Topological Signatures of Altered Brain Network Centrality in ADHD: A TDA Mapper Study

Ali Nabi Duman
Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations, PMLR 282:160-173, 2026.

Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder widely hypothesized to stem from alterations in large-scale brain connectivity. However, neuroimaging studies have yielded inconsistent findings, motivating the need for advanced analytical methods capable of capturing the complex, dynamic nature of brain function. In this study, we apply Topological Data Analysis (TDA), specifically the Mapper algorithm, to resting-state functional magnetic resonance imaging (fMRI) data from the multi-site ADHD-200 dataset. We constructed graphical representations of brain state dynamics for participants with ADHD and typically developing controls (TDC) from three independent sites. The topological structure of these graphs was quantified using network centrality measures (betweenness, closeness, and degree). Our results reveal a significant increase in centrality measures in the ADHD group compared to TDC in three cohorts. Furthermore, we observed a weak but significant positive correlation between centrality and symptom severity in one of the cohorts. We conclude that TDA-derived centrality measures can detect alterations in the dynamical organization of brain activity in ADHD, potentially reflecting a less efficient or more rigid network topology.

Cite this Paper

BibTeX
@InProceedings{pmlr-v282-duman26a, title = {Topological Signatures of Altered Brain Network Centrality in ADHD: A TDA Mapper Study}, author = {Duman, Ali Nabi}, booktitle = {Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations}, pages = {160--173}, year = {2026}, editor = {Acosta, Francisco and Azeglio, Simone and Tolooshams, Bahareh and van de Geijn, Chase and Shewmake, Christian and Sanborn, Sophia and Miolane, Nina}, volume = {282}, series = {Proceedings of Machine Learning Research}, month = {14 Dec 2024--07 Dec 2025}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v282/main/assets/duman26a/duman26a.pdf}, url = {https://proceedings.mlr.press/v282/duman26a.html}, abstract = {Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder widely hypothesized to stem from alterations in large-scale brain connectivity. However, neuroimaging studies have yielded inconsistent findings, motivating the need for advanced analytical methods capable of capturing the complex, dynamic nature of brain function. In this study, we apply Topological Data Analysis (TDA), specifically the Mapper algorithm, to resting-state functional magnetic resonance imaging (fMRI) data from the multi-site ADHD-200 dataset. We constructed graphical representations of brain state dynamics for participants with ADHD and typically developing controls (TDC) from three independent sites. The topological structure of these graphs was quantified using network centrality measures (betweenness, closeness, and degree). Our results reveal a significant increase in centrality measures in the ADHD group compared to TDC in three cohorts. Furthermore, we observed a weak but significant positive correlation between centrality and symptom severity in one of the cohorts. We conclude that TDA-derived centrality measures can detect alterations in the dynamical organization of brain activity in ADHD, potentially reflecting a less efficient or more rigid network topology.} }
Endnote
%0 Conference Paper %T Topological Signatures of Altered Brain Network Centrality in ADHD: A TDA Mapper Study %A Ali Nabi Duman %B Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations %C Proceedings of Machine Learning Research %D 2026 %E Francisco Acosta %E Simone Azeglio %E Bahareh Tolooshams %E Chase van de Geijn %E Christian Shewmake %E Sophia Sanborn %E Nina Miolane %F pmlr-v282-duman26a %I PMLR %P 160--173 %U https://proceedings.mlr.press/v282/duman26a.html %V 282 %X Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder widely hypothesized to stem from alterations in large-scale brain connectivity. However, neuroimaging studies have yielded inconsistent findings, motivating the need for advanced analytical methods capable of capturing the complex, dynamic nature of brain function. In this study, we apply Topological Data Analysis (TDA), specifically the Mapper algorithm, to resting-state functional magnetic resonance imaging (fMRI) data from the multi-site ADHD-200 dataset. We constructed graphical representations of brain state dynamics for participants with ADHD and typically developing controls (TDC) from three independent sites. The topological structure of these graphs was quantified using network centrality measures (betweenness, closeness, and degree). Our results reveal a significant increase in centrality measures in the ADHD group compared to TDC in three cohorts. Furthermore, we observed a weak but significant positive correlation between centrality and symptom severity in one of the cohorts. We conclude that TDA-derived centrality measures can detect alterations in the dynamical organization of brain activity in ADHD, potentially reflecting a less efficient or more rigid network topology.
APA
Duman, A.N.. (2026). Topological Signatures of Altered Brain Network Centrality in ADHD: A TDA Mapper Study. Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations, in Proceedings of Machine Learning Research 282:160-173 Available from https://proceedings.mlr.press/v282/duman26a.html.

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