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14 results on '"Hlinka, Jaroslav"'

1. Towards Definition of Higher Order Causality in Complex Systems

Author

Kořenek, Jakub, Sanda, Pavel, and Hlinka, Jaroslav

Subjects
Statistics - Machine Learning, Computer Science - Information Theory, Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability
Abstract

The description of the dynamics of complex systems, in particular the capture of the interaction structure and causal relationships between elements of the system, is one of the central questions of interdisciplinary research. While the characterization of pairwise causal interactions is a relatively ripe field with established theoretical concepts and the current focus is on technical issues of their efficient estimation, it turns out that the standard concepts such as Granger causality or transfer entropy may not faithfully reflect possible synergies or interactions of higher orders, phenomena highly relevant for many real-world complex systems. In this paper, we propose a generalization and refinement of the information-theoretic approach to causal inference, enabling the description of truly multivariate, rather than multiple pairwise, causal interactions, and moving thus from causal networks to causal hypernetworks. In particular, while keeping the ability to control for mediating variables or common causes, in case of purely synergetic interactions such as the exclusive disjunction, it ascribes the causal role to the multivariate causal set but \emph{not} to individual inputs, distinguishing it thus from the case of e.g. two additive univariate causes. We demonstrate this concept by application to illustrative theoretical examples as well as a biophysically realistic simulation of biological neuronal dynamics recently reported to employ synergetic computations.

Published
2024

2. Integral Betti signature confirms the hyperbolic geometry of brain, climate, and financial networks

Author

Caputi, Luigi, Pidnebesna, Anna, and Hlinka, Jaroslav

Subjects
Mathematics - Algebraic Topology, Physics - Data Analysis, Statistics and Probability, Quantitative Biology - Neurons and Cognition, Quantitative Finance - Computational Finance
Abstract

This paper extends the possibility to examine the underlying curvature of data through the lens of topology by using the Betti curves, tools of Persistent Homology, as key topological descriptors, building on the clique topology approach. It was previously shown that Betti curves distinguish random from Euclidean geometric matrices - i.e. distance matrices of points randomly distributed in a cube with Euclidean distance. In line with previous experiments, we consider their low-dimensional approximations named integral Betti values, or signatures that effectively distinguish not only Euclidean, but also spherical and hyperbolic geometric matrices, both from purely random matrices as well as among themselves. To prove this, we analyse the behaviour of Betti curves for various geometric matrices -- i.e. distance matrices of points randomly distributed on manifolds of constant sectional curvature, considering the classical models of curvature 0, 1, -1, given by the Euclidean space, the sphere, and the hyperbolic space. We further investigate the dependence of integral Betti signatures on factors including the sample size and dimension. This is important for assessment of real-world connectivity matrices, as we show that the standard approach to network construction gives rise to (spurious) spherical geometry, with topology dependent on sample dimensions. Finally, we use the manifolds of constant curvature as comparison models to infer curvature underlying real-world datasets coming from neuroscience, finance and climate. Their associated topological features exhibit a hyperbolic character: the integral Betti signatures associated to these datasets sit in between Euclidean and hyperbolic (of small curvature). The potential confounding ``hyperbologenic effect'' of intrinsic low-rank modular structures is also evaluated through simulations.

Published
2024

3. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Author

McWhinney, Sean, Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle, Corkum, Emily, Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda, Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick, Cannon, Dara, Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa, Fortea, Lydia, Fullerton, Janice, Goltermann, Janik, Gotlib, Ian, Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur, Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth, Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip, Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel, Ortuño, Maria, Overs, Bronwyn, Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew, Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter, Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott, Stein, Dan, Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia, Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje, Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils, Yatham, Lakshmi, Thompson, Paul, Andreassen, Ole, Ching, Christopher, and Hajek, Tomas

Subjects
MRI, bipolar disorder, body mass index, obesity, principal component analysis, psychiatry, Humans, Bipolar Disorder, Principal Component Analysis, Adult, Female, Male, Magnetic Resonance Imaging, Middle Aged, Obesity, Schizophrenia, Cerebral Cortex, Cluster Analysis, Young Adult, Brain
Abstract

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Published
2024

10. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Author

Onderzoeksgroep 1, McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., Hajek, Tomas, Onderzoeksgroep 1, McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., and Hajek, Tomas

Published
2024

11. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Author

McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., Hajek, Tomas, McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., and Hajek, Tomas

Abstract

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associ

Published
2024

14. Global functional connectivity reorganization reflects cognitive processing speed deficits and fatigue in multiple sclerosis.

Author

Hok, Pavel, Thai, Quang Thong, Bučková, Barbora Rehák, Domin, Martin, Řasová, Kamila, Tintěra, Jaroslav, Lotze, Martin, Grothe, Matthias, and Hlinka, Jaroslav

Subjects
*COGNITIVE processing speed, *FATIGUE (Physiology), *FUNCTIONAL magnetic resonance imaging, *FUNCTIONAL connectivity, *MULTIPLE sclerosis
Abstract

Background and purpose Methods Results Conclusions Cognitive impairment (CI) in multiple sclerosis (MS) is associated with bidirectional changes in resting‐state centrality measures. However, practicable functional magnetic resonance imaging (fMRI) biomarkers of CI are still lacking. The aim of this study was to assess the graph‐theory‐based degree rank order disruption index (kD) and its association with cognitive processing speed as a marker of CI in patients with MS (PwMS) in a secondary cross‐sectional fMRI analysis.Differentiation between PwMS and healthy controls (HCs) using kD and its correlation with CI (Symbol Digit Modalities Test) was compared to established imaging biomarkers (regional degree, volumetry, diffusion‐weighted imaging, lesion mapping). Additional associations were assessed for fatigue (Fatigue Scale for Motor and Cognitive Functions), gait and global disability.Analysis in 56 PwMS and 58 HCs (35/27 women, median age 45.1/40.5 years) showed lower kD in PwMS than in HCs (median −0.30/−0.06, interquartile range 0.55/0.54; p = 0.009, Mann–Whitney U test), yielding acceptable yet non‐superior differentiation (area under curve 0.64). kD and degree in medial prefrontal cortex (MPFC) correlated with CI (kD/MPFC Spearman's ρ = 0.32/−0.45, p = 0.019/0.001, n = 55). kD also explained fatigue (ρ = −0.34, p = 0.010, n = 56) but neither gait nor disability.kD is a potential biomarker of CI and fatigue warranting further validation. [ABSTRACT FROM AUTHOR]

Published
2024
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