Biology subjects

Spisak, T.

Publications and source records attributed to Spisak, T..

16 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↗

bioRxiv2026

Cerebellar activation in human placebo analgesia: Bridging findings from mice to humans

Placebo analgesia has traditionally been explained by top-down cortical regulation of brainstem and spinal pathways. Recent circuit-level work in animal models identified a rostral anterior cingulate-pontine-cerebellar pathway that contributes to expectation-based analgesia, implicating cerebellum circuits in placebo effects1. Building on these findings, we examined pontine and cerebellar contributions within a large individual-participant meta-analysis of human neuroimaging studies of placebo analgesia2 (n = 603). We found that the effects of human pain and placebo converge in cerebellar territories embedded in higher-order cognitive3,4 and action-mode networks5. These regions exhibit placebo-induced anticipatory increases and reduced responses during painful stimulation, which correlate with the magnitude of placebo analgesia, consistent with predictive configuration of the system. Pontine responses also correlate with individual differences in placebo analgesia. In independent Human Connectome Project data (n = 820), pontine activity is functionally connected with cingulate and cerebellar regions implicated in placebo analgesia. Together, these findings support a model in which expectation effects are implemented via predictive configuration of a cortico-pontine-cerebellar system.

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↗

bioRxiv2025

Predicting individual differences of fear and cognitive learning and extinction

The abilities to acquire new information and to modify previously learned knowledge are critical in an ever-changing world. However, the efficacy of learning is notably variable among individuals, with extinction learning being the epitome of such variability. Abundant studies have identified a core network of brain regions including amygdala, hippocampus, dorsal anterior cingulate cortex (ACC), ventromedial prefrontal cortex (PFC) and, more recently, the cerebellum, as key players in learning and extinction. Yet, the precise interactions within this network and their relationship to individual learning abilities and extinction have remained largely unexplored. In the present study, we examined how functional (FC), effective (EC), and structural (SC) connectivity patterns in the core learning network allow predicting individual differences in the efficacy of learning, extinction, and renewal. Analysing a large dataset of over 500 participants across a multitude of paradigms, our results revealed that FC predicted better acquisition, with a central role of ACC and hippocampus, whereas SC, involving ACC and amygdala, predicted higher levels of extinction learning. EC results suggested a predominantly inhibitory coupling among core learning network nodes, with paradigm-specific EC connectivity patterns predicting learning. Our predictions not only generalised between fear and cognitive predictive learning paradigms but were also successful in predicting learning from task-related FC and simulated data. Together, these results describe the multimodal neural determinants of learning, extinction, and renewal, and may inform individualised interventions for affective disorders based on neural connectivity patterns.

neuroscience↗

bioRxiv2025

Common and distinct neural mechanisms of aversive and appetitive pain-related learning

Appetitive and aversive conditioning are both fundamental to adaptive behaviour, yet there remains limited understanding of how they differ on the behavioural and neural level. We investigated the two processes during acquisition and extinction using functional magnetic resonance imaging and behavioural measures. In a within-subject differential conditioning paradigm (preregistration DRKS00027448), aversive learning was induced by pairing visual cues with a temperature increase (pain rise), while appetitive learning involved a temperature decrease (pain reduction). Valence and contingency ratings confirmed successful learning for both types of learning, though only the appetitive condition showed a return to baseline ratings during extinction, suggesting incomplete extinction in the aversive condition. On the neural level, both engaged the visual cortex during acquisition (with increased functional connectivity with the right frontal operculum) and the ventromedial prefrontal cortex (vmPFC) during extinction. However, aversive learning showed a stronger activation increase in the mediodorsal thalamus with heightened connectivity with the locus coeruleus during acquisition, as well as sustained parahippocampal activity during extinction. Moreover, incomplete extinction in the aversive condition (as indicated by contingency ratings) was associated with sustained activity in the visual cortex during pain anticipation. These results suggest that while appetitive and aversive learning share activation in regions involved in sensory processing (occipital lobe) and learning (vmPFC), aversive learning uniquely engages areas promoting rapid acquisition (mediodorsal thalamus and locus coeruleus) and cautious unlearning, in line with the notion of a better-safe-than-sorry strategy.

