Biology subjects

Palva, J. M.

Publications and source records attributed to Palva, J. M..

30 records · Page 2Linked to original sources

bioRxiv2022

Brain criticality predicts individual synchronization levels in humans

Moderate levels of synchronization of neuronal oscillations are essential for healthy brain dynamics. Synchronization levels exhibit large inter-individual variability the origins of which are unknown. Neuronal systems have been postulated to operate near a critical transition point or in an extended regime between disorder (subcritical) and order (supercritical phase) characterized by moderate synchronization and emergent power-law long-range temporal correlations (LRTCs). We investigated whether inter-individual variability in synchronization levels is explained by the individual position along the critical regime by analyzing magnetoencephalography (MEG) and intra-cerebral stereo-electroencephalography (SEEG) human resting-state data. Here we show that variability in synchronization levels exhibits a positive linear and quadratic relationships with LRTCs in healthy participants and brain areas. In the epileptogenic zone this correlation was negative. These results show that variability in synchronization levels is regulated by the individual position along an extended critical-like regime, with healthy brain areas tending to operate in its subcritical and epileptogenic areas in its supercritical side.

neuroscience↗

bioRxiv2022

Critical-like bistable dynamics in the resting-state human brain

Brain activity exhibits scale-free avalanche dynamics and power-law long-range temporal correlations (LRTCs) across the nervous system. This has been thought to reflect "brain criticality", i.e., brains operating near a critical phase transition between disorder and excessive order. Neuronal activity is, however, metabolically costly and may be constrained by activity-limiting mechanisms and resource depletion, which could make the phase transition discontinuous and bistable. Observations of bistability in awake human brain activity have nonetheless remained scarce and its functional significance unclear. First, using computational modelling where bistable synchronization dynamics emerged through local positive feedback, we found bistability to occur exclusively in a regime of critical-like dynamics. We then assessed bistability in vivo with resting-state magnetoencephalography and stereo-encephalography. Bistability was a robust characteristic of cortical oscillations throughout frequency bands from {delta} (3-7 Hz) to high-{gamma} (100-225 Hz). As predicted by modelling, bistability and LRTCs were positively correlated. Importantly, while moderate levels of bistability were positively correlated with executive functioning, excessive bistability was associated with epileptic pathophysiology and predictive of local epileptogenicity. Critical bistability is thus a salient feature of spontaneous human brain dynamics in awake resting-state and is both functionally and clinically significant. These findings expand the framework of brain criticality and show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.

neuroscience↗

bioRxiv2021

Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations

Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. However, the genetic underpinnings of oscillatory dynamics have remained poorly understood. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in Catechol-O-methyltransferase (COMT) Val158Met and brain-derived neurotrophic factor (BDNF) Val66Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in local and large-scale synchronization dynamics of neuronal oscillations. Comparison of these results to computational modelling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework.

neuroscience↗

bioRxiv2021

Biophysical network models of phase-synchronization in MEG resting-state

Magnetoencephalography (MEG) is used extensively to study functional connectivity (FC) networks of phase-synchronization, but the relationship of these networks to their biophysical substrates is poorly understood. Biophysical Network Models (BNMs) have been used to produce networks corresponding to MEG-derived networks of phase-synchronization, but the roles of inter-regional conduction delays, the structural connectome and dynamics of model of individual brain regions, in obtaining this correspondence remain unknown. In this study, we investigated the roles of conduction delays, the structural connectome, and dynamics of models of individual regions, in obtaining a correspondence between model-generated and MEG-derived networks between left-hemispheric regions. To do this, we compared three BNMs, respectively comprising Wilson-Cowan oscillators interacting with diffusion Magnetic Resonance Imaging (MRI)-based patterns of structural connections through zero delays, constant delays and distance-dependent delays respectively. For the BNM whose networks corresponded most closely to the MEG-derived network, we used comparisons against null models to identify specific features of each model component, e.g. the pattern of connections in the structure connectome, that contributed to the observed correspondence. The Wilson-Cowan zero delays model produced networks with a closer correspondence to the MEG-derived network than those produced by the constant delays model, and the same as those produced by the distance-dependent delays model. Hence, there is no evidence that including conduction delays improves the correspondence between model-generated and MEG-derived networks. Given this, we chose the Wilson-Cowan zero delays model for further investigation. Comparing the Wilson-Cowan zero delays model against null models revealed that both the pattern of structural connections and Wilson-Cowan oscillatory dynamics contribute to the correspondence between model-generated and MEG-derived networks. Our investigations yield insight into the roles of conduction delays, the structural connectome and dynamics of models of individual brain regions, in obtaining a correspondence between model-generated and MEG-derived networks. These findings result in a parsimonious BNM that produces networks corresponding closely to MEG-derived networks of phase-synchronization. HighlightsO_LISimple biophysical model produces close match ({rho}=0.49) between model and MEG networks C_LIO_LINo evidence for conduction delays improving match between model and MEG networks C_LIO_LIPattern of structural connections contributes to match between model and MEG networks C_LIO_LIWilson-Cowan oscillatory dynamics contribute to match between model and MEG networks C_LI

