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Positive effects of a beta-frequency binaural beats programme on memory

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Positive effects of a beta-frequency binaural beats programme on memory

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Positive effects of a beta-frequency binaural beats programme on memory

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Tema e grupo → até 24 de fevereiro

Questão de investigação e objetivos → até 7 de março

Enquadramento teórico → até 21 de março

Método → até 11 de abril

Resultados esperados → até 2 de maio

Discussão e conclusão → até 23 de maio

Data de entrega → até 28 de maio

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De que forma é que a qualidade de sono influência os processos de consolidação da memória em estudantes universitários?

Hipoteses:

H0: A qualidade de sono não influência os processos de memória.

H1: A qualidade de sono influência os processos de memória.

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Informações:

O período de sono promove a consolidação, codificação e recuperação da memória. A consolidação ocorre durante o sono, após a codificação da informação durante o período de vigília, a consolidação da informação é um processo que transforma memórias novas em representações estáveis na rede de memória a longo prazo pré-existente.

De acordo com a literatura, existem grande benefícios após dormir 8 horas, mas também após sestas com duração de 1-2h ajudam na melhoria da memória processual (responsável por armazenar habilidades e hábitos adquiridos através da prática repetida). Posto isto, a investigação cientifica comprova que existem benefícios de consolidação e reorganização da memória no decorrer do sono 3h horas após a aprendizagem.

Neste caso, o período de sono atrás mais benefícios para as memórias que são codificadas de forma explícita, ou seja, quando a pessoa anteriormente realiza uma aprendizagem de forma consciente fazendo um esforço para memorizar. No fundo, o sujeito acaba por consolidar melhor essa informação porque foi memorizada de forma consciente e que será necessária no futuro.

O sono é composto por ciclos de aproximadamente 90 minutos, alternando entre o sono não REM (NREM) e o sono REM.

O sono não REM, ocorre na primeira metade da noite, sendo crucial para a consolidação da memória declarativa, ou seja, factos e eventos. Durante esse período, existe uma reativação de memória que foi recém adquirida no hipocampo, depois é transferida gradualmente para as redes corticais, deixando a informação mais estável e integrada. Neste processo ocorre por meio de oscilação cerebrais, ou seja, um padrão de ritmo de atividade elétrica no cérebro que regula a comunicação entre neurônios e ajuda na consolidação da memória durante o sono. Entre essas oscilações, destacam-se as oscilações lentas (<1 Hz), que sincronizam a atividade cortical e favorecem a plasticidade neuronal, os fusos do sono (12-16 Hz), gerados no tálamo e responsáveis pelo fortalecimento das sinapses, e as ondas agudas hipocampais (100-300 Hz), que reativam sequências neurais codificadas durante a vigília. Esse conjunto de mecanismos apoia a consolidação sistémica da memória, permitindo que a informação seja armazenada a longo prazo e evitando interferências com novas aprendizagens. Além disso, de acordo com a hipótese da homeostase sináptica, o sono SWS desempenha um papel fundamental na regulação da força sináptica, reduzindo conexões irrelevantes e reforçando aquelas essenciais para a retenção da memória.

Já o sono REM ocorre na segunda metade da noite e caracteriza-se por uma atividade cerebral rápida, associada a sonhos intensos e a uma elevada atividade colinérgica, essencial para os processos de plasticidade sináptica. Esse período é determinante para a consolidação da memória processual, relacionada a habilidades motoras e cognitivas, assim como para a memória emocional, facilitando a conexão entre a amígdala e o neocórtex. Durante o sono REM, ocorre a potenciação a longo prazo (LTP), um mecanismo de neuroplasticidade que fortalece e estabiliza sinapses previamente ativadas. A ativação de genes relacionados à plasticidade sináptica, como Arc e Egr1, contribui para a manutenção das conexões neurais e a integração das novas informações nos circuitos cerebrais. Além disso, oscilações específicas, como as ondas ponto-genículo-occipitais (PGO) e as oscilações teta (4-8 Hz), desempenham um papel essencial na reorganização da memória e na consolidação de sequências motoras. Esse processo garante que novas aprendizagens sejam refinadas e estabilizadas, permitindo um desempenho mais eficiente em tarefas motoras e cognitivas após o despertar.

Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews. Neuroscience, 11(2), 114–126. https://doi.org/10.1038/nrn2762

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Período de Vigília - ocorre antes de dormir e quando estamos quase a dormir

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Effects of sleep deprivation on cognitive and physical performance
in university students

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Effects of sleep deprivation on cognitive and physical performance
in university students

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Patrick, Y., Lee, A., Raha, O., Pillai, K., Gupta, S., Sethi, S., Mukeshimana, F., Gerard, L., Moghal, M. U., Saleh, S. N., Smith, S. F., Morrell, M. J., & Moss, J. (2017). Effects of sleep deprivation on cognitive and physical performance in university students. Sleep and Biological Rhythms, 15(3), 217–225. https://doi.org/10.1007/s41105-017-0099-5

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Investigating the effect of daily sleep on memory capacity in college students

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Investigating the effect of daily sleep on memory capacity in college students

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Phan, D., Chan, C., Pan, R., Yang, N., Hsu, H., Ting, H., Lai, K. R., & Lin, K. (2018). Investigating the effect of daily sleep on memory capacity in college students. Technology and Health Care, 27(2), 183–194. https://doi.org/10.3233/thc-181350

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Neurociências - Tabela de Sistematização.docx

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Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1

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Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1

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Embora a função do sono permaneça desconhecida, é amplamente reconhecido que o sono é crucial para a função cerebral adequada. Sendo que a memória e aprendizagem são melhorada no momento de sono, no hipocampos, quando ocorre uma privação de sono estes processos acabam por ser interrompidos. Quando ocorre alterações dentre o periodo de vigília e o sono profundo acaba por afetar diretamente a plasticidade sináptica.

Neste caso a plasticidade sináptica é um processo que ajuda na conexões entre as sinapses, sendo que pode fortalecer ou enfraquer as restas das atividade neural que por sua vez esta diretamente relacionado com a aprendizagem e a memória. A estrutura sináptica é alterada pela influencia do sono.

Cofilina é uma proteína essencial que regula a estrutura sinapses que afetam diretamente a formação e a eliminação das dendríticos, que por sua vez são importantes para a comunicação entre os diferentes neurónios, que são essenciais para a plasticidade sináptica que ajuda no processo de aprendizagem e memória.

De acordo com a literatura 5h de privação do sono leva com que exista a perda de espinhas dendríticas do CA1, o processo é facilmente revertido com apenas 3h de sono como forma de recuperar o período de tempo perdido, conseguindo por sua vez recuperar/ restaurar a perda de coluna vertebral nos neurônios e o comprimento dendrito e normalizar os déficits de memória e plasticidade sináptica causados pela privação do sono.

conclusão: a privação do sono afeta negativamente o desempenho cognitivo por causa das alterações que ocorrem na plasticidades sináptica no hipocampo. Porto isto, as investigações mostram que o sono desempenha um papel essencial na consolidação da memória porque ajuda a manter a estrutura sináptica saudável.

Havekes, R., Park, A. J., Tudor, J. C., Luczak, V. G., Hansen, R. T., Ferri, S. L., Bruinenberg, V. M., Poplawski, S. G., Day, J. P., Aton, S. J., Radwańska, K., Meerlo, P., Houslay, M. D., Baillie, G. S., & Abel, T. (2016). Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1. eLife, 5. https://doi.org/10.7554/elife.13424

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The role of sleep in regulating structural plasticity and synaptic strength: Implications for memory and cognitive function 

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The role of sleep in regulating structural plasticity and synaptic strength: Implications for memory and cognitive function 

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Raven, F., Van Der Zee, E. A., Meerlo, P., & Havekes, R. (2017). The role of sleep in regulating structural plasticity and synaptic strength: Implications for memory and cognitive function. Sleep Medicine Reviews, 39, 3–11. https://doi.org/10.1016/j.smrv.2017.05.002

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Sleep Benefits prose memory consolidation in university students

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Sleep Benefits prose memory consolidation in university students

