Biology subjects

Feld, G. B.

Publications and source records attributed to Feld, G. B..

12 recordsLinked to original sources

bioRxiv2026

Global structure of new relational knowledge networks is represented in retrosplenial complex, and node-distance in hippocampus

Much of our knowledge is structured in networks, but how the brain represents such networks remains unclear. In most nested knowledge structures a subset of the nodes will be highly connected to other nodes in the network. High levels of connection can exist either locally (high degree centrality) or at a global level (high closeness/betweenness centrality). Here, we explored how the human brain represents network structure using functional magnetic resonance imaging (fMRI) and a novel learning paradigm. During training, participants learned the transition structure for travel between a set of fictious partially connected alien planets. Despite encountering only individual connections, participants choices indicated they could infer the broader network structure. Next day, they viewed each stimulus during a cover task while undergoing fMRI. Representational-similarity analysis showed that shortest-path distances within the learned graph were encoded in the posterior hippocampus and right retrosplenial complex, whereas global connectivity (closeness/ betweenness centrality) was represented in the left retrosplenial complex. These findings extend the view that brain networks associated with spatial navigation also process abstract relational knowledge using principles akin to mapping physical space. The results are also consistent with proposals that the hippocampus represents distance information and that the retrosplenial cortex acts as hub for integrating recently acquired knowledge.

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bioRxiv2026

FASTIMAGES: Validating replay detection methods in human Neuroimaging using a combined MEG and fMRI dataset

Studies in rodents and humans using invasive electrophysiology have established that neural replay is a ubiquitous phenomenon in the brain that is associated with a wide range of cognitive functions, including memory, planning and decision making. Yet, invasively recording in humans remains difficult, and hence knowledge about replay in humans remains scarce. Hence, to comprehensively understand replay in humans, we need reliable approaches that can detect it non-invasively. Several main non-invasive approaches have been proposed, but we lack a full comparative validation against known ground truth signals. In this study, we present FASTIMAGES, a benchmark dataset from seventy participants with parallel fMRI (n = 40, previously published) and MEG (n=30) recordings containing known neural sequences evoked by fast visual stimulation as well as functional localizer trials. The neural sequences were elicited by five different visual stimuli shown in sequences at speeds of 132, 164, 228 and 612 milliseconds onset-to-onset intervals. Using this dataset, we investigate two existing statistical methods for sequence detection, namely Temporally Delayed Linear Modelling (TDLM, developed for MEG by Liu et al., 2021) and Slope Order Dynamic Analysis (SODA, developed for fMRI by Wittkuhn & Schuck, 2021). We examine the underlying assumptions of each method, analyse their resulting strengths and weaknesses in application to MEG and fMRI. We demonstrate that both approaches excel in their native modality (TDLM for MEG and SODA for fMRI), with comparable effect sizes given idealized conditions in this benchmark. Cross-modality transfer remains challenging. Finally, the FASTIMAGES dataset provides data with known and clearly expressed sequences and can be used to benchmark and validate future sequence detection methods under idealized conditions.

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bioRxiv2025

Challenges in Replay Detection by TDLM in Post-Encoding Resting State

Using temporally delayed linear modelling (TDLM) and magnetoencephalography (MEG), we investigated whether items associated with an underlying graph structure are replayed during a post-learning resting state. In these same data, we previously provided evidence for replay during on-line (non-rest) memory retrieval. Despite successful decoding of brain activity during a localizer task, and contrary to predictions, we found no evidence for replay during a post-learning resting state. To better understand this, we performed a hybrid simulation analysis in which we inserted synthetic replay events into a control resting state recorded prior to the actual experiment. This simulation revealed that replay detection using our current pipeline requires an extremely high replay density to reach significance (>1 replay sequence per second, with "replay" defined as a sequence of reactivations within a certain time lag). Furthermore, when scaling the number of replay events with a behavioural measure, we were unable to induce a strong correlation between sequenceness and this measure. We infer that even if replay was present at plausible rates in our resting state dataset, we would lack statistical power to detect it with TDLM. Finally, contrasting our novel hybrid simulation to existing purely synthetic simulations indicated that the latter approaches overestimate the sensitivity of TDLM. We discuss approaches that might optimize the analytic methodology, including identifying boundary conditions under which TDLM can be expected to detect replay. We conclude that solving these methodological constraints will be crucial for optimizing the non-invasive measurement of human replay using MEG.