neuroscience↗

bioRxiv2021

Modules in connectomes of phase-synchronization comprise anatomically contiguous, spatially related regions

Modules in brain functional connectomes are essential to balancing segregation and integration of neuronal activity. Connectomes are the complete set of pairwise connections between brain regions. Non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) have been used to identify modules in connectomes of phase-synchronization. However, their resolution is suboptimal because of spurious phase-synchronization due to EEG volume conduction or MEG field spread. Here, we used invasive, intracerebral recordings from stereo-electroencephalography (SEEG, N = 67), to identify modules in connectomes of phase-synchronization. To generate SEEG-based group-level connectomes affected only minimally by volume conduction, we used submillimeter accurate localization of SEEG contacts and referenced electrode contacts in cortical grey matter to their closest contacts in white matter. Combining community detection methods with consensus clustering, we found that the connectomes of phase-synchronization were characterized by distinct and stable modules at multiple spatial scales, across frequencies from 3 to 320 Hz. These modules were highly similar within canonical frequency bands. Unlike the distributed brain systems identified with functional Magnetic Resonance Imaging (fMRI), modules up to the high-gamma frequency band comprised only anatomically contiguous regions. Notably, the identified modules comprised cortical regions involved in shared repertoires of sensorimotor and cognitive functions including memory, language and attention. These results suggest that the identified modules represent functionally specialised brain systems, which only partially overlap with the brain systems reported with fMRI. Hence, these modules might regulate the balance between functional segregation and functional integration through phase-synchronization. HighlightsO_LILarge-cohort SEEG used for phase-synchronization connectomics C_LIO_LIConnectomes of phase-synchronization possess distinct and stable modules C_LIO_LIModules in connectomes are highly similar within canonical frequency bands C_LIO_LIModules in connectomes comprise anatomically contiguous regions C_LIO_LIModules in connectomes comprise functionally related regions C_LI

neuroscience↗

bioRxiv2021

Phase-based cortical synchrony is affected by prematurity

Inter-areal synchronization by phase-phase correlations (PPC) of cortical oscillations mediates many higher neurocognitive functions, which are often affected by prematurity, a globally prominent neurodevelopmental risk factor. Here, we used electroencephalography (EEG) to examine brain-wide cortical PPC networks at term-equivalent age, comparing human infants after early prematurity to a cohort of healthy controls. We found that prematurity affected these networks in a sleep state-specific manner, and the differences between groups were also frequency-selective, involving brain-wide connections. The strength of synchronization in these networks was predictive of clinical outcomes in the preterm infants. These findings show that prematurity affects PPC networks in a clinically significant manner suggesting early functional biomarkers of later neurodevelopmental compromise to be used in clinical and translational studies after early neonatal adversity.

neuroscience↗

bioRxiv2019

Resting-state cross-frequency coupling networks in human electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that inter-areal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at meso-scale resolution from stereo-electroencephalography (SEEG) and at macro-scale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from non-sinusoidal signals ubiquitous in neuronal activity. We show that genuine inter-areal CFC is present in human resting-state activity in both MEG and SEEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for inter-areal CFS and PAC being two distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

neuroscience↗

bioRxiv2018

Influence of multiple hypothesis testing on reproducibility in neuroimaging research

BackgroundReproducibility of research findings has been recently questioned in many fields of science, including psychology and neurosciences. One factor influencing reproducibility is the simultaneous testing of multiple hypotheses, which increases the number of false positive findings unless the p-values are carefully corrected. While this multiple testing problem is well known and has been studied for decades, it continues to be both a theoretical and practical problem.\n\nNew MethodHere we assess the reproducibility of research involving multiple-testing corrected for family-wise error rate (FWER) or false discovery rate (FDR) by techniques based on random field theory (RFT), cluster-mass based permutation testing, adaptive FDR, and several classical methods. We also investigate the performance of these methods under two different models.\n\nResultsWe found that permutation testing is the most powerful method among the considered approaches to multiple testing, and that grouping hypotheses based on prior knowledge can improve power. We also found that emphasizing primary and follow-up studies equally produced most reproducible outcomes.\n\nComparison with Existing Method(s)We have extended the use of two-group and separate-classes models for analyzing reproducibility and provide a new open-source software \"MultiPy\" for multiple hypothesis testing.\n\nConclusionsOur results suggest that performing strict corrections for multiple testing is not sufficient to improve reproducibility of neuroimaging experiments. The methods are freely available as a Python toolkit \"MultiPy\" and we aim this study to help in improving statistical data analysis practices and to assist in conducting power and reproducibility analyses for new experiments.