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Conte, F., Malloggi, S., De Rosa, O., Ficca, G., Righi, S., Viggiano, M. P., & Giganti, F. (2025). Sleep Benefits prose memory consolidation in university students. Brain Sciences, 15(3), 265. https://doi.org/10.3390/brainsci15030265

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Comparing the Effects of Sleep and Rest on Memory Consolidation

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Comparing the Effects of Sleep and Rest on Memory Consolidation

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Durante o periodo de sono, existe uma redução no processamento sensorial por isso o cérebro foca-se em favorecer a consolidação das memórias. Durante nossas horas de vigília, passamos por períodos em que estamos ativamente focados no processamento de estímulos ambientais (“online” ou vigília ativa), mas também vivenciamos períodos de desligamento do ambiente sensorial, nos quais nosso foco se volta para pensamentos e sentimentos internos (“offline” ou vigília silenciosa). Esses estados de vigília offline, em particular, foram propostos para facilitar a consolidação da memória, bem como outros processos cognitivos importantes, como a formação de insights (por exemplo, momentos “aha”).

Embora tenha sido demonstrado que os cochilos diurnos beneficiam diversos tipos de memória (por exemplo, declarativa, motora, espacial, perceptiva, e aprendizagem de conceitos ), o benefício dos estados cerebrais de vigília para a memória só foi investigado recentemente.

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Tucker, M. A., Humiston, G. B., Summer, T., & Wamsley, E. (2020). Comparing the Effects of Sleep and Rest on Memory Consolidatio. Nature and Science of Sleep, Volume 12, 79–91. https://doi.org/10.2147/nss.s223917

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Mechanisms of systems memory consolidation during sleep

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Mechanisms of systems memory consolidation during sleep

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Klinzing, J. G., Niethard, N., & Born, J. (2019). Mechanisms of systems memory consolidation during sleep. Nature Neuroscience, 22(10), 1598–1610. https://doi.org/10.1038/s41593-019-0467-3

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Foram identificadas duas respostas significativas no EEG:

Um aumento da potência na faixa delta/theta (2–8,5 Hz) entre 235–975 ms após o início do som.

Esta resposta ocorreu durante a apresentação dos sons emocionais emparelhados às memórias.

Esta atividade foi significativamente influenciada pela valência emocional dos sons:

Quanto mais negativo o som, maior a resposta delta/theta (r = 0.56, p = 0.006).

Tendência de correlação positiva com o benefício de memória (CB):

Houve uma tendência (não significativa após correção de múltiplas comparações) para que maior atividade delta/theta estivesse associada a maior retenção de memória (r = 0.47, p = 0.03).

O estudo conclui que:

A atividade delta/theta parece mediar o reforço das memórias emocionais durante o sono SWS.

Este padrão sugere que o cérebro está a reprocessar essas memórias de forma ativa e seletiva.

Apoia a noção de que as oscilações lentas contribuem para o replay e consolidação das memórias armazenadas temporariamente no hipocampo, transferindo-as para o neocórtex.

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Participants

Healthy young participants were recruited from colleges in the greater Boston area. A total of 185 participants consented to the experiment, of which 123 (age: 21.6 ± 2.7; 63.9% female; see below for details) provided usable datasets for analysis. Participants reported no abnormal sleep patterns, history of psychiatric or neurological disorders, or current medication use except birth control. They were instructed to keep a regular sleep schedule for the three nights preceding the experiment and were asked to refrain from recreational drugs and alcohol for 48 h and caffeine in the morning before their visits. All participants provided written consent approved by the Institutional Review Board of Beth Israel Deaconess Medical Center. All ethical regulations relevant to human research participants were followed.

Participants were assigned to one of 5 groups. Emotional SWS (E-SWS) and emotional REM (E-REM) groups learned emotional items and were then exposed to reactivations during SWS or REM, respectively. Similarly, a neutral SWS (N-SWS) group learned neutral items with reactivations in SWS. We also included emotional nap (E-Nap) and emotional wake (E-Wake) groups, which learned emotional items and took a nap without any reactivations or did not take a nap but rested quietly for a similar period before testing. Although a neutral REM group was also planned, this was abandoned due to the time constraint imposed by an upcoming halt in laboratory operations. Groups were matched in age (F = .54, p = 0.71) and sex ( X 2 = 2.4, p  = 0.66).