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bioRxiv2025

High intensity exercise before sleep boosts memory encoding the next morning

The importance of sleep for memory consolidation has been extensively studied, but its role for memory encoding remains less well characterized. At the molecular and cellular level, the renormalization of synaptic weights during sleep has received substantial support, which is thought to free capacity to encode new information at the behavioral level. However, at the systems level and behaviorally, support for this process playing a major role for memory function remains scarce. In the current study, we investigated the utility of moderate- and high-intensity evening exercise as a low-cost low-tech intervention to modulate sleep and its influence on subsequent encoding in the morning. Our findings indicate that high-intensity interval training (HIIT) improved post-sleep memory performance with effects lasting up to 24 hours after initial encoding. In addition, we show that especially the early parts of the encoding task were affected by the HIIT intervention, which is in line with increases in synaptic homeostasis being targeted by the exercise. Intriguingly, low-performing participants seemed to benefit more from the HIIT intervention suggesting it not only as a tool for basic research but also as a candidate for applications to boost memory performance in mental disorders or in the elderly. These results provide first evidence that acute exercise can affect learning processes even hours after it occurs.

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bioRxiv2023

Reactivation strength during cued recall is modulated by graph distance within cognitive maps

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8-minute consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic (MEG) recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

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bioRxiv2022

Automated real-time EEG sleep spindle detection for brain state-dependent brain stimulation

Sleep spindles are a hallmark electroencephalographic (EEG) feature of non-rapid eye movement (NREM) sleep and believed to be instrumental for sleep-dependent memory reactivation and consolidation. However, direct proof of their causal relevance is hard to obtain, and our understanding of their immediate neurophysiological consequences is limited. To investigate their causal role, spindles need to be targeted in real-time with sensory or non-invasive brain stimulation techniques. While fully automated offline detection algorithms are well established, spindle detection in real time is highly challenging due to their spontaneous and transient nature. Here, we present the real-time spindle detector (RTSD), a robust multi-channel EEG signal processing algorithm that enables the automated triggering of stimulation during sleep spindles in a phase-specific manner. We validated the RTSD method by streaming pre-recorded sleep EEG datasets to a real-time computer system running a Simulink(R) Real-Time implementation of the algorithm. Sleep spindles were detected with high levels of sensitivity ([~]83%) and precision ([~]78%) and an F1-score of [~]0.81 in reference to state-of-the-art offline algorithms (which reached similar levels when compared to each other), for both naps and full nights, and largely independent of sleep scoring information. Detected spindles were comparable in frequency, duration, amplitude, and symmetry, and showed the typical time-frequency characteristics as well as a centroparietal topography. Spindles were detected close to their center and reliably at the predefined target phase. The RTSD algorithm therefore empowers researchers to target spindles during human sleep and apply the stimulation method and experimental paradigm of their choice.

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bioRxiv2021

Learning graph networks: sleep targets highly connected global and local nodes for consolidation

Much of our long-term knowledge is organised in complex networks. Sleep is thought to be critical for abstracting knowledge and enhancing important item memory for long-term retention. Thus, sleep should aid the development of memory for networks and the abstraction of their structure for efficient storage. However, this remains unknown because past sleep studies have focused on discrete items. Here we explored the impact of sleep (night-sleep/day-wake within-subject paradigm) on memory for graph-networks where some items were important due to dense local connections (degree centrality) or, independently, important due to greater global connections (closeness / betweenness centrality). A network of 27 planets (nodes) sparsely interconnected by 36 teleporters (edges) was learned via discrete associations without explicit indication of any network structure. Despite equivalent exposure to all connections in the network, we found that memory for the links between items with high local centrality or high global centrality were better retained after sleep. These results highlight that sleep has the capacity for strengthening both global and local structure from the world and abstracting over multiple experiences to efficiently form internal networks of knowledge.

