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Frässle, S.

Publications and source records attributed to Frässle, S..

7 recordsLinked to original sources
bioRxiv2023

Dynamic Causal Modelling Highlights the Importance of Decreased Self-Inhibition of the Sensorimotor Cortex in Motor Fatigability

Motor fatigability emerges when challenging motor tasks must be maintained over an extended period of time. It is a frequently observed phenomenon in everyday life which affects patients as well as healthy individuals. Motor fatigability can be measured using simple tasks like finger tapping at maximum speed for 30s. This typically results in a rapid decrease of tapping frequency, a phenomenon called motor slowing. In a previous study (Bachinger et al. 2019), we showed that motor slowing goes hand in hand with a gradual increase of activation in the primary sensorimotor cortex (SM1), supplementary motor area (SMA), and dorsal premotor cortex (PMd). Previous electrophysiological measurements further suggested that the increase in SM1 activity might reflect a breakdown of inhibition and, particularly, a breakdown of surround inhibition which might have led to heightened coactivation of antagonistic muscles. It is unclear what drives the activity increase in SM1 caused by motor slowing and whether motor fatigability affects the dynamic interactions between SM1 and upstream motor areas like SMA and PMd. Here, we performed dynamic causal modelling to answer this question. Our main findings revealed that motor slowing was associated with a significant reduction in SM1 self-inhibition which is in line with previous electrophysiological results. Additionally, the model revealed a significant decrease in the driving input to premotor areas suggesting that structures other than cortical motor areas might cause motor fatigability.

neuroscience↗
bioRxiv2021

The Unique Cytoarchitecture and Wiring of the Human Default Mode Network

The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage post-mortem histology and in vivo neuroimaging to characterise the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialised for unimodal, heteromodal, and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which mechanistic accounts of the broad role the DMN plays in human brain function and cognition can be developed.

neuroscience↗
bioRxiv2021

Test-retest reliability of regression dynamic causal modeling

AO_SCPLOWBSTRACTC_SCPLOWRegression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability - a test-theoretical property of particular importance for clinical applications - together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project (HCP) dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean ICC 0.24-0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates to measures of functional connectivity, rDCM performed favorably - particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications. AO_SCPLOWUTHORC_SCPLOW SO_SCPLOWUMMARYC_SCPLOWTest-retest reliability is an important prerequisite for the validity of connectivity estimates in many situations, particularly in clinical applications. Here, using different datasets from the Human Connectome Project, we demonstrate that regression DCM (rDCM) yields good to excellent test-retest reliability when focusing on strong connections. Comparing this to the test-retest reliability of functional connectivity measures, rDCM performed favorably in most cases. Furthermore, we show that reliability is not homogeneously distributed: we identified several regions (primarily in frontal and temporal lobe) that were linked via highly-reliable connections, regardless of the paradigm. Finally, we demonstrate that individual connectivity profiles are sufficiently unique that participants can be identified with high accuracy. Our findings emphasize the potential of rDCM for robust inference on directed "connectivity fingerprints" from fMRI data.

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

Technical Note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to delays due to the finite transmission speed of axonal connections. Ordinary differential equations are therefore not adequate to capture the ensuing circuit dynamics, and delay differential equations (DDEs) are required instead. Previous work has illustrated that the integration of DDEs in DCMs benefits from sophisticated integration schemes in order to ensure rigorous parameter estimation and correct model identification. However, integration schemes that have been proposed for DCMs either emphasise speed (at the possible expense of accuracy) or robustness (but with computational costs that are problematic in practice). In this technical note, we propose an alternative integration scheme that overcomes these shortcomings and offers high computational efficiency while correctly preserving the nature of delayed effects. This integration scheme is available as open-source code in the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) toolbox and can be easily integrated into existing software (SPM) for the analysis of DCMs for electrophysiological data. While this paper focuses on its application to the convolution-based formalism of DCMs, the new integration scheme can be equally applied to more advanced formulations of DCMs (e.g. conductance based models). Our method provides a new option for electrophysiological DCMs that offers the speed required for scientific projects, but also the accuracy required for rigorous translational applications, e.g. in computational psychiatry.

neuroscience↗
bioRxiv2020

Regression dynamic causal modeling for resting-state fMRI

AO_SCPLOWBSTRACTC_SCPLOW"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI - regression dynamic causal modeling (rDCM) - extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

neuroscience↗
bioRxiv2020

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

neuroscience↗