Predictive Neuroimaging Lab

The Predictive Neuroimaging Lab of the University Hospital Essen is an interdisciplinary junior research group led by Tamas Spisak.

Our research group focuses on predictive modelling in neuroimaging (functional and structural MRI and DTI) and aims to fuse the latest advances of machine learning and artificial intelligence into brain imaging research.

Brain Network Analysis

Exploit the rich information in structural and functional (dynamic) brain network data to build predictive models.

Methodological innovations

Develop novel statistical and feature engineering methods helping the fusion of machine learning techniques and neuroimaging.


Develop novel canditates for imaging-based and multi-modal composite biomarkers in the context of cognition, learning, treatment expectations, pain and placebo.

Research Products

Share and deploy predictive models and methodological developments as containeerized software tools (e.g. BIDS-apps)

Research & Development


Brain Wide Association Studies (BWAS)

BWAS replicability and sample size requirements



A resting state connectivity-based predictive signature of individual pain sensitivity.



A cortical thickness-based predictive signature of individual pain sensitivity.



Tools for analyzing and quantifying effects of counfounder variables on machine learning model predictions.


Pain-related learning signature

A functional connectivity signature of pain-related learning.



Probabilistic Threshold-Free Cluster Enhancement of Neuroimages.



A nipype-based modular pipeline development system focusing on connectivity-based predictive modelling.



Flexible modelling of spatial inaccuracies in individual activation maps with machine learning. In progress...


Motion Confounds in Predictive Modelling

Precise assesment of motion confounds in predictive modelling. In progress...


Palcebo Meta-Prediction

Meta-analysis and prediction of placebo response in collaboration with the Bingel-Lab and the placebo imaging collaboration. In progress...


(generated automatically based on PubMed RSS)

  • Multivariate BWAS can be replicable with moderate sample sizes in some cases

    Nature (accepted on 2022-12-15)

    Spisak T, Bingel U, Wager TD


    Brain-Wide Association Studies (BWAS) — which correlate individual differences in phenotypic traits with measures of brain structure and function — have become a dominant method for linking mind and brain over the past 30 years. Univariate BWAS typically test tens to hundreds of thousands of brain voxels individually, whereas 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 circuits. Multivariate predictive models address many of these concerns, and offer substantial promise for delivering brain-based measures of behavioral and clinical states and traits. In their recent paper4, Marek, Tervo-Clemmens (T-C) et al. evaluated the effects of sample size on univariate and multivariate BWAS in three large-scale neuroimaging datasets 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, and (b) multivariate BWAS reveal substantially larger effects and are therefore more highly powered. Marek, T-C, et al. find that multivariate BWAS provide “inflated in-sample associations” that often fail to replicate (i.e., are underpowered) unless thousands of participants are included. This implies that effect size estimates from the discovery sample are necessarily inflated. However, we distinguish between the effect size estimation method (in-sample vs. cross-validated) and the sample (discovery vs. replication), and show that with appropriate cross-validation the in-sample “inflation” Marek, T-C, et al. report in the discovery sample can be entirely eliminated. With additional analyses, we demonstrate that multivariate BWAS effects in high quality datasets can be replicable with substantially smaller sample sizes in some cases. Specifically, applying a standard multivariate prediction algorithm to functional connectivity in the Human Connectome Project yielded replicable effects with sample sizes of 75-500 for 5 of 6 phenotypes tested.

    DOI: N/A

  • Statistical quantification of confounding bias in machine learning models

    GigaScience 2022-08-26

    Spisak T


    Background The lack of nonparametric statistical tests for confounding bias significantly hampers the development of robust, valid, and generalizable predictive models in many fields of research. Here I propose the partial confounder test, which, for a given confounder variable, probes the null hypotheses of the model being unconfounded. Results The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in machine learning. Applying the proposed test on models trained on large-scale functional brain connectivity data (N= 1,865) (i) reveals previously unreported confounders and (ii) shows that state-of-the-art confound mitigation approaches may fail preventing confounder bias in several cases. Conclusions The proposed test (implemented in the package mlconfound; can aid the assessment and improvement of the generalizability and validity of predictive models and, thereby, fosters the development of clinically useful machine learning biomarkers.


