Biology subjects

Reichert, C.

Publications and source records attributed to Reichert, C..

16 recordsLinked to original sources

bioRxiv2026

The Effect of Visual Impairment on the Controllability of a Brain-Computer Interface Using Visual Spatial Attention

The primary goal of a brain-computer interface (BCI) is to enable interactions with the environment by generating control signals directly from brain activity, independent of physical movement including gaze shifts. Attentional selection of peripheral objects has been used to control a BCI but requires sufficient visual acuity to reliably recognize the target stimulus. Here, we systematically investigated the effect of different stages of visual impairment on event-related potentials (ERPs) and BCI performance while healthy participants directed covert attention to peripheral visual stimuli. We hypothesized that the effect of visual impairments such as blurred or monocular vision might alter attention-related visual ERPs, but has only minor or no impact at all on BCI performance when color is the target feature. Early attention sensitive visual components were altered in monocular vision compared to binocular vision. Simulated vision degradation resulted in reduction of ERPs in the P300 range. The differences in ERPs across vision conditions did not affect the decoding accuracy of the BCI. The study not only revealed differences of visual ERPs in different viewing conditions but primarily demonstrated that a BCI can be reliably controlled by focusing attention on a visual stimulus whose distinguishing features such as color, can be recognized even with visual impairments. This provides a promising approach to enable paralyzed persons to communicate using a non-invasive, gaze-independent BCI.

neuroscience↗

bioRxiv2025

Individualized Phenotyping of Functional ALS Pathology in Sensorimotor Cortex

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease characterized by the loss of motor neurons in primary motor cortex (MI), leading to muscle weakness, atrophy, and death within a median of three years. Even though ALS is characterized by different disease subtypes affecting different body parts, individiualized phenotyping of functional ALS pathology has so far not been achieved. We recorded 7 Tesla functional MRI (7T-fMRI) data while ALS patients and matched controls moved affected and non-affected body parts in the MR scanner. We applied robust Shared Response Modeling (rSRM) for capturing ALS-specific shared responses for group classification, and Partial Least Squares (PLS) regression for relating the latent variables to clinical subtypes and the degree of disease progression. We show that both functional connectivity and functional activation in MI are a predictor for disease onset site. However, disease severity could best be predicted by functional connectivity rather than pure activation changes. Critically, we show that functionally disease-defining information in MI is not strongest in the area that is behaviorally first-affected, deviating from the behavioral phenotype of the patients. When computing the models weight distribution of the King stage classification and projecting them back into voxel space, the highest mean weights are present in the foot and tongue/face regions that seem to drive disease progress. Our data highlight the importance of 7T-fMRI task-based functional connectivity measures for classifying ALS-patients, and provide evidence that a single 7T-MRI scan can be used for identifying a disease signature of each individual ALS patient.

neuroscience↗

bioRxiv2024

Broadband high frequency Activity initializes Distractor Suppression

Selective attention requires fast and accurate distractor suppression. We investigated if broadband high-frequency activity (BHA; 80 - 150 Hz), indicative of local neuronal population dynamics in early sensory cortices, indexes rapid processing of distracting information. In the first experiment we tested whether BHA distinguishes targets from distracting information in a visual search paradigm using tilted gratings as targets and distractors. In the second experiment, we examined whether BHA distractor processing can be trained by statistical learning. In both experiments, BHA preceded the low-frequency target enhancement (NT) and distractor suppression (PD; 1-40 Hz) components and distinguished between targets and distractors. Only the BHA but not low-frequency component amplitude correlated with participants performance and was higher for lateral distractors versus lateral targets. Furthermore, BHA predicted the strength of the PD. These results indicate that BHA initiates stimulus discrimination via distractor suppression. Significance StatementSelective attention is the net result of successful target selection and distractor suppression, representing a key mechanism of humans everyday life. Here, we shed light on the neural mechanisms of distractor processing by showing that the early occipital broadband high-frequency activity (BHA) is linked to successful distractor suppression. Specifically, the BHA predicted individual performance and PD strength, a component associated with distractor suppression. We found the BHA and later the PD increased in amplitude during implicit statistical learning indicative of a trainable distractor suppression mechanism. We demonstrate that the BHA serves as an early marker for a successful initiation of distractor suppression.