Design and procedures

The protocol included two visits (Fig. 5A). On the first visit, participants arrived at the laboratory at around 11:00 AM. After consenting, they filled out the Epworth Sleepiness Scale (Johns, 1991) and questionnaires about their sleep patterns and quality for the preceding three nights. They were then wired for EEG (detailed below), and a brief (7-minute) resting state EEG was recorded with eyes closed. We do not report on the EEG data from the rest periods in this paper.

A Timeline of the study procedures for different groups are depicted. In all groups, the first visit started at 11:00 AM. After training and practice, a baseline test (T1) was carried out immediately before the nap. The first retest (T2) was approximately 45 min after waking up. The second retest (T3) was approximately 1 week after the first visit. B During training, participants passively viewed emotional or neutral pictures appearing at different locations on a grid, while a paired sound was played. During practice and test, pictures appeared in the center of the screen, and participants were asked to move them to their correct location.

We used a modified version of the TMR task described in previous publications (Rudoy et al., 2009; Creery et al., 2015). Learning included two successive phases: training and practice. During training, participants viewed 50 neutral or negative pictures appearing in different locations on a grid, in random order, while simultaneously hearing a 1-second sound that was naturally linked with that object (Fig. 5B, left). During practice, pictures appeared in the center of the screen while their corresponding sound played (Fig. 5B, right). Participants were instructed to move the objects to their original location and press the mouse button. After the mouse was clicked, the picture was moved to its correct location, providing feedback. In the first two runs of the practice phase, all 50 objects were tested. In subsequent rounds, items that had been placed within 150 pixels of their correct locations in two successive rounds no longer appeared. The practice phase continued until all objects had been removed from the testing pool. The baseline test (T1) followed immediately after learning. This phase was similar to practice, except participants placed each picture only once, and no feedback was provided. Following Test 1, a second 7-min resting state EEG was collected.

For participants in the sleep groups, lights were turned off shortly after the baseline test, usually around 2 PM, and a 2-h nap opportunity was provided. For all sleep participants, white noise was played through bedside speakers, starting with lights off and continuing until lights on. In the reactivation groups, half of the sounds ( n  = 25) were presented in a random order, with 5-second interstimulus intervals, with sound intensity of ~39 dB at the pillow. Cue presentations were started manually ~1.5 min after SWS or REM onset. Cues were selected by a computer algorithm so that memory accuracy at T1 for the cued and non-cued items was similar. Sounds were presented until the specific sleep stage (SWS or REM) ended. If the participant entered the target sleep stage again, reactivation was resumed. Data from a participant were included in analyses only if all 25 cues were played at least once. After lights on, a final resting state EEG was collected. Wake participants were also wired for EEG and spent an equal amount of time in the bedroom, doing relaxing activities such as reading, while being observed to ensure continued wakefulness.

Retests were identical to T1. The immediate retest (T2) took place approximately 45 min after the lights on or at the end of the rest period for the wake group. Before they left the laboratory, participants were instructed that their memory would be tested in the same way at their second visit. The second visit was approximately one week later and included only the delayed retest (T3), which took place at 4:45 PM, to match the timing of Test 2. EEG was not monitored at this visit.

Before training and each retest, participants filled out the Stanford Sleepiness Scale (Supplementary Table 1) and a two-item questionnaire about their ability to concentrate and their level of "feeling refreshed."

We focused on results from T2, as the majority of the TMR studies indicated memory benefits evident shortly after sleep. Assessment at T3 was included as exploratory, given that some studies showed that TMR benefits might be longer lasting.

Stimuli

50 emotional and 50 neutral images from online picture databases, the International Affective Pictures System and, the Geneva Affective Picture Database, and Google image searches were used. Images were 150 × 150 pixels and were displayed on a 67.5 cm × 57.25 cm monitor with a viewing resolution of 1440 × 900 pixels. Emotional images were all negative. 1-second sounds that were naturally linked to each of these images were then taken from the database Pond5 ( www.pond5.com) and paired with each image.

