Biology subjects

Ahrends, C.

Publications and source records attributed to Ahrends, C..

6 recordsLinked to original sources

bioRxiv2026

Test-retest reliable and site-robust Hidden Markov Model framework for discovering whole-brain beta activity

Sensorimotor beta activity (13-30 Hz) is a key neuronal signature in the human sensorimotor system, and its features can be effectively measured using functional brain imaging methods such as magnetoencephalography (MEG). In addition to its importance in healthy brain processing, beta activity has been shown to be altered in several neurological diseases, underscoring its potential as a biomarker. To serve as biomarkers, features must be reliably defined, stable across measurements and, ideally, amenable to automated analysis, yet current approaches to beta characterization require subjective decisions and manual work. We here describe a hidden Markov model (HMM) based approach to automatically segment beta events from source level MEG beta band activity into discrete high- and low-beta states. We demonstrate the differences between the proposed HMM based approach and a commonly used amplitude-envelope based approach to analyse high- and low-beta modulation. We show that the methods complement each other both when applied to resting data and task related passive movement data. Furthermore, we assess the test-retest reliability of the proposed pipeline within individuals using intraclass correlation coefficients (ICC), and test if HMM constructed at one measurement site can be applied to data acquired at another site, thereby evaluating its multisite transferability. We show that the proposed approach produces stable results within subjects and across sites for many of the features. The ICC values were excellent for high-beta state (86-100% of brain areas), while low-beta state test-retest reliability was more modest. Most of the features showed statistically significant differences between sites only in a few brain areas, indicating very good multisite stability. The proposed approach can serve as an automated, reproducible analysis pipeline for, e.g., clinical applications, and appears suitable for multi-site datasets.

neuroscience↗

bioRxiv2025

Prediction of individual melodic contour processing in sensory association cortices from resting state functional connectivity

Recent studies suggest that it is possible to predict an individual brains spatial activation pattern in response to a paradigm from their functional connectivity at rest (rsFC). However, it is unclear whether this prediction works across the brain. We here aim to understand whether individual task activation can be best predicted in local regions that are highly specialised to the task at hand or whether there are domain-independent regions in the brain that carry most information about the individual. To answer this question, we used fMRI data from participants (nonmusicians, N=52) at rest and during an auditory oddball paradigm while watching a silent movie. We then predicted individual differences in brain responses to melodic deviants from their rsFC both across the whole brain (global parcellation in 22 regions) and within the auditory cortices (local parcellation in 22 regions). Predictability was consistently higher in specific brain areas. These areas are centred around sensory association cortices: In the local (auditory cortex) parcellation, the best predicted area is the right superior temporal gyrus (STG), an auditory association area. The right STG is a critical region in melodic contour processing, a capacity that is central to the paradigm at hand. Interestingly, the best predicted network in the global parcellation is the bilateral visual association cortex. Sensory association cortices may carry more information specific to an individual regardless of the particular paradigm. Our results indicate that individual differences can be predicted in paradigm-relevant areas or general areas with higher inter-individual variability.

neuroscience↗

bioRxiv2023

Stacking models of brain dynamics improves prediction of subject traits in fMRI

Beyond structural and time-averaged functional connectivity brain measures, the way brain activity dynamically unfolds can add important information when investigating individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, there are two potential sources of variation in the models estimation which will in turn affect the predictions: first, in certain cases, the estimation variability due to different initialisations or choice of inference method; and second, the variability induced by the choice of the model hyperparameters that determine the complexity of the model. Rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. We propose stacking, a prediction-driven approach for model selection, to leverage this variability. Specifically, we combine predictions from multiple models of brain dynamics to generate predictions that are accurate and robust across multiple cognitive traits. We demonstrate the approach using the Hidden Markov Model, a probabilistic generative model of brain network dynamics. We show that stacking can significantly improve the prediction of subject-specific phenotypes, which is crucial for the clinical translation of findings.

neuroscience↗

bioRxiv2023

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

Predicting an individuals cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individuals brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individuals time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.

neuroscience↗

bioRxiv2021

Data and model considerations for estimating time-varying functional connectivity in fMRI

Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the models ability to detect temporal changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the models ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model. HighlightsO_LITime-varying FC models sometimes fail to detect temporal changes in fMRI data C_LIO_LIBetween- and within-subject FC variability affect model stasis C_LIO_LIThe choice of parcellation affects model stasis in real fMRI data C_LIO_LIThe number of observations and free parameters critically affect model stasis Keywords: fMRI; time-varying FC; Hidden Markov Model (HMM); resting state C_LI

neuroscience↗

bioRxiv2020

Sweet anticipation: Predictability of familiar music in autism

Autism has been characterised by different behavioural and cognitive profiles compared to typically developing (TD) individuals, and increasingly these differences have been associated with differences in structural and functional brain connectivity. It is currently unknown as to whether autistic and TD listeners process music in the same way: emotionally, mnemonically, and perceptually. The present study explores the brains dynamical landscape linked to music familiarity in an fMRI dataset from autistic and TD individuals. Group analysis using leading eigenvector dynamics analysis (LEiDA) revealed significantly higher probability of occurrence of a brain network in TD compared to autistic individuals during listening to familiar music. This network includes limbic and paralimbic areas (amygdala, hippocampus, parahippocampal gyrus, and temporal pole). No significant differences were found between autistic and TD individuals while listening to a scrambled, i.e. unfamiliar and more unpredictable, version of the same music track. These findings provide novel neuroimaging insights into how autistic prediction monitoring may shape brain networks during listening to familiar musical excerpts.

neuroscience↗