neuroscience↗

bioRxiv2018

Effect of fluoxetine on adult amblyopia: a placebo-controlled study combining neuroplasticity-enhancing pharmacological intervention and perceptual training

Amblyopia is a common visual disorder that is treatable in childhood. However, therapies have limited efficacy in adult patients with amblyopia. Fluoxetine can reinstate early-life critical period-like neuronal plasticity and has been used to recover functional vision in adult rats with amblyopia. This phase 2, randomized, double-blind (fluoxetine vs. placebo), multicenter clinical trial examined whether or not fluoxetine can improve visual acuity in amblyopic adults. This interventional trial included 42 participants diagnosed with moderate to severe amblyopia. Subjects were randomized to receive either 20 mg fluoxetine (n=22) or placebo (n=20). During the 10-week treatment period, all subjects performed daily computerized perceptual training and eye patching. There was no significant difference in treatment efficacy between the groups. Visual acuity at the primary endpoint had significantly improved over baseline in both the fluoxetine (-0.167 logMAR) and placebo (-0.194 logMAR) groups (both p < 0.001). Because patching alone is not effective in adults, the visual acuity improvement likely resulted from perceptual training. There was a positive correlation between visual acuity improvement and the perceptual training time. While this study failed to provide evidence that fluoxetine enhances neuroplasticity, our data support the usefulness of perceptual training for vision improvement in adults with amblyopia.

clinical trials↗

bioRxiv2017

Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures

When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or \"ghost\" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations.\n\nHighlights{checkmark} Reliable estimation of neuronal coupling with MEG and EEG is challenged by signal mixing\n{checkmark}A number of coupling techniques attempt to overcome this limitation by excluding zero-lag interactions\n{checkmark}Contrary to what is commonly admitted, our simulations illustrate that such interaction metrics will still yield false positives\n{checkmark}Spurious, or \"ghost\", interactions are generally detected between sources in the vicinity of true phase-lagged interacting sources\n{checkmark}Signal mixing also severely affects the mutual separability of phase and amplitude correlations

neuroscience↗

bioRxiv2017

Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses

Inter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively recorded only with magneto- and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed MEG/EEG data, signal mixing, or \"source leakage\", is a significant confounder for FC analyses and network localization.\n\nSignal mixing leads to two distinct kinds of false-positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source-reconstructed MEG/EEG data.\n\nOur approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis.\n\nHighlightsO_LIA true interaction often is \"ghosted\" into a multitude of spurious edges (SI)\nC_LIO_LIEffective in controlling and illustrating SI\nC_LIO_LIHyperedges have much improved TPR and graph quality\nC_LIO_LIAdvantages in visualizing connectivity\nC_LI

neuroscience↗

bioRxiv2017

High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention

Visuospatial attention prioritizes processing of attended visual stimuli. It is characterized by lateralized alpha-band (8-14 Hz) amplitude suppression in visual cortex and increased neuronal activity in a network of frontal and parietal areas. It has remained unknown what mechanisms coordinate neuronal processing among frontoparietal network and visual cortices and implement the attention-related modulations of alpha-band amplitudes and behavior. We investigated whether large-scale network synchronization could be such a mechanism. We recorded human cortical activity with magnetoencephalography (MEG) during a visuospatial attention task. We then identified the frequencies and anatomical networks of inter-areal phase synchronization from source localized MEG data. We found that visuospatial attention is associated with robust and sustained long-range synchronization of cortical oscillations exclusively in the high-alpha (10-14 Hz) frequency band. This synchronization connected frontal, parietal and visual regions and was observed concurrently with amplitude suppression of low-alpha (6-9 Hz) band oscillations in visual cortex. Furthermore, stronger high-alpha phase synchronization was associated with decreased reaction times to attended stimuli and larger suppression of alpha-band amplitudes. These results thus show that high-alpha band phase synchronization is functionally significant and could coordinate the neuronal communication underlying the implementation of visuospatial attention.

neuroscience↗