Characterizing Network Search Algorithms Developed for Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. However, the development of such methods is cumbersome in the case of large model-spaces. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. For test data a publicly available fMRI dataset of 60 subjects was used. First, we reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package, increasing computational speed during model estimation. Then, three network search algorithms have been adapted for DCM, and we demonstrated how modifications to these methods, based on DCM posterior parameter estimates, can enhance search performance. Comparison of the results are based on model evidence, structural similarities and the number of model estimations needed during search. An analytical approach using Bayesian model reduction (BMR) for efficient network discovery is already available for DCM. Comparing model search methods we found that topological algorithms often outperform analytical methods for single-subject analysis and achieve similar results for recovering common network properties of the winning model family, or set of models, obtained by multi-subject family-wise analysis. However, network search methods show their limitations in higher level statistical analysis of parametric empirical Bayes. Optimizing such linear modeling schemes the BMR methods are still considered the recommended approach. We envision the freely available database of estimated model-spaces to help further studies of the DCM model-space, and the ReDCM package to be a useful contribution for Bayesian inference within and beyond the field of neuroscience

Többértékű logikai függvények dekompozíciója

Az elektronika egyik fontos területe a logikai tervezés. Az áramkörökben fellépő feszültséget valamiképp logikailag manipulálva tudjuk az elektromosságot saját hasznunkra fordítani. Ahogy a technika fejlődésével összetettebb felépítésű architektúrák jelentek meg, például a harmadik generációs, integrált áramkörök, úgy a bonyolultabb logikai hálózatok építésére is lehetőség nyílt. Mivel adott esetben a megvalósítandó logika erőforrásigénye igencsak nagy lehet, felmerülhet a kérdés, hogy lehet-e egyszerűsíteni egy tetszőleges áramkört.Bscmérnök-informatiku

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI.

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons

Population-level Correction of Systematic Motion Artifacts in fMRI in Patients with Ischemic Stroke

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Central sensitization-related changes of effective and functional connectivity in the rat inflammatory trigeminal pain model.

Central sensitization is a key mechanism in the pathology of several neuropathic pain disorders. We aimed to investigate the underlying brain connectivity changes in a rat model of chronic pain. Non-noxious whisker stimulation was used to evoke blood-oxygen-level-dependent (BOLD) responses in a block-design functional Magnetic Resonance Imaging (fMRI) experiment on 9.4T. Measurements were repeated two days and one week after injecting complete Freund's adjuvant into the rats' whisker pad. We found that acute pain reduced activation in the barrel cortex, most probably due to a plateau effect. After one week, increased activation of the anterior cingulate cortex was found. Analyses of effective connectivity driven by stimulus-related activation revealed that chronic pain-related central sensitization manifested as a widespread alteration in the activity of the somatosensory network. Changes were mainly mediated by the anterior cingulate cortex and the striatum and affected the somatosensory and motor cortices and the superior colliculus. Functional connectivity analysis of nested BOLD oscillations justified that the anterior cingular-somatosensory interplay is a key element of network changes. Additionally, a decreased cingulo-motor functional connectivity implies that alterations also involve the output tract of the network. Our results extend the knowledge about the role of the cingulate cortex in the chronification of pain and indicate that integration of multiple connectivity analysis could be fruitful in studying the central sensitization in the pain matrix

Central sensitization-related changes of effective and functional connectivity in the rat inflammatory trigeminal pain model

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Filled contour plots visualizing the Regional Displacement Interaction (RDI) effect: how the predicted connectivity strength (color-coded) changes depending on the simultaneously varying values of and , in case of no nuisance regression (A∶NOREG) and all investigated first-level nuisance regression methods, i.e., NOREG+M6 (E), WMSCF (B), COMPCORR (C), GSREG (D), WMSCF+M6 (F), COMPCORR+M6 (G), GSREG+M6 (H), and SAT36 (I).

<p>Vertical and horizontal axes of plots B-I are the same as those of plot A. Gray bars next to the legends indicate the (−1,1) interval to ease interpretation of color-coded Z-score values.</p