Evidence for genetic regulation of the human parieto-occipital 10-Hz rhythmic activity

Human Sensorimotor Beta Event Characteristics and Aperiodic Signal Are Highly Heritable

Individuals' phenotypes, including the brain's structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns that can be characterized noninvasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor (rolandic) beta rhythm, shows intermittent high amplitude "events" that predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic (1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm events and aperiodic activity in 210 healthy male and female adult siblings' spontaneous MEG activity. The most heritable trait was the aperiodic 1/f signal, with a heritability of 0.87 in the right hemisphere. Time-resolved beta event amplitude parameters were also highly heritable, whereas the heritabilities for overall beta power, peak frequency, and measures of event duration remained nonsignificant. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that these differences partially reflect different underlying signal-generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.

A genetic variation in the adenosine A2A receptor gene contributes to variability in oscillatory alpha power in wake and sleep EEG and A1 adenosine receptor availability in the human brain

The EEG alpha rhythm (∼ 8-13 Hz) is one of the most salient human brain activity rhythms, modulated by the level of attention and vigilance and related to cerebral energy metabolism. Spectral power in the alpha range in wakefulness and sleep strongly varies among individuals based on genetic predisposition. Knowledge about the underlying genes is scarce, yet small studies indicated that the variant rs5751876 of the gene encoding A2A adenosine receptors (ADORA2A) may contribute to the inter-individual variation. The neuromodulator adenosine is directly linked to energy metabolism as product of adenosine tri-phosphate breakdown and acts as a sleep promoting molecule by activating A1 and A2A adenosine receptors. We performed sleep and positron emission tomography studies in 59 healthy carriers of different rs5751876 alleles, and quantified EEG oscillatory alpha power in wakefulness and sleep, as well as A1 adenosine receptor availability with 18F-CPFPX. Oscillatory alpha power was higher in homozygous C-allele carriers (n = 27, 11 females) compared to heterozygous and homozygous carriers of the T-allele (n(C/T) = 23, n(T/T) = 5, 13 females) (F(18,37) = 2.35, p = 0.014, Wilk's Λ = 0.487). Furthermore, a modulatory effect of ADORA2A genotype on A1 adenosine receptor binding potential was found across all considered brain regions (F(18,40) = 2.62, p = 0.006, Wilk's Λ = 0.459), which remained significant for circumscribed occipital region of calcarine fissures after correction for multiple comparisons. In female participants, a correlation between individual differences in oscillatory alpha power and A1 receptor availability was observed. In conclusion, we confirmed that a genetic variant of ADORA2A affects individual alpha power, while a direct modulatory effect via A1 adenosine receptors in females is suggested.

The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study

Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.

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. We hypothesized that this variability could be partially explained by genetic polymorphisms in neuromodulatory genes. 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 neuronal oscillation dynamics. Comparison of these results to computational modeling 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.

Brief segments of neurophysiological activity enable individual differentiation

Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.

Fxr1 regulates sleep and synaptic homeostasis

The fragile X autosomal homolog 1 (Fxr1) is regulated by lithium and has been GWAS-associated with schizophrenia and insomnia. Homeostatic regulation of synaptic strength is essential for the maintenance of brain functions and involves both cell-autonomous and system-level processes such as sleep. We examined the contribution of Fxr1 to cell-autonomous homeostatic synaptic scaling and neuronal responses to sleep loss, using a combination of gene overexpression and Crispr/Cas9-mediated somatic knockouts to modulate gene expression. Our findings indicate that Fxr1 is downregulated during both scaling and sleep deprivation via a glycogen synthase kinase 3 beta (GSK3β)-dependent mechanism. In both conditions, downregulation of Fxr1 is essential for the homeostatic modulation of surface AMPA receptors and synaptic strength. Preventing the downregulation of Fxr1 during sleep deprivation results in altered EEG signatures. Furthermore, sequencing of neuronal translatomes revealed the contribution of Fxr1 to changes induced by sleep deprivation. These findings uncover a role of Fxr1 as a shared signaling hub between cell-autonomous homeostatic plasticity and system-level responses to sleep loss, with potential implications for neuropsychiatric illnesses and treatments.