  • Temporal dynamics of fMRI signal changes during conditioned interoceptive pain-related fear and safety acquisition and extinction

    Behavioural Brain Research, 2022-06-03

    Labrenz F, Spisak T, Ernst TM, Gomes CA, Quick HH, Axmacher N, Elsenbruch S, Timmann D


    Associative learning and memory mechanisms drive interoceptive signaling along the gut-brain axis, thus shaping affective-emotional reactions and behavior. Specifically, learning to predict potentially harmful, visceral pain is assumed to succeed within very few trials. However, the temporal dynamics of cerebellar and cerebral fMRI signal changes underlying early acquisition and extinction of learned fear signals and the concomitant evolvement of safety learning remain incompletely understood. 3 T fMRI data of healthy individuals from three studies were uniformly processed across the whole brain and the cerebellum. All studies employed differential delay conditioning (N = 94) with one visual cue (CS+) being repeatedly paired with visceral pain as unconditioned stimulus (US) while a second cue remained unpaired (CS-). During subsequent extinction (N = 51), all CS were presented without US. Behavioral results revealed increased CS+-aversiveness and CS--pleasantness after conditioning and diminished valence ratings for both CS following extinction. During early acquisition, the CS- induced linearly increasing neural activation in the insula, midcingulate cortex, hippocampus, precuneus as well as cerebral and cerebellar somatomotor regions. The comparison between acquisition and extinction phases yielded a CS--induced linear increase in the posterior cingulate cortex and precuneus during early acquisition, while there was no evidence for linear fMRI signal changes for the CS+ during acquisition and for both CS during extinction. Based on theoretical accounts of discrimination and temporal difference learning, these results suggest a gradual evolvement of learned safety cues that engage emotional arousal, memory, and cortical modulatory networks. As safety signals are presumably more difficult to learn and to discriminate from learned threat cues, the underlying temporal dynamics may reflect enhanced salience and prediction processing as well as increasing demands for attentional resources and the integration of multisensory information. Maladaptive responses to learned safety signals are a clinically relevant phenotype in multiple conditions, including chronic visceral pain, and can be exceptionally resistant to modification or extinction. Through sustained hypervigilance, safety seeking constitutes one key component in pain and stress-related avoidance behavior, calling for future studies targeting the mechanisms of safety learning and extinction to advance current cognitive-behavioral treatment approaches.


  • Resting-state functional heterogeneity of the right insula contributes to pain sensitivity

    Scientific Reports, 2021-11-25

    Veréb, D., Kincses B, Spisak T, Schlitt F, Szabó N, Faragó P, Kocsis K, Bozsik B, Tóth E, Király A, Zunhammer M, Schmidt-Wilcke T, Bingel U, Kincses ZT,


    Previous studies have described the structure and function of the insular cortex in terms of spatially continuous gradients. Here we assess how spatial features of insular resting state functional organization correspond to individual pain sensitivity. From a previous multicenter study, we included 107 healthy participants, who underwent resting state functional MRI scans, T1-weighted scans and quantitative sensory testing on the left forearm. Thermal and mechanical pain thresholds were determined. Connectopic mapping, a technique using non-linear representations of functional organization was employed to describe functional connectivity gradients in both insulae. Partial coefficients of determination were calculated between trend surface model parameters summarizing spatial features of gradients, modal and modality-independent pain sensitivity. The dominant connectopy captured the previously reported posteroanterior shift in connectivity profiles. Spatial features of dominant connectopies in the right insula explained significant amounts of variance in thermal (R2 = 0.076; p < 0.001 and R2 = 0.031; p < 0.029) and composite pain sensitivity (R2 = 0.072; p < 0.002). The left insular gradient was not significantly associated with pain thresholds. Our results highlight the functional relevance of gradient-like insular organization in pain processing. Considering individual variations in insular connectopy might contribute to understanding neural mechanisms behind pain and improve objective brain-based characterization of individual pain sensitivity.


  • Network properties and regional brain morphology of the insular cortex correlate with individual pain thresholds

    Scientific Reports, 2021-10-15

    Neumann L, Wulms N, Witte V, Spisak T, Zunhammer M, Ulrike Bingel, Tobias Schmidt-Wilcke


    Pain thresholds vary considerably across individuals and are influenced by a number of behavioral, genetic and neurobiological factors. However, the neurobiological underpinnings that account for individual differences remain to be fully elucidated. In this study, we used voxel-based morphometry (VBM) and graph theory, specifically the local clustering coefficient (CC) based on resting-state connectivity, to identify brain regions, where regional gray matter volume and network properties predicted individual pain thresholds. As a main finding, we identified a cluster in the left posterior insular cortex (IC) reaching into the left parietal operculum, including the secondary somatosensory cortex, where both regional gray matter volume and the local CC correlated with individual pain thresholds. We also performed a resting-state functional connectivity analysis using the left posterior IC as seed region, demonstrating that connectivity to the pre- as well as postcentral gyrus bilaterally; that is, to the motor and primary sensory cortices were correlated with individual pain thresholds. To our knowledge, this is the first study that applied VBM in combination with voxel-based graph theory in the context of pain thresholds. The co-location of the VBM and the local CC cluster provide first evidence that both structure and function map to the same brain region while being correlated with the same behavioral measure; that is, pain thresholds. The study highlights the importance of the posterior IC, not only for pain perception in general, but also for the determination of individual pain thresholds.


