Biology subjects

Mayr, A.

Publications and source records attributed to Mayr, A..

11 recordsLinked to original sources

bioRxiv2026

Neural Integration of Affective Prosodic and Semantic Cues in Non-literal Forms of Speech Understanding

Emotions in speech are conveyed through both semantics and prosody--voice melody, yet how listeners integrate these cues in the brain remains unclear. We investigated non-literal forms of speech, such as irony and sarcasm, where understanding beyond literal meaning relies on the dynamic interplay between affective prosodic and semantic cues, alongside theory of mind (ToM). We used functional magnetic resonance imaging while participants listened to short dialogues between two characters, which varied in prosody and semantics to convey either literal or non-literal meanings. Behaviorally, semantics and prosody interacted in shaping participants evaluations, although the data suggest a prosody dominance effect. Non-literal speech engaged a distributed network including the bilateral inferior frontal gyrus, temporal speech regions, and ToM areas, with ROI analyses revealing heterogeneous prosody-semantics integration profiles across regions and tasks. Taken together, our data clarify the behavioral and neural underpinnings of the integration of prosody and semantics in non-literal speech and open new venues in this field. Author noteThe authors made the following contributions. Adrien Wittmann: Conceptualization, Methodology, Data curation, Formal analysis, Software, Investigation, Validation, Visualisation, Writing - Original Draft Preparation, Writing - Review & Editing; Leonardo Ceravolo: Methodology, Software, Writing - Review & Editing; Audrey Mayr: Conceptualization, Software, Methodology, Investigation, Validation; Didier Grandjean: Funding acquisition, Supervision, Conceptualization, Writing - Review & Editing.

neuroscience↗

bioRxiv2025

Machine Learning-Driven Optimization of Specific, Compact, and Efficient Base Editors via Single-Round Diversification

Base editing shows great potential in research and clinical applications. Current iterations of the deaminases used to create precise single-nucleotide changes via base editing exhibit undesirable effects, including off-targeting, off-base editing, and bystander editing. Current deaminases are derived from either larger eukaryotic deaminases, which exhibit high levels of Cas-independent DNA targeting, or from evolved variants of the smaller E. coli TadA protein (ecTadA), which exhibits off-base editing. To overcome the limitations inherent to using a single protein sequence for engineering, we diversified newly identified TadA orthologs by DNA shuffling to yield millions of training sequences for measuring base editor efficiency. We trained generative models on the performance data from the pools of variants and drew on information-theoretic insights to efficiently explore the sequence space to generate diverse and high-performing deaminases. From a single round of diversification, we created a small set of novel and specific cytosine and adenosine deaminases that were markedly distinct in sequence from published base editor deaminases. We found that our model created deaminases generally outperform those we identified through typical directed evolution. The novel compact deaminases identified here show high on-base activity, comparable to the leading published base editors, and with demonstrably lower off-base activity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/666807v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@cd25c1org.highwire.dtl.DTLVardef@69ad98org.highwire.dtl.DTLVardef@412e4org.highwire.dtl.DTLVardef@1744100_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

bioengineering↗

bioRxiv2022

Boosting polygenic risk scores

Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in the field of clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise L2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types.

bioinformatics↗

bioRxiv2022

Statistical learning for sparser fine-mapped polygenic models: the prediction of LDL-cholesterol

Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models that tend to be more robust regarding deviations from the target population.

genetics↗

bioRxiv2022

Investigation on How Dynamic Effective Connectivity Patterns Encode the Fluctuating Pain Intensity in Chronic Migraine

Chronic migraine is characterised by persistent headaches for more than 15 days per month; the intensity of the pain is fluctuating over time. Here, we explored the dynamic interplay of connectivity patterns between regions known to be related to pain processing and their relation to the ongoing dynamic pain experience. We recorded EEG from 80 sessions (20 chronic migraine patients in 4 separate sessions of 25 minutes). The patients were asked to continuously rate the intensity of their endogenous headache. On different time-windows, a dynamic causal model (DCM) of cross spectral responses was inverted to estimate connectivity strengths. For each patient and session, the evolving dynamics of effective connectivity were related to pain intensities and to pain intensity changes by using a Bayesian linear model. Hierarchical Bayesian modelling was further used to examine which connectivity-pain relations are consistent across sessions and across patients. The results reflect the multi-facetted clinical picture of the disease. Across all sessions, each patient with chronic migraine exhibited a distinct pattern of pain intensity-related cortical connectivity. The diversity of the individual findings are accompanied by inconsistent relations between the connectivity parameters and pain intensity or pain intensity changes at group level. This suggests a rejection of the idea of a common neuronal core problem for chronic migraine.

