Biology subjects

Krekelberg, B.

Publications and source records attributed to Krekelberg, B..

6 recordsLinked to original sources

bioRxiv2026

α-tACS Modulates Reward-Dependent Pupil Responses and Corticostriatal Connectivity

IntroductionNoninvasive brain stimulation can help clarify the neural basis of reward processing and potentially inform treatments for disorders involving reward dysfunction. However, widely used methods such as transcranial magnetic and electrical stimulation cannot directly stimulate deep-brain regions like the striatum. Here, we tested whether stimulating the ventrolateral prefrontal cortex (VLPFC)--a cortical region strongly connected to the striatum-- could indirectly influence reward-related neural and physiological responses. MethodsIn a within-subjects design, participants performed a card-guessing task involving monetary rewards for correct guesses and punishments for incorrect guesses. During the task, participants underwent functional magnetic resonance imaging (fMRI) and pupillometry while receiving concurrent 10 Hz transcranial alternating current stimulation (-tACS). Stimulation targeted either the VLPFC or a control region (temporoparietal junction). We measured pupil dilation, brain activation (BOLD signal), and functional connectivity between the ventral striatum and dorsal anterior cingulate cortex (VS-dACC). ResultsVLPFC stimulation increased pupil size during reward and punishment outcomes, indicating greater physiological arousal. At the neural level, -tACS enhanced VLPFC activation during reward and suppressed its responses during punishment. Stimulation also changed VS- dACC connectivity in a context-dependent manner. Importantly, stimulation-driven increases in pupil size during reward correlated positively with stimulation-induced changes in VS-dACC connectivity. Exploratory moderated mediation analyses suggested that stimulation influenced the degree to which striatal responses mediated the relationship between task outcomes and pupil size changes. ConclusionsTargeting VLPFC with -tACS modulates local cortical activity and corticostriatal networks during reward processing, providing a promising noninvasive approach to influence reward circuitry. HighlightsO_LI-tACS over VLPFC increases pupil responses to reward and punishment. C_LIO_LIStimulation alters reward-related VLPFC activity without enhancing striatal BOLD. C_LIO_LI-tACS modulates ventral striatum-dACC connectivity in a task-dependent manner. C_LIO_LIConnectivity changes predict pupil dilation, linking brain and autonomic responses. C_LI

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bioRxiv2025

Predicting Spiking Activity from Scalp EEG

Despite decades of electroencephalography (EEG) research, the relationship between EEG and underlying spiking dynamics remains unclear. This limits our ability to infer local neural dynamics reflected in intracranial signals from EEG, a critical step to bridge electrophysiological findings across species and to develop non-invasive brain-machine interfaces (BMIs). We recorded spiking activity from a 32-channel floating microarray permanently implanted in parafoveal V1 and scalp-EEG in a male macaque monkey. While the animal fixated, the screen flickered at different temporal frequencies to induce steady-state visual evoked potentials (SSVEP). We analyzed the relationship between the V1 multi-unit spiking activity envelope (MUAe) and EEG frequency bands to predict MUAe at each time point from EEG. We extracted instantaneous spectrotemporal features of the EEG signal, including phase, amplitude, and phase-amplitude coupling of its frequency bands. Although the relationship between these features and the V1 MUAe was complex and frequency-dependent, it was reliably predictive of the MUAe. Specifically, in a multivariate linear regression predicting MUAe from EEG, each EEG feature (phase, amplitude, coupling) contributed to model predictions. In addition, we found that MUAe predictions were better in shallow than deep cortical layers, and that the phase of stimulus frequency further improved MUAe predictions. Our study shows that a comprehensive account of spectrotemporal features of non-invasive EEG can inform about underlying spiking activity, which is beyond what is available when the amplitude or phase of the EEG signal is considered separately. This demonstrates the richness of the EEG signal and its complex relationship with neural spiking activity and suggests that using more comprehensive spectrotemporal signatures could improve BMI applications.

