EEG/ECoG/EMG publications

RESUMO Objetivo Analisar o Benefício do Mascaramento Modulado (BMM) em medidas eletrofisiológicas e comportamentais em indivíduos jovens e adultos normouvintes. Método Estudo analítico observacional e transversal, com amostra final da pesquisa composta por 40 participantes, sendo 20 indivíduos de 18 a 30 anos (jovens-adultos) e 20 indivíduos de 31 a 50 anos (adultos), para realização de avaliação comportamental (Teste de reconhecimento de sentença na presença de ruído estável e modulado) e eletrofisiológica (Potencial Evocado Auditivo Cortical) para investigação do BMM. Os resultados foram analisados através do Test-t pareado e ANOVA para medidas repetidas, seguido pelo teste post-hoc de Bonferroni (p-valor <0,05). Resultados Foi identificada uma menor interferência do ruído modulado nas medidas de latência e amplitude dos componentes corticais, gerando uma redução significativa na latência de P1 e aumento da amplitude de P2 em ambos os grupos de participantes. O ruído estável gerou limiares eletrofisiológicos e comportamentais mais elevados comparado ao ruído modulado. Uma maior magnitude do BMM foi observada no grupo jovem-adulto. Conclusão Identificou-se em ambos os grupos de participantes uma menor interferência do ruído modulado no tempo de codificação da resposta auditiva neural e no processo de discriminação neural da fala. Além disso, os limiares comportamentais e eletrofisiológicos foram tipicamente mais elevados diante do ruído estável quando comparado ao ruído modulado, apontando para uma correspondência da medida do BMM entre os domínios da audição. A magnitude do BMM superior no grupo de jovens-adultos, especialmente no domínio eletrofisiológico, sugere uma maior habilidade de resolução temporal em indivíduos mais jovens.

Objective: To analyze the Benefit of Modulated Masking (BMM) on hearing in young, adult and elderly normal-hearing individuals. Methods: The sample included 60 normal-hearing individuals aged 18-75 years who underwent behavioral assessment (sentence recognition test in the presence of steady and modulated noise) and electrophysiological assessment (cortical Auditory Evoked Potential) to investigate BMM. The results were analyzed comparatively using the paired t-test and ANOVA for repeated measures, followed by the Bonferroni post-hoc test (p-value < 0.05). Results: A decrease in latencies and an increase in amplitudes of cortical components (P1-N1-P2) was observed due to noise modulation in all age groups. Modulated noise generated better auditory threshold responses (electrophysiological and behavioral), compared to steady noise. The elderly presented a higher threshold in both hearing domains, compared to the other participants, as well as a lower BMM magnitude. Conclusion: It was possible to conclude that the modulated noise generated less interference in the magnitude of the neural response (smaller latencies) and in the neural processing time (larger amplitudes) for the speech stimulus in all participants. The higher auditory thresholds (electrophysiological and behavioral) and the lower BMM magnitude observed in the elderly group, even in the face of noise modulation, suggest a lower temporal auditory performance in this population, and may indicate a deficit in the temporal resolution capacity, associated with the process of aging. Level of evidence: 3:

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity.

Binaural beating is a perceptual auditory illusion occurring when presenting two neighboring frequencies to each ear separately. Several controversial claims have been attributed to binaural beats regarding their ability to entrain human brain activity and mood, in both the scientific literature and the marketing realm. Here, we sought to address those questions in a robust fashion using a single-blind, active-controlled protocol. To do so, we compared the effects of binaural beats with a control beat stimulation (monaural beats, known to entrain brain activity but not mood) across four distinct levels in the human auditory pathway: subcortical and cortical entrainment, scalp-level functional connectivity and self-reports. Both stimuli elicited standard subcortical responses at the pure tone frequencies of the stimulus [i.e., frequency following response (FFR)], and entrained the cortex at the beat frequency [i.e., auditory steady state response (ASSR)]. Furthermore, functional connectivity patterns were modulated differentially by both kinds of stimuli, with binaural beats being the only one eliciting cross-frequency activity. Despite this, we did not find any mood modulation related to our experimental manipulation. Our results provide evidence that binaural beats elicit cross frequency connectivity patterns, but weakly entrain the cortex when compared with monaural beat stimuli. Whether binaural beats have an impact on cognitive performance or other mood measurements remains to be seen and can be further investigated within the proposed methodological framework.