neuroscience↗

bioRxiv2021

Increased Functional Connectivity Characterises the Prodromal Phase of the Migraine Cycle

IntroductionEpisodic migraine is reflected by cyclic changes in behaviour and cortical processing. We aimed to identify how Functional connectivities change over the entire migraine cycle. MethodsBy using longitudinal neuroimaging and a whole-brain connectivity analysis approach, we tested 12 episodic migraine patients across 82 FMRI recordings during spontaneous headache attacks with Follow-up measurements over the pain-free interval without any external stimulation. ResultsWe Found that Functional connectivities linearly increased over the interictal interval. In the prodromal phase, we observed the strongest connections between the anterior agranular insula and the posterior orbitofrontal cortex with sensory, motor, and cingulate areas. The strengths of the connections dropped during the headache. Peak connectivity during the prodromal phase and its collapse during the headache can be regarded as a mechanism of normalising cortical processing. ConclusionsThe strongest connections during the ictal phase of the migraine cycle may contribute to the variety of symptoms of migraine attacks including headache, sensory hypersensitivity, and autonomous symptoms. We speculate about a malfunction at the molecular level in agranular frontal and insular regions, which needs to be addressed in subsequent studies. Article highlightsO_LIWe investigated functional connectivities over an entire migraine cycle. C_LIO_LIWe found cycle-related connectivity changes for two proximate agranular regions. C_LIO_LIThe prodromal increase and the collapse of connectivity during the headache may reflect normalising cortical processing. C_LI

neuroscience↗

bioRxiv2021

Individually Unique Dynamics of Cortical Connectivity Reflect the Ongoing Intensity of Chronic Pain

BackgroundChronic pain diseases are characterised by an ongoing and fluctuating endogenous pain, yet it remains to be elucidated how this is reflected by the dynamics of ongoing functional cortical connections. The present study addresses this disparity by taking the individual perspective of pain patients into account, which is the varying intensity of endogenous pain. MethodsTo this end, we investigated the cortical encoding of 20 chronic back pain patients and 20 chronic migraineurs in four repeated fMRI sessions. During the recording, the patients were asked to continuously rate their pain intensity. A brain parcellation approach subdivided the whole brain into 408 regions. A 10 s sliding-window connectivity analysis computed the pair-wise and time-varying connectivity between all brain regions across the entire recording period. Linear mixed effects models were fitted for each pair of brain regions to explore the relationship between cortical connectivity and the observed trajectory of the patients fluctuating endogenous pain. ResultsTwo pain processing entities were taken into account: pain intensity (high, middle, low pain) and the direction of pain intensity changes (rising vs. falling pain). Overall, we found that periods of high and increasing pain were predominantly related to low cortical connectivity. For chronic back pain this applies to the pain intensity-related connectivity for limbic and cingulate areas, and for the precuneus. The change of pain intensity was subserved by connections in left parietal opercular regions, right insular regions, as well as large parts of the parietal, cingular and motor cortices. The change of pain intensity direction in chronic migraine was reflected by decreasing connectivity between the anterior insular cortex and orbitofrontal areas, as well as between the PCC and frontal and ACC regions. ConclusionsInterestingly, the group results were not mirrored by the individual patterns of pain-related connectivity, which is suggested to deny the idea of a common neuronal core problem for chronic pain diseases. In a similar vein, our findings are supported by the experience of clinicians, who encounter patients with a unique composition of characteristics: personality traits, various combinations of symptoms, and a wide range of individual responses to treatment. The diversity of the individual cortical signatures of chronic pain encoding results adds to the understanding of chronic pain as a complex and multifaceted disease. The present findings support recent developments for more personalised medicine.

neuroscience↗

bioRxiv2021

Pain and the Emotional Brain: Affective Rather than Cognitive Processes Drive the Cortical Encoding of Pain

