High Frequential Resolution Networks: Considerations on a New Functional Brain Connectivity Framework.

Connectivity analyses are widely used to assess the interaction brain networks. This type of analyses is usually conducted considering the well-known classical frequency bands: delta, theta, alpha, beta, and gamma. However, this parcellation of the frequency content can bias the analyses, since it does not consider the between-subject variability or the particular idiosyncrasies of the connectivity patterns that occur within a band. In this study, we addressed these limitations by introducing the High Frequential Resolution Networks (HFRNs). HFRNs were constructed, using a narrow-bandwidth FIR bank filter of 1 Hz bandwidth, for two different connectivity metrics (Amplitude Envelope Correlation, AEC, and Phase Lag index, PLI) and for 3 different databases of MEG and EEG recordings. Results showed a noticeable similarity between the frequential evolution of PLI, AEC, and the Power Spectral Density (PSD) from MEG and EEG signals. Nonetheless, some technical remarks should be considered: (i) results at the gamma band should exclude the frequency range around 50 Hz due to abnormal connectivity patterns, consequence of the previously applied 50 Hz notch-filter; (ii) HFRNs patterns barely vary with the connection distance; and (iii) a low sampling frequency can exert a remarkable influence on HFRNs. To conclude, we proposed a new framework to perform connectivity analyses that allow to further analyze the frequency-based distribution of brain networks.

Influence of PICALM and CLU risk variants on beta EEG activity in Alzheimer's disease patients.

PICALM and CLU genes have been linked to alterations in brain biochemical processes that may have an impact on Alzheimer's disease (AD) development and neurophysiological dynamics. The aim of this study is to analyze the relationship between the electroencephalographic (EEG) activity and the PICALM and CLU alleles described as conferring risk or protective effects on AD patients and healthy controls. For this purpose, EEG activity was acquired from: 18 AD patients and 12 controls carrying risk alleles of both PICALM and CLU genes, and 35 AD patients and 12 controls carrying both protective alleles. Relative power (RP) in the conventional EEG frequency bands (delta, theta, alpha, beta, and gamma) was computed to quantify the brain activity at source level. In addition, spatial entropy (SE) was calculated in each band to characterize the regional distribution of the RP values throughout the brain. Statistically significant differences in global RP and SE at beta band (p-values < 0.05, Mann-Whitney U-test) were found between genotypes in the AD group. Furthermore, RP showed statistically significant differences in 58 cortical regions out of the 68 analyzed in AD. No statistically significant differences were found in the control group at any frequency band. Our results suggest that PICALM and CLU AD-inducing genotypes are involved in physiological processes related to disruption in beta power, which may be associated with physiological disturbances such as alterations in beta-amyloid and neurotransmitter metabolism.

Risk Variants in Three Alzheimer's Disease Genes Show Association with EEG Endophenotypes.

BACKGROUND: Dementia due to Alzheimer's disease (AD) is a complex neurodegenerative disorder, which much of heritability remains unexplained. At the clinical level, one of the most common physiological alterations is the slowing of oscillatory brain activity, measurable by electroencephalography (EEG). Relative power (RP) at the conventional frequency bands (i.e., delta, theta, alpha, beta-1, and beta-2) can be considered as AD endophenotypes. OBJECTIVE: The aim of this work is to analyze the association between sixteen genes previously related with AD: APOE, PICALM, CLU, BCHE, CETP, CR1, SLC6A3, GRIN2 ß, SORL1, TOMM40, GSK3 ß, UNC5C, OPRD1, NAV2, HOMER2, and IL1RAP, and the slowing of the brain activity, assessed by means of RP at the aforementioned frequency bands. METHODS: An Iberian cohort of 45 elderly controls, 45 individuals with mild cognitive impairment, and 109 AD patients in the three stages of the disease was considered. Genomic information and brain activity of each subject were analyzed. RESULTS: The slowing of brain activity was observed in carriers of risk alleles in IL1RAP (rs10212109, rs9823517, rs4687150), UNC5C (rs17024131), and NAV2 (rs1425227, rs862785) genes, regardless of the disease status and situation towards the strongest risk factors: age, sex, and APOE ɛ4 presence. CONCLUSION: Endophenotypes reduce the complexity of the general phenotype and genetic variants with a major effect on those specific traits may be then identified. The found associations in this work are novel and may contribute to the comprehension of AD pathogenesis, each with a different biological role, and influencing multiple factors involved in brain physiology.