1
|
Chen B, Sun W, Yan C. Controllability in attention deficit hyperactivity disorder brains. Cogn Neurodyn 2024; 18:2003-2013. [PMID: 39104674 PMCID: PMC11297865 DOI: 10.1007/s11571-023-10063-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 11/23/2023] [Accepted: 12/19/2023] [Indexed: 08/07/2024] Open
Abstract
The role of network metrics in exploring brain networks of mental illness is crucial. This study focuses on quantifying a node controllability index (CA-scores) and developing a novel framework for studying the dysfunction of attention deficit hyperactivity disorder (ADHD) brains. By analyzing fMRI data from 143 healthy controls and 102 ADHD patients, the controllability metric reveals distinct differences in nodes (brain regions) and subsystems (functional modules). There are significantly atypical CA-scores in the Rolandic operculum, superior medial orbitofrontal cortex, insula, posterior cingulate gyrus, supramarginal gyrus, angular gyrus, precuneus, heschl gyrus, and superior temporal gyrus of ADHD patients. A comparison with measures of connection strength, eigenvector centrality, and topology entropy suggests that the controllability index may be more effective in identifying abnormal regions in ADHD brains. Furthermore, our controllability index could be extended to investigate functional networks associated with other psychiatric disorders. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10063-z.
Collapse
Affiliation(s)
- Bo Chen
- Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, 310018 People’s Republic of China
| | - Weigang Sun
- Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, 310018 People’s Republic of China
| | - Chuankui Yan
- College of Mathematics and Physics, Wenzhou University, Wenzhou, 325024 People’s Republic of China
| |
Collapse
|
2
|
Jamalabadi H, Hahn T, Winter NR, Nozari E, Ernsting J, Meinert S, Leehr EJ, Dohm K, Bauer J, Pfarr JK, Stein F, Thomas-Odenthal F, Brosch K, Mauritz M, Gruber M, Repple J, Kaufmann T, Krug A, Nenadić I, Kircher T, Dannlowski U, Derntl B. Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers. Front Aging Neurosci 2023; 15:1085153. [PMID: 37920384 PMCID: PMC10618679 DOI: 10.3389/fnagi.2023.1085153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 09/25/2023] [Indexed: 11/04/2023] Open
Abstract
Background Controllability is a measure of the brain's ability to orchestrate neural activity which can be quantified in terms of properties of the brain's network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging. Methods In a sample of 814 healthy individuals (aged 33.9 ± 12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children. We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability). Results and conclusion In nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.
Collapse
Affiliation(s)
- Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R. Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Erfan Nozari
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
- Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, United States
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Elisabeth J. Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Marco Mauritz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| |
Collapse
|
3
|
Stocker JE, Nozari E, van Vugt M, Jansen A, Jamalabadi H. Network controllability measures of subnetworks: implications for neurosciences. J Neural Eng 2023; 20. [PMID: 36633267 DOI: 10.1088/1741-2552/acb256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Objective:Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics. Critically though, contrary to the engineering context in which control theory was originally developed, a mathematical understanding of the network nodes and connections in neurosciences cannot be assumed. For instance, in the case of psychological systems such as those studied to understand psychiatric disorders, a potentially large set of related variables are unknown. As such, while the measures offered by network control theory would be mathematically correct, in that they can be calculated with high precision, they could have little translational values with respect to their putative role suggested by controllability metrics. It is therefore critical to understand if and how the controllability metrics estimated over subnetworks would deviate, if access to the complete set of variables, as is common in neurosciences, cannot be taken for granted.Approach:In this paper, we use a host of simulations based on synthetic as well as structural magnetic resonance imaging (MRI) data to study the potential deviation of controllability metrics in sub- compared to the full networks. Specifically, we estimate average- and modal-controllability, two of the most widely used controllability measures in neurosciences, in a large number of settings where we systematically vary network type, network size, and edge density.Main results:We find out, across all network types we test, that average and modal controllability are systematically, over- or underestimated depending on the number of nodes in the sub- and full network and the edge density.Significance:Finally, we provide formal theoretical proof that our observations generalize to any network type and discuss the ramifications of this systematic bias and potential solutions to alleviate the problem.
Collapse
Affiliation(s)
- Julia Elina Stocker
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Erfan Nozari
- Department of Mechanical Engineering, University of California, Riverside, United States of America.,Department of Electrical and Computer Engineering, University of California, Riverside, United States of America.,Department of Bioengineering, University of California, Riverside, United States of America
| | - Marieke van Vugt
- Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany.,Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| |
Collapse
|
4
|
Hahn T, Jamalabadi H, Nozari E, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Grumbach P, Fisch L, Leenings R, Sarink K, Blanke J, Vennekate LK, Emden D, Opel N, Grotegerd D, Enneking V, Meinert S, Borgers T, Klug M, Leehr EJ, Dohm K, Heindel W, Gross J, Dannlowski U, Redlich R, Repple J. Towards a network control theory of electroconvulsive therapy response. PNAS NEXUS 2023; 2:pgad032. [PMID: 36874281 PMCID: PMC9982063 DOI: 10.1093/pnasnexus/pgad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/09/2022] [Accepted: 01/17/2023] [Indexed: 02/04/2023]
Abstract
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.
Collapse
Affiliation(s)
- Tim Hahn
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Erfan Nozari
- Department of Mechanical Engineering, University of California, 92521 Riverside, USA
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Marco J Mauritz
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Pascal Grumbach
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Julian Blanke
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Leon Kleine Vennekate
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Verena Enneking
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Institute for Translational Neuroscience, University of Münster, 48149 Münster, Germany
| | - Tiana Borgers
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Melissa Klug
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Walter Heindel
- Institute of Clinical Radiology, University of Münster, 48149 Münster, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University Hospital Münster, 48149 Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Department of Psychology, University of Halle, 06099 Halle (Saale), Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| |
Collapse
|
5
|
Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
Collapse
Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
| |
Collapse
|