neuroscience↗

bioRxiv2024

Temporal kinetics of brain state effects on visual perception

We investigated the effects of brain states on human perception and early visual response comparing focused wakefulness (ON state) to external inattention (OFF state). In two experiments, we investigated the temporal kinetics of brain states changes during stimulus processing and assessed fluctuations across extended periods of time. We used a classifier to distinguish between these states on a single trial level using theta activity in MEG sensors. We found that participants shifted from an ON to an OFF state as rapidly as two seconds. Visual target discrimination was comparable in both states, but reaction times were slower and more variable during the OFF state. Broad band high-frequency activity (BHA) recorded in MEG sensors covering the occipital cortex tracked target grating orientation. BHA was reduced during the OFF state but participants were still able to distinguish sensory information highlighting the role of BHA in visual perception across cognitive brain states.

neuroscience↗

bioRxiv2024

Physical activity modulates early visual response and improves target detection in humans

Brain state changes affect visual perception by altering spatial resolution. Attention enhances the spatial resolution decorrelating neuronal activity in early nonhuman primate (NHP) visual cortex. Physical activity (PA) amplifies these attentional effects in rodents but impact of PA on visual perception in humans remains uncertain. We investigated the relationship between broadband high-frequency activity (BHA: 80-150 Hz) recorded with magnetoencephalography (MEG) and visual detection performance. We found that PA enhanced visual target detection predicted by a reduction of early BHA responses (<90 msec). This effect may be due to reduced interneuronal correlation to improve spatial resolution. Moreover, PA improved spatial integration time, as indicated by a linear relationship between reaction times and BHA variation with target eccentricity. These findings provide evidence that PA influences neuronal activity critical for early visual perception, optimizing visual processing at the initial stages of the visual hierarchy.

neuroscience↗

bioRxiv2024

Physical Exercise Improves Working Memory through Ripple-Spindle Coupling

Spindle-ripple coupling enhances memory consolidation during sleep. Ripples, representing the compressed reactivation of environmental information, provide a mechanism for retaining memory information in chronological order and are also crucial for working memory (WM) during wakefulness. Brief sessions of physical exercise (PE) are proposed to boost WM. In concurrent EEG/MEG sessions, we investigated the role of PE in WM performance and high-frequency-ripple to spindle coupling. Ripples, identified in MEG sensors covering the medial temporal lobe (MTL) region, predicted individual WM performance. Ripples were locked to robust oscillatory patterns in the EEG defined spindle band. Spindle activity and ripples decrease during initial stimulus presentation and rebound after 1 sec. Behaviorally, PE enhanced WM performance. Neurophysiologically, PE scaled the ripple rate with the number of items to be kept in WM and strengthened the coupling between ripple events and spindle oscillations. These findings reveal that PE enhances WM by coordinating ripple-spindle interaction.

neuroscience↗

bioRxiv2024

Anterior thalamus functional connectivity with cortical regions underpinning prospective memory: a 7-Tesla fMRI seed-based study

Prospective memory, or memory for future intentions, engages particular cortical regions. Lesion studies also implicate the thalamus, with prospective memory deterioration following thalamic stroke. Neuroimaging, anatomical, and lesion studies suggest the ANT (anterior nuclei of the thalamus) in particular are involved in episodic memory, with electrophysiological studies suggesting an active role in selecting neural assemblies underlying particular memory traces. Here we hypothesized that the ANT are engaged in realizing prospectively-encoded intentions, detectable using ultra-high-field strength functional MRI. Using a within-subject design, participants (N = 14; age 20-35 years) performed an ongoing n-back working memory task with two cognitive loads, each with and without a prospective memory component, during 7-Tesla functional MRI. Seed-to-voxel whole brain functional connectivity analyses were performed to establish whether including a prospective memory component in an ongoing task results in greater connectivity between ANT and cortical regions engaged in prospective memory. Repeated measures ANOVAs were applied to behavioral and connectivity measures, with the factors Task Type (with prospective memory or not) and N-Back (2-back or 3-back). Response accuracy was greater and reaction times faster without the prospective memory component, and accuracy was higher in the 2-than 3-back condition. Task Type had a main effect on connectivity with an ANT seed, with greater ANT-DLPFC (dorsolateral prefrontal cortex) and ANT-STG (superior temporal gyrus) connectivity when including a prospective memory component. Post hoc testing based on a significant interaction showed greater ANT-DLPFC connectivity (p-FWE = 0.007) when prospective memory was included with the low and ANT-STG connectivity (p-FWE = 0.019) with the high cognitive load ongoing task. Direct comparison showed greater functional connectivity between these areas and the ANT than DMNT (dorsomedial nucleus of the thalamus) during prospective remembering. Enhanced ANT-DLPFC connectivity, a brain region with an established role in strategic monitoring for prospective memory cues, arose with a low cognitive load ongoing task that enabled monitoring. This connectivity was significantly less on direct comparison with increasing the cognitive load of the ongoing task without prospective memory, suggesting specificity for prospective memory. Greater ANT-STG connectivity on prospective memory inclusion in the higher cognitive load ongoing task fits with reported STG activation on prospective memory through spontaneous retrieval. Lower connectivity on direct comparison with a DMNT seed suggests ANT specificity. The findings fit with a coordinating role for the ANT in prospective remembering. Given the small sample, these findings should be considered preliminary, with replication required.

