Biology subjects

Cohen Kadosh, R.

Publications and source records attributed to Cohen Kadosh, R..

10 recordsLinked to original sources

bioRxiv2026

Hippocampal stimulation timed to memory reactivation shapes human sleep oscillatory dynamics and consolidation

Memory consolidation during sleep depends on the precisely timed coordination of hippocampal reactivation with cortical slow oscillations and spindles. In humans this coupling has been characterised correlationally, while causal investigations have required intracranial stimulation in restricted patient cohorts or used non-invasive approaches targeting cortical regions, often obscuring the underlying sleep rhythms. Here we stimulated the human hippocampus during sleep for the first time, applying temporal interference stimulation to the left hippocampal head either concurrently with or prior to induced memory reactivation. Stimulation concurrent with reactivation reduced associative memory forgetting relative to mistimed stimulation and enhanced fast spindle amplitude, preserving the association between slow oscillation-spindle coupling and memory. This behavioural benefit was mediated by a distributed, left-lateralised set of spindle and coupling features. Non-invasively engaging deep hippocampal circuits during sleep offers both a means to probe human memory and a scalable basis for intervention where consolidation fails.

neuroscience↗

bioRxiv2022

Dissecting the chain of information processing and its interplay with neurochemicals and fluid intelligence across development

Previous research has highlighted the role of glutamate and gamma-aminobutyric acid (GABA) in perceptual, cognitive, and motor tasks. However, the exact involvement of these neurochemical mechanisms in the chain of information processing, and across human development, are unclear. In a cross-sectional longitudinal design, we used a computational approach to dissociate cognitive, decision, and visuomotor processing in 293 individuals spanning early childhood to adulthood. We found that glutamate and GABA within the intraparietal sulcus (IPS) explained unique variance in visuomotor processing, with higher glutamate predicting poorer visuomotor processing in younger participants but better visuomotor processing in mature participants, while GABA showed the opposite pattern. These findings were functionally, neuroanatomically, and neurochemically specific, and were replicated ~1.5 years later and were generalized in two further different behavioral tasks. Using resting fMRI, we revealed that the relationship between IPS neurochemicals and visuomotor processing is mediated by functional connectivity in the visuomotor network. We then extended our findings to high-level cognitive behavior by predicting fluid intelligence performance. We showed that fluid intelligence performance is explained by IPS GABA and glutamate and is mediated by visuomotor processing, and moderated by the developmental stage. These results provide an integrative biological and psychological mechanistic explanation that links cognitive processes and neurotransmitters across human development and establishes their involvement in intelligent behavior.

neuroscience↗

bioRxiv2022

Explaining and Predicting the Effects of Neurostimulation via Neuronal Excitation/Inhibition

Previous research has highlighted the role of the excitation/inhibition ratio (E/I) for typical and atypical development, mental health, cognition, and learning. Other research has highlighted the benefits of high-frequency random noise stimulation (tRNS)--an excitatory form of neurostimulation--on learning. We examined the E/I as a potential mechanism and studied whether tRNS effect on learning depends on E/I as measured by the aperiodic exponent as its putative marker. In addition to manipulating E/I using tRNS, we also manipulated the level of learning (learning/overlearning) that has been shown to influence E/I. Participants (n=102) received either sham stimulation or 20 min tRNS over the dorsolateral prefrontal cortex (DLPFC) during a mathematical learning task. We showed that tRNS increased E/I, as reflected by the aperiodic exponent, and that lower E/I predicted greater benefit from tRNS specifically for the learning task. In contrast to previous Magnetic Resonance Spectroscopy-based E/I studies, we found no effect of the level of learning on E/I. A further analysis using a different data set suggest that both measures of E/I (EEG vs MRS) may reflect, at least partly, different biological mechanisms. Our results highlight the role of E/I as a marker for neurostimulation efficacy and learning. This mechanistic understanding provides better opportunities for augmented learning and personalized interventions.

neuroscience↗

bioRxiv2022

Blinding efficacy and adverse events following repeated transcranial alternating current, direct current, and random noise stimulation.

As transcranial electrical stimulation (tES) protocols advance, assumptions underlying the technique need to be retested to ensure they still hold. Whilst the safety of stimulation has been demonstrated mainly for a small number of sessions, and small sample size, adverse events (AEs) following multiple sessions remain largely untested. Similarly, whilst blinding procedures are typically assumed to be effective, the effect of multiple stimulation sessions on the efficacy of blinding procedures also remains under question. This is especially relevant in multisite projects where small unintentional variations in protocol could lead to inter-site difference. We report AE and blinding data from 1,019 participants who received up to 11 semi-consecutive sessions of active or sham transcranial alternating current stimulation (tACS), direct current stimulation (tDCS), and random noise stimulation (tRNS), at 4 sites in the UK and US. We found that AEs were often best predicted by factors other than tES, such as testing site or session number. Results from the blinding analysis suggested that blinding was less effective for tDCS and tACS than tRNS. The occurrence of AEs did not appear to be linked to tES despite the use of smaller electrodes or repeated delivery. However, blinding efficacy was impacted in tES conditions with higher cutaneous sensation, highlighting a need for alternative stimulation blinding protocols. This may be increasingly necessary in studies wishing to deliver stimulation with higher intensities.

neuroscience↗

bioRxiv2022

Predicting Math Ability using Working Memory, Number Sense, and Neurophysiology in Children and Adults

