Biology subjects

Iglesias, S.

Publications and source records attributed to Iglesias, S..

4 recordsLinked to original sources

bioRxiv2021

Interoception of breathing and its relationship with anxiety

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. While interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modelling and high-field (7 Tesla) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low vs. moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multimodal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

neuroscience↗

bioRxiv2021

Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Pathophysiological theories suggest that such dysfunction might be partially caused by aberrant interactions of different modulatory neurotransmitters with NMDARs, which could explain heterogeneity among patients with schizophrenia and their treatment response. Understanding the differential impact of different neuromodulators on readouts of NMDAR function is therefore of high clinical relevance. Here, we report results from two studies (N=81 each) which systematically tested whether the MMN is sensitive to diminishing and enhancing cholinergic vs. dopaminergic function. Both studies used a double-blind, placebo-controlled between-subject design and monitored individual drug plasma levels. Using a novel variant of the auditory oddball paradigm, we contrasted phases with stable versus volatile probabilities of tone switches. In the first study, we found that the muscarinic acetylcholine receptor antagonist biperiden reduced and/or delayed mismatch responses, particularly during stable phases of the experiment, whereas this effect was absent for amisulpride, a dopamine D2/D3 receptor antagonist. The direct comparison between biperiden and amisulpride indicated a significant drug x mismatch interaction. In the second study, neither elevating acetylcholine nor dopamine levels via administration of galantamine and levodopa, respectively, exerted significant effects on MMN. Overall, our results indicate differential sensitivity of the MMN to changes in cholinergic (muscarinic) versus dopaminergic receptor function. This finding may prove useful for developments of future tools for predicting individual treatment responses in disorders that show abnormal MMN, such as schizophrenia.

neuroscience↗

bioRxiv2021

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

neuroscience↗

bioRxiv2020

A Hilbert-based method for processing respiratory timeseries

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (translationalneuromodeling.org/tapas). HighlightsO_LIWe introduce a new estimator for respiratory volume per unit time from respiratory recordings. C_LIO_LIWe demonstrate how this is able to accurately characterise atypical breathing events. C_LIO_LIThis removes significantly more variance when used as a confound regressor for fMRI data. C_LIO_LIOur implementation will be included in PhysIO, released as part of TAPAS: translationalneuromodeling.org/tapas. C_LI

neuroscience↗