  • Deciphering the scopolamine challenge rat model by preclinical functional MRI.

    Scientific Reports, 2021-05-25

    Somogyi G, Hlatky D, Spisak T, Spisak Z, Nyitrai G, Czurkó A.


    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.


  • Meta-analysis of neural systems underlying placebo analgesia from individual participant fMRI data

    Nature Communications, 2021-03-02

    Zunhammer M, Spisak T, Wager TD, Bingel U; Placebo Imaging Consortium.


    The brain systems underlying placebo analgesia are insufficiently understood. Here we performed a systematic, participant-level meta-analysis of experimental functional neuroimaging studies of evoked pain under stimulus-intensity-matched placebo and control conditions, encompassing 603 healthy participants from 20 (out of 28 eligible) studies. We find that placebo vs. control treatments induce small, widespread reductions in pain-related activity, particularly in regions belonging to ventral attention (including mid-insula) and somatomotor networks (including posterior insula). Behavioral placebo analgesia correlates with reduced pain-related activity in these networks and the thalamus, habenula, mid-cingulate, and supplementary motor area. Placebo-associated activity increases occur mainly in frontoparietal regions, with high between-study heterogeneity. We conclude that placebo treatments affect pain-related activity in multiple brain areas, which may reflect changes in nociception and/or other affective and decision-making processes surrounding pain. Between-study heterogeneity suggests that placebo analgesia is a multi-faceted phenomenon involving multiple cerebral mechanisms that differ across studies.


  • Resection of cerebellar tumours causes widespread and functionally relevant white matter impairments

    Human Brain Mapping, 2021-01-07

    Gomes CA, Steiner KM, Ludolph N, Spisak T, Ernst TM, Mueller O, Göricke SL, Labrenz F, Ilg W, Axmacher N, Timmann D.


    Several diffusion tensor imaging studies reveal that white matter (WM) lesions are common in children suffering from benign cerebellar tumours who are treated with surgery only. The clinical implications of WM alterations that occur as a direct consequence of cerebellar disease have not been thoroughly studied. Here, we analysed structural and diffusion imaging data from cerebellar patients with chronic surgical lesions after resection for benign cerebellar tumours. We aimed to elucidate the impact of focal lesions of the cerebellum on WM integrity across the entire brain, and to investigate whether WM deficits were associated with behavioural impairment in three different motor tasks. Lesion symptom mapping analysis suggested that lesions in critical cerebellar regions were related to deficits in savings during an eyeblink conditioning task, as well as to deficits in motor action timing. Diffusion imaging analysis of cerebellar WM indicated that better behavioural performance was associated with higher fractional anisotropy (FA) in the superior cerebellar peduncle, cerebellum's main outflow path. Moreover, voxel-wise analysis revealed a global pattern of WM deficits in patients within many cerebral WM tracts critical for motor and non-motor function. Finally, we observed a positive correlation between FA and savings within cerebello-thalamo-cortical pathways in patients but not in controls, showing that saving effects partly depend on extracerebellar areas, and may be recruited for compensation. These results confirm that the cerebellum has extended connections with many cerebral areas involved in motor/cognitive functions, and the observed WM changes likely contribute to long-term clinical deficits of posterior fossa tumour survivors.


  • Effective connectivity differences in motor network during passive movement of paretic and non-paretic ankles in subacute stroke patients

    PeerJ, 2020-05-29

    Nagy M, Aranyi C, Opposits G, Papp T, Lánczi L, Berényi E, Vér C, Csiba L, Katona P, Spisak T, Emri M.