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bioRxiv2021

Brain-Wide Inferiority and Equivalence Tests in fMRI Group Analyses: Selected Applications

Null hypothesis significance testing is the major statistical procedure in the field of fMRI, but provides only a rather limited picture of the effects in a data set. When sample size and power is low relying only on strict significance testing may lead to a host of false negative findings. In contrast, with very large data sets virtually every voxel might become significant. It is thus desirable to complement significance testing with procedures like inferiority and equivalence tests that allow to formally compare effect sizes within and between data sets and offer novel approaches to obtain insight into fMRI data. The major component of these tests are estimates of standardized effect sizes and their confidence intervals. Here we show how Hedges g, the bias corrected version of Cohens d, and its confidence interval can be obtained from SPM t maps. We then demonstrate how these values can be used to evaluate whether non-significant effects are really statistically smaller than significant effects to obtain "regions of undecidability" within a data set, and to test for the replicability and lateralization of effects. This method allows the analysis of fMRI data beyond point estimates enabling researchers to take measurement uncertainty into account when interpreting their findings.

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bioRxiv2020

Specific changes in sleep oscillations after blocking human metabotropic glutamate receptor 5 in the absence of altered memory function

BackgroundSleep consolidates declarative memory by repeated replay linked to the cardinal oscillations of NonREM sleep. However, there is so far little evidence of classical glutamatergic plasticity induced by this replay. Rather, we have previously reported that blocking NMDA or AMPA receptors does not affect sleep-dependent consolidation of declarative memory. AimsInvestigate the role of metabotropic glutamate receptor 5 (mGluR5) on memory processing during sleep. MethodsIn two placebo-controlled within-subject cross-over experiments with 20 healthy humans each, we used fenobam to block mGluR5 during sleep. In Experiment I, participants learned word-pairs (declarative task) and a finger sequence (procedural task) in the evening, then received the drug and recall was tested in the next morning. To cover possible effects on synaptic renormalization processes during sleep, in Experiment II, participants learned new word-pairs in the morning after sleep. Results/OutcomesSurprisingly, fenobam neither reduced retention of memory across sleep nor new learning after sleep, although it severely altered sleep architecture and memoryrelevant EEG oscillations. In NonREM sleep, fenobam suppressed 12-15 Hz spindles but augmented 2-4 Hz delta waves, whereas in REM sleep it suppressed 4-8 Hz theta and 16-22 Hz beta waves. Notably, under Fenobam NonREM spindles became more consistently phase-coupled to the slow oscillation. Conclusions/InterpretationsOur findings indicate that mGluR5-related plasticity is not essential for memory processing during sleep, even though mGlurR5 are strongly implicated in the regulation of the cardinal sleep oscillations. Declaration of interest/FundingThe authors have nothing to disclose and funders had no influence on the research presented here.

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bioRxiv2020

Vast amounts of encoded items nullify but do not reverse the effect of sleep on declarative memory

Sleep strengthens memories by repeatedly reactivating associated neuron ensembles. Our studies show that although long-term memory for a medium number of word-pairs (160) benefits from sleep, a large number (320) does not. This suggest an upper limit to the amount of information that has access to sleep-dependent declarative memory consolidation, which is possibly linked to the availability of reactivation opportunities. Due to competing processes of global forgetting that are active during sleep, we hypothesised that even larger amounts of information would enhance the proportion of information that is actively forgotten during sleep. In the present study, we aimed to induce such forgetting by challenging the sleeping brain with vast amounts of to be remembered information. For this, 80 participants learned a very large number of 640 word-pairs over the course of an entire day and then either slept or stayed awake during the night. Recall was tested after another night of regular sleep. Results revealed comparable retention rates between the sleep and wake groups. Although this null-effect can be reconciled the concept of limited capacities available for sleep-dependent consolidation, it contradicts our hypothesis that sleep would increase forgetting compared to the wake group. Additional exploratory analyses relying on equivalence testing and Bayesian statistics reveal that there is evidence against sleep having a detrimental effect on the retention of declarative memory at high information loads. We argue that forgetting occurs in both wake and sleep states through different mechanisms, i.e., through increased interference and through global synaptic downscaling, respectively. Both of these processes might scale similarly with information load.

