Biology subjects

Grosse-Wentrup, M.

Publications and source records attributed to Grosse-Wentrup, M..

5 recordsLinked to original sources

bioRxiv2026

A right-hemispheric language network at single-neuron resolution

Human language depends on highly specialized left-hemispheric brain networks. Damage to these networks causes severe language impairments (aphasia), one of the most common, debilitating and costly consequences of left-hemispheric brain injury, especially stroke. The limited recovery of aphasia despite intensive rehabilitation efforts emphasizes the need to understand the basis of residual language abilities at the single-neuron level, which has remained unexplored so far. Here, we report large-scale microelectrode recordings with single-unit resolution over a period of ten months from the right-hemispheric prefrontal and parietal association cortex of an individual with stroke-induced chronic non-fluent aphasia. Single neurons exhibited regionally specific responses during comprehension, retrieval and articulation of words, the core operations of language. Distinct subpopulations encoded linguistic information in a task-specific manner, despite correlated firing patterns across tasks. Both single-neuron activity and temporally coordinated population dynamics were predicted by semantic and phonological embeddings derived from large language models (LLMs), revealing a regional dissociation in which semantic features preferentially accounted for prefrontal activity and phonological features for parietal activity. Our findings suggest that right-hemispheric circuits, homotopic to the left language network, can support language processing through structured, functionally organised activity at the level of single neurons. This study opens an avenue for developing mechanistically specific neurorehabilitation and neurorestorative strategies for aphasia, such as brain-computer interfaces (BCIs), that leverage right-hemispheric language resources.

neuroscience↗

bioRxiv2023

Neuro-Cognitive Multilevel Causal Modeling: A Framework that Bridges the Explanatory Gap between Neuronal Activity and Cognition

Explaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. We introduce neuro-cognitive multilevel causal modeling (NC-MCM), a framework that bridges the explanatory gap between neuronal activity and cognition by construing cognitive states as (behaviorally and dynamically) causally consistent abstractions of neuronal states. Multilevel causal modeling allows us to interchangeably reason about the neuronal- and cognitive causes of behavior while maintaining a physicalist (in contrast to a strong dualist) position. We introduce an algorithm for learning cognitive-level causal models from neuronal activation patterns and demonstrate its ability to learn cognitive states of the nematode C. elegans from calcium imaging data. We show that the cognitive-level model of the NC-MCM framework provides a concise representation of the neuronal manifold of C. elegans and its relation to behavior as a graph, which, in contrast to other neuronal manifold learning algorithms, supports causal reasoning. We conclude the article by arguing that the ability of the NC-MCM framework to learn causally interpretable abstractions of neuronal dynamics and their relation to behavior in a purely data-driven fashion is essential for understanding more biological systems whose complexity prohibits the development of hand-crafted computational models.

neuroscience↗

bioRxiv2023

BunDLe-Net: Neuronal Manifold Learning Meets Behaviour

Neuronal manifold learning techniques represent high-dimensional neuronal dynamics in low-dimensional embeddings to reveal the intrinsic structure of neuronal manifolds. A common goal of these techniques is to learn embeddings that allow a good reconstruction of the original data. We introduce a novel neuronal manifold learning technique, BunDLe-Net, that learns a low-dimensional Markovian embedding of the neuronal dynamics which pre-serves only those aspects of the neuronal dynamics that are relevant for a given behavioural context. In this way, BunDLe-Net eliminates neuronal dynamics that are irrelevant for decoding behaviour, effectively de-noising the data to reveal better the intricate relationships between neuronal dynamics and behaviour. We show that BunDLe-Net learns highly consistent manifolds across animals that reveal the building blocks of their neuronal manifolds on a variety of data sets, ranging from calcium imaging data recorded in the nematode C. elegans to spiking data from the rat hippocampus and primate somatosensory cortex.

animal behavior and cognition↗

bioRxiv2020

Intrinsic neural timescales related to sensory processing: Evidence from abnormal behavioural states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioural states. We address these questions by investigating the brains intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anaesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activitys intrinsic neural timescale is related to specifically sensory rather than motor information processing in the healthy brain. HighlightsO_LIEEG resting-state shows a hierarchy of intrinsic neural timescales. C_LIO_LISensory deficits as in disorders of consciousness lead to prolonged intrinsic neuraltimescales. C_LIO_LIClinical conditions with motor deficits do not show changes in intrinsic neural timescales.20 C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/229161v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1893c45org.highwire.dtl.DTLVardef@d4176dorg.highwire.dtl.DTLVardef@4dfaa8org.highwire.dtl.DTLVardef@183621e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Stratification of behavioral response to transcranial current stimulation by resting-state electrophysiology

Transcranial alternating current stimulation (tACS) enables the non-invasive stimulation of brain areas in desired frequencies, intensities and spatial configurations. These attributes have raised tACS to a widely used tool in cognitive neuroscience and a promising treatment in the field of motor rehabilitation. Nevertheless, considerable heterogeneity of its behavioral effects has been reported across individuals. We present a machine learning pipeline for predicting the behavioral response to 70 Hz contralateral motor cortex-tACS from Electroencephalographic resting-state activity preceding the stimulation. Specifically, we show in a cross-over study design that high-gamma (90-160 Hz) resting-state activity predicts arm-speed response to the stimulation in a concurrent reaching task. Moreover, we show in a prospective stimulation study that the behavioral effect size of stimulation significantly increases after the stratification of subjects with our prediction method. Finally, we discuss a plausible neurophysiological mechanism that links high resting-state gamma power in motor areas to stimulation response. As such, we provide a method that can distinguish responders from non-responders to tACS, prior to the stimulation treatment. This contribution could eventually bring us a step closer towards translating tACS into a safe and effective clinical treatment tool.

neuroscience↗