Publications

Review for "Predictive processing: is the future just a memory?"

Brain Activations in Schizophrenia During a Graded Memory Task Studied With Functional Neuroimaging

Functional neuroimaging experiments have implicated prefrontal cortex (PFC) in memory processes. Several studies of schizophrenic patients have shown failure of activation in the dorsolateral region of PFC (DLPFC). We used a graded memory challenge to characterize functional neuroanatomical differences between schizophrenic and control subjects. The graded manipulation of task demands enabled us to assess group differences in the context of normal and abnormal psychological task performance.Memory-related activity was assessed using positron emission tomography in schizophrenic patients and age-matched controls during performance of a graded memory task. Subjects underwent scanning while learning and recalling word lists of variable length.We used a model that assessed linear and nonlinear effects of memory load. Nonlinear group differences in DLPFC activation were observed. Controls showed a steepening slope of DLPFC increase as task demands increased. By contrast, schizophrenic subjects showed initial DLPFC increases that fell away with increasing memory load. The DLPFC response in schizophrenic subjects was closely related to measured task performance. In addition, schizophrenic subjects failed to show task-related decreases in activity in the left superior temporal and inferior parietal gyrus.Patients with schizophrenia showed a failure in DLPFC activation only in the face of diminished performance measures, suggesting that a full characterization of task-related changes in DLPFC activation must consider performance levels. However, striking failures of deactivation in superior temporal and inferior parietal regions were independent of task performance, possibly reflecting a core abnormality of the condition.

Prediction, precision, and context: dynamic causal modelling of MEG and ECoG

A heuristic for the degrees of freedom of statistics based on multiple variance parameters

Dynamic changes in effective connectivity characterized by variable parameter regression and kalman filtering

Attention to visual motion can increase the responsiveness of the motion-selective cortical area V5 and the posterior parietal cortex. We addressed attentional modulation of effective connectivity using variable parameter regression and functional magnetic resonance imaging. We present data from a single subject scanned under identical stimulus conditions (visual motion) while varying only the attentional component of the task. Variable parameter regression of the influence of V5 on PP revealed increased effective connectivity during attention to visual motion. With this dynamic measure of effective connectivity we were able to make inferences about the source of modulation by looking for regions that predicted the observed changes in connectivity. Using an ordinary regression analysis, we showed that activity in the prefrontal cortex could explain these changes and was sufficient to account for these modulatory influences on connections in the dorsal visual pathway.

The sentient organoid?

Citation: Friston K. The sentient organoid? Front Sci (2023) 1:1147911. doi: 10.3389/fsci.2023.1147911

Markov Blankets in the Brain

Recent characterisations of self-organising systems depend upon the presence of a Markov blanket: a statistical boundary that mediates the interactions between what is inside of and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. This depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level. The nuance afforded by partitioning neural systems in this way highlights certain limitations of modular perspectives of brain function that only consider a single level of description.

Relating rapid mental simulation to past experience

Methylphenidate and brain activity in a reward/conflict paradigm: Role of the insula in task performance

Comparing visual discrimination and detection: the special status of ‘no’ responses

Making a decision about whether something is there or not (detection) is qualitatively different from making a decision about what is there (discrimination). A key aspect of detection is the asymmetry in the availability of evidence for 'yes' and for 'no' responses. While discrimination requires a comparison between the relative evidence for different options, in a detection setting, evidence is only available for the presence of a stimulus and not for its absence. This means that confidence in the absence of a stimulus cannot rely on the magnitude of "evidence for absence" and must instead be based on other information. In line with this conceptual difference, previous studies identified behavioural dissimilarities between 'yes' and 'no' responses: confidence in 'no' responses is generally lower, and is less predictive of objective accuracy (Kanai, Walsh, & Tseng, 2010; Meuwese, van Loon, Lamme, & Fahrenfort, 2014) not only in visual detection but also in detection-like tasks (such as recognition memory; Higham, Perfect, & Bruno, 2009). Here, we set out to further characterise the distinct cognitive processes involved in reporting stimulus absence compared to reporting stimulus presence or identity. We present the results from two psychophysical experiments in which participants were asked to perform detection and discrimination tasks on the same class of stimuli, in different blocks. Although there was no difference in overall accuracy between the two tasks, we find markedly different behavioural signatures for discrimination and detection responses. Specifically, we replicate findings of a weaker relationship between accuracy and confidence for 'No' responses (lower area under the type 2 ROC) and show that 'No' responses exhibit a weaker negative association between reaction-time and confidence compared to both 'Yes' and discrimination responses. We discuss our observations in the context of possible cognitive models, such as unequal-variance SDT and temporal evidence accumulation models.