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bioRxiv2019

The gap junction blocker mefloquine impairs sleep-dependent declarative memory consolidation in humans

During sleep, the time-compressed replay of engrams acquired during preceding wakefulness drives memory consolidation. We demonstrate in healthy humans that direct electrical coupling between neurons via gap junctions, i.e., electrical synapses, contributes to this beneficial effect of sleep. Twenty male participants learned a declarative word-pair task and a procedural finger sequence tapping task before receiving the antimalarial mefloquine that is known to block electrical synapses. Retrieval was tested after a retention interval of approximately 20.5 hours that included nocturnal sleep. As predicted, mefloquine given before sleep impaired the retention of declarative memory. In contrast, this effect was absent in control groups, which stayed awake or received mefloquine after sleep. Irrespective of sleep or administration time, mefloquine enhanced retention performance on the procedural memory control task. We conclude that sleep-dependent processes relying on electrical neuronal coupling enable hippocampus-dependent declarative memory consolidation, presumably via time-compressed hippocampal replay of memory traces within sharp-wave/ripple complexes. The recruitment of this understudied form of neuronal information transfer may be necessary to achieve fast-paced memory reprocessing during sleep. Considering that drugs targeting neurochemical synapses have recently fallen short of substantially advancing the treatment of memory impairments in Alzheimers disease, schizophrenia or during normal aging, unraveling the contribution of gap junctions to sleep-dependent declarative memory formation can be expected to open new therapeutic avenues. Significance statementSleep supports the strengthening and transformation of memory content via the active replay of previously encoded engrams. Surprisingly, blocking neurochemical synaptic transmission does not impair this function of sleep. Here we demonstrate that the direct electrical coupling between neurons via electrical synapses (gap junctions) is essential for the sleep-dependent formation of declarative memory, i.e., memory for episodes and facts. These findings are in line with the assumption that electrical synapses enable time-compressed neuronal firing patterns that emerge during sleep and drive declarative memory consolidation. Electrical synapses have so far not been linked to higher-order brain functions in humans; their contribution to sleep-dependent memory processing may provide a novel target for sleep-related clinical interventions.

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bioRxiv2017

Overnight memory consolidation facilitates rather than interferes with new learning of similar materials - a study probing NMDA-receptors

Whereas sleep-dependent consolidation and its neurochemical underpinnings have been strongly researched, less is known about how consolidation during sleep affects subsequent learning. Since sleep enhances memory, it can be expected to pro-actively interfere with learning after sleep, in particular of similar materials. This pro-active interference should be enhanced by substances that benefit consolidation during sleep, such as D-cycloserine. We tested this hypothesis in two groups (Sleep, Wake) of young healthy participants receiving on one occasion D-cycloserine (175 mg) and on another occasion placebo, according to a double-blind balanced cross-over design. Treatment was administered after participants had learned a set of word-pairs (A-B list) and before nocturnal retention periods of sleep vs. wakefulness. After D-cycloserine blood plasma levels had dropped to negligible amounts, i.e., the next day in the evening, participants learned, in three sequential runs, new sets of word-pairs. One list - to enhance interference - consisted of the same cue words as the original set paired with a new target word (A-C list) and the other of completely new cue words (D-E set). Unexpectedly, during post-retention learning the A-C interference list was generally better learned than the completely new D-E list, which suggests that consolidation of previously encoded similar material enhances memory integration rather than pro-active interference. Consistent with this view, new learning of word-pairs was better after sleep than wakefulness. Similarly, D-cycloserine generally enhanced learning of new word-pairs, compared to placebo. This effect being independent of sleep or wakefulness, leads us to speculate that D-cycloserine, in addition to enhancing sleep-dependent consolidation, might mediate a time-dependent process of active forgetting.

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