No association between peripheral serotonin-gene-related DNA methylation and brain serotonin neurotransmission in the healthy and depressed state.

BACKGROUND: Methylation of serotonin-related genes has been proposed as a plausible gene-by-environment link which may mediate environmental stress, depressive and anxiety symptoms. DNA methylation is often measured in blood cells, but little is known about the association between this peripheral epigenetic modification and brain serotonergic architecture. Here, we evaluated the association between whole-blood-derived methylation of four CpG sites in the serotonin transporter (SLC6A4) and six CpG sites of the tryptophan hydroxylase 2 (TPH2) gene and in-vivo brain levels of serotonin transporter (5-HTT) and serotonin 4 receptor (5-HT4) in a cohort of healthy individuals (N = 254) and, for 5-HT4, in a cohort of unmedicated patients with depression (N = 90). To do so, we quantified SLC6A4/TPH2 methylation using bisulfite pyrosequencing and estimated brain 5-HT4 and 5-HTT levels using positron emission tomography. In addition, we explored the association between SLC6A4 and TPH2 methylation and measures of early life and recent stress, depressive and anxiety symptoms on 297 healthy individuals. RESULTS: We found no statistically significant association between peripheral DNA methylation and brain markers of serotonergic neurotransmission in patients with depression or in healthy individuals. In addition, although SLC6A4 CpG2 (chr17:30,236,083) methylation was marginally associated with the parental bonding inventory overprotection score in the healthy cohort, statistical significance did not remain after accounting for blood cell heterogeneity. CONCLUSIONS: We suggest that findings on peripheral DNA methylation in the context of brain serotonin-related features should be interpreted with caution. More studies are needed to rule out a role of SLC6A4 and TPH2 methylation as biomarkers for environmental stress, depressive or anxiety symptoms.

Moderate associations between BDNF Val66Met gene polymorphism, musical expertise, and mismatch negativity.

Auditory predictive processing relies on a complex interaction between environmental, neurophysiological, and genetic factors. In this view, the mismatch negativity (MMN) and intensive training on a musical instrument for several years have been used for studying environment-driven neural adaptations in audition. In addition, brain-derived neurotrophic factor (BDNF) has been shown crucial for both the neurogenesis and the later adaptation of the auditory system. The functional single-nucleotide polymorphism (SNP) Val66Met (rs6265) in the BDNF gene can affect BDNF protein levels, which are involved in neurobiological and neurophysiological processes such as neurogenesis and neuronal plasticity. In this study, we hypothesised that genetic variation within the BDNF gene would be associated with different levels of neuroplasticity of the auditory cortex in 74 musically trained participants. To achieve this goal, musicians and non-musicians were recruited and divided in Val/Val and Met- (Val/Met and Met/Met) carriers and their brain activity was measured with magnetoencephalography (MEG) while they listened to a regular auditory sequence eliciting different types of prediction errors. MMN responses indexing those prediction errors were overall enhanced in Val/Val carriers who underwent intensive musical training, compared to Met-carriers and non-musicians with either genotype. Although this study calls for replications with larger samples, our results provide a first glimpse of the possible role of gene-regulated neurotrophic factors in the neural adaptations of automatic predictive processing in the auditory domain after long-term training.

Brain recognition of previously learned versus novel temporal sequences: a differential simultaneous processing.

Memory for sequences is a central topic in neuroscience, and decades of studies have investigated the neural mechanisms underlying the coding of a wide array of sequences extended over time. Yet, little is known on the brain mechanisms underlying the recognition of previously memorized versus novel temporal sequences. Moreover, the differential brain processing of single items in an auditory temporal sequence compared to the whole superordinate sequence is not fully understood. In this magnetoencephalography (MEG) study, the items of the temporal sequence were independently linked to local and rapid (2-8 Hz) brain processing, while the whole sequence was associated with concurrent global and slower (0.1-1 Hz) processing involving a widespread network of sequentially active brain regions. Notably, the recognition of previously memorized temporal sequences was associated to stronger activity in the slow brain processing, while the novel sequences required a greater involvement of the faster brain processing. Overall, the results expand on well-known information flow from lower- to higher order brain regions. In fact, they reveal the differential involvement of slow and faster whole brain processing to recognize previously learned versus novel temporal information.

Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study.

Brain network analysis represents a powerful technique to gain insights into the connectivity profile characterizing individuals with different levels of fluid intelligence (Gf). Several studies have used diffusion tensor imaging (DTI) and slow-oscillatory resting-state fMRI (rs-fMRI) to examine the anatomical and functional aspects of human brain networks that support intelligence. In this study, we expand this line of research by investigating fast-oscillatory functional networks. We performed graph theory analyses on resting-state magnetoencephalographic (MEG) signal, in addition to structural brain networks from DTI data, comparing degree, modularity and segregation coefficient across the brain of individuals with high versus average Gf scores. Our results show that high Gf individuals have stronger degree and lower segregation coefficient than average Gf participants in a significantly higher number of brain areas with regards to structural connectivity and to the slower frequency bands of functional connectivity. The opposite result was observed for higher-frequency (gamma) functional networks, with higher Gf individuals showing lower degree and higher segregation across the brain. We suggest that gamma oscillations in more intelligent individuals might support higher local processing in segregated subnetworks, while slower frequency bands would allow a more effective information transfer between brain subnetworks, and stronger information integration.

Applying Spike-density component analysis for high-accuracy auditory event-related potentials in children.

OBJECTIVE: Overlapping neurophysiological signals are the main obstacle preventing from using cortical auditory event-related potentials (AEPs) in clinical settings. Children AEPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently developed, to isolate with high accuracy the neural components of auditory responses of 8-year-old children. METHODS: Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard AEP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC). RESULTS: SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected. CONCLUSION: Superior accuracy in isolating neural components at the individual level was demonstrated for SCA-HSAC over other SCA approaches even for children AEPs. SIGNIFICANCE: Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults.

Brain predictive coding processes are associated to COMT gene Val158Met polymorphism.

Predicting events in the ever-changing environment is a fundamental survival function intrinsic to the physiology of sensory systems, whose efficiency varies among the population. Even though it is established that a major source of such variations is genetic heritage, there are no studies tracking down auditory predicting processes to genetic mutations. Thus, we examined the neurophysiological responses to deviant stimuli recorded with magnetoencephalography (MEG) in 108 healthy participants carrying different variants of Val158Met single-nucleotide polymorphism (SNP) within the catechol-O-methyltransferase (COMT) gene, responsible for the majority of catecholamines degradation in the prefrontal cortex. Our results showed significant amplitude enhancement of prediction error responses originating from the inferior frontal gyrus, superior and middle temporal cortices in heterozygous genotype carriers (Val/Met) vs homozygous (Val/Val and Met/Met) carriers. Integrating neurophysiology and genetics, this study shows how the neural mechanisms underlying optimal deviant detection vary according to the gene-determined cathecolamine levels in the brain.