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

Replicable multivariate BWAS with moderate sample sizes

Brain-Wide Association Studies (BWAS) have become a dominant method for linking mind and brain over the past 30 years. Univariate models test tens to hundreds of thousands of brain voxels individually, whereas multivariate models ( multivariate BWAS) integrate signals across brain regions into a predictive model. Numerous problems have been raised with univariate BWAS, including lack of power and reliability and an inability to account for pattern-level information embedded in distributed neural circuits1-3. Multivariate predictive models address many of these concerns, and offer substantial promise for delivering brain-based measures of behavioral and clinical states and traits2,3. In their recent paper4, Marek et al. evaluated the effects of sample size on univariate and multivariate BWAS in three large-scale neuroimaging dataset and came to the general conclusion that "BWAS reproducibility requires samples with thousands of individuals". We applaud their comprehensive analysis, and we agree that (a) large samples are needed when conducting univariate BWAS of individual differences in trait measures, and (b) multivariate BWAS reveal substantially larger effects and are therefore more highly powered. However, we disagree with Marek et al.s claims that multivariate BWAS provide "inflated in-sample associations" that often fail to replicate (i.e., are underpowered), and that multivariate BWAS consequently require thousands of participants when predicting trait-level individual differences. Here we substantiate that (i) with appropriate methodology, the reported in-sample effect size inflation in multivariate BWAS can be entirely eliminated, and (ii) in most cases, multivariate BWAS effects are replicable with substantially smaller sample sizes (Figure 1). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/497072v1_fig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@180764borg.highwire.dtl.DTLVardef@d64a2forg.highwire.dtl.DTLVardef@a0865aorg.highwire.dtl.DTLVardef@d4b14c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Multivariate BWAS provide unbiased effect sizes and high replicability with low-moderate sample sizes. (a) In-sample effects in multivariate BWAS are only inflated if estimates are obtained without cross-validation. (b) Cross-validation fully eliminates in-sample effect size inflation and, as a consequence, provides higher replicability. Each point in (a) and (b) corresponds to one bootstrap subsample, as in Fig. 4b of Marek et al. Dotted lines denote the threshold for p=0.05 with n=495. (c) The inflation of in-sample effect size obtained without cross-validation (red) is reduced, but does not disappear, at higher sample sizes. Conversely, cross-validated estimates (blue) are slightly pessimistic with low sample sizes and become quickly unbiased as sample size is increased. (d) Without cross-validation, in-sample effect size estimates are non-zero (r{approx}0.5, red) even when predicting permuted outcome data. Cross-validation eliminates systematic bias across all sample sizes (blue). Dashed lines in (c) and (d) denote 95% parametric confidence intervals, and shaded areas denote bootstrap and permutation-based confidence intervals. (e-f) Cross-validated analysis reveals that sufficient in-sample power (e) and out-of-sample replication probability (P(rep)) (f) can be achieved for a variety of phenotypes at low or moderate sample sizes. 80% power and P(rep) are achievable in <500 participants for half the phenotypes tested (colored bars) using the prediction algorithm in Marek et al. (top panels in (e) and (f), sample size required for 80% power or P(rep) shown). Other phenotypes require sample sizes >500 (bars with arrows). Power and P(rep) can be substantially improved with a ridge regression-based model recommended in some comparison studies10,11 (bottom panels in (e) and (f)), with 80% power and P(rep) with sample sizes as low as n=100 and n=75, respectively, when predicting cognitive ability, and sample sizes between 75 and 375 for other investigated variables, except inhibition assessed with the flanker task. (g) We estimated interactions between sample size and publication bias by computing effect size inflation (rdiscovery - rreplication) only for those bootstrap cases where prediction performance was significant (p>0.05) in the replication sample. Our analysis shows that the effect size inflation due to publication bias is modest (<10%) with <500 participants for half the phenotypes using the Marek et al. model and all phenotypes but the flanker using the ridge model. C_FIG

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↗

bioRxiv2020

Deciphering the scopolamine challenge rat model by preclinical functional MRI

During preclinical drug testing, the systemic administration of scopolamine (SCO), a cholinergic antagonist, is widely used. However, it suffers important limitations, like non-specific behavioural effects partly due to its peripheral side-effects. Therefore, neuroimaging measures would enhance its translational value. To this end, in Wistar rats, we measured whisker-stimulation induced functional MRI activation after SCO, peripherally acting butylscopolamine (BSCO), or saline administration in a cross-over design. Besides the commonly used gradient-echo echo-planar imaging (GE EPI), we also used an arterial spin labeling method in isoflurane anesthesia. With the GE EPI measurement, SCO decreased the evoked BOLD response in the barrel cortex (BC), while BSCO increased it in the anterior cingulate cortex. In a second experiment, we used GE EPI and spin-echo (SE) EPI sequences in a combined (isoflurane + i.p. dexmedetomidine) anesthesia to account for anesthesia-effects. Here, we also examined the effect of donepezil. In the combined anesthesia, with the GE EPI, SCO decreased the activation in the BC and the inferior colliculus (IC). BSCO reduced the response merely in the IC. Our results revealed that SCO attenuated the evoked BOLD activation in the BC as a probable central effect in both experiments. The likely peripheral vascular actions of SCO with the given fMRI sequences depended on the type of anesthesia or its dose.

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↗

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↗