neuroscience↗

bioRxiv2023

Reduced dimension stimulus decoding and column-based modeling reveal architectural differences of primary somatosensory finger maps between younger and older adults

The primary somatosensory cortex (SI) contains fine-grained tactile representations of the body, arranged in an orderly fashion. Using ultra-high resolution fMRI data to describe such detailed individual topographic maps or to detect group differences is challenging, because group alignment often does not preserve the high spatial detail of the data. Here, we use shared response modeling (SRM), a technique that allows group analyses by mapping individual stimulus-driven responses to a lower dimensional shared feature space, to detect age-related differences in sensory representations between younger and older adults using 7T-fMRI data. Using this method, we show that finger representations are more precise in Brodmann-Area (BA) 3b and BA1 compared to BA2 and motor areas, and that this hierarchical processing is preserved across age groups. By combining SRM with column-based decoding (C-SRM), we further show that the number of columns that optimally describes finger maps in SI is higher in younger compared to older adults in BA1, indicating a greater columnar size in older adults SI. Taken together, we conclude that SRM is suitable for finding fine-grained group differences in SI fMRI data at ultra-high-resolution, and we provide first evidence that the columnar architecture of a functional area changes with increasing age.

neuroscience↗

bioRxiv2021

Time to recover from daily caffeine intake.

Caffeine elicits widespread effects in the central nervous system and is the most frequently consumed psychostimulant worldwide. First evidence indicates that, during daily intake, the elimination of caffeine may slow down and the primary metabolite, paraxanthine, may accumulate. The neural impact of such adaptions is virtually unexplored. In this report, we leveraged the data of a laboratory study with N= 20 participants and three within-subject conditions: caffeine (150 mg caffeine x 3/day x 10 days), placebo (150 mg mannitol x 3/day x 10 days), and withdrawal (caffeine x 9 days, afterwards placebo x 1 day). Using liquid chromatography-mass spectrometry coupled with tandem mass spectrometry, we determined the course of salivary caffeine and paraxanthine, measured regularly at day 10. We assessed grey matter (GM) intensity and cerebral blood flow (CBF) in the withdrawal condition as compared to their changes in caffeine in our previous report. The results indicate high remaining levels of paraxanthine and of caffeine carried overnight during daily intake, and the levels of paraxanthine remained higher than in placebo during withdrawal. After 36 h of withdrawal, the previously reported caffeine-induced GM reduction was partially mitigated, while CBF was elevated compared to placebo. Our findings unveil that conventional daily caffeine intake does not provide sufficient time to clear up psychoactive compounds and restore cerebral responses, even after 36 hours of abstinence. They also suggest investigating consequences of a paraxanthine accumulation during daily caffeine intake.

pharmacology and toxicology↗

bioRxiv2021

Working Memory Performance after Daily Caffeine Intake: Compromised Performance and Reduced Hippocampal Activity.