A systematic framework for functional connectivity measures

Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures – based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

Correction: A Neurocomputational Model of the Mismatch Negativity

Beginning at “with physiologically grounded models of neuroimaging data…” in the first paragraph of the Introduction, the citations are incorrect throughout the text. The correct citation(s) for each citation block, delineated by [X], can be found two blocks further on in the text. For example: in the Introduction section, the sentence “…with physiologically grounded models of neuroimaging data [4,5]” should instead cite reference [1] from the paragraph below. Consequently the last line of the manuscript is incorrect and should read: “Similarly, extensions of this model could also be used to better understand the effects of drugs, such as ketamine [70,71], or neuromodulators, such as acetylcholine [72], on the MMN. We hope to pursue this avenue of research in future work.”

What might interoceptive inference reveal about consciousness?

The mainstream science of consciousness offers a few predominate views of how the brain gives rise to awareness. Chief among these are the Higher Order Thought Theory, Global Neuronal Workspace Theory, Integrated Information Theory, and hybrids thereof. In parallel, rapid development in predictive processing approaches have begun to outline concrete mechanisms by which interoceptive inference shapes selfhood, affect, and exteroceptive perception. Here, we consider these new approaches in terms of what they might offer our empirical, phenomenological, and philosophical understanding of consciousness and its neurobiological roots.

Inferring Effective Connectivity from fMRI Data

PsPM-trSP3: SCR measurement in response to aversive/arousing/neutral IAPS pictures while subjected to auditory distractors

This dataset includes skin conductance response (SCR) measurements for each of 40 healthy unmedicated participants (20 males and 20 females aged 21.9 +/- 3.8 years) in response to the 16 most arousing negative, and most arousing positive (excluding explicit nude) and 16 least arousing neutral IAPS pictures, presented for 1 s each in 1 block, while listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was 4 s, plus a variable delay of around 0.4 s for image loading.

Neural diffusivity and pre-emptive epileptic seizure intervention

The propagation of epileptic seizure activity in the brain is a widespread pathophysiology that, in principle, should yield to intervention techniques guided by mathematical models of neuronal ensemble dynamics. During a seizure, neural activity will deviate from its current dynamical regime to one in which there are significant signal fluctuations. In silico treatments of neural activity are an important tool for the understanding of how the healthy brain can maintain stability, as well as of how pathology can lead to seizures. The hope is that, contained within the mathematical foundations of such treatments, there lie potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the dynamic causal modelling neuronal state equation generalises to a Fokker-Planck formalism if one extends the framework to model the ways in which activity propagates along the structural connections of neural systems. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity-i.e., by lowering the rate at which neuronal fluctuations disperse to neighbouring regions. We show, for neural systems prone to epileptic seizures, that such a reduction in diffusivity can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror the activity profile of a pathological region in its functionally connected areas. This counter-intuitive method is intended to be used pre-emptively-i.e., in order to mitigate the effects of the seizure, or ideally even prevent it from occurring in the first place. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network prior to seizure onset. Our hope is that this technique can form the basis for future real-time monitoring and intervention devices that are capable of treating epilepsy in a non-invasive manner.

Detecting subject-specific activations using fuzzy clustering

Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure-function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure-function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner.

Network constraints in scale free dynamical systems

Scale free dynamics are observed in a variety of physical and biological systems. These include neural activity in which evidence for scale freeness has been reported using a range of imaging modalities. Here, we derive the ways in which connections within a network must transform - relative to system size - in order to maintain scale freeness and test these theoretical transformations via simulations. First, we explore the known invariance of planetary motion for orbits varying in size. Using parametric empirical Bayesian modelling and a generic dynamical systems model, we show that we recover Kepler's third law from orbital timeseries, using our proposed transformations; thereby providing construct validation. We then demonstrate that the dynamical critical exponent is inversely proportional to the time rescaling exponent, in the context of coarse graining operations. Using murine calcium imaging data, we then show that the dynamical critical exponent can be estimated in an empirical biological setting. Specifically, we compare dynamical critical exponents - associated with spontaneous and task states in two regions of imaged cortex - that are classified as task-relevant and task-irrelevant. We find, consistently across animals, that the task-irrelevant region exhibits higher dynamical critical exponents during spontaneous activity than during task performance. Conversely, the task-relevant region is associated with higher dynamical critical exponents in task vs. spontaneous states. These data support the idea that higher dynamical critical exponents, within relevant cortical structures, underwrite neuronal processing due to the implicit increase in cross-scale information transmission.