Discovering heritable modes of MEG spectral power

Brain structure and many brain functions are known to be genetically controlled, but direct links between neuroimaging measures and their underlying cellular-level determinants remain largely undiscovered. Here, we adopt a novel computational method for examining potential similarities in high-dimensional brain imaging data between siblings. We examine oscillatory brain activity measured with magnetoencephalography (MEG) in 201 healthy siblings and apply Bayesian reduced-rank regression to extract a low-dimensional representation of familial features in the participants' spectral power structure. Our results show that the structure of the overall spectral power at 1-90 Hz is a highly conspicuous feature that not only relates siblings to each other but also has very high consistency within participants' own data, irrespective of the exact experimental state of the participant. The analysis is extended by seeking genetic associations for low-dimensional descriptions of the oscillatory brain activity. The observed variability in the MEG spectral power structure was associated with SDK1 (sidekick cell adhesion molecule 1) and suggestively with several other genes that function, for example, in brain development. The current results highlight the potential of sophisticated computational methods in combining molecular and neuroimaging levels for exploring brain functions, even for high-dimensional data limited to a few hundred participants.

A study for the mechanism of sensory disorder in restless legs syndrome based on magnetoencephalography

In spite of the relatively high incidence rate, the etiology and pathogenesis of restless legs syndrome (RLS) are still unclear. Long-term drug treatments fail to achieve satisfying curative effects, which is reflected by rebound and augmentation of related symptoms. An electrophysiological endophenotype experiment was done to investigate the mechanism of somatosensory disorder among RLS patients. Together with 15 normal subjects as the control group, with comparable ages and genders to the RLS patients, 15 primitive RLS patients were scanned by Magnetoencephalography (MEG) under natural conditions; furthermore, the somatosensory evoked magnetic field (SEF) with single and paired stimuli, was also measured. Compared to the control group, the SEF intensities of RLS patients' lower limbs were higher, and the paired-pulse depression (PPD) for SEF in RLS patients was attenuated. It was also revealed by time-frequency analysis of somatosensory induced oscillation (SIO) in RLS patients, that 93.3% of somatosensory induced Alpha (8-12 Hz) oscillations were successfully elicited, while 0% somatosensory induced Gamma (30-55 Hz) oscillations were elicited; which was significantly different from the control group. Additionally, in RLS patients exhibit increased excitability of the sensorimotor cortex, a remarkable abnormality existing in early somatosensory gating control (GC) and an attenuated inhibitory interneuron network, which consequently results in a compensatory mechanism through which RLS patients increase their attention-driven lower limb sensory gating control via somatosensory-induced Alpha (8-12 Hz) oscillation. This hyperexcitability, partially due to an electrocortical disinhibition, may have an important therapeutical implication, and become an important target of neuromodulatory interventions.

Neuregulin 3 and its roles in schizophrenia risk and presentation

Neuregulins, a four-member family of epidermal growth factor-like signaling molecules, have been studied for over two decades. They were first implicated in schizophrenia in 2002 with the detection of linkage and association at the NRG1 locus followed after a few years by NRG3. However, the associations with disease have not been very consistently observed. In contrast, association of NGR3 variants with disease presentation, specifically the presence of delusions, has been more consistent. This appears to be mediated by quantitative changes in the alternative splicing of the gene, which has also been consistently observed. Additional diseases and phenotypes, psychiatric or not, have also been connected with NRG3. These results demonstrate two important aspects of behavioral genetics research. The first is that if we only consider simple risk and fail to examine the details of each patient's individual phenotype, we will miss important insights on the disease biology. This is an important aspect of the goals of precision medicine. The second is that the functional consequences of variants are often more complex than simple alterations in levels of transcription of a particular gene, including, among others, regulation of alternative splicing. To accurately model and understand the biological consequences of phenotype-associated genetic variants, we need to study the biological consequences of each specific variant. Simply studying the consequences of a null allele of the orthologous gene in a model system, runs the risk of missing the many nuances of hypomorphic and/or gain of function variants in the genome of interest.

Fine Structure of Posterior Alpha Rhythm in Human EEG: Frequency Components, Their Cortical Sources, and Temporal Behavior

Heterogeneity of the posterior alpha rhythm (AR) is a widely assumed but rarely tested phenomenon. We decomposed the posterior AR in the cortical source space with a 3-way PARAFAC technique, taking into account the spatial, frequency, and temporal aspects of mid-density EEG. We found a multicomponent AR structure in 90% of a group of 29 healthy adults. The typical resting-state structure consisted of a high-frequency occipito-parietal component of the AR (ARC1) and a low-frequency occipito-temporal component (ARC2), characterized by individual dynamics in time. In a few cases, we found a 3-component structure, with two ARC1s and one ARC2. The AR structures were stable in their frequency and spatial features over weeks to months, thus representing individual EEG alpha phenotypes. Cortical topography, individual stability, and similarity to the primate AR organization link ARC1 to the dorsal visual stream and ARC2 to the ventral one. Understanding how many and what kind of posterior AR components contribute to the EEG is essential for clinical neuroscience as an objective basis for AR segmentation and for interpreting AR dynamics under various conditions, both normal and pathological, which can selectively affect individual components.