Biology subjects

Kincses, B.

Publications and source records attributed to Kincses, B..

10 recordsLinked to original sources

bioRxiv2026

Beyond pain: a brain-based biomarker predicts individual pain relief

Effective pain relief is a central goal of medical care, yet objective biomarkers of pain relief are lacking. Using task-based functional MRI and a capsaicin-induced tonic heat pain model, we experimentally elicited both pain exacerbation and pain relief within the same individuals. Existing brain-based signatures, including the Neurologic Pain Signature (NPS), reliably detected pain increases, but failed to capture pain relief. We therefore developed the PAin and RElief Signature (PARES), a multivariate brain signature trained to predict bidirectional changes in pain perception in n = 61 healthy controls. PARES robustly predicted both pain increases and relief and generalized to an independent cohort of people with chronic back pain (n = 58), who underwent the same experimental procedures. Together these findings establish a neural signature of pain relief and provide a potential biomarker for treatment stratification and analgesic development.

neuroscience↗

bioRxiv2026

Reinforcement Learning via Brain Feedback for real-time fMRI-based adaptive stimulus generation

Traditional fMRI studies rely on predefined task paradigms, where fixed stimulus designs limit the flexibility with which brain-stimulus relationships can be explored. Here, we introduce Reinforcement Learning via Brain Feedback (RLBF), a framework and open-source software package for adaptive stimulus optimization using real-time fMRI. RLBF reverses the conventional direction of inference by using neural responses to guide the exploration of stimulus spaces through reinforcement learning, enabling optimization of predefined brain targets such as regional activity or multivariate neural signatures. The accompanying Python-based software provides a modular framework integrating real-time fMRI data processing, reinforcement learning agents, adaptive stimulus generation, simulation-based testing, and experiment monitoring. Its flexible architecture allows researchers to customize preprocessing pipelines, reward functions, stimulus spaces, and RL strategies for diverse closed-loop neuroimaging applications. We validate the framework in a proof-of-concept study (N=10), demonstrating real-time optimization of a simple visual stimulus space by adapting checkerboard contrast and frequency to maximize primary visual cortex (V1) responses within a single 10-minute fMRI session. RLBF provides an extensible foundation for brain-guided stimulus optimization and enables new approaches for investigating neural specificity, individualized brain-stimulus relationships, and adaptive experimental design.

bioinformatics↗

bioRxiv2026

Layer-specific cortical processing dissociates sensory and cognitive influences on pain

Pain arises from the integration of nociceptive input with cognitive and affective processes, yet how these signals are organized within cortical circuits remains unclear. Here, using submillimeter-resolution 7T fMRI, we tested whether bottom-up (BU) and top-down (TD) influences on pain are segregated across cortical layers in human sensory-discriminative regions. Participants underwent a factorial manipulation of nociceptive input and cognitive modulation, enabling dissociation of BU and TD processes. BU processing was strongest in middle cortical layers. In contrast, TD modulation preferentially engaged superficial layers, consistent with cortico-cortical feedback mechanisms. Critically, individual differences in this laminar segregation predicted the magnitude of distraction-induced analgesia, linking circuit-level organization to behavior. These findings provide evidence that cognitive modulation of pain is implemented through layer-specific cortical computations and extend canonical microcircuit models to human pain processing. More generally, they establish laminar fMRI as a powerful approach for linking cortical circuit architecture to subjective experience.

neuroscience↗

bioRxiv2025

Meta-analytic evidence for distinct neural correlates of conditioned vs. verbally induced placebo analgesia

Placebo analgesia demonstrates that belief and expectation can significantly alter pain, even without active treatment. Placebo analgesia can be induced through verbal suggestion, classical conditioning, or their combination, though the role of conditioned neural responses above and beyond effects of verbal instructions remains unclear. We conducted a systematic meta-analysis of individual participant data from 16 within-participant placebo neuroimaging studies (n = 409), employing univariate and multivariate analyses to identify shared and distinct mechanisms of placebo analgesia induced by suggestions alone versus suggestions combined with conditioning. Both techniques increased activity during pain in the dorsolateral prefrontal and inferior parietal cortices and decreased activation in the insula, putamen, and primary sensory areas. Adding conditioning enhanced engagement of regions associated with context representation and pain modulation (e.g., dorsolateral/dorsomedial prefrontal cortices) and decreases in nociceptive regions (e.g., primary sensory and insular areas). Conditioning also strengthened the association between analgesia and nociceptive activity, as reflected in the Neurologic Pain Signature. Combining conditioning with instructions yielded greater analgesia, mediated by increased ventromedial prefrontal and dorsal caudate activity, alongside decreased sensory-nociceptive and cerebellar activity. These findings suggest the two strategies rely on partially distinct mechanisms, which could be combined to optimize placebo analgesias clinical application.