Previous work has shown relations between domain-general processes, domain-specific processes, and mathematical ability. However, the underlying neurophysiological effects of mathematical ability are less clear. Recent evidence highlighted the potential role of beta oscillations in mathematical ability. Here we investigate whether domain-general (working memory) and domain-specific (number sense) mediate the relation between resting-state beta oscillations and mathematical ability, and how this may differ as a function of development (children vs. adults). We compared a traditional analysis method normally used in EEG studies with a more recently developed parameterization method that separates periodic from aperiodic activity. Regardless of methods chosen, we found no support for mediation of working memory and number sense, neither for children nor for adults. However, we found subtle differences between the methods. Additionally, we showed that the traditional EEG analysis method conflates periodic activity with aperiodic activity, and the latter is strongly related to mathematical ability and this relation differs between children and adults. At the cognitive level, our findings do not support previous suggestions of a mediation of working memory and number sense. At the neurophysiological level our findings suggest that aperiodic, rather than periodic, activity is linked to mathematical ability as a function of development.

neuroscience↗

bioRxiv2021

Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants--personalized Bayesian optimization (pBO)--that searches available parameter combinations to optimize an intervention as a function of an individuals ability. This novel technique was utilized to identify transcranial alternating current stimulation frequency and current strength combinations most likely to improve arithmetic performance, based on a subjects baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, stimulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

neuroscience↗

bioRxiv2020

Is it all in our head? When subjective beliefs about receiving an intervention are better predictors of experimental results than the intervention itself

In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individuals subjective experience of the type of treatment s/he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment--the thought of receiving the active or placebo condition during an experiment--can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N=387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Study 1 & 2; transcranial direct current stimulation: Study 3 & 4). Our findings show that the inclusion of subjective treatment can provide a better model fit, either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions. Significance statementWe demonstrate that individual differences in subjective treatment--the belief of receiving the active or placebo condition during an experiment--can explain variability in research outcomes better than objective treatment, the actual treatment to which participants are assigned. Even though it is a standard practice for intervention studies to collect data on subjective treatment, its contribution to research outcomes has been overlooked. By demonstrating the explanatory power of subjective treatment beyond objective treatment in four independent datasets, we show its potential to provide further insights into the effectiveness of different interventions. We, therefore, encourage researchers to adopt our approach in existing and new studies, to improve experimental design and ultimately increase the rigour and robustness of clinical and non-clinical interventions.

neuroscience↗

bioRxiv2020

Impact of chronic transcranial Random-Noise Stimulation (tRNS) on prefrontal cortex excitation-inhibition balance in juvenile mice.

Transcranial random noise stimulation (tRNS), a non-invasive neuromodulatory technique capable of altering cortical activity, has been proposed to improve the signal-to-noise ratio at the neuronal level and the sensitivity of the neurons following an inverted U-function. The aim of this study was to examine the effects of tRNS on vGLUT1 and GAD 65-67 and its safety in terms of pathological changes. For that, juvenile mice were randomly distributed in three different groups: "tRNS 1x" receiving tRNS at the density current used in humans (0.3 A/m2, 20 min), "tRNS 100x" receiving tRNS at two orders of magnitude higher (30.0 A/m2, 20 min) and "sham" (0.3 A/m2, 15 s). Nine tRNS sessions during five weeks were administered to the prefrontal cortex of alert animals. No detectable tissue macroscopic lesions were observed after tRNS sessions. Post-stimulation immunohistochemical analysis of GAD 65-67 and vGLUT1 immunoreactivity showed a reduced GAD 65-67 immunoreactivity levels in the region directly beneath the electrode for tRNS 1x group with no significant effects in the tRNS 100x nor sham group. The observed results points to an excitatory effect associated with a decrease in GABA levels in absence of major histopathological alterations providing a novel mechanistic explanation for tRNS effects.

neuroscience↗

bioRxiv2020

How can noise alter neurophysiology in order to improve human behaviour? A combined transcranial random noise stimulation and electroencephalography study

Random noise has been shown to improve the detection of suboptimal signals in humans and machines. Based on that, transcranial random noise stimulation (tRNS) has aimed to improve human behaviour by targeting neuronal activity. To uncover the poorly understood mechanistic underpinnings of tRNS, we recorded electroencephalography data during arithmetic training while delivering active or sham tRNS above the dorsolateral prefrontal cortex (dlPFC). By successfully removing the tRNS artefact in the time and frequency domains, we examined the mechanisms that underlie its behavioural improvement. We found that active tRNS improved arithmetic performance and impacts specific ERPs components that are associated with attentional mechanisms. Furthermore, the tRNS effect was maximal in individuals with suboptimal arithmetic ability and neurophysiological measures of top-down control and excitation/inhibition ratio. These results providing a novel mechanistic explanation for the effect of tRNS on human behaviour and highlight how suboptimal task-specific behaviour and neurophysiology predicts its effect.

neuroscience↗

bioRxiv2017

The Neurocognitive Architecture of Individual Differences in Math Anxiety in Typical Children

Math Anxiety (MA) is characterized by a negative emotional response when facing math-related situations. MA is distinct from general anxiety and can emerge during primary education. Prior studies typically comprise adults and comparisons between high-versus low-MA, where neuroimaging work has focused on differences in network activation between groups when completing numerical tasks. The present study used voxel-based morphometry to identify the structural brain correlates of MA in a sample of 79 healthy children aged 7-12 years. Given that MA is thought to develop in later primary education, the study focused on the level of MA, rather than categorically defining it as present or absent. Using a battery of cognitive- and numerical-function tasks, this study identified that increased MA was associated with reduced attention, working memory and math achievement. Increased MA was associated with reduced grey matter in the left anterior intraparietal sulcus. This region was also associated with attention, suggesting that baseline differences in morphology may underpin attentional differences. Future studies should clarify whether poorer attentional capacity due to reduced grey matter density results in the later emergence of MA. Further, our data highlight the role of working memory in propagating reduced math achievement in children with higher MA.

neuroscience↗