Biology subjects

Kourtzi, Z.

Publications and source records attributed to Kourtzi, Z..

6 recordsLinked to original sources

bioRxiv2026

Expression levels of α5 subunit-containing GABA-A receptors in the prelimbic cortex are associated with visual perceptual learning

Perceptual learning is a dynamic process involving the acquisition and integration of sensory information necessary for adaptive decision making. Resolving the neural basis of perceptual learning may have relevance for our understanding of schizophrenia and other neurodevelopmental disorders that implicate impaired perceptual acuity. We developed a novel touchscreen task that utilizes orientation discrimination and variable attentional load to elucidate the perceptual and attentional components of visual perceptual learning (VPL) in male and female rats. Based on previous evidence we hypothesised that individual variation in VPL may depend in part on inhibitory neurotransmission mediated by {gamma}-amino butyric acid (GABA). Segregating subjects based on poor and good learning after task pretraining revealed dose-dependent improvements in VPL in poor learners following the administration of an 5 subunit specific GABA-A (GABRA5) positive allosteric modulator (Alogabat) and a GABA-B agonist (R-baclofen) early in learning. Poor VPL performance was associated with significantly reduced GABRA5 mRNA expression in dorsal regions of the prefrontal cortex (PFC), notably the prelimbic cortex. Decreased GABRA5 expression in this region was co-localized to somatostatin- and parvalbumin-expressing interneurons. These findings reveal a potential link between GABRA5 expression in selective PFC populations of inhibitory interneurons and the speed and acuity of VPL. Significance statementCortical inhibitory microcircuits underlie visual perceptual learning (VPL) and may contribute to altered perceptual learning in neurodevelopmental disorders. However, the mechanisms underlying VPL are poorly understood. This study implemented a translationally relevant touchscreen task to assess visual perception and attention. We report that 5-subunit containing GABA-A receptors (GABRA5) are decreased in expression in the prelimbic subregion of the prefrontal cortex in animals that are slower to acquire the VPL task compared with animals showing superior VPL. We also report that VPL can be improved in a subset of poor learning animals by a positive allosteric modulator of GABRA5. These findings suggest that GABRA5 may be a potential therapeutic target to improve visual perception in schizophrenia and other neurodevelopmental disorders.

neuroscience↗

bioRxiv2025

Reactivation-coupled brain stimulation enables complete learning generalization

Generalization of learned knowledge to new contexts is essential for adaptive behavior. Despite extensive research on the brain plasticity mechanisms underlying learning specificity, the mechanisms that facilitate generalization remain poorly understood. Here, we investigate whether using brain stimulation to disrupt offline consolidation in visual cortex promotes learning generalization. Separate groups of participants (N = 144) were trained on visual detection tasks using either a reactivation-based protocol or conventional full-practice, combined with anodal or sham transcranial direct current stimulation (tDCS) over the visual cortex. Strikingly, only combination of reactivation-based learning with anodal tDCS produced complete generalization from trained to untrained stimuli, an effect consistently replicated across features (orientation, motion direction). In contrast, reactivation-based learning alone and conventional full-practice - whether with or without brain stimulation - yielded stimulus-specific learning. Importantly, reactivation-coupled brain stimulation achieved generalization with an 80% reduction in training trials while maintaining learning gains comparable to full-practice. These findings demonstrate that reactivation and neuromodulation interact to unlock learning generalization, revealing a key brain plasticity mechanism and offering a rapid, translatable strategy for sensory rehabilitation.

neuroscience↗

bioRxiv2021

Brain stimulation boosts perceptual learning by altering sensory GABAergic plasticity and functional connectivity

Interpreting cluttered scenes --a key skill for successfully interacting with our environment-- relies on our ability to select relevant sensory signals while filtering out noise. Training is known to improve our ability to make these perceptual judgements by altering local processing in sensory brain areas. Yet, the brain-wide network mechanisms that mediate our ability for perceptual learning remain largely unknown. Here, we combine transcranial direct current stimulation (tDCS) with multi-modal brain measures to modulate cortical excitability during training on a signal-in-noise task (i.e. detection of visual patterns in noise) and test directly the link between processing in visual cortex and its interactions with decision-related areas (i.e. posterior parietal cortex). We test whether brain stimulation alters inhibitory processing in visual cortex, as measured by magnetic resonance spectroscopy (MRS) of GABA and functional connectivity between visual and posterior parietal cortex, as measured by resting state functional magnetic resonance imaging (rs-fMRI). We show that anodal tDCS during training results in faster learning and decreased GABA+ during training, before these changes occur for training without stimulation (i.e. sham). Further, anodal tDCS decreases occipito-parietal interactions and time-varying connectivity across the visual cortex. Our findings demonstrate that tDCS boosts learning by accelerating visual GABAergic plasticity and altering interactions between visual and decision-related areas, suggesting that training optimises gain control mechanisms (i.e. GABAergic inhibition) and functional inter-areal interactions to support perceptual learning.

neuroscience↗

bioRxiv2020

Predicting future regional tau accumulation in asymptomatic and early Alzheimer's disease

The earliest stages of Alzheimers disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers ({beta}-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to {beta}-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD. One Sentence SummaryOur machine learning approach combines baseline multimodal data to make individualised predictions of future pathological tau accumulation at prodromal and asymptomatic stages of Alzheimers disease with high accuracy and regional specificity.

neuroscience↗

bioRxiv2020

Recurrent plasticity mechanisms for perceptual decisions in the human brain

Learning and experience are critical for translating ambiguous sensory information from our environments to perceptual decisions. Yet, evidence on how training molds the adult human brain remains controversial, as fMRI at standard resolution does not allow us to discern the finer-scale mechanisms that underlie sensory plasticity. Here, we combine ultra-high field (7T) functional imaging at sub-millimetre resolution with orientation discrimination training to interrogate experience-dependent plasticity across cortical depths. Our results provide evidence for recurrent plasticity, by contrast to sensory encoding vs. feedback mechanisms. We demonstrate that learning alters orientation-specific representations in superficial rather than middle V1 layers, suggesting changes in read-out rather than input signals. Further, learning increases feedforward rather than feedback layer-to-layer connectivity in occipito-parietal regions, suggesting that sensory plasticity gates perceptual decisions. Our findings propose finer-scale plasticity mechanisms that re-weight sensory signals to inform improved decisions, bridging the gap between micro- and macro-circuits of experience-dependent plasticity.

neuroscience↗

bioRxiv2020

Suppressive recurrent and feedback computations for adaptive processing in the human brain

Adapting to the environment statistics by reducing brain responses to repetitive sensory information is key for efficient information processing. Yet, the fine-scale computations that support this adaptive processing in the human brain remain largely unknown. Here, we capitalize on the sub-millimetre resolution afforded by ultra-high field imaging to examine BOLD-fMRI signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate adaptive visual processing. We demonstrate suppressive recurrent processing within visual cortex, as indicated by stronger BOLD decrease in superficial than middle and deeper layers for gratings that were repeatedly presented at the same orientation. Further, we show dissociable connectivity mechanisms for adaptive processing: enhanced feedforward connectivity within visual cortex, while feedback occipito-parietal connectivity, reflecting top-down influences on visual processing. Our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.

neuroscience↗