This work represents an attempt to bring together two important themes in neuronal dynamics. The first is the characterization of dynamic correlations in multiunit recordings of spike activity using joint-peri-stimulus time histograms (J-PSTHs) [Aertsen and Preissl, 1991: Non Linear Dynamics and Neural Networks]. The second is transient phase-locking at high (gamma) frequencies, either in terms of spiking in separable spike trains [e.g., Eckhorn et al., 1988: Biol Cybern 60:121–130, Gray and Singer, 1989 Proc Natl Acad Sci USA 86:1698–1702], or using continuous electrical or biomagnetic signals [e.g., Desmedt and Tomberg, 1994 Neurosci Lett 168:126–129]. In this paper we suggest that transient phase-locking is necessary for frequency-specific, dynamic event-related correlations. This point is demonstrated using the gamma-frequency (36 Hz) component of neuromagnetic signals measured in the prefrontal and partial regions of a subject during self-paced movements. A J-PSTH analysis revealed dynamic changes in prefronto-parietal correlations in relation to movement onset. These frequency-specific dynamic correlations were associated with changes in the degree of phase-locking, of the sort reported by Desmedt and Tomberg [1994 Neurosci Lett 168:126–129]. Hum. Brain Mapping 5:48–57, 1997. © 1997 Wiley-Liss, Inc.
A recent letter to Nature (Vaadia et al. Nature, Lond. 373, 515-518 (1995)) presented compelling results concerning neuronal interactions in monkey cortex. Vaadia et al. made two fundamental points: (i) it is possible that cortical function is mediated by dynamic modulation of coherent firing among neurons; and (ii) these time-dependent changes in correlations can emerge without modulation of firing rates. These observations have severe implications for models of neural coding and empirical approaches that are based on firing rates (e.g. neuroimaging). This communication presents a simpler explanation for the results presented in Vaadia et al., by noting they are consistent with the correlated expression of stereotyped neuronal transients following (or preceding) a salient event. This re-formulation is important because: (i) correlations measured in terms of transients are not time-dependent, allowing prevailing models of neural coding to be 'reinstated'; and (ii) it suggests a powerful analysis based on singular value decomposition of firing rates.
This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs.
This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic <em>consequences</em> of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present—and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations.
Pain and tinnitus share common pathophysiological mechanisms, clinical features, and treatment approaches. A source-localized resting-state EEG study was conducted in 150 participants: 50 healthy controls, 50 pain, and 50 tinnitus patients. Resting-state activity as well as functional and effective connectivity was computed in source space. Pain and tinnitus were characterized by increased theta activity in the pregenual anterior cingulate cortex, extending to the lateral prefrontal cortex and medial anterior temporal lobe. Gamma-band activity was increased in both auditory and somatosensory cortex, irrespective of the pathology, and extended to the dorsal anterior cingulate cortex and parahippocampus. Functional and effective connectivity were largely similar in pain and tinnitus, except for a parahippocampal-sensory loop that distinguished pain from tinnitus. In tinnitus, the effective connectivity between parahippocampus and auditory cortex is bidirectional, whereas the effective connectivity between parahippocampus and somatosensory cortex is unidirectional. In pain, the parahippocampal-somatosensory cortex is bidirectional, but parahippocampal auditory cortex unidirectional. These modality-specific loops exhibited theta-gamma nesting. Applying a Bayesian brain model of brain functioning, these findings suggest that the phenomenological difference between auditory and somatosensory phantom percepts result from a vicious circle of belief updating in the context of missing sensory information. This finding may further our understanding of multisensory integration and speaks to a universal treatment for pain and tinnitus-by selectively disrupting parahippocampal-somatosensory and parahippocampal-auditory theta-gamma activity and connectivity.
Cognitive scientists have traditionally specified the functional components of cognitive skills on the basis of behavioural studies of normal and neurologically impaired subjects. The results of functional imaging studies are challenging these classical models because there is a high degree of overlap among the neural systems activated by tasks that share no cognitive components. This suggests that a given neuronal structure can perform multiple functions that depend on the areas with which it interacts. However, there will be a limited range of functions that an area can perform given that its anatomical (intrinsic and extrinsic) connectivity is fixed. Assigning labels that encompass the operations that each area performs should enable a task to be re-described in terms of the functions of the areas activated. In other words, function should predict the structure and conversely structure should predict function. These systematic descriptions are referred to as ontologies. We argue that a systematic ontology for cognition would facilitate the integration of cognitive and anatomical models and organise the cognitive components of diverse tasks into a single framework. These points are illustrated with cognitive and anatomical models of reading and object recognition.
Psychopharmacological activation, in conjunction with positron emission tomographic measurements of regional cerebral blood flow (rCBF), was used to investigate the neurotransmitter basis of a specific cognitive function in man. Monoaminergic neurotransmission was pharmacologically manipulated during performance of auditory - verbal memory tasks. Statistical parametric mapping was used to identify the brain sites of interaction between memory-induced increases in rCBF and active drugs. Memory task-induced increases in rCBF in the left prefrontal cortex were attenuated by apomorphine, a non-selective dopamine agonist, whilst buspirone, a serotonin1A partial agonist, augmented rCBF increases in this area. In addition, apomorphine and buspirone augmented memory-induced increases in rCBF centred in the posterior cingulate cortex, whilst buspirone alone attenuated rCBF increases in the retrosplenial cortex and posterior parahippocampal gyrus. These regionally selective interactions may represent neuromodulatory effects of monoaminergic neurotransmission on a specific cognitive function in man.