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bioRxiv2024

EFMouse: a Matlab toolbox to model electric fields in the mouse brain

Research into the mechanisms underlying neuromodulation by tES using in-vivo animal models is key to overcoming experimental limitations in humans and essential to building a detailed understanding of the in-vivo consequences of tES. Insights from such animal models are needed to develop targeted and effective therapeutic applications of non-invasive brain stimulation in humans. The sheer difference in scale and geometry between animal models and the human brain contributes to the complexity of designing and interpreting animal studies. Here, we introduce EFMouse, a toolbox that extends previous approaches to model intracranial electric fields and generate predictions that can be tested with in-vivo recordings in mice. Novel functionality includes the ability to capture typical surgical approaches in the mouse (e.g., cranial recording windows), the placement of stimulation electrodes anywhere in or on the animal, and novel ways to report field predictions, including some refined measures of focality and direction homogeneity, and quantification based on regions defined in the Allen Mouse Brain Atlas. Although the EFMouse toolbox is generally applicable to planning and designing tES studies in mice, we illustrate its use by posing questions about transcranial direct current stimulation (tDCS) experiments with the goal of targeting the left visual cortex of the mouse. The EFMouse toolbox is publicly available at https://github.com/klabhub/EFMouse. Author summaryTranscranial electrical stimulation offers opportunities for studying brain activity and developing neuromodulation therapies. Even though this technique is used extensively in humans, understanding its neural consequences is still limited. Mouse models offer an opportunity to bridge this gap. However, major differences between human and mouse brains, such as brain size and cortical folding, pose a challenge to the design of appropriate stimulation protocols in mice. To address this, we developed EFMouse, an open-source computational toolbox that predicts intracranial electrical fields in the mouse brain during stimulation. This toolbox allows researchers to design experiments by simulating electrode arrangements and quantifying properties of the predicted electric field in specific brain regions. By doing so, EFMouse can guide the optimization of stimulation techniques to achieve targeted and reproducible effects. We illustrate its use by comparing a series of electrode arrangements, in terms of the strength, focality, and direction of their induced electric field. By making EFMouse publicly available, we hope to advance fundamental neuroscience research and the development of future clinical applications. HighlightsEFMouse is a novel, open-source, Matlab-based electric field simulator for the mouse brain. EFMouse quantifies induced field focality and homogeneity in regions of the Allen Mouse Brain Atlas. Montages with a return on the mouses back generate homogeneous fields perpendicular to the cortical surface. Montages with a small distance between stimulation and return electrodes on the mouses head can generate focal, but relatively weak fields.

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bioRxiv2020

Brain Network Mechanisms of Visual Shape Completion

Visual shape completion represents object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the processs underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form visually completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 34 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across all 20 observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal, dorsal attention, default mode, and cingulo-opercular networks. Each parcels task activation difference could be modeled via its resting-state connections with the remaining parcels (r=.62, p<10-9), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modeling activation differences in the secondary visual network and across all remaining networks. Taken together, these results suggest that shape completion relies upon a distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks. HighlightsO_LIShape completion differentially activates regions distributed across five networks C_LIO_LIThe secondary visual network plays the clearest role in shape completion C_LIO_LIDorsal attention functional connections likely coordinate activity across networks C_LIO_LIPosterior temporal region, PH, played a highly consistent role in completion C_LI

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bioRxiv2019

Transcranial alternating current stimulation reduces BOLD adaptation and increases functional connectivity

Transcranial alternating current stimulation (tACS) is used as a non-invasive tool for cognitive enhancement and clinical applications. The physiological effects of tACS, however, are complex and poorly understood (Liu et al. 2018). Most studies of tACS focus on its ability to entrain brain oscillations (Herrmann et al. 2013), but our behavioral results in humans (Kar and Krekelberg 2014a) and extracellular recordings in nonhuman primates (Kar et al. 2017) support the view that tACS at 10 Hz additionally affects brain function by reducing sensory adaptation. Our primary goal here was to test this hypothesis using BOLD imaging in human subjects. Using a motion adaptation paradigm developed to quantify BOLD adaptation (Huk et al. 2001) and concurrent fMRI and tACS, we found that tACS significantly attenuated adaptation in the human motion area (hMT+). In addition, an exploratory analysis showed that tACS increased functional connectivity between the stimulated hMT+ and the rest of the brain, in particular the dorsal attention network. We conclude that weak 10 Hz currents applied to the scalp affect both local and global measures of brain activity.\n\nNew and NoteworthyConcurrent transcranial alternating current stimulation (tACS) and fMRI show that tACS affects the human brain by attenuating adaptation and increasing functional connectivity. This work is important for our basic understanding of what tACS does, but also for therapeutic applications, which need insight into the full range of ways in which tACS affects the brain.

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bioRxiv2018

An intrinsic neural eye tracker in primary visual cortex

Humans and other primates rely on eye movements to explore visual scenes and to track moving objects. As a result, the image that is projected onto the retina - and propagated throughout the visual cortical hierarchy - is almost constantly changing and makes little sense without taking into account the momentary direction of gaze. How is this achieved in the visual system? Here we show that in primary visual cortex (V1), the earliest stage of cortical vision, neural representations carry an embedded \"eye tracker\" that signals the direction of gaze associated with each image. Using chronically implanted multi-electrode arrays, we recorded the activity of neurons in V1 during tasks requiring fast (exploratory) and slow (pursuit) eye movements. Neurons were stimulated with flickering, full-field luminance noise at all times. As in previous studies 1-4, we observed neurons that were sensitive to gaze direction during fixation, despite comparable stimulation of their receptive fields. We trained a decoder to translate neural activity into metric estimates of (stationary) gaze direction. This decoded signal not only tracked the eye accurately during fixation, but also during fast and slow eye movements, even though the decoder had not been exposed to data from these behavioural states. Moreover, this signal lagged the real eye by approximately the time it took for new visual information to travel from the retina to cortex. Using simulations, we show that this V1 eye position signal could be used to take into account the sensory consequences of eye movements and map the fleeting positions of objects on the retina onto their stable position in the world.

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