Biology subjects

Kincses, Z. T.

Publications and source records attributed to Kincses, Z. T..

4 recordsLinked to original sources

bioRxiv2024

Enhancing Retrieval Capacity of the Predictive Brain through Dorsolateral Prefrontal Cortex Intervention

The ability to extract spatial or temporal regularities across experiences is crucial for skill development and predictive processes. The prefrontal cortex (PFC) plays a key role in modulating competitive memory systems, supporting declarative/episodic memory as opposed to statistical learning. This regulatory role may explain findings of improved acquisition and consolidation of statistical regularities following the suppression of dorsolateral PFC (DLPFC) by repetitive transcranial magnetic stimulation (rTMS). This raises a key question: Is access to models and prior statistical knowledge also modulated by the DLPFC? This preregistered study provides new insights by examining the role of the DLPFC in retrieving pre-existing knowledge of temporally distributed statistical regularities. Using a probabilistic learning task, healthy participants engaged in implicit statistical learning for 25 minutes. After a 24-hour consolidation period, participants received either 1 Hz rTMS or sham stimulation over the left, right, or bilateral DLPFC for 10 minutes before retesting. We found more effective access to statistical regularities in the bilateral DLPFC group compared to the sham group. Our results suggest that DLPFC suppression enhances the retrieval of statistical knowledge, particularly when interhemispheric compensatory mechanisms are prevented. These findings contribute to understanding competitive memory systems and offer implications for cognitive enhancement strategies.

neuroscience↗

bioRxiv2022

Gray matter cortical thickness predicts individual pain sensitivity: a multi-center machine learning approach

Pain sensitivity is known to considerably vary across individuals. While the variability in pain has been linked to structural neural correlates, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity utilising structural MRI-based cortical thickness data from a multi-center dataset (3 centers, 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearsons r = 0.36, p < 0.0005). The predictions were found to be specific to pain sensitivity and not biased towards potential confounding effects (e.g., anxiety, stress, depression, center-effects). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain. HighlightsO_LIWe present a robust, brain structure-based multi-center predictive model for pain sensitivity. C_LIO_LIOur model based on gray matter cortical thickness explains 13% of the variance in pain sensitivity and generalizes to out-of-center data. C_LIO_LIThe predictions are specific to pain sensitivity and not driven by potential confounders such as stress, depression, anxiety, and center-effects. C_LIO_LIThe most important predictors were rostral anterior cingulate cortex, parahippocampal gyrus and temporal pole, all negatively correlated with pain sensitivity. C_LI

neuroscience↗

bioRxiv2019

Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

Individual differences in pain perception are of key interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual susceptibility to pain is reflected in the pain-free resting-state brain activity and functional connectivity.\n\nHere, we identified and validated a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature (https://spisakt.github.io/RPN-signature) allows assessing the individual susceptibility to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have broad implications for translational research and the development and assessment of analgesic treatment strategies.

neuroscience↗

bioRxiv2018

Purkinje cell number-correlated cerebrocerebellar circuit anomaly in the valproate model of autism

While cerebellar alterations may play a crucial role in the development of core autism spectrum disorder (ASD) symptoms, their pathophysiology on the function of cerebrocerebellar circuit loops is largely unknown. We combined multimodal MRI (9.4 T) brain assessment of the prenatal rat valproate (VPA) model and correlated immunohistological analysis of the cerebellar Purkinje cell number to address this question. We hypothesized that a suitable functional MRI (fMRI) paradigm might show some altered activity related to disrupted cerebrocerebellar information processing. Two doses of maternal VPA (400 and 600 mg/kg, s.c.) were used, and while the higher VPA dose induced a global decrease in whole brain volume, the lower dose induced a focal gray matter density decrease in the cerebellum and brainstem. Increased cortical BOLD responses to whisker stimulation were detected in both VPA groups, but it was more pronounced and extended to cerebellar regions in the 400 mg/kg VPA group. Immunohistological analysis revealed a decreased number of Purkinje cells in both VPA groups. In a detailed analysis, we revealed that the Purkinje cell number interacts with the cerebral BOLD response distinctively in the two VPA groups that highlights atypical function of the cerebrocerebellar circuit loops with potential translational value as an ASD biomarker.

neuroscience↗