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7. Interindividual variability of functional connectome in schizophrenia

Author

Santo-Angles, Aniol, Salvador, Raymond, Gomar, Jesús J., Guerrero-Pedraza, Amalia, Ramiro, Núria, Tristany, Josep, Teixidó, Cristina, Ortiz-Gil, Jordi, Aguirre, Candibel, Bosque, Clara, López-Araquistain, Laura, Maristany, Teresa, Salgado-Pineda, Pilar, Sarró, Salvador, McKenna, Peter J., Bernardo, Miquel, Pomarol-Clotet, Edith, and Schwarzbach, Jens

Published
2021
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9. Interventions and Implications

Author

Brewer, Judson, primary, de Wit, Harriet, additional, Cortese, Aurelio, additional, Denys, Damiaan, additional, Hanlon, Colleen A., additional, Holmes, Emily A., additional, Paulus, Martin P., additional, Schwarzbach, Jens, additional, and Tse, Peter, additional

Published
2021
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18. An automated pipeline for obtaining labeled ICA‐templates corresponding to functional brain systems.

Author

Tahedl, Marlene and Schwarzbach, Jens V.

Subjects
*INDEPENDENT component analysis, *GRAY matter (Nerve tissue), *WHITE matter (Nerve tissue), *LARGE-scale brain networks, *REGRESSION analysis
Abstract

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data‐driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study‐specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two‐step procedure to obtain ICA‐templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open‐source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group‐ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP‐provided ICA‐map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric‐based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z‐values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks. [ABSTRACT FROM AUTHOR]

Published
2023
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22. Additional file 1 of Early remission in multiple sclerosis is linked to altered coherence of the Cerebellar Network

Author

Tahedl, Marlene, Levine, Seth M., Weissert, Robert, Kohl, Zacharias, Lee, De-Hyung, Linker, Ralf A., and Schwarzbach, Jens V.

Abstract

Additional file 1: Figure S1. Probability that a given network is the “original network”, whose functional coherence is changed in MS patients following their entrance into remission. Open circles are data from individual patients; closed circles depict the mean, and error bars are SEM. (A) Results from the first set of 9 patients. (B) Results from the second set of 9 patients. Note that in both cases the cerebellar network is, by far, the most likely network to change its functional coherence.

Published
2022
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30. An updated and extended atlas for corresponding brain activation during task and rest

Author

Tahedl, Marlene and Schwarzbach, Jens V.

Subjects
Similarity (geometry), Human Connectome Project, medicine.diagnostic_test, business.industry, Computer science, Pattern recognition, Independent component analysis, Task (project management), Neuroimaging, Component (UML), medicine, Artificial intelligence, business, Functional magnetic resonance imaging
Abstract

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data-driven method to compute group differences between such networks. To aid interpretation of functional network analyses, Smith and colleagues proposed a template of ten functional networks identified in 36 healthy participants. They labeled them those components according to their similarity with statistical parameter maps from a metaanalysis of task-based functional magnetic resonance imaging studies (Smith et al., 2009). However, those original templates show substantial distortion with respect to what up-to date correction methods can achieve, such that trying to capture relevant effects within several cortical regions, especially the sensorimotor and orbitofrontal cortices, as well as the cerebellum may yield suboptimal results. Here, we provide a technical update and extension to the original templates. Using correlation analyses, we identified the best matching maps of each of the original ten templates to ICA maps from the Human Connectome Project (HCP). The HCP provides group-parcellations of a large dataset (n = 1003) with high-quality data. This approach yields a better fit of spatial component maps with anatomical borders and gray-/white-matter-boundaries. Additionally, we provide a version of the updated templates in CIFTI file-format, an emerging format in the neuroimaging community that combines surface-based data with subcortical/cerebellar data in volumetric space. The two formats we provide here offer an improvement on the templates by Smith et al., which should enhance sensitivity and interpretability of future research that compares functional networks between groups.

