Tuesday, May 14th, 2024

Time	Event
12:31p	Cerebrovascular Health Mediates Processing Speed Change Through Anterior White Matter Alterations: A UK Biobank Study Cerebrovascular disease is associated with an increased likelihood of developing dementia. While cardiovascular risk factors are modifiable and may reduce the risk of later-life cognitive dysfunction, the relationship between cerebrovascular risk factors, brain integrity and cognition remains poorly characterised. Using a large UK Biobank sample of predominantly middle-aged adults, without neurological disease, our structural equation mediation models showed that poor cerebrovascular health, indicated by the presence of cerebrovascular risk factors, was associated with slowed processing speed. This effect was best explained by anterior white matter microstructure changes, rather than posterior changes. Effects were also significantly reduced when considering other forms of cognition, demonstrating both regional- and cognitive-specificity of our effects. Critically, our findings also demonstrate that including measures of risk factor duration may be particularly important for improving estimations of cerebrovascular burden. In summary, our study demonstrates the specific impact of early cerebrovascular burden on brain structure and cognitive function, highlighting the necessary next steps for improving cerebrovascular burden quantification and improving clinical predictions. (Comment on this)
12:31p	A direct neural signature of serial dependence in working memory Serial dependence describes the phenomenon that current object representations are attracted to previously encoded and reported representations. While attractive biases have been observed reliably and across various modalities in behavioral reports, a direct neural correlate has not been established. Previous studies have either shown a reactivation of past information without observing a neural signal related to the bias of the current information, or a repulsive distortion of current neural representations contrasting the behavioral bias. The present study recorded neural signals with magnetoencephalography during a working memory task to identify neural correlates of serial dependence. Participants encoded and memorized two sequentially presented motion directions per trial, one of which was later retro-cued for report. Multivariate analyses provided reliable reconstructions of both motion directions. Importantly, the reconstructed directions in the current trial were attractively shifted towards the target direction of the previous trial. This neural bias mirrored the behavioral attractive bias, thus reflecting a direct neural signature of serial dependence. The use of a retro-cue task in combination with magnetoencephalography allowed us to determine that this neural bias emerged at later, post-encoding time points. This timing suggests that serial dependence in working memory affects memorized information during read-out and reactivation processes that happen after the initial encoding. Taken together, we identified a direct neural signature of serial dependence, which occurs during later processing stages of working memory representations. (Comment on this)
2:34p	Deep Neural Network for Direct Prediction of Analytic Signals from Neural Oscillations Objective: A method for real-time prediction of analytic signals is needed for state-informed stimulation in electroencephalography (EEG) experiments. The currently available methods lack sufficient prediction accuracy or have a complicated selection process of experimental parameters. Approach: The proposed method uses a deep neural network (DNN) to predict current and future EEG phases and amplitudes from raw EEG data for real-time signal processing. Main results: The proposed method predicted EEG phases and amplitudes more accurately than the conventional method based on an autoregressive model for actual EEG data. Furthermore, the DNN incorporates a missing signal imitation layer to make the model robust against missing data due to recording failures in EEG experiments. Significance: The DNN allows the current EEG phase to be estimated directly from the raw EEG data, which reduces the number of experimental parameters required. The end-to-end prediction framework can help simplify experimental operations and facilitate the implementation of the proposed method in various applications concerning the modulation of neural dynamics. (Comment on this)

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