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1.
Brain Behav ; 9(4): e01263, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30887701

RESUMO

INTRODUCTION: Brain processes of working memory involve oscillatory activities at multiple frequencies in local and long-range neural networks. The current study addressed the specific roles of alpha oscillations during memory encoding and retention, supporting the hypothesis that multiple functional mechanisms of alpha oscillations exist in parallel. METHOD: We recorded magnetoencephalography (MEG) in 25 healthy young adults, who performed a variant of a Sternberg working memory task. A sequential list of five consonant letters was visually presented and was followed after a 2.0 s retention interval by a probe of a pair of two letters from the study list. Participants responded whether the probe pair was in same or reversed order in the list. RESULT: Reaction time (RT) was shortest for the first letters in the list, increased with increasing serial position, and shorter for the last position. RT was substantially longer for the probe in reversed order. Time-frequency analysis of the MEG revealed event-related desynchronization (ERD) of alpha oscillations during the encoding interval and an alpha power increase (ERS) during memory retention. Alpha ERD during encoding occurred at 10 Hz and ERS during retention at 12 Hz, suggesting different alpha mechanisms. Analysis of alpha coherence and alpha-gamma cross-spectral coupling, applied to MEG beamformer source activity, revealed connectivity across brain areas. Additionally, alpha-gamma coupling identified centers of local computation. The connectivity between occipital and frontotemporal areas was correlated with alpha ERS during memory retention. Cross-frequency coupling between alpha phase and gamma amplitude depicted a hierarchy of information flow from frontal to temporal and occipital brain areas. CONCLUSION: Alpha decrease during encoding indicates an active state of visual processing, while subsequent ERS indicates inhibition of further visual input for protecting the memory, and phasic timing of temporal and occipital gamma oscillations is related to a long-range working memory networks.


Assuntos
Ritmo alfa/fisiologia , Encéfalo/fisiologia , Memória de Curto Prazo/fisiologia , Retenção Psicológica/fisiologia , Adulto , Mapeamento Encefálico , Feminino , Humanos , Magnetoencefalografia , Masculino , Testes Neuropsicológicos , Tempo de Reação/fisiologia , Adulto Jovem
2.
J Neurosci Methods ; 262: 41-55, 2016 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-26777472

RESUMO

BACKGROUND: Synchrony between neuroelectric oscillations in distant brain areas is currently used as an indicator of functional connectivity between the involved neural substrates. Coherence measures, which quantify synchrony, are affected by concurrent brain activities, commonly subsumed as noise. NEW METHOD: Using Monte-Carlo simulation, we analysed the properties of circular statistics and how those are affected by noise. We considered three different models of neuroelectric signal generation, which are an additive model, phase-reset, and reciprocal phase-interaction. Using the receiver-operating characteristic method, we compared the performances of currently implemented algorithms for coherence detection such as phase-coherence or phase-locking factor, magnitude-squared coherence, and phase-lagging index, all based on circular statistics, and a more general approach to synchrony, using measures of mutual information. We compared inter-trial coherence as a method for signal detection with coherence between multiple sources as measure of source interaction and connectivity. RESULTS: Charts of performance characteristics showed that the choice of methods depend on the underlying signal generation model. Detection of coherence requires in general a higher signal-to-noise ratio than detection of the signal itself, and again, the difference in performance depends strongly on the underlying model of signal generation. COMPARISON WITH EXISTING METHODS: Previous comparisons of the performances of different algorithms for signal detection and coherence have not considered systematically the underlying neural generation mechanisms. CONCLUSION: Detection of coherence generated by additive signals or a phase-reset requires largely higher signal-to-noise ratio compared to signal detection. Only in case of true phase interaction, signal detection and coherence measures are similarly sensitive.


Assuntos
Córtex Auditivo/fisiologia , Modelos Neurológicos , Processamento de Sinais Assistido por Computador , Razão Sinal-Ruído , Adulto , Idoso , Algoritmos , Simulação por Computador , Feminino , Humanos , Magnetoencefalografia , Masculino , Pessoa de Meia-Idade , Método de Monte Carlo , Curva ROC , Adulto Jovem
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