Published
2020

40. Resting-state functional connectivity alterations in obsessive-compulsive disorder

Author

Ploner, Markus (Prof. Dr.), Koch, Kathrin (Prof. Dr.), Schwarzbach, Jens (Prof. Dr.), Gürsel, Deniz Asena, Ploner, Markus (Prof. Dr.), Koch, Kathrin (Prof. Dr.), Schwarzbach, Jens (Prof. Dr.), and Gürsel, Deniz Asena

Abstract

Neuroimaging studies have led to a better characterization of the neuropsychopathology of obsessive-compulsive disorder (OCD). These findings mainly demonstrate disturbances in fronto-striatal circuits as well as impairments in large-scale networks, encompassing fronto-parietal-limbic areas, defined by functionally connected brain regions, reflecting spontaneous brain activity. Nevertheless, there is inconsistency in the reported results. For instance, while some studies point towards increased connectivity between such large-scale networks, others report decreased connectivity. Furthermore, most of these studies employed hypothesis-driven approaches, basing their assumptions on models derived from neurobiological (i.e., animal, lesion) studies and reports from positron emission tomography (PET) studies. Findings from data-driven approaches are scarce, and it is poorly understood whether the findings of the two approaches converge. To tackle these issues, we conducted two studies that represent the core of this thesis. First, to overcome inconsistencies and to find a consistent and pathologically relevant pattern of connectivity alterations in OCD, we performed a coordinate-based meta-analysis of 18 whole-brain resting-state functional magnetic resonance imaging (fMRI) studies comparing OCD patients and healthy controls (total sample of 541 patients, 572 healthy controls). The meta-analysis resulted in (1) consistent hypoconnectivity within frontoparietal and salience network, and between salience, frontoparietal and default-mode network, and (2) consistent general dysconnectivity (no specific direction of connectivity change) within default-mode and frontoparietal network, as well as between frontoparietal and default-mode, salience network, limbic network, but also with the striatum and thalamus. Second, we conducted a resting-state fMRI study in a sample of OCD patients and healthy controls and employed independent component and sliding time window analysis to in, Neuroimaging-Studien haben zu einer besseren Charakterisierung der Neuropsychopathologie der Zwangsstörung (OCD) geführt. Die Ergebnisse dieser Studien zeigen hauptsächlich Störungen in frontostriatalen Schleifen sowie Beeinträchtigungen in großen Netzwerken, die frontoparietal-limbische Areale umfassen, welche durch funktionell verbundene Hirnregionen definiert sind und die spontane Hirnaktivität widerspiegeln. Trotzdem gibt es Inkonsistenzen in den berichteten Ergebnissen. Während einige Studien auf eine erhöhte Konnektivität zwischen solch großen Netzwerken hinweisen, berichten andere von einer verringerten Konnektivität. Darüber hinaus verwendeten die meisten dieser Studien hypothesengetriebene Ansätze, wobei ihre Annahmen auf Modellen beruhten, die aus neurobiologischen (d.h. Tier-, Läsions-) Studien und Positronen-Emissions-Tomographie (PET) -Studien abgeleitet waren. Erkenntnisse aus datengetriebenen Ansätzen sind selten, und es ist unklar, ob die Ergebnisse der beiden Ansätze übereinstimmen. Um diese Probleme anzugehen, haben wir zwei Studien durchgeführt, die den Kern dieser Arbeit darstellen. Um Inkonsistenzen zu überwinden und ein konsistentes und pathologisch relevantes Muster von Konnektivitätsänderungen bei Zwangsstörungen zu finden, führten wir zunächst eine koordinatenbasierte Metaanalyse von 18 Untersuchungen zur funktionellen Magnetresonanztomographie (fMRI) unter Ruhebedingungen (d.h. ohne Aufgabe) durch, in denen Patienten mit Zwangsstörung und gesunde Kontrollen verglichen wurden (Gesamtstichprobe von 541 Patienten, 572 gesunde Kontrollen). Die Metaanalyse ergab (1) eine konsistente Hypokonnektivität innerhalb des Frontoparietalen- und Salienz-Netzwerks sowie zwischen dem Salienz-, dem Frontoparietalen- und dem Default-Mode-Netzwerk und (2) eine allgemein konsistente Dyskonnektivität (d.h. keine spezifische Richtung der Konnektivitätsänderung) innerhalb des Default-Mode- und des Frontoparietalen-Netzwerks, sowie zwischen Frontoparietalem- und De