Prior to the main study, a pilot study was carried out to confirm that the emotional and neutral stimulus sets were significantly different in emotion ratings. This study confirmed that both emotional sounds and emotional sound-picture pairs were significantly more negative than their neutral counterparts (see Supplementary Materials).

EEG acquisition and preprocessing

EEG data was acquired from 57 channels (positioned according to the 10-20 system). Additional electrodes were placed on the left and right mastoids (for EEG references), above the right eye and below the left eye (for EOG), and on the chin (two; for EMG), forehead (recording reference), and collarbone (ground). Data were collected at 400 Hz with an Aura-LTM64 amplifier and TWin software (Grass Technologies). All impedances were kept to <25 kOhm.

Sleep scoring was performed using TWin software and MATLAB (The MathWorks, Natick, MA) according to AASM criteria. Subsequent EEG analyses were performed in MATLAB using custom scripts. First, all EEG channels were re-referenced to the average of the two mastoids, high-pass filtered at 0.3 Hz and notch filtered at 60 Hz. Data were then artifact-rejected based on visual inspection, with bad segments of data being marked and removed from subsequent analyses. Bad channels were identified by visual inspection and interpolated using a spherical spline algorithm. Artifact-free data were then subjected to further analysis.

EEG data analysis

Filtered, artifact-free data were segmented into epochs that extended from 1 s before stimulus-onset to 3 s after and baseline adjusted to the mean voltage during the 1 second before cue onset. Complex Morlet wavelets were used to decompose the epoched and baseline-adjusted time series data into time-frequency representations, with spectral power being extracted at 30 logarithmically spaced frequencies from 2 to 40 Hz and the number of wavelet cycles increasing from 3 to 10 in 30 logarithmically spaced steps to match frequency bins. For analysis, power was decibel-normalized within-subject (10× log10(power/baseline)), where the baseline was mean power in the 200-500 ms prior to cue-onset. This baseline period was chosen to mitigate contamination of the baseline period by post-stimulus activity. As such, positive values reflect relative increases in power following sound cues compared to the pre-cue baseline, whereas negative values reflect decreases. To reduce EEG data complexity, we focused time-frequency analyses on electrode Cz as this site has previously shown to be sensitive to TMR-related effects.

Data reduction

A participant's data was excluded if (i) less than one full round of reactivation was achieved ( n = 32; 17%), (ii) a full round of reactivation was carried out in the wrong sleep stage ( n = 4; 2%), (iii) sleep was too short or fragmented (sleep <45 min or WASO > 30 min; n = 18; 10%), (iv) an equipment failure occurred ( n = 5; 3%), (v) the protocol was not completed ( n = 2; 1%), or healthy control criteria were not met ( n  = 1; <1%). In addition, 1 participant from the E-REM group was removed because their error at T1, as well as at T2, was more than three standard deviations from the mean. The final sample included 24 participants in E-SWS, 25 in E-REM, 31 in N-SWS, 22 in E-Nap, and 20 in E-Wake. Finally, the EEG of 1 participant in N-SWS could not be used in spectral analyses due to a recording error.

Statistics and reproducibility

Recall accuracy was measured as the distance between where participants placed the pictures and their correct locations, measured in pixels, with smaller errors in placement indicating better recall accuracy. Because error was not normally distributed within subjects, we used subject medians for each test in all analyses. The effect of reactivation on recall accuracy was examined using LMMs. In these models, the % change in error from T1 (%ΔError) was included as the dependent variable. Larger %ΔError values indicated more forgetting. 'Reactivation' (reactivated vs. non-reactivated) was included as a fixed effect. In the analyses that compared the effect of reactivation between groups, 'Group' and 'Group × Reactivation' interaction were included as additional fixed effects. Our primary interest was in the two-way interaction between 'Group' and 'Reactivation'; however, we used full-factorial models, which also included the three-way interaction ('Reactivation × Group × SWS×REM product') for completeness. In all models, compound symmetry covariance structure was used for the repeated measures, with 'Subject' included as a random effect.

