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Zavaglia M, Malherbe C, Schlaadt S, Nachev P, Hilgetag CC. Ground-truth validation of uni- and multivariate lesion inference approaches. Brain Commun 2024; 6:fcae251. [PMID: 39291162 PMCID: PMC11406464 DOI: 10.1093/braincomms/fcae251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 05/15/2024] [Accepted: 07/25/2024] [Indexed: 09/19/2024] Open
Abstract
Lesion analysis aims to reveal the causal contributions of brain regions to brain functions. Various strategies have been used for such lesion inferences. These approaches can be broadly categorized as univariate or multivariate methods. Here we analysed data from 581 patients with acute ischaemic injury, parcellated into 41 Brodmann areas, and systematically investigated the inferences made by two univariate and two multivariate lesion analysis methods via ground-truth simulations, in which we