Abstract In cognitive neuroscience, electrical brain activity is most commonly recorded at the scalp. In order to infer the contributions and connectivity of underlying neuronal sources within the brain, it is necessary to reconstruct sensor data at the source level. Several approaches to this reconstruction have been developed, thereby solving the so-called implicit inverse problem (Michel et al. 2004). However, a unifying premise against which to validate these source reconstructions is seldom available. The dataset provided in this work, in which brain activity is simultaneously recorded on the scalp (non-invasively) by electroencephalography (EEG) and on the cortex (invasively) by electrocorticography (ECoG), can be of a great help in this direction. These multimodal recordings were obtained from a macaque monkey under wakefulness and sedation. Our primary goal was to establish the connectivity architecture between two sources of interest (frontal and parietal), and to assess how their coupling changes over the conditions. We chose these sources because previous studies have shown that the connections between them are modified by anaesthesia (Boly et al. 2012). Our secondary goal was to evaluate the consistency of the connectivity results when analyzing sources recorded from invasive data (128 implanted ECoG sources) and source activity reconstructed from scalp recordings (19 EEG sensors) at the same locations as the ECoG sources. We conclude that the directed connectivity in the frequency domain between cortical sources reconstructed from scalp EEG is qualitatively similar to the connectivity inferred directly from cortical recordings, using both data-driven (directed transfer function; DTF) and biologically grounded (dynamic causal modelling; DCM) methods. Furthermore, the connectivity changes identified were consistent with previous findings (Boly et al. 2012). Our findings suggest that inferences about directed connectivity based upon non-invasive electrophysiological data have construct validity in relation to invasive recordings.
<ns4:p>This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.</ns4:p>
Metastability is a key source of itinerant dynamics in the brain; namely, spontaneous spatiotemporal reorganization of neuronal activity. This itinerancy has been the focus of numerous dynamic functional connectivity (DFC) analyses - developed to characterize the formation and dissolution of distributed functional patterns over time, using resting state fMRI. However, aside from technical and practical controversies, these approaches cannot recover the neuronal mechanisms that underwrite itinerant (e.g., metastable) dynamics-due to their descriptive, model-free nature. We argue that effective connectivity (EC) analyses are more apt for investigating the neuronal basis of metastability. To this end, we appeal to biologically-grounded models (i.e., dynamic causal modelling, DCM) and dynamical systems theory (i.e., heteroclinic sequential dynamics) to create a probabilistic, generative model of haemodynamic fluctuations. This model generates trajectories in the parametric space of EC modes (i.e., states of connectivity) that characterize functional brain architectures. In brief, it extends an established spectral DCM, to generate functional connectivity data features that change over time. This foundational paper tries to establish the model's face validity by simulating non-stationary fMRI time series and recovering key model parameters (i.e., transition probabilities among connectivity states and the parametric nature of these states) using variational Bayes. These data are further characterized using Bayesian model comparison (within and between subjects). Finally, we consider practical issues that attend applications and extensions of this scheme. Importantly, the scheme operates within a generic Bayesian framework - that can be adapted to study metastability and itinerant dynamics in any non-stationary time series.
This report summarises our recent attempts to integrate action and perception within a single optimisation framework. We start with a statistical formulation of Helmholtz's ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses.
This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work.
Abstract Background Recent reports based on conventional SEIR models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities that far exceed the first wave. These models suggest non-pharmaceutical interventions would have limited impact without intermittent national lockdowns and consequent economic and health impacts. We used Bayesian model comparison to revisit these conclusions, when allowing for heterogeneity of exposure, susceptibility, and viral transmission. Methods We used dynamic causal modelling to estimate the parameters of epidemiological models and, crucially, the evidence for alternative models of the same data. We compared SEIR models of immune status that were equipped with latent factors generating data; namely, location, symptom, and testing status. We analysed daily cases and deaths from the US, UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany, and Canada over the period 25-Jan-20 to 15-Jun-20. These data were used to estimate the composition of each country’s population in terms of the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed, and (iii) not infectious when susceptible to infection. Findings Bayesian model comparison found overwhelming evidence for heterogeneity of exposure, susceptibility, and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain the large differences in mortality rates across countries. The best model of UK data predicts a second surge of fatalities will be much less than the first peak (31 vs . 998 deaths per day. 95% CI: 24-37)—substantially less than conventional model predictions. The size of the second wave depends sensitively upon the loss of immunity and the efficacy of find-test-trace-isolate-support (FTTIS) programmes. Interpretation A dynamic causal model that incorporates heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes. Research in context Evidence before this study Hundreds of modelling papers have been published recently, offering predictions and projections of the current coronavirus outbreak. These range from peer-reviewed publications to rapid reports from learned societies. Many, if not most, of these modelling initiatives commit to a particular kind of epidemiological model that precludes heterogeneity in viral exposure, susceptibility, and transmission. The ensuing projections can be fantastical in terms of fatalities and ensuing public health responses. Added value of this study This study revisits the evidence for conventional epidemiological modelling assumptions using dynamic causal modelling and Bayesian model comparison. It provides overwhelming evidence for heterogeneity, and the interaction between lockdown and herd immunity in suppressing viral transmission. Implications of all the available evidence Heterogeneity of this sort means that low seroprevalence (<20%) is consistent with levels of population immunity that play a substantive role in attenuating viral transmission and, crucially, facilitating public health measures.
