Biology subjects

Treder, M. S.

Publications and source records attributed to Treder, M. S..

7 recordsLinked to original sources

bioRxiv2024

Targeted memory reactivation elicits temporally compressed reactivation linked to spindles

Memories reactivate during sleep, however the properties of such reactivation and its relationship to encoding strength and subsequent memory performance are not well understood. We set out to examine memory reactivations associated with a serial reaction time task (SRTT). 48 human participants performed the SRTT, and then slept in the lab while we deliberately induced reactivation in Slow Wave Sleep (SWS) using a Targeted Memory Reactivation (TMR) design. We detected reactivation after TMR cues using multiclass classification that adapted to sleep data by using sleep activity for training and wake activity for testing. We then examined the temporal properties of reactivation in relation to behavioural performance and sleep spindles. In keeping with the rodent literature, the observed reactivation was 3 to 20 times faster than waking activity. Furthermore, we report an inverted-U shaped relationship between TMR-related behavioural improvement and encoding strength, with very strong and very weak memories benefiting little from cueing while medium-strength memories benefit the most. Finally, reactivation was more frequently observed in trials with high sigma power, supporting the idea that sleep spindles are associated with memory reactivation during sleep. These findings bring us closer to understanding the characteristics of human memory reactivation after TMR, demonstrate when cueing is effective, and provide evidence for the positive relationship between the detectability of reactivation and memory consolidation.

neuroscience↗

bioRxiv2022

Quality assessment of anatomical MRI images from Generative Adversarial Networks: human assessment and image quality metrics

BackgroundGenerative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human eye to more subtle statistical relationships, a more automated approach towards evaluating GANs is required. New methodWe investigated to what extent visual quality can be assessed using image quality metrics and we used group analysis and spatial independent components analysis to verify that the GAN reproduces multivariate statistical relationships found in real data. Reference human data was obtained by recruiting neuroimaging experts to assess real Magnetic Resonance (MR) images and images generated by a Wasserstein GAN. Image quality was manipulated by exporting images at different stages of GAN training. Results: Experts were sensitive to changes in image quality as evidenced by ratings and reaction times, and the generated images reproduced group effects (age, gender) and spatial correlations moderately well. We also surveyed a number of image quality metrics which consistently failed to fully reproduce human data. While the metrics Structural Similarity Index Measure (SSIM) and Naturalness Image Quality Evaluator (NIQE) showed good overall agreement with human assessment for lower-quality images (i.e. images from early stages of GAN training), only a Deep Quality Assessment (QA) model trained on human ratings was sensitive to the subtle differences between higher-quality images. ConclusionsWe recommend a combination of group analyses, spatial correlation analyses, and both distortion metrics (SSIM, NIQE) and perceptual models (Deep QA) for a comprehensive evaluation and comparison of brain images produced by GANs.

neuroscience↗

bioRxiv2021

Targeting targeted memory reactivation: characteristics of cued reactivation in sleep

Targeted memory reactivation (TMR) is a technique in which sensory cues associated with memories during wake are used to trigger memory reactivation during subsequent sleep. The characteristics of such cued reactivation, and the optimal placement of TMR cues, remain to be determined. We built an EEG classification pipeline that discriminated reactivation of right- and left-handed movements and found that cues which fall on the up-going transition of the slow oscillation (SO) are more likely to elicit a classifiable reactivation. We also used a novel machine learning pipeline to predict the likelihood of eliciting a classifiable reactivation after each TMR cue using the presence of spindles and features of SOs. Finally, we found that reactivations occurred either immediately after the cue or one second later. These findings greatly extend our understanding of memory reactivation and pave the way for development of wearable technologies to efficiently enhance memory through cueing in sleep.

neuroscience↗

bioRxiv2021

Targeted memory reactivation in human REM sleep elicits recurrent, temporally compressed reactivation

Several studies show that memories are reactivated during non-rapid eye movement (NREM) sleep, but the question of whether equivalent reactivation can be detected in rapid eye movement (REM) sleep is hotly debated. To examine this, we used a technique called targeted memory reactivation (TMR) in which sounds are paired with learned material in wake, and then re-presented in subsequent sleep to trigger reactivation. We then used machine learning classifiers to identify reactivation in REM related to the encoded wake activity. The reactivation we measured was mediated by high theta activity and was sometimes temporally compressed and sometimes dilated compared to wakeful experience. Reactivation strength positively predicted overnight performance improvement. These findings provide the first evidence for wake-like memory reactivation in human REM sleep after TMR.