Published
2019

41. Resting-state functional connectivity alterations in obsessive-compulsive disorder

Author

Koch, Kathrin (Prof. Dr.), Schwarzbach, Jens (Prof. Dr.);Ploner, Markus (Prof. Dr.), Gürsel, Deniz Asena, Koch, Kathrin (Prof. Dr.), Schwarzbach, Jens (Prof. Dr.);Ploner, Markus (Prof. Dr.), and Gürsel, Deniz Asena

Abstract

Neuroimaging studies have led to a better characterization of the neuropsychopathology of obsessive-compulsive disorder (OCD). These findings mainly demonstrate disturbances in fronto-striatal circuits as well as impairments in large-scale networks, encompassing fronto-parietal-limbic areas, defined by functionally connected brain regions, reflecting spontaneous brain activity. Nevertheless, there is inconsistency in the reported results. For instance, while some studies point towards increased connectivity between such large-scale networks, others report decreased connectivity. Furthermore, most of these studies employed hypothesis-driven approaches, basing their assumptions on models derived from neurobiological (i.e., animal, lesion) studies and reports from positron emission tomography (PET) studies. Findings from data-driven approaches are scarce, and it is poorly understood whether the findings of the two approaches converge. To tackle these issues, we conducted two studies that represent the core of this thesis. First, to overcome inconsistencies and to find a consistent and pathologically relevant pattern of connectivity alterations in OCD, we performed a coordinate-based meta-analysis of 18 whole-brain resting-state functional magnetic resonance imaging (fMRI) studies comparing OCD patients and healthy controls (total sample of 541 patients, 572 healthy controls). The meta-analysis resulted in (1) consistent hypoconnectivity within frontoparietal and salience network, and between salience, frontoparietal and default-mode network, and (2) consistent general dysconnectivity (no specific direction of connectivity change) within default-mode and frontoparietal network, as well as between frontoparietal and default-mode, salience network, limbic network, but also with the striatum and thalamus. Second, we conducted a resting-state fMRI study in a sample of OCD patients and healthy controls and employed independent component and sliding time window analysis to in, Neuroimaging-Studien haben zu einer besseren Charakterisierung der Neuropsychopathologie der Zwangsstörung (OCD) geführt. Die Ergebnisse dieser Studien zeigen hauptsächlich Störungen in frontostriatalen Schleifen sowie Beeinträchtigungen in großen Netzwerken, die frontoparietal-limbische Areale umfassen, welche durch funktionell verbundene Hirnregionen definiert sind und die spontane Hirnaktivität widerspiegeln. Trotzdem gibt es Inkonsistenzen in den berichteten Ergebnissen. Während einige Studien auf eine erhöhte Konnektivität zwischen solch großen Netzwerken hinweisen, berichten andere von einer verringerten Konnektivität. Darüber hinaus verwendeten die meisten dieser Studien hypothesengetriebene Ansätze, wobei ihre Annahmen auf Modellen beruhten, die aus neurobiologischen (d.h. Tier-, Läsions-) Studien und Positronen-Emissions-Tomographie (PET) -Studien abgeleitet waren. Erkenntnisse aus datengetriebenen Ansätzen sind selten, und es ist unklar, ob die Ergebnisse der beiden Ansätze übereinstimmen. Um diese Probleme anzugehen, haben wir zwei Studien durchgeführt, die den Kern dieser Arbeit darstellen. Um Inkonsistenzen zu überwinden und ein konsistentes und pathologisch relevantes Muster von Konnektivitätsänderungen bei Zwangsstörungen zu finden, führten wir zunächst eine koordinatenbasierte Metaanalyse von 18 Untersuchungen zur funktionellen Magnetresonanztomographie (fMRI) unter Ruhebedingungen (d.h. ohne Aufgabe) durch, in denen Patienten mit Zwangsstörung und gesunde Kontrollen verglichen wurden (Gesamtstichprobe von 541 Patienten, 572 gesunde Kontrollen). Die Metaanalyse ergab (1) eine konsistente Hypokonnektivität innerhalb des Frontoparietalen- und Salienz-Netzwerks sowie zwischen dem Salienz-, dem Frontoparietalen- und dem Default-Mode-Netzwerk und (2) eine allgemein konsistente Dyskonnektivität (d.h. keine spezifische Richtung der Konnektivitätsänderung) innerhalb des Default-Mode- und des Frontoparietalen-Netzwerks, sowie zwischen Frontoparietalem- und De

