Karl John Friston

Embodied Dynamic Causal Modelling: Brain and Cardiac Effective Connectivity

This paper introduces Embodied Dynamic Causal Modelling to estimate effective connectivity across the brain-heart axis using multimodal data from an auditory Go/No-Go task.

Inferential planning in the frontal cortex

This study shows that inferential planning in the frontal cortex generates simultaneous neural representations of sequence elements through probabilistic inference, unifying planning, memory, and motor preparation findings.

A Canonical Microcircuit for Estimating Excitation/Inhibition (E/I) Balance

This paper introduces a canonical microcircuit model to estimate E/I balance from non-invasive M/EEG recordings and shows its relevance to schizophrenia biomarkers.

Cross-Frequency Coupling as a Neural Substrate for Prediction Error Evaluation: A Laminar Neural Mass Modeling Approach

This paper proposes that cross-frequency coupling (CFC) mechanisms in a laminar neural mass model support prediction error evaluation in predictive coding, with implications for disorders like Alzheimer's disease and the effects of serotonergic psychedelics.

Time-Varying Dynamic Causal Modelling for Sequential Responses: Neural Mechanisms of Slow Cortical Potentials, Preparation, Planning and Beyond

This paper introduces DCM-SR, a new framework for modeling time-varying neural dynamics, and applies it to study slow cortical potentials in cognitive tasks.

Event-Related Warping: A Toolbox for Temporal Alignment and Jitter Correction in Sequential Experimental Paradigms

This paper introduces Event-Related Warping (ERW), a method for temporal alignment and jitter correction in sequential experimental paradigms, improving response recovery and preserving inter-channel relationships.

When the beat drops - The functional anatomy of cardiac-induced sensory attenuation of auditory processing

This paper uses MEG and dynamic causal modelling to show that cardiac systole suppresses auditory deviant responses through local inhibitory gain control, not hierarchical attentional modulation.

The methodological foundations of lesion network mapping remain sound

The paper defends lesion network mapping by showing that it provides meaningful differences through specificity testing, refuting criticisms that it reflects normative connectome properties.

Heterogeneity in neural network engagement supports individual differences in top-down attention

The paper examines individual differences in top-down attention using MEG and pupillometry, finding that sensory processing and strategic resource allocation shape heterogeneous neural network engagement.

Topographic variation in neurotransmitter receptor densities explains differences in intracranial EEG spectra

This paper shows that incorporating neurotransmitter receptor densities improves biophysically informed models of intracranial EEG spectra, linking molecular characteristics to cortical population dynamics.

Apathy as a Loss of Prior Precision on Action Outcomes

This study investigates apathy as a reduction in prior precision of action outcome beliefs, finding a negative correlation between prior precision and apathy in healthy adults.

Impaired context disambiguation as a key determinant of post-traumatic psychopathology: A neurocomputational account

This paper develops a neurocomputational model of PTSD by linking Bayesian belief-updating and active inference to impaired context disambiguation and maladaptive behavior.

The functional anatomy of nociception: effective connectivity in chronic pain and placebo responders

The paper compares effective connectivity in chronic pain patients and placebo responders using resting-state fMRI to identify markers of nociception and placebo response.

A computational account of joint SSRI and anti-inflammatory treatment

The paper develops a computational model to explore how inflammation, serotonin levels, and brain activity interact, and how anti-inflammatory and antidepressant treatments affect depressive symptoms.

Interictal discharges spread along local recurrent networks between tubers and surrounding cortex

This study links interictal epileptiform discharge spread to network coupling in tuberous sclerosis patients using EEG and neural mass modeling.

Increasing spectral DCM flexibility and speed by leveraging Julia's ModelingToolkit and automated differentiation

The paper introduces a Julia package that enhances spectral DCM flexibility and speed using ModelingToolkit and automatic differentiation for parameter estimation in neuroimaging data.

Stimulus-repetition effects on macaque V1 and V4 microcircuits explain gamma-synchronization increase

The paper models learning effects in macaque visual cortex using Dynamic Causal Modeling to explain repetition-induced increases in gamma synchronization.

Cerebellar state estimation enables resilient coupling across behavioural domains

This paper proposes a cerebellar model for state estimation that links neuronal dynamics with behavioral coordination, using the free energy principle to explain contextual behavior.

Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

The paper examines the relationship between intracranial EEG and fMRI in the sensorimotor cortex, finding that high gamma power correlates with fMRI changes during motor tasks and rest.

Degeneracy in the neurological model of auditory speech repetition

The paper uses dynamic causal modelling to study effective connectivity during auditory repetition, finding bidirectional and inhibitory interactions between Wernickes and Brocas areas with individual variability.

Attentional effects on local V1 microcircuits explain selective V1-V4 communication

The paper models selective visual attention in macaques using Dynamic Causal Modeling to show that intrinsic V1 modulation explains attention effects in V1-V4 communication through changes in inhibition.

Human hippocampal ripples tune cortical responses in uncertain visual contexts

The paper shows that hippocampal ripples predict visual stimulus unpredictability and modulate cortical responses in humans.

Multi-modal and multi-model interrogation of large-scale functional brain networks

The paper compares two models for simulating brain activity across modalities, showing they can replicate functional connectivity and dynamics but with varying performance across modalities.

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

This paper develops a Bayesian framework integrating MEG and 7T-MRS to infer synaptic connectivity influenced by neurotransmitter concentrations, confirming GABA and glutamate effects on specific cortical connections.

Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex

The paper uses dynamic causal modelling to show that intrinsic disinhibition in higher visual cortex contributes to alpha power differences between eyes-open and eyes-closed states.

In vitro neurons learn and exhibit sentience when embodied in a simulated game-world

The paper demonstrates that in vitro neurons can learn and exhibit goal-directed behavior in a simulated game environment.

Experimental validation of the free-energy principle with in vitro neural networks

The paper validates the free-energy principle in vitro using rat cortical neurons, showing that free energy minimization predicts network self-organization and plasticity.

In the Body’s Eye: The Computational Anatomy of Interoceptive Inference

This paper introduces a formal model of cardiac active inference to explain how cardiac signals influence exteroceptive perception and confidence, and demonstrates its implications for fear perception and physiological reactivity.

Enhanced top-down sensorimotor processing in somatic anxiety

This study identifies enhanced top-down effective connectivity in sensorimotor cortices as a marker of trait somatic anxiety using resting-state fMRI and DCM.

Metastable Oscillatory Modes emerge from Synchronization in the Brain Spacetime Connectome

The paper shows that metastable oscillatory modes in the brain emerge from synchronization in the spacetime connectome, matching MEG data from healthy individuals.