Neuroprotective effects of caffeine have been frequently reported in the context of disease and cognitive dysfunction as well as in epidemiological studies in humans. However, evidence on caffeine effects on neural and memory functions during daily intake in a healthy cognitive state remains scarce. This randomized double-blind placebo-controlled crossover study investigated working memory functions by N-back tasks and functional magnetic resonance imaging (fMRI) after daily caffeine intake compared to a placebo baseline and to acute caffeine withdrawal in 20 young healthy volunteers. Each volunteer was given 3 times 150 mg caffeine for 10 days in the daily caffeine condition, 3 times 150 mg mannitol for 10 days in the placebo condition, and 9-day caffeine plus 1-day mannitol in the acute withdrawal condition. During the 10th day, participants performed 4 N-back sessions (two loads each: 0- and 3-back) under controlled laboratory conditions. During the 4th session of N-Back (i.e. at 5.5 h, 36.5 h and > 10 days after the last caffeine intake in the caffeine, withdrawal, and placebo condition, respectively) we assessed blood-oxygen-level-dependent (BOLD) activity. During the entire 10th day, in 0-back tasks, we observed longer reaction times (RTs) in the withdrawal compared to the placebo (Cohens d = 0.7) and caffeine condition (Cohens d = 0.6), but no significant effects of conditions on error rates. In contrast, in 3-back tasks (controlled for 0-back), the RTs in the caffeine condition were longer compared to placebo (Cohens d = 0.6) and withdrawal (Cohens d = 0.5). Error rates were higher during both caffeine and withdrawal conditions compared to placebo (Cohens d of both contrasts = 0.4). Whole-brain analyses on fMRI data did not reveal significant condition-dependent differences in activities between task loads. Across task loads, however, we observed a reduced hippocampal activation (Cohens d = -1.3) during the caffeine condition compared to placebo, while no significant difference in brain activities between withdrawal and placebo conditions. Taken together, the worse working memory function and the hippocampal hypoactivation implicate a potential detrimental effect of daily caffeine intake on neurocognitive functions of healthy adults. Moreover, they echo the hippocampal volumetric reduction reported previously in the same volunteers. Lastly, acute withdrawal from daily caffeine intake impairs both low-order cognitive processes and working memory performance. Taking earlier studies on acute caffeine effects into account, our findings indicate that daily caffeine intake elicits a dynamic change in cerebral activities during the course of repeated consumption, with unknown consequences in the long run.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2019

Caffeine-induced Plasticity of Grey Matter Volume in Healthy Brains: A placebo-controlled multimodal within-subject study

Disturbed sleep homeostatic states have been found to alter neuronal homeostasis and reduce grey matter (GM) volume. Caffeine intake that interferes with sleep homeostasis through antagonizing adenosine receptors can impair hippocampal synaptic strength, neurogenesis, as well as memory and learning in rats. In this study, reduced medial temporal GM volume was observed after daily caffeine intake in humans (3 x 150 mg x 10 days compared to 10-day placebo administration). The potential bias from reduced cerebral blood flow was controlled, and the GM reduction was independent of the change in sleep pressure. A decrease in working memory during daily caffeine intake was observed, albeit no association with the magnitude of GM changes. The findings indicate that daily caffeine intake might induce rapid cerebral plasticity and be detrimental for higher order cognitive performance in the long run. They may call into question whether the neuroprotective effects of caffeine found in acute or low dose administration in animals are generalizable onto the daily usage in humans.

neuroscience↗

bioRxiv2019

Caffeine-dependent changes of sleep-wake regulation: evidence for adaptation after repeated intake

To enhance wakefulness, daily consumption of caffeine in the morning and afternoon is highly common. However, it is unknown whether such a regular intake pattern affects timing and quality of wakefulness, as regulated by an interplay of circadian and sleep-homeostatic mechanisms. Thus, we investigated the effects of daily caffeine intake and its withdrawal on circadian rhythms and wake-promotion in 20 male young habitual caffeine consumers. We applied a double-blind, within-subject design with a caffeine (150 mg, 3 x daily), a placebo, and a withdrawal condition each lasting ten days. Starting on day nine of treatment, salivary melatonin and cortisol, evening nap sleep, as well as sleepiness and vigilance performance throughout day and night were quantified during 43 h under controlled laboratory conditions. Neither the time course of melatonin (i.e., onset, amplitude, or area under the curve) nor the time course of cortisol were significantly affected by caffeine intake or its withdrawal. During withdrawal, however, volunteers reported increased sleepiness, showed more attentional lapses, as well as polysomnography-derived markers of elevated sleep propensity in the late evening compared to both, placebo and caffeine conditions. Thus, the typical timing of habitual caffeine intake in humans may not necessarily shift circadian phase nor lead to clear-cut benefits in alertness. The time-of-day independent effects of caffeine withdrawal suggest an adaptation to the substance, presumably in the homeostatic aspect of sleep-wake regulation.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