Biology subjects

Grundei, M.

Publications and source records attributed to Grundei, M..

4 recordsLinked to original sources

bioRxiv2026

Time- and performance-dependent enhancement of tactospatial working memory caused by online anodal tDCS of the posterior parietal cortex

BackgroundSpatial working memory (WM) relies on posterior parietal cortex (PPC) within a distributed fronto-parietal network, yet whether online anodal transcranial direct current stimulation (tDCS) of PPC modulates tactile WM, and how behavioral effects unfold over time, remains unclear. ObjectiveWe tested whether online anodal tDCS over left PPC modulates performance in a tactospatial WM task and whether stimulation effects vary over time and as a function of baseline performance. MethodsIn a double-blind, sham-controlled, within-subject crossover design, 32 healthy adults completed two counterbalanced sessions (Active, Sham). Each session comprised baseline, stimulation, and post-stimulation phases while participants performed a retro-cued delayed match-to-sample task with vibrotactile patterns delivered to the left index finger. During the stimulation phase, participants received either 2 mA anodal or sham tDCS over left PPC for 15 min, depending on session. Behavioral accuracy was analyzed using a sliding-window approach combined with cluster-based permutation testing. ResultsActive stimulation induced a significant, gradual improvement in WM accuracy relative to sham, emerging approximately eight minutes after stimulation onset and corresponding to a [~]5% performance increase (Hedges g = 0.43). Stimulation effects were baseline-dependent, with individuals showing lower initial WM performance exhibiting larger behavioral gains, whereas high performers showed minimal benefits. ConclusionsThese findings show that online anodal stimulation of left PPC can modulate tactile WM performance in a time- and baseline-dependent manner. More broadly, the results suggest that behavioral effects of parietal tDCS emerge gradually during ongoing stimulation and depend on initial cognitive state. HighlightsO_LIOnline anodal PPC-tDCS enhances tactile working memory C_LIO_LIBehavioral gains emerge after [~]8 min of stimulation C_LIO_LIActive stimulation improves accuracy by [~]5% versus sham C_LIO_LILower baseline performers show larger stimulation benefits C_LIO_LISliding-window analyses capture dynamic online tDCS effects C_LI

neuroscience↗

bioRxiv2026

Retest Reliability of Task-related fMRI BOLD Signals during Sequential Decision Making

IntroductionFunctional magnetic resonance imaging (fMRI) is widely used to study neural processes of behavior, but evaluations of test-retest reliability (TRR) of task-related blood-oxygen-level-dependent (BOLD) responses are scarce for many cognitive tasks. Such information is particularly important for longitudinal and intervention research. The ability to learn associations between choices and outcomes across decision stages is crucial for daily behavior. We assessed the measurement reliability of behavioral performance and fMRI BOLD signals during value-based sequential decision making to evaluate the TRR of task-relevant regions for future research on non-invasive brain stimulations. MethodsTwenty healthy adults (22 to 40 years) completed two task-fMRI sessions, at least 2 weeks apart. During scanning, participants performed two variants of a three-stage Markov decision task with conditions varied in temporal contingency (immediate vs. delayed) and magnitude of choice outcomes (high vs. low). Both sessions were conducted under sham tDCS via a focal 3 x 1 montage targeting left dorsolateral prefrontal cortex (DLPFC). TRR was assessed using intraclass correlation coefficients (ICC) with a two-way mixed-effects consistency model for decision performance and task-related fMRI signals at voxel level and summarized in key regions defined by the extended Human Connectome Project atlas (HCPex). ResultsDecision performance was lower with delayed than immediate outcomes (p < 0.001). Higher outcome magnitude improved performance (p < 0.001). Decision performance increased across learning bins (p < 0.001). The behavioral TRR was in the moderate to good level (ICC(3,1) = 0.744 for accuracy; ICC(3,1) = 0.788 for reaction time). At the whole-brain level, contrasting brain activities in delayed with immediate condition revealed suprathreshold cluster peaks in several frontal-parietal (e.g., orbitofrontal, bilateral dorsolateral prefrontal, and medial parietal cortices) and striatal regions (e.g., bilateral putamen). Voxel-wise ICCs revealed variable but often moderate-to-good TRR across task-relevant regions, with stronger reliability in several striatal, orbitofrontal, and left dorsolateral prefrontal parcels, and more variable reliability across anterior cingulate and medial prefrontal parcels. ConclusionThese results from a 2-session tDCS sham-sham stimulation study establish the validity of using the three-stage Markov decision task in future studies about intervention effects on the frontal-parietal-striatal network.

neuroscience↗

bioRxiv2022

EEG mismatch responses in a multi-modal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation and vision.

The human brain is constantly subjected to a multi-modal stream of probabilistic sensory inputs. EEG signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni- and cross-modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri-modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by uni-modal transition probabilities and cross-modal conditional dependencies. We found modality specific signatures of MMN ([~]100-200ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized pre-frontal sources. Additionally, we identified a cross-modal signature of mismatch processing in the P3a time range ([~]300-350ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, the P3a responses across modalities were increased for mispredicted compared to predicted and unpredictable stimuli, suggesting sensitivity to cross-modal predictive information. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking uni-modal stimulus transitions as well as cross-modal conditional dependencies.

neuroscience↗

bioRxiv2020

Neural surprise in somatosensory Bayesian learning

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Primary somatosensory cortex is found to encode Bayesian surprise, reflecting model updating, from around 140ms. As such, this dissociation indicates that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its precise mechanisms. Author summaryOur environment features statistical regularities, such as a drop of rain predicting imminent rainfall. Despite the importance for behavior and survival, much remains unknown about how these dependencies are learned, particularly for somatosensation. As surprise signalling about novel observations indicates a mismatch between ones beliefs and the world, it has been hypothesized that surprise computation plays an important role in perceptual learning. By analyzing EEG data from human participants receiving sequences of tactile stimulation, we compare different formulations of surprise and investigate the employed underlying learning model. Our results indicate that the brain estimates transitions between observations. Furthermore, we identified different signatures of surprise computation and thereby provide a dissociation of the neural correlates of belief inadequacy and belief updating. Specifically, early surprise responses from around 70ms were found to signal the need for changes to the model, with encoding of its subsequent updating occurring from around 140ms. These results provide insights into how somatosensory surprise signals may contribute to the learning of environmental statistics.

neuroscience↗