located on the right or left side of the forearm. Subjects reported their choice by pressing a button with the opposite hand. Our results showed that discrimination, as opposed to simply detection, activated several brain areas. Most were common to both discrimination tasks. These included regions of prefrontal cortex, right postcentral gyrus and inferior parietal lobule, basal ganglia, and cerebellum. However, activation of pre-SMA and anterior cingulate was found to be specific to the TD task. This suggests that these two frontal regions may play a role in the temporal processing of somatosensory events.

Modelling event-related responses in the brain

Active inference and oculomotor pursuit: The dynamic causal modelling of eye movements

This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models – usually biophysical models of neuronal activity. Our key innovation is to use a generative model based on a normative (Bayes-optimal) model of active inference to model oculomotor pursuit in terms of subjects' beliefs about how visual targets move and how their oculomotor system responds. Our aim here is to establish the face validity of the approach, by manipulating the content and precision of sensory information – and examining the ensuing changes in the subjects' implicit beliefs. These beliefs are inferred from their eye movements using the normative model. We show that on average, subjects respond to an increase in the 'noise' of target motion by increasing sensory precision in their models of the target trajectory. In other words, they attend more to the sensory attributes of a noisier stimulus. Conversely, subjects only change kinetic parameters in their model but not precision, in response to increased target speed. Using this technique one can estimate the precisions of subjects' hierarchical Bayesian beliefs about target motion. We hope to apply this paradigm to subjects with schizophrenia, whose pursuit abnormalities may result from the abnormal encoding of precision.

Causal Hierarchy within the Thalamo-Cortical Network in Spike and Wave Discharges

Generalised spike wave (GSW) discharges are the electroencephalographic (EEG) hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM) to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges.

Canonical Cortical Field Theories

We characterise the dynamics of neuronal activity, in terms of field theory, using neural units placed on a 2D-lattice modelling the cortical surface. The electrical activity of neuronal units was analysed with the aim of deriving a neural field model with a simple functional form that still able to predict or reproduce empirical findings. Each neural unit was modelled using a neural mass and the accompanying field theory was derived in the continuum limit. The field theory comprised coupled (real) Klein-Gordon fields, where predictions of the model fall within the range of experimental findings. These predictions included the frequency spectrum of electric activity measured from the cortex, which was derived using an equipartition of energy over eigenfunctions of the neural fields. Moreover, the neural field model was invariant, within a set of parameters, to the dynamical system used to model each neuronal mass. Specifically, topologically equivalent dynamical systems resulted in the same neural field model when connected in a lattice; indicating that the fields derived could be read as a canonical cortical field theory. We specifically investigated non-dispersive fields that provide a structure for the coding (or representation) of afferent information. Further elaboration of the ensuing neural field theory, including the effect of dispersive forces, could be of importance in the understanding of the cortical processing of information.

Functional asymmetry and the consequences of action

High-Dimensional Image Registration Using Symmetric Priors

Degeneracy in the neurological model of auditory speech repetition

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.

Nonlinear Coupling in the Human Motor System

The synchronous discharge of neuronal assemblies is thought to facilitate communication between areas within distributed networks in the human brain. This oscillatory activity is especially interesting, given the pathological modulation of specific frequencies in diseases affecting the motor system. Many studies investigating oscillatory activity have focused on same frequency, or linear, coupling between areas of a network. In this study, our aim was to establish a functional architecture in the human motor system responsible for induced responses as measured in normal subjects with magnetoencephalography. Specifically, we looked for evidence for additional nonlinear (between-frequency) coupling among neuronal sources and, in particular, whether nonlinearities were found predominantly in connections within areas (intrinsic), between areas (extrinsic) or both. We modeled the event-related modulation of spectral responses during a simple hand-grip using dynamic casual modeling. We compared models with and without nonlinear connections under conditions of symmetric and asymmetric interhemispheric connectivity. Bayesian model comparison suggested that the task-dependent motor network was asymmetric during right hand movements. Furthermore, it revealed very strong evidence for nonlinear coupling between sources in this distributed network, but interactions among frequencies within a source appeared linear in nature. Our results provide empirical evidence for nonlinear coupling among distributed neuronal sources in the motor system and that these play an important role in modulating spectral responses under normal conditions.

Temporal Difference Models and Reward-Related Learning in the Human Brain

Temporal difference learning has been proposed as a model for Pavlovian conditioning, in which an animal learns to predict delivery of reward following presentation of a conditioned stimulus (CS). A key component of this model is a prediction error signal, which, before learning, responds at the time of presentation of reward but, after learning, shifts its response to the time of onset of the CS. In order to test for regions manifesting this signal profile, subjects were scanned using event-related fMRI while undergoing appetitive conditioning with a pleasant taste reward. Regression analyses revealed that responses in ventral striatum and orbitofrontal cortex were significantly correlated with this error signal, suggesting that, during appetitive conditioning, computations described by temporal difference learning are expressed in the human brain.

The slice-timing problem in event-related fMRI

Analysis of fMRI data using the general linear statistical model

Spatial registration and normalization of images

This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time‐series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley‐Liss, Inc.