neuroscience↗

bioRxiv2024

On the replicability of diffusion weighted MRI-based brain-behavior models

Establishing replicable inter-individual brain-wide associations is key to advancing our understanding of the crucial links between brain structure, function, and behavior, as well as applying this knowledge in clinical contexts. While the replicability and sample size requirements for anatomical and functional MRI-based brain-behavior associations have been extensively discussed recently, systematic replicability assessments are still lacking for diffusion-weighted imaging (DWI), despite it being the dominant non-invasive method to investigate white matter microstructure and structural connectivity. We report results of a comprehensive evaluation of the replicability of various DWI-based multivariate brain-behavior models. This evaluation is based on large-scale data from the Human Connectome Project, including five different DWI-based brain features (from fractional anisotropy to structural connectivity) and 58 different behavioral phenotypes. Our findings show an overall moderate replicability, with 24-31% of phenotypes replicable with sample sizes of fewer than 500. As DWI yields trait-like brain features, we restricted the analysis to trait-like phenotypes, such as cognitive and motor skills, and found much more promising replicability estimates, with 67-75% of these phenotypes replicable with n<500. Contrasting our empirical results to analytical replicability estimates substantiated that the replicability of DWI-based models is primarily a function of the true, unbiased effect size. Our work highlights the potential of DWI to produce replicable brain-behavior associations. However, it shows that achieving replicability with small-to-moderate samples requires stable, reliable and neurobiologically relevant target phenotypes. Our work highlights the potential of DWI to produce replicable brain-behavior associations, but only for stable, reliable and neurobiologically relevant target phenotypes. HIGHLIGHTSO_LIModerate replicability in DWI-based models: Overall replicability of DWI-based brain-behavior associations ranges from 24-31% with sample sizes under 500. C_LIO_LIImproved replicability for trait-like phenotypes: Trait-like phenotypes e.g., cognitive and motor skills exhibit higher replicability estimates of 67-75%, compared to state-like phenotypes such as emotion. C_LIO_LIEffect size as a key factor: Replicability is primarily influenced by the true, unbiased effect size, highlighting the importance of targeting stable and reliable phenotypes. C_LIO_LIPromise of -based multivariate associations: DWI-based brain-behaviour models should focus on phenotypes that display a sufficient temporal stability and test-retest reliability. C_LI

neuroscience↗

bioRxiv2023

External validation of machine learning models - registered models and adaptive sample splitting

Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data pre-processing and feature engineering pipelines can lead to overfitting and poor generalizability. An unbiased evaluation of predictive models necessitates external validation, which involves testing the finalized model on independent data. Despite its importance, external validation is often neglected in practice due to the associated costs. Here we propose that, for maximal credibility, model discovery and external validation should be separated by the public disclosure (e.g. pre-registration) of feature processing steps and model weights. Furthermore, we introduce a novel approach to optimize the trade-off between efforts spent on training and external validation in such studies. We show on data involving more than 3000 participants from four different datasets that, for any "sample size budget", the proposed adaptive splitting approach can successfully identify the optimal time to stop model discovery so that predictive performance is maximized without risking a low powered, and thus inconclusive, external validation. The proposed design and splitting approach (implemented in the Python package "AdaptiveSplit") may contribute to addressing issues of replicability, effect size inflation and generalizability in predictive modeling studies.

bioinformatics↗

bioRxiv2023

Connectome-Based Attractor Dynamics Guide Brain Activity in Rest, Task, and Disease

Functional brain connectivity has been instrumental in uncovering the large-scale organization of the brain and its relation to various behavioral and clinical phenotypes. Understanding how this functional architecture relates to the brains dynamic activity repertoire is an essential next step towards interpretable generative models of brain function. We propose functional connectivity-based Attractor Neural Networks (fcANNs), a theoretically inspired model of macro-scale brain dynamics, simulating recurrent activity flow among brain regions based on first principles of self-organization. In the fcANN framework, brain dynamics are understood in relation to attractor states; neurobiologically meaningful activity configurations that minimize the free energy of the system. We provide the first evidence that large-scale brain attractors - as reconstructed by fcANNs - exhibit an approximately orthogonal organization, which is a signature of the self-orthogonalization mechanism of the underlying theoretical framework of free-energy-minimizing attractor networks. Analyses of 7 distinct datasets demonstrate that fcANNs can accurately reconstruct and predict brain dynamics under a wide range of conditions, including resting and task states, and brain disorders. By establishing a formal link between connectivity and activity, fcANNs offer a simple and interpretable computational alternative to conventional descriptive analyses. Key PointsO_LIWe present a simple yet powerful generative computational model for large-scale brain dynamics C_LIO_LIBased on the theory of artificial attractor neural networks emerging from first principles of self-organization C_LIO_LIModel dynamics accurately reconstruct several characteristics of resting-state brain dynamics and confirm theoretical predictions of emergent attractor self-orthogonalization C_LIO_LIOur model captures both task-induced and pathological changes in brain activity C_LIO_LIfcANNs offer a simple and interpretable computational alternative to conventional descriptive analyses of brain function C_LI Project website (with interactive manuscript)https://pni-lab.github.io/connattractor

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↗

bioRxiv2019

Optimal choice of parameters in functional connectome-based predictive modelling might be biased by motion: comment on Dadi et al.

In a recent study, Dadi and colleagues make recommendations on optimal parameters for functional connectome-based predictive models. While the authors acknowledge that \"optimal choices of parameters will differ on datasets with very different properties\", some questions regarding the universality of the recommended \"default values\" remain unanswered.\n\nNamely, as already briefly discussed by Dadi et al., the datasets used in the target study might not be representative regarding the sparsity of the (hidden) ground truth (i.e. the number of non-informative connections), which might affect the performance of L1- and L2-regularization approaches and feature selection.\n\nHere we exemplify that, at least in one of the investigated datasets systematic motion artefacts might bias the discriminative signal towards \"non-sparsity\", which might lead to underestimating the performance of L1-regularized models and feature selection.\n\nWe conclude that the expected sparsity of the discriminative signal should be carefully considered when planning predictive modelling workflows and the neuroscientific validity of predictive models should be investigated to account for non-neural confounds.

bioinformatics↗