    A better understanding of the neural changes associated with paresis in stroke patients could have important implications for therapeutic approaches. Dynamic Causal Modeling (DCM) for functional magnetic resonance imaging (fMRI) is commonly used for analyzing effective connectivity patterns of brain networks due to its significant property of modeling neural states behind fMRI signals. We applied this technique to analyze the differences between motor networks (MNW) activated by continuous passive movement (CPM) of paretic and non-paretic ankles in subacute stroke patients. This study aimed to identify CPM induced connectivity characteristics of the primary sensory area (S1) and the differences in extrinsic directed connections of the MNW and to explain the hemodynamic differences of brain regions of MNW. For the network analysis, we used ten stroke patients’ task fMRI data collected under CPMs of both ankles. Regions for the MNW, the primary motor cortex (M1), the premotor cortex (PM), the supplementary motor area (SMA) and the S1 were defined in a data-driven way, by independent component analysis. For the network analysis of both CPMs, we compared twelve models organized into two model-families, depending on the S1 connections and input stimulus modeling. Using DCM, we evaluated the extrinsic connectivity strengths and hemodynamic parameters of both stimulations of all patients. After a statistical comparison of the extrinsic connections and their modulations of the “best model”, we concluded that three contralateral self-inhibitions (cM1, cS1 and cSMA), one contralateral inter-regional connection (cSMA→cM1), and one interhemispheric connection (cM1→iM1) were significantly different. Our research shows that hemodynamic parameters can be estimated with the Balloon model using DCM but the parameters do not change with stroke. Our results confirm that the DCM-based connectivity analyses combined with Bayesian model selection may be a useful technique for quantifying the alteration or differences in the characteristics of the motor network in subacute stage stroke patients and in determining the degree of MNW changes.


  • Brain MRI Diffusion Encoding Direction Number Affects Tract-Based Spatial Statistics Results in Multiple Sclerosis

    J Neuroimaging, 2020-05-25

    Kincses B, Spisak T, Faragó P, Király A, Szabó N, Veréb D, Kocsis K, Bozsik B, Tóth E, Vécsei L, Kincses ZT.


    BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) is a promising approach to detect the underlying brain pathology. These alterations can be seen in several diseases such as multiple sclerosis. Tract-based spatial statistics (TBSS) is an easy to use and robust way for analyzing diffusion data. The effect of acquisition parameters of DTI on TBSS has not been evaluated, especially the number of diffusion encoding directions (NDED), which is directly proportional with scan time. METHODS: We analyzed a large set of DTI data of healthy controls (N = 126) and multiple sclerosis patients (N = 78). The highest NDED (60 directions) was reduced and a tensor calculation was done separately for every subset. We calculated the mean and standard deviation of DTI parameters under the white matter mask. Moreover, the FMRIB Software Library TBSS pipeline was used on DTI images with 15, 30, 45, and 60 directions to compare differences between groups. Mean DTI parameters were compared between groups as a function of NDED. RESULTS: The mean value of FA and AD decreased with increasing number of directions. This was more pronounced in areas with smaller FA values. RD and MD were constant. The skeleton size reduced with elevating NDED along with the number of significant voxels. The TBSS analysis showed significant differences between groups throughout the majority of the skeleton and the group difference was associated with NDED. CONCLUSION: Our results suggested that results of TBSS depended on the NDED, which should be considered when comparing DTI data with varying protocols.

    PMID:32447822 | DOI:10.1111/jon.12705

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

    Nature Communications, 2020-01-12

    Spisak T, Kincses B, Schlitt F, Zunhammer M, Schmidt-Wilcke T, Kincses ZT, Bingel U.


    Individual differences in pain perception are of 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 sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate 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 allows assessing the individual sensitivity 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 implications for translational research and the development and assessment of analgesic treatment strategies.

    PMID:31924769 | PMC:PMC6954277 | DOI:10.1038/s41467-019-13785-z

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

    Scientific Reports, 2019-06-27

    Spisak T, Román V, Papp E, Kedves R, Sághy K, Csölle CK, Varga A, Gajári D, Nyitrai G, Spisák Z, Kincses ZT, Lévay G, Lendvai B, Czurkó A.


    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. The higher VPA dose induced 3% smaller whole brain volume, the lower dose induced 2% smaller whole brain volume and additionally 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.

    PMID:31239528 | PMC:PMC6592903 | DOI:10.1038/s41598-019-45667-1

  • Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power

    Neuroimage, 2018-10-09

    Spisak T, Spisak Z, Zunhammer M, Bingel U, Smith S, Nichols T, Kincses T.


