Biology subjects

Hinrichs, H.

Publications and source records attributed to Hinrichs, H..

8 recordsLinked to original sources

bioRxiv2023

Contextual modulation of primary visual cortex by temporal predictability during motion extrapolation

Predicting future events is a fundamental cognitive ability which often depends on the volatility of the environment. Previous studies on apparent motion reported that when the brain is confronted with low levels of predictability, activity in low-level sensory areas is increased, including primary visual cortex. However, other studies on temporal predictability reported opposite effects potentially due to the influence of attention. It remains unclear, however, how temporal predictability modulates brain responses in a more ecologically valid real motion paradigm. Our study investigated whether motion extrapolation in high and low predictable contexts would differently modulate fMRI-responses in subject-specific primary visual cortex during visible and partially occluded stimulation. To this end, participants performed a modified version of the interception paradigm in visible and occluded phases, in which they observed a stimulus moving horizontally, then vertically at two different velocities. They were instructed to press when and where the stimulus would reach a given point-of-contact. In high predictable context, the velocity was identical during horizontal and vertical (occluded) movement; whereas, in low predictable context, the velocity could change during the vertical trajectory. MVPA results revealed accuracies above chance for all classification analyses carried out with low and highly predictable context data. Moreover, trial-history analysis showed that a change in trial type (constant velocity after change in velocity and vice versa) increased BOLD-responses in V1. This pattern of results suggests motion extrapolation can enhance activity in primary visual cortex, regardless of trial-specific predictability, but it is affected by recent trial history. Highlights* Ignoring trial history high and low temporal predictability similarly enhance response in V1 * In recent trial-history analysis, low temporal predictability enhanced responses in V1 compared to high temporal predictability * Shared regions inside primary visual cortex encode visible and partially occluded information

neuroscience↗

bioRxiv2023

Ventrointermediate thalamic stimulation improves motor learning in humans

Ventrointermediate thalamic stimulation (VIM-DBS) modulates oscillatory activity in a cortical network including primary motor cortex, premotor cortex, and parietal cortex. Here we show that, beyond the beneficial effects of VIM-DBS on motor execution, this form of invasive stimulation facilitates production of sequential finger movements that follow a fixed sequence but not when the sequence is selected at random. These results highlight the role of thalamo-cortical activity in motor learning.

neuroscience↗

bioRxiv2022

Seeing and Extrapolating motion trajectories share common informative activation patterns in primary visual cortex

The natural environment is dynamic and moving objects become constantly occluded, engaging the brain in a challenging completion process to estimate where and when the object might reappear. Although motion extrapolation is critical in daily life - imagine crossing the street while an approaching car is occluded by a larger standing vehicle - its neural underpinnings are still not well understood. While the engagement of low-level visual cortex during dynamic occlusion has been postulated, most of the previous group-level fMRI-studies failed to find evidence for an involvement of low-level visual areas during occlusion. In this fMRI-study, we therefore used individually-defined retinotopic maps and multivariate pattern analysis to characterize the neural basis of visible and occluded motion in humans. To this end, participants learned velocity-direction pairings (slow motion-upwards; fast motion-downwards or vice versa) during a training phase without occlusion and judged the stimulus direction, based on its velocity, during a following test phase with occlusion. We find that occluded motion direction can be predicted from the activity patterns during visible motion within low-level visual areas, supporting the notion of a mental representation of motion trajectory in these regions during occlusion. Highlights* Dynamically occluded information is processed in low-level visual cortex * Specific regions inside low-level visual areas encode visible and dynamically occluded information * Overlap of visible and occluded informative activity patterns in the visual field suggest shared computational circuits in primary visual cortex

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bioRxiv2020

Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface

ObjectiveOne of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze-direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts. ApproachWe present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component - a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure. Main resultsDespite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks. SignificanceOur method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.

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bioRxiv2020

Local sleep during mind-wandering enhances processes of spatial attention allocation

Mind wandering (MW) is a subjective, cognitive phenomenon, in which thoughts move away from the task towards an internal train of thoughts, possibly during phases of neuronal sleep-like activity (local sleep, LS). MW decreases cortical processing of external stimuli and is assumed to decouple attention from the external world. Here, we directly tested how indicators of LS, cortical processing and attentional selection change in a pop-out visual search task during phases of MW. Participants brain activity was recorded using magnetoencephalography, MW was assessed via self-report using randomly interspersed probes. As expected, MW worsened performance being accompanied by a decrease in high frequency activity (HFA, 80-150Hz) and an increase in slow wave activity (SWA, 1-6Hz), consistent with the occurrence of LS. In contrast, visual attentional selection as indexed by the N2pc component was enhanced during MW with the N2pc amplitude being directly linked to participants performance. This observation clearly contradicts accounts of attentional decoupling predicting a decrease in attention-related responses to external stimuli during MW. Together our results suggest that MW occurs during phases of LS with processes of attentional target selection being upregulated, potentially to compensate for the mental distraction during MW.

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bioRxiv2019

Multiscale criticality measures as general-purpose gauges of proper brain function

The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). We applied MsCr to MEG and EEG data in four telling degraded information processing scenarios: disorders of consciousness, mild cognitive impairment, schizophrenia and preictal activity. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.

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bioRxiv2019

Convolutional Neural Networks for Decoding of Covert Attention Focus and Saliency Maps for EEG Feature Visualization

ObjectiveConvolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process.\n\nApproachWe developed a CNN model to decode the covert focus of attention from EEG event-related potentials during object selection. We compared the CNN and the commonly used linear discriminant analysis (LDA) classifier performance, applied to datasets with different dimensionality, and analyzed transfer learning capacity. Moreover, we validated the impact of single model components by systematically altering the model. Furthermore, we investigated the use of saliency maps as a tool for visualizing the spatial and temporal features driving the model output.\n\nMain resultsThe CNN model and the LDA classifier achieved comparable accuracy on the lower-dimensional dataset, but CNN exceeded LDA performance significantly on the higher-dimensional dataset (without hypothesis-driven preprocessing), achieving an average decoding accuracy of 90.7% (chance level = 8.3%). Parallel convolutions, tanh or ELU activation functions, and dropout regularization proved valuable for model performance, whereas the sequential convolutions, ReLU activation function, and batch normalization components, reduced accuracy or yielded no significant difference. Saliency maps revealed meaningful features, displaying the typical spatial distribution and latency of the P300 component expected during this task.\n\nSignificanceFollowing systematic evaluation, we provide recommendations for when and how to use CNN models in EEG decoding. Moreover, we propose a new approach for investigating the neural correlates of a cognitive task by training CNN models on raw high-dimensional EEG data and utilizing saliency maps for relevant feature extraction.

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bioRxiv2018

Direct evidence for prediction signals in frontal cortex independent of prediction error

Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction-error signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from prediction-error signals has been challenging. Critically, while many studies found indirect evidence for predictive coding in the form of prediction-error signals, direct evidence for the prediction signal is mostly lacking. Here we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex generates prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The amplitude of high frequency broadband (HFB) neural activity was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single trial HFB amplitudes predicted deviations and correlated with post-stimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of prediction-error signals.

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