Published
2019

45. Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions

Author

Tahedl, Marlene, Levine, Seth M., Greenlee, Mark W., Weissert, Robert, and Schwarzbach, Jens V.

Subjects
Neurology, 150 Psychologie, fMRI, functional connectivity, fMRI, functional connectivity, multiple sclerosis, resting state, neuroimaging biomarker, ddc:150, Review, Neurology (clinical), multiple sclerosis, resting state, neuroimaging biomarker
Abstract

Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.

Published
2018
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46. Identifying emotion subtypes from individualized emotion space

Author

Levine, Seth, Alahäivälä, Aino, Wackerle, Anja, Rupprecht, Rainer, and Schwarzbach, Jens

Subjects
FOS: Psychology, Other Psychology, Psychology, Experimental Analysis of Behavior, Social and Behavioral Sciences
Abstract

Many investigations into emotion processing contend that emotions can be reduced to a set of lower dimensions (e.g., valence and arousal). Additionally, emotion dysregulation is associated with numerous psychiatric disorders, whose treatment(s) may require inspiration from personalized medicine. To translate emotion research to the clinical domain, one may therefore need to investigate at the individual level, employing data-driven methods and forgoing classical assumptions regarding emotions. To this end, we explored the relative structure of emotion information resulting from 85 participants organizing emotionally-charged images following their own emotional responses to the pictures. Using cluster analyses and multidimensional scaling, we investigated the underlying composition of individuals’ emotion spaces. Hierarchical clustering revealed five subtypes that reflect differing layouts of the emotion space; multidimensional scaling of each subtype’s representative emotion space demonstrated that, although valence explained the primary organization of all emotion spaces, arousal as a secondary explanatory variable played a reduced role differentially for the subtypes, suggesting intrinsic differences in emotion information processing. Such data-driven methods yield new, unbiased ways of studying emotions and may reveal limitations of classic models or idiosyncrasies of individuals, which can inform future neuroimaging research and offer new approaches for studying emotions and emotion dysfunctions in psychiatric disorders.

Published
2018

49. Supracategorical fear information revealed by aversively conditioning multiple categories.

Author

Levine, Seth M, Kumpf, Miriam, Rupprecht, Rainer, and Schwarzbach, Jens V

Subjects
FUNCTIONAL magnetic resonance imaging, SURVIVAL analysis (Biometry)
Abstract

Fear-generalization is a critical function for survival, in which an organism extracts information from a specific instantiation of a threat (e.g., the western diamondback rattlesnake in my front yard on Sunday) and learns to fear – and accordingly respond to – pertinent higher-order information (e.g., snakes live in my yard). Previous work investigating fear-conditioning in humans has used functional magnetic resonance imaging (fMRI) to demonstrate that activity patterns representing stimuli from an aversively-conditioned category (CS+) are more similar to each other than those of a neutral category (CS-). Here we used fMRI and multiple aversively-conditioned categories to ask whether we would find only similarity increases within the CS+ categories or also similarity increases between the CS+ categories. Using representational similarity analysis, we correlated several models to activity patterns underlying different brain regions and found that, following fear-conditioning, between-category and within-category similarity increased for the CS+ categories in the insula, superior frontal gyrus (SFG), and the right temporal pole. When specifically investigating fear-generalization, these between- and within-category effects were detected in the SFG. These results advance prior pattern-based neuroimaging work by exploring the effect of aversively-conditioning multiple categories and indicate an extended role for such regions in potentially representing supracategorical information during fear-learning. [ABSTRACT FROM AUTHOR]

Published
2021
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