neuroscience↗

bioRxiv2020

Distributional changes in myelin-specific MRI markers uncover dynamics in the fornix following spatial navigation training

Increasing evidence implicates white matter (WM) dynamics supporting learning in the mature brain. Recent MRI studies, mostly using diffusion tensor MRI (DT-MRI), have demonstrated learning-induced WM changes at the microstructural level. However, while DT-MRI-derived measures have sensitivity to general WM microstructural changes, they lack compartmental specificity, making them difficult to relate to underlying cellular mechanisms, stymying deeper understanding of mechanisms supporting training-induced gains in performance. Gaining a deeper understanding demands a more detailed characterization of changes in specific WM sub-components. To this end, four microstructural MRI techniques were employed to study alterations in rat brains after 5-days of water maze training: DT-MRI; Composite Hindered and Restricted Model of Diffusion (CHARMED); magnetization transfer (MT) imaging; quantitative susceptibility mapping and R2*. The hypothesis tested here was that microstructural changes would be: (i) observed in tracts supporting spatial navigation, i.e., fornix and corpus callosum (CC); and (ii) more pronounced in the myelin-specific measures. Medians and distributions of microstructural parameters were derived along the fornix, CC and cingulum (as a comparison tract) using the tractometry approach. Summary measures were derived from different metrics using unsupervised data reduction. Significant pre-vs-post training differences were found in the medians of two principal components loading on: (i) anisotropy indices; and (ii) MT ratio. The most striking effect, however, was seen in the distributions of pre-vs-post training MT ratio in the fornix, consistent with the primary hypothesis, and highlighting the value of this alternative to the standard approach (i.e., comparing means/medians of DT-MRI parameters) for studying neuroplasticity in vivo. Significance statementRecent MRI-studies have demonstrated that white matter (WM) dynamics support learning, even in the mature brain. However, most studies are based on diffusion tensor MRI (DT-MRI) measures, which although providing sensitivity to WM microstructural changes, do not provide a direct translation to the underlying cellular mechanisms making interpretation difficult. Using alternative quantitative MRI approaches, this study provided more specific subcomponent microstructural insights into the brains WM response to water-maze training. We show that myelin-specific MR measures show more marked changes than axonal-specific and DT-MRI measures, in WM tracts responsible for spatial navigation. The results promote adoption of these alternative approaches to DT-MRI for studying neuroplasticity in vivo.

neuroscience↗

bioRxiv2018

Evidence for a reversal of the neural information flow between object perception and object reconstruction from memory

Remembering is a reconstructive process. Surprisingly little is known about how the reconstruction of a memory unfolds in time in the human brain. We used reaction times and EEG time-series decoding to test the hypothesis that the information flow is reversed when an event is reconstructed from memory, compared to when the same event is initially being perceived. Across three experiments, we found highly consistent evidence supporting such a reversed stream. When seeing an object, low-level perceptual features were discriminated faster behaviourally, and could be decoded from brain activity earlier, than high-level conceptual features. This pattern reversed during associative memory recall, with reaction times and brain activity patterns now indicating that conceptual information was reconstructed more rapidly than perceptual details. Our findings support a neurobiologically plausible model of human memory, suggesting that memory retrieval is a hierarchical, multi-layered process that prioritizes semantically meaningful information over perceptual detail.

neuroscience↗

bioRxiv2017

Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in cognitive neuroscience. They allow for the unique opportunity to record brain activity with a high spatial and temporal resolution. To extract the local signal of interest in stereotactic EEG (S-EEG) data, a common pre-processing choice is to re-reference the data with a bipolar montage.\n\nWith bipolar reference, each channel is subtracted from its neighbour in order to reduce commonalities between channels and isolate activity that is spatially confined. We here challenge the assumption that bipolar reference can effectively perform this task. We argue that in order to extract local activity, the distribution of the signal source of interest, as well as the distribution of interfering distant signals and noise sources need to be considered. Those can have a variable spatial extent and are modulated by electrode spacing, location and anatomical characteristics. Those factors are not accounted for by a fixed referencing scheme and bipolar reference can therefore not only decrease the signal to noise ratio (SNR) of the data, but also lead to mislocalization of activity and consequently to misinterpretation of results.\n\nWe promote the perspective of regarding referencing as a spatial filtering operation with fixed coefficients. As an alternative, we propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. We argue that ICA performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and can therefore be used to identify activity that is local. We first describe and demonstrate this procedure on human S-EEG recordings. In a simulation with real data, we then quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when it comes to revealing local time series from the superposition of neighbouring channels.

neuroscience↗