A one-way MANOVA was used to compare the sleep variables (total sleep time [TST], wake after sleep onset [WASO], %N1, %N2, %SWS, %REM, and SWS×REM product) between the reactivation groups, with Tukey's test for post-hoc analyses. Pearson's correlation was used for correlations between normally distributed variables. Spearman's correlation was used if variables were not normally distributed.

To analyze cue-evoked EEG data, we first identified clusters of time-frequency points for which post-cue activity was significantly different from zero. For each group, we conducted one-sample t tests across participants to detect points in the time-frequency space at which spectral power was systematically different from zero ( p < 0.05), false discovery rate (FDR) adjusted; see ref. for a similar approach. Clusters of significant activity were identified using the bwlabeln function in MATLAB. Within each cluster, spectral power was averaged across all time-frequency points in that cluster, producing for each participant a single spectral power value for each cluster.

All hypothesis testing was carried out using two-sided tests. The significance threshold was set at p  < 0.05. For correlations, Bonferroni correction was used to control family wise error rate.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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Both slow wave and rapid eye movement sleep contribute to emotional memory consolidation - Communications Biology

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Sleep supports memory consolidation, but the specific roles of different sleep stages in this process remain unclear. While rapid eye movement sleep (REM) has traditionally been linked to the processing of emotionally charged material, recent evidence suggests that slow wave sleep (SWS) also plays a role in strengthening emotional memories.

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O Sleep Slow Wave (SWS) tem um papel importante na consolidação de memórias declarativas dependentes do hipocampo. Durante o sono, as memórias recentes são reativadas e gradualmente redistribuídas pelas redes neocorticais. Esse processo ativo de consolidação envolve uma interação sincronizada entre as redes neocorticais e hipocampais. Durante a SWS, as oscilações lentas sincronizam a atividade dos neurônios, permitindo que as informações do tálamo (fusos talamocorticais) e do hipocampo (reativações de memória acompanhadas de ondas agudas) sejam comunicadas ao neocórtex. Essa sincronização é fundamental para redistribuir as representações de memória para as redes neocorticais, onde são armazenadas a longo prazo.

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Slow-wave sleep and the consolidation of long-term memory - PubMed

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Slow-wave sleep (SWS) has been shown to play an important role in the reinforcement of declarative memory. A dialogue between the neocortex and hippocampus is important during this consolidation and appears to be largely regulated by <1 Hz electroencephalographic (EEG) slow oscillations. Events e …

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o sono não rem e o sono rem apresenta diferentes funções no processo de memória e consolidam diferentes tipos de memória.

Estabilidade da memoria ocorre durante o sono rem, consolida a memoria não declarativa e processual

A integração da memória do conhecimento ocorre durante o sono não rem, beneficia a memoria declarativa

A consolidação a longo prazo da memória depende da interação entre o hipocampo e o córtex, e as oscilações dependentes do sono atuam como veículos de transferência e estabilização de memória. Muitas excelentes revisões têm resumido o processo de consolidação da memória dependente do sono.

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Kumar, D., Yanagisawa, M., & Funato, H. (2024). Sleep-dependent memory consolidation in young and aged brains. Aging Brain, 6, 100124. https://doi.org/10.1016/j.nbas.2024.100124

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Enhancing Working Memory and Reducing Anxiety in University Students: A Neurofeedback Approach

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Enhancing Working Memory and Reducing Anxiety in University Students: A Neurofeedback Approach

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University Students: A Neurofeedback Approach

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Shining a Light on the Mechanisms of Sleep for Memory Consolidation

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Shining a Light on the Mechanisms of Sleep for Memory Consolidation

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Closed-loop auditory stimulation method

to modulate sleep slow waves and motor

learning performance in rats

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Closed-loop auditory stimulation method

to modulate sleep slow waves and motor

learning performance in rats

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A Meta-Analysis of the Impact of Short-Term Sleep Deprivation on Cognitive Variables

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A substantial amount of research has been conducted in an effort to understand the impact of short-term (<48 hr) total sleep deprivation (SD) on outcomes in various cognitive domains. Despite this wealth of information, there has been disagreement…

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