    The threshold-free cluster enhancement (TFCE) approach integrates cluster information into voxel-wise statistical inference to enhance detectability of neuroimaging signal. Despite the significantly increased sensitivity, the application of TFCE is limited by several factors: (i) generalisation to data structures, like brain network connectivity data is not trivial, (ii) TFCE values are in an arbitrary unit, therefore, P-values can only be obtained by a computationally demanding permutation-test. Here, we introduce a probabilistic approach for TFCE (pTFCE), that gives a simple general framework for topology-based belief boosting. The core of pTFCE is a conditional probability, calculated based on Bayes' rule, from the probability of voxel intensity and the threshold-wise likelihood function of the measured cluster size. In this paper, we provide an estimation of these distributions based on Gaussian Random Field theory. The conditional probabilities are then aggregated across cluster-forming thresholds by a novel incremental aggregation method. pTFCE is validated on simulated and real fMRI data. The results suggest that pTFCE is more robust to various ground truth shapes and provides a stricter control over cluster "leaking" than TFCE and, in many realistic cases, further improves its sensitivity. Correction for multiple comparisons can be trivially performed on the enhanced P-values, without the need for permutation testing, thus pTFCE is well-suitable for the improvement of statistical inference in any neuroimaging workflow. Implementation of pTFCE is available at

    PMID:30296561 | PMC:PMC6834440 | DOI:10.1016/j.neuroimage.2018.09.078

  • Stepwise occlusion of the carotid arteries of the rat: MRI assessment of the effect of donepezil and hypoperfusion-induced brain atrophy and white matter microstructural changes

    PLoS One, 2018-06-01

    Nyitrai G, Spisak T, Spisak Z, Gajári D, Diószegi P, Kincses TZ, Czurkó A.


    Bilateral common carotid artery occlusion (BCCAo) in the rat is a widely used animal model of vascular dementia and a valuable tool for preclinical pharmacological drug testing, although the varying degrees of acute focal ischemic lesions it induces could interfere with its translational value. Recently, a modification to the BCCAo model, the stepwise occlusion of the two carotid arteries, has been introduced. To acquire objective translatable measures, we used longitudinal multimodal magnetic resonance imaging (MRI) to assess the effects of semi-chronic (8 days) donepezil treatment in this model, with half of the Wistar rats receiving the treatment one week after the stepwise BCCAo. With an ultrahigh field MRI, we measured high-resolution anatomy, diffusion tensor imaging, cerebral blood flow measurements and functional MRI in response to whisker stimulation, to evaluate both the structural and functional effects of the donepezil treatment and stepwise BCCAo up to 5 weeks post-occlusion. While no large ischemic lesions were detected, atrophy in the striatum and in the neocortex, along with widespread white matter microstructural changes, were found. Donepezil ameliorated the transient drop in the somatosensory BOLD response in distant cortical areas, as detected 2 weeks after the occlusion but the drug had no effect on the long term structural changes. Our results demonstrate a measurable functional MRI effect of the donepezil treatment and the importance of diffusion MRI and voxel based morphometry (VBM) analysis in the translational evaluation of the rat BCCAo model.

    PMID:29851990 | PMC:PMC5979036 | DOI:10.1371/journal.pone.0198265

  • Gray Matter Atrophy Is Primarily Related to Demyelination of Lesions in Multiple Sclerosis: A Diffusion Tensor Imaging MRI Study

    Frontiers in Neuroanatomy, 2017-04-21

    Tóth E, Szabó N, Csete G, Király A, Faragó P, Spisak T, Bencsik K, Vécsei L, Kincses ZT.


    Objective: Cortical pathology, periventricular demyelination, and lesion formation in multiple sclerosis (MS) are related (Hypothesis 1). Factors in the cerebrospinal fluid close to these compartments could possibly drive the parallel processes. Alternatively, the cortical atrophy could be caused by remote axonal transection (Hypothesis 2). Since MRI can differentiate between demyelination and axon loss, we used this imaging modality to investigate the correlation between the pattern of diffusion parameter changes in the periventricular- and deep white matter and the gray matter atrophy. Methods: High-resolution T1-weighted, FLAIR, and diffusion MRI images were acquired in 52 RRMS patients and 50 healthy, age-matched controls. We used EDSS to estimate the clinical disability. We used Tract Based Spatial Statistics to compare diffusion parameters (fractional anisotropy, mean, axial, and radial diffusivity) between groups. We evaluated global brain, white, and gray matter atrophy with SIENAX. Averaged, standard diffusion parameters were calculated in four compartment: periventricular lesioned and normal appearing white matter, non-periventricular lesioned and normal appearing white matter. PLS regression was used to identify which diffusion parameter and in which compartment best predicts the brain atrophy and clinical disability. Results: In our diffusion tensor imaging study compared to controls we found extensive alterations of fractional anisotropy, mean and radial diffusivity and smaller changes of axial diffusivity (maximal p > 0.0002) in patients that suggested demyelination in the lesioned and in the normal appearing white matter. We found significant reduction in total brain, total white, and gray matter (patients: 718.764 ± 14.968, 323.237 ± 7.246, 395.527 ± 8.050 cm3, controls: 791.772 ± 22.692, 355.350 ± 10.929, 436.422 ± 12.011 cm3; mean ± SE), (p < 0.015; p < 0.0001; p < 0.009; respectively) of patients compared to controls. The PLS analysis revealed a combination of demyelination-like diffusion parameters (higher mean and radial diffusivity in patients) in the lesions and in the non-lesioned periventricular white matter, which best predicted the gray matter atrophy (p < 0.001). Similarly, EDSS was best predicted by the radial diffusivity of the lesions and the non-lesioned periventricular white matter, but axial diffusivity of the periventricular lesions also contributed significantly (p < 0.0001). Interpretation: Our investigation showed that gray matter atrophy and white matter demyelination are related in MS but white matter axonal loss does not significantly contribute to the gray matter pathology.