BackgroundThe experience of pain has been dissociated into two interwoven aspects: a sensory-discriminative aspect and an affective-motivational aspect. We aimed to explore which of the pain descriptors is more deeply rooted in the human brain. FindingsParticipants were asked to evaluate applied cold pain. The majority of the trials showed distinct ratings: some were rated higher for unpleasantness and others for intensity. We compared the relationship between functional data recorded from 7 tesla MRI with unpleasantness and intensity ratings and revealed a stronger relationship between cortical data and unpleasantness ratings. ConclusionsThe present study underlines the importance of the emotional-affective aspects of pain-related cortical processes in the brain. The findings corroborate previous studies showing a higher sensitivity to pain unpleasantness compared to ratings of pain intensity. For the processing of pain in healthy subjects, this effect may reflect the more direct and intuitive evaluation of emotional aspects of the pain system, which is to prevent harm and to preserve the physical integrity of the body.

neuroscience↗

bioRxiv2021

Intrinsic Network Activity Reflects the Fluctuating Experience of Tonic Pain

Although we know sensation is continuous, research on long-lasting and continuously changing stimuli is scarce and the dynamic nature of ongoing cortical processing is largely neglected. In a longitudinal study with 152 fMRI sessions, participants were asked to continuously rate the intensity of applied tonic heat pain for 20 minutes. Using group independent component analysis and dual-regression, we extracted the subjects time courses of intrinsic network activity. The relationship between the dynamic fluctuation of network activity with the varying time courses of three pain processing entities was computed: pain intensity, the direction of pain intensity changes and temperature. We were able to dissociate the spatio-temporal patterns of objective (temperature) and subjective (pain intensity/changes of pain intensity) aspects of pain processing in the human brain. We found two somatosensory networks with distinct functions: one network which encodes the small fluctuations in temperature and consists mainly of bilateral SI. A second right-lateralised network that encodes the intensity of the subjective experience of pain consists of SI, SII, the PCC, and the thalamus. We revealed the somatosensory dynamics that build up towards a current subjective percept of pain. The timing suggests a cascade of subsequent processing steps towards the current pain percept.

neuroscience↗

bioRxiv2021

Intrinsic Network Activity Reflects the Ongoing Experience of Chronic Pain

Analyses of intrinsic network activity have been instrumental in revealing cortical processes that are altered in chronic pain patients. However, such studies have not accounted for variable time courses of network activity and subjective pain experience. In a novel approach, we aimed to elucidate how intrinsic functional networks evolve in regard to the fluctuating intensity of the experience of chronic pain. In a longitudinal study with 156 fMRI sessions, 20 chronic back pain patients and 20 chronic migraine patients were asked to continuously rate the intensity of their endogenous pain. Using group independent component analysis and dual-regression, we extracted the time courses of 100 independent components separately for chronic back pain and chronic migraine. We investigated the relationship between the fluctuation of intrinsic network activity with the time course of subjective pain ratings. For chronic back pain, we found increased cortical network activity for the salience network and a local pontine network, as well as decreased network activity in the anterior and posterior default mode network for higher pain intensities. Higher pain intensities in chronic migraine were accompanied with lower activity in a prefrontal cortical network. By taking the perspective of the individual, we focused on the processes that matter for each patient, which are phases of relatively low pain and more straining phases of relatively high pain. The present design of ongoing assessment of the endogenous pain can be a powerful and promising tool to assess the signature of a patients endogenous pain encoding over weeks and months.

neuroscience↗

bioRxiv2020

Chronic Pain Patients Exhibit Individually Unique Cortical Signatures of Pain

We investigated how the trajectory of pain patients ongoing and fluctuating pain is encoded in the brain. In repeated fMRI sessions, 20 chronic back pain patients and 20 chronic migraineurs were asked to continuously rate the intensity of their endogenous pain. Linear mixed effects models were used to disentangle cortical processes related to pain intensity and to pain intensity changes. We found that the intensity of pain in chronic back pain patients is encoded in the anterior insula, the frontal operculum, and the pons; the change of pain of chronic back pain and chronic migraine patients is mainly encoded in the anterior insula. At the individual level, we identified a more complex picture where each patient exhibited their own signature of endogenous pain encoding. The diversity of the individual cortical signatures of chronic pain encoding results adds to the understanding of chronic pain as a complex and multifaceted disease.

neuroscience↗