Jaakko Matias Palva

The paper shows that working memory operations involve dynamic changes in neural subspace geometry, driven by aperiodic activity and slow oscillations.

The paper identifies five distinct depression phenotypes using MEG-oscillation functional connectivity and low-dimensional brain-symptom associations.

This paper describes a surgical protocol for creating a large, resealable cranial window in mice to enable long-term, multi-modal electrophysiological recordings of the Default Mode Network.

This study shows that early life stress increases neuronal avalanche spread in the prefrontal-amygdala network in male rats but not females, indicating sex-dependent effects on brain network dynamics.

This study investigates the relationship between delta-band synchrony and beta-gamma bistability in sleep-related epilepsy, finding they synergistically contribute to seizure generation.

The paper examines how REM sleep affects large-scale brain network dynamics and links this to its reduced epileptogenicity by analyzing SEEG recordings from epilepsy patients.

CROCOpy is a Python toolbox for analyzing neuronal oscillations and functional connectivity metrics in electrophysiological data.

The paper examines developmental changes in neuronal avalanches in the mPFC-BLA circuit of rats, finding distinct critical dynamics in each region.

This study uses advanced EEG analysis to assess cortical maturation in preterm infants by examining oscillatory dynamics during different vigilance states.

The paper develops a whole-brain modeling approach to mitigate activity mixing in neuroimaging data, improving brain-symptom correlation estimates and localization.

The paper develops a probabilistic model to identify epileptogenic networks using interictal SEEG data, achieving high classification accuracy with a reduced feature space.

The paper uses a computational model of delay-coupled oscillators to match MEG functional connectivity patterns, finding that meta-stable dynamics with small delays explain observed oscillatory coupling.

The paper shows that individual differences in cognitive flexibility are linked to specific neural oscillations and feedback sensitivity during rule learning.

This paper introduces a hierarchical Kuramoto model to simulate critical synchronization dynamics in the human brain and shows it aligns with MEG data on the subcritical side of a critical regime.

The paper examines how node centrality in MEG resting-state networks relates to neurotransmitter receptor and transporter densities across different frequency bands.

This study compares Biophysical Network Models (BNMs) with different delay specifications to determine their role in generating alpha-band phase synchronization networks in MEG data.

The paper uses SEEG data to identify stable, frequency-specific modules in phase-synchronization connectomes that are anatomically contiguous and functionally related.

This study investigates how infra-slow fluctuations and respiratory rhythms influence fast cortical EEG oscillations during wakefulness and sleep, finding that slow signals drive fast oscillations with stronger coupling during sleep.

This study uses MEG data to show that aberrant brain criticality, measured by long-range temporal correlations, characterizes early stages of Alzheimer's disease and can classify individuals with subjective cognitive decline and mild cognitive impairment.

This study examines hippocampal oscillations in rats during learning, finding distinct neural signatures for perception and associative learning.

The paper investigates how ADHD symptoms relate to brain criticality, finding that intermediate symptom scores correlate with peak criticality and potential functional benefits.

The paper shows that individual differences in brain synchronization levels correlate with power-law long-range temporal correlations, with variations in healthy and epileptic brain regions.

This study shows that COMT and BDNF genetic variants affect neuronal oscillation synchronization and dynamics in humans.

This paper identifies critical-like bistable dynamics in the resting-state human brain, showing their functional and clinical significance across frequency bands.

This study examines how prematurity affects phase-phase cortical synchrony networks in infants, finding sleep-state and frequency-specific differences linked to clinical outcomes.

This study investigates how biophysical network models can replicate MEG-derived phase-synchronization networks, finding that conduction delays do not improve the match but structural connections and Wilson-Cowan dynamics do.

This study shows that high-gamma activity (HGA) in human cortex can be synchronized across distant brain regions, revealing a new form of neuronal communication distinct from lower-frequency synchronization.

The paper identifies genuine cross-frequency coupling networks in human resting-state brain activity using MEG and SEEG data, showing distinct mechanisms for coupling oscillations across frequencies.

This study evaluates methods for multiple hypothesis testing in neuroimaging to improve reproducibility, finding permutation testing is most powerful and prior knowledge grouping enhances results.

The paper shows that even advanced MEG/EEG connectivity measures can produce false positive 'ghost' interactions due to signal mixing, and quantifies this effect through simulations.