    PMID:28424595 | PMC:PMC5372801 | DOI:10.3389/fnana.2017.00023

  • Central sensitization-related changes of effective and functional connectivity in the rat inflammatory trigeminal pain model

    Neuroscience, 2016-12-23

    Spisak T, Pozsgay Z, Aranyi C, Dávid S, Kocsis P, Nyitrai G, Gajári D, Emri M, Czurkó A, Kincses ZT.


    Central sensitization is a key mechanism in the pathology of several neuropathic pain disorders. We aimed to investigate the underlying brain connectivity changes in a rat model of chronic pain. Non-noxious whisker stimulation was used to evoke blood-oxygen-level-dependent (BOLD) responses in a block-design functional Magnetic Resonance Imaging (fMRI) experiment on 9.4T. Measurements were repeated two days and one week after injecting complete Freund's adjuvant into the rats' whisker pad. We found that acute pain reduced activation in the barrel cortex, most probably due to a plateau effect. After one week, increased activation of the anterior cingulate cortex was found. Analyses of effective connectivity driven by stimulus-related activation revealed that chronic pain-related central sensitization manifested as a widespread alteration in the activity of the somatosensory network. Changes were mainly mediated by the anterior cingulate cortex and the striatum and affected the somatosensory and motor cortices and the superior colliculus. Functional connectivity analysis of nested BOLD oscillations justified that the anterior cingular-somatosensory interplay is a key element of network changes. Additionally, a decreased cingulo-motor functional connectivity implies that alterations also involve the output tract of the network. Our results extend the knowledge about the role of the cingulate cortex in the chronification of pain and indicate that integration of multiple connectivity analysis could be fruitful in studying the central sensitization in the pain matrix.

    PMID:28003158 | DOI:10.1016/j.neuroscience.2016.12.018

  • Population-Level Correction of Systematic Motion Artifacts in fMRI in Patients with Ischemic Stroke

    J Neuroimaging, 2016-11-19

    Aranyi C, Opposits G, Nagy M, Berényi E, Vér C, Csiba L, Katona P, Spisak T, Emri M.


    BACKGROUND: The aim of this study was to reveal potential sources of systematic motion artifacts in stroke functional magnetic resonance imaging (fMRI) focusing on those causing stimulus-correlated motion on the individual-level and separate the motion effect on the fMRI signal changing from the activation-induced alteration at population level. METHODS: Eleven ischemic stroke patients were examined by fMRI. The fMRI paradigm was based on passive ankle movement on both the healthy and the paretic leg's side. Three individual-level motion correction strategies were compared and we introduced five measures to characterize each subjects' in-scanner relative head movement. After analyzing the correlation of motion parameters and the subjects' physiological scale scores, we selected a parameter to model the motion-related artifacts in the second-level analysis. RESULTS: At first (individual) level analysis, the noise-component correction-based CompCor method provided the highest -log10(p) value of cluster-level occurrence probability at 12.4/13.6 for healthy and paretic side stimulus, respectively, with a maximal z-value of 15/16.3. Including the motion parameter at second (group) level resulted in lower cluster occurrence values at 10.9/5.55 while retaining the maximal z-value. CONCLUSIONS: We proposed a postprocessing pipeline for ischemic stroke fMRI data that combine the CompCor correction at first level with the modeling of motion effect at second-level analysis by a parameter obtained from fMRI data. Our solution is applicable for any fMRI-based stroke rehabilitation study since it does not require any MRI-compatible motion capture system and is based on commonly used methods.