A 'pattern alternation paradigm' has been previously used in human ERP recordings to investigate the brain encoding of complex auditory regularities, but prior studies on regularity encoding in animal models to examine mechanisms of adaptation of auditory neuronal responses have used primarily oddball stimulus sequences to study stimulus-specific adaptation alone. In order to examine the sensitivity of neuronal adaptation to expected and unexpected events embedded in a complex sound sequence, we used a similar patterned sequence of sounds. We recorded single unit activity and compared neuronal responses in the rat inferior colliculus (IC) to sound stimuli conforming to pattern alternation regularity with those to stimuli in which occasional sound repetitions violated that alternation. Results show that some neurons in the rat inferior colliculus are sensitive to the history of patterned stimulation and to violations of patterned regularity, demonstrating that there is a population of subcortical neurons, located as early as the level of the midbrain, that can detect more complex stimulus regularities than previously supposed and that are as sensitive to complex statistics as some neurons in primary auditory cortex. Our findings indicate that these pattern-sensitive neurons can extract temporal and spectral regularities between successive acoustic stimuli. This is important because the extraction of regularities from the sound sequences will result in the development of expectancies for future sounds and hence, the present results are compatible with predictive coding models. Our results demonstrate that some collicular neurons, located as early as in the midbrain level, are involved in the generation and shaping of prediction errors in ways not previously considered and thus, the present findings challenge the prevailing view that perceptual organization of sound only emerges at the auditory cortex level.

The scalp-recorded frequency-following response (FFR) is a measure of the auditory nervous system's representation of periodic sound, and may serve as a marker of training-related enhancements, behavioural deficits, and clinical conditions. However, FFRs of healthy normal subjects show considerable variability that remains unexplained. We investigated whether the FFR representation of the frequency content of a complex tone is related to the perception of the pitch of the fundamental frequency. The strength of the fundamental frequency in the FFR of 39 people with normal hearing was assessed when they listened to complex tones that either included or lacked energy at the fundamental frequency. We found that the strength of the fundamental representation of the missing fundamental tone complex correlated significantly with people's general tendency to perceive the pitch of the tone as either matching the frequency of the spectral components that were present, or that of the missing fundamental. Although at a group level the fundamental representation in the FFR did not appear to be affected by the presence or absence of energy at the same frequency in the stimulus, the two conditions were statistically distinguishable for some subjects individually, indicating that the neural representation is not linearly dependent on the stimulus content. In a second experiment using a within-subjects paradigm, we showed that subjects can learn to reversibly select between either fundamental or spectral perception, and that this is accompanied both by changes to the fundamental representation in the FFR and to cortical-based gamma activity. These results suggest that both fundamental and spectral representations coexist, and are available for later auditory processing stages, the requirements of which may also influence their relative strength and thus modulate FFR variability. The data also highlight voluntary mode perception as a new paradigm with which to study top-down vs bottom-up mechanisms that support the emerging view of the FFR as the outcome of integrated processing in the entire auditory system.

Stimulus-specific adaptation (SSA) in single neurons of the auditory cortex was suggested to be a potential neural correlate of the mismatch negativity (MMN), a widely studied component of the auditory event-related potentials (ERP) that is elicited by changes in the auditory environment. However, several aspects on this SSA/MMN relation remain unresolved. SSA occurs in the primary auditory cortex (A1), but detailed studies on SSA beyond A1 are lacking. To study the topographic organization of SSA, we mapped the whole rat auditory cortex with multiunit activity recordings, using an oddball paradigm. We demonstrate that SSA occurs outside A1 and differs between primary and nonprimary cortical fields. In particular, SSA is much stronger and develops faster in the nonprimary than in the primary fields, paralleling the organization of subcortical SSA. Importantly, strong SSA is present in the nonprimary auditory cortex within the latency range of the MMN in the rat and correlates with an MMN-like difference wave in the simultaneously recorded local field potentials (LFP). We present new and strong evidence linking SSA at the cellular level to the MMN, a central tool in cognitive and clinical neuroscience.

The aim of this paper is twofold: first, to introduce a neural field model motivated by a well-known neural mass model; second, to show how one can estimate model parameters pertaining to spatial (anatomical) properties of neuronal sources based on EEG or LFP spectra using Bayesian inference. Specifically, we consider neural field models of cortical activity as generative models in the context of dynamic causal modeling (DCM). This paper considers the simplest case of a single cortical source modeled by the spatiotemporal dynamics of hidden neuronal states on a bounded cortical surface or manifold. We build this model using multiple layers, corresponding to cortical lamina in the real cortical manifold. These layers correspond to the populations considered in classical (Jansen and Rit) neural mass models. This allows us to formulate a neural field model that can be reduced to a neural mass model using appropriate constraints on its spatial parameters. In turn, this enables one to compare and contrast the predicted responses from equivalent neural field and mass models respectively. We pursue this using empirical LFP data from a single electrode to show that the parameters controlling the spatial dynamics of cortical activity can be recovered, using DCM, even in the absence of explicit spatial information in observed data.