Biology subjects

Cohen Kadosh, K.

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

4 recordsLinked to original sources

bioRxiv2025

Empirical evidence of the neuroactive potential of the gut on neurochemistry in the gut-brain axis in humans

The gut microbiome produces a wide range of neuroactive metabolites capable of influencing brain neurotransmitter systems. While preclinical studies suggest these microbial pathways modulate cognitive and emotional processes, evidence in humans remains limited. This study investigates the relationship between gut microbiome-derived neuroactive pathways and in vivo brain neurotransmitter concentrations in healthy young females. Using proton magnetic resonance spectroscopy ({superscript 1}H-MRS), we quantified GABA and glutamate levels in the dorsolateral prefrontal cortex (dlPFC), anterior cingulate cortex (ACC), and inferior occipital gyrus (IOG). Parallel gut microbiome profiling identified functional pathways associated with the synthesis and degradation of GABA, glutamate, short-chain fatty acids, p-cresol, and inositol. We observed significant, region-specific associations between microbial neuroactive pathways and both neurotransmitter concentrations and excitatory/inhibitory (E/I) balance, a key regulator of neuroplasticity and mental health. Notably, microbial GABA synthesis pathways were inversely related to brain glutamate levels in the dlPFC, while glutamate synthesis pathways correlated with GABA levels in the ACC. Additional pathways, including p-cresol and SCFA metabolism, showed region-dependent relationships with brain neurochemistry and E/I ratios. These neurochemical findings were complemented by associations between the same microbial pathways and psychological outcomes, including anxiety, depressive symptoms, and sleep quality. These findings provide new evidence of gut-derived metabolic influence on human brain neurotransmitter dynamics and mental wellbeing, highlighting the gut-brain axis as a promising target for microbiome-based interventions supporting cognitive resilience and emotional health.

neuroscience↗

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗

bioRxiv2018

Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks during adolescence

Research has shown that difficulties with emotion regulation abilities in childhood and adolescence increase the risk for developing symptoms of mental disorders, e.g anxiety. We investigated whether functional magnetic resonance imaging (fMRI)-based neurofeedback (NF) can modulate brain networks supporting emotion regulation abilities in adolescent females. We performed three studies (total N=63). We first compared different NF implementations regarding their effectiveness of modulating prefrontal cortex (PFC)-amygdala functional connectivity (fc). Further we assessed the effects of fc-NF on neural measures, emotional/metacognitive measures and their associations. Finally, we probed the mechanism underlying fc-NF by examining concentrations of inhibitory and excitatory neurotransmitters. Results showed that NF implementations differentially modulate PFC-amygdala fc. Using the most effective NF implementation we observed important relationships between neural and emotional/metacognitive measures, such as practice-related change in fc was related with change in thought control ability. Further, we found that the relationship between state anxiety prior to the MRI session and the effect of fc-NF was moderated by GABA concentrations in the PFC and anterior cingulate cortex. To conclude, we were able to show that fc-NF can be used in adolescent females to shape neural and emotional/metacognitive measures underlying emotion regulation. We further show that neurotransmitter concentrations moderate fc-NF-effects.

neuroscience↗