    PMID:27859975 | DOI:10.1111/jon.12408

  • The Effect of Passive Movement for Paretic Ankle-Foot and Brain Activity in Post-Stroke Patients

    Eur Neurol, 2016-09-01

    Vér C, Emri M, Spisak T, Berényi E, Kovács K, Katona P, Balkay L, Menyhárt L, Kardos L, Csiba L.


    BACKGROUND: This study aims at investigating the short-term efficacy of the continuous passive motion (CPM) device developed for the therapy of ankle-foot paresis and to investigate by fMRI the blood oxygen level-dependent responses (BOLD) during ankle passive movement (PM). METHODS: Sixty-four stroke patients were investigated. Patients were assigned into 2 groups: 49 patients received both 15 min manual and 30 min device therapy (M + D), while the other group (n = 15) received only 15 min manual therapy (M). A third group of stroke patients (n = 12) was investigated by fMRI before and immediately after 30 min CPM device therapy. There was no direct relation between the fMRI group and the other 2 groups. All subjects were assessed using the Modified Ashworth Scale (MAS) and a goniometer. RESULTS: Mean MAS decreased, the ankle's mean plantar flexion and dorsiflexion passive range of motion (PROM) increased and the equinovalgus improved significantly in the M + D group. In the fMRI group, the PM of the paretic ankle increased BOLD responses; this was observed in the contralateral pre- and postcentral gyrus, superior temporal gyrus, central opercular cortex, and in the ipsilateral postcentral gyrus, frontal operculum cortex and cerebellum. CONCLUSION: Manual therapy with CPM device therapy improved the ankle PROM, equinovalgus and severity of spasticity. The ankle PM increased ipsi- and contralateral cortical activation.

    PMID:27577057 | DOI:10.1159/000448033

  • Increased resting-state EEG functional connectivity in benign childhood epilepsy with centro-temporal spikes

    Seizure, 2016-01-23

    Clemens B, Puskás S, Spisak T, Lajtos I, Opposits G, Besenyei M, Hollódy K, Fogarasi A, Kovács NZ, Fekete I, Emri M.


    PURPOSE: To explore intrahemispheric, cortico-cortical EEG functional connectivity (EEGfC) in benign childhood epilepsy with rolandic spikes (BECTS). METHODS: 21-channel EEG was recorded in 17 non-medicated BECTS children and 19 healthy controls. 180s of spike- and artifact-free activity was selected for EEGfC analysis. Correlation of Low Resolution Electromagnetic Tomography- (LORETA-) defined current source density time series were computed between two cortical areas (region of interest, ROI). Analyses were based on broad-band EEGfC results. Groups were compared by statistical parametric network (SPN) method. Statistically significant differences between group EEGfC values were emphasized at p<0.05 corrected for multiple comparison by local false discovery rate (FDR). RESULTS: (1) Bilaterally increased beta EEGfC occurred in the BECTS group as compared to the controls. Greatest beta abnormality emerged between frontal and frontal, as well as frontal and temporal ROIs. (2) Locally increased EEGfC emerged in all frequency bands in the right parietal area. CONCLUSIONS: Areas of increased EEGfC topographically correspond to cortical areas that, based on relevant literature, are related to speech and attention deficit in BECTS children.

    PMID:26794010 | DOI:10.1016/j.seizure.2016.01.001

  • Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI

    PLoS One, 2014-09-05

    Spisak T, Jakab A, Kis SA, Opposits G, Aranyi C, Berényi E, Emri M.


    Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

    PMID:25188284 | PMC:PMC4154676 | DOI:10.1371/journal.pone.0104947

  • Uppermost synchronized generators of spike-wave activity are localized in limbic cortical areas in late-onset absence status epilepticus

    Seizure, 2013-12-28

    Piros P, Puskas S, Emri M, Opposits G, Spisak T, Fekete I, Clemens B.


    PURPOSE: Absence status (AS) epilepticus with generalized spike-wave pattern is frequently found in severely ill patients in whom several disease states co-exist. The cortical generators of the ictal EEG pattern and EEG functional connectivity (EEGfC) of this condition are unknown. The present study investigated the localization of the uppermost synchronized generators of spike-wave activity in AS. METHOD: Seven patients with late-onset AS were investigated by EEG spectral analysis, LORETA (Low Resolution Electromagnetic Tomography) source imaging, and LSC (LORETA Source Correlation) analysis, which estimates cortico-cortical EEGfC among 23 ROIs (regions of interest) in each hemisphere. RESULTS: All the patients showed generalized ictal EEG activity. Maximum Z-scored spectral power was found in the 1-6 Hz and 12-14 Hz frequency bands. LORETA showed that the uppermost synchronized generators of 1-6 Hz band activity were localized in frontal and temporal cortical areas that are parts of the limbic system. For the 12-14 Hz band, abnormally synchronized generators were found in the antero-medial frontal cortex. Unlike the rather stereotyped spectral and LORETA findings, the individual EEGfC patterns were very dissimilar. CONCLUSION: The findings are discussed in the context of nonconvulsive seizure types and the role of the underlying cortical areas in late-onset AS. The diversity of the EEGfC patterns remains an enigma. Localizing the cortical generators of the EEG patterns contributes to understanding the neurophysiology of the condition.

    PMID:24370317 | DOI:10.1016/j.seizure.2013.11.017

  • Autistic traits in neurotypical adults: correlates of graph theoretical functional network topology and white matter anisotropy patterns

    PLoS One, 2013-04-18

    Jakab A, Emri M, Spisak T, Szeman-Nagy A, Beres M, Kis SA, Molnar P, Berenyi E.


    Attempts to explicate the neural abnormalities behind autism spectrum disorders frequently revealed impaired brain connectivity, yet our knowledge is limited about the alterations linked with autistic traits in the non-clinical population. In our study, we aimed at exploring the neural correlates of dimensional autistic traits using a dual approach of diffusion tensor imaging (DTI) and graph theoretical analysis of resting state functional MRI data. Subjects were sampled from a public neuroimaging dataset of healthy volunteers. Inclusion criteria were adult age (age: 18-65), availability of DTI and resting state functional acquisitions and psychological evaluation including the Social Responsiveness Scale (SRS) and Autistic Spectrum Screening Questionnaire (ASSQ). The final subject cohort consisted of 127 neurotypicals. Global brain network structure was described by graph theoretical parameters: global and average local efficiency. Regional topology was characterized by degree and efficiency. We provided measurements for diffusion anisotropy. The association between autistic traits and the neuroimaging findings was studied using a general linear model analysis, controlling for the effects of age, gender and IQ profile. Significant negative correlation was found between the degree and efficiency of the right posterior cingulate cortex and autistic traits, measured by the combination of ASSQ and SRS scores. Autistic phenotype was associated with the decrease of whole-brain local efficiency. Reduction of diffusion anisotropy was found bilaterally in the temporal fusiform and parahippocampal gyri. Numerous models describe the autistic brain connectome to be dominated by reduced long-range connections and excessive short-range fibers. Our finding of decreased efficiency supports this hypothesis although the only prominent effect was seen in the posterior limbic lobe, which is known to act as a connector hub. The neural correlates of the autistic trait in neurotypicals showed only limited similarities to the reported findings in clinical populations with low functioning autism.

    PMID:23593367 | PMC:PMC3618514 | DOI:10.1371/journal.pone.0060982


Our Team

Dr. Tamas Spisak, PhD

junior research group leader

Head of the PNI-Lab

Dr. Balint Kincses, MD, PhD

PostDoc (also affiliated with the Bingel Lab)

physicist & physician

Dr. Raviteja Kotikalapudi, PhD

PostDoc (from March 2021)

physics and computing in medicine and biology

Jialin Li, MSc

PhD Candidate (affiliated to the Max Planck School of Cognition)


Robert Englert, MSc

PhD student

electrical/medical engineer

Giuseppe Gallitto, MSc

PhD student

psychology (Neuroscience)

Evgenij Knorr

IT technician

IT Specialist

Kevin Hoffschlag

student assistant

computer science (BSc in progress)

Mohamad Abouras

student assistant

computer science (BSc in progress)

Maria Berjano

student assistant (on lab rotation)

computer science (MSc in progress)

Advisory Board

Latest Posts


PhD-position - EXPIRED

PhD position - EXPIRED We are currently seeking a PhD student (m/f/d)** in the field of “Machine learning / Deep learning for Neuroimaging”

Read more
View All Post


Contact Details

Predictive NeuroImaging Lab

Institut for Artificial Intelligence in Medicine

Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany

Predictive Neuroimaging Lab
    Büro "SHIP" (Smart Hospital Information Platform) / Universitätsmedizin
    Girardethaus, Girardetstr. 6, 45131, Essen, Germany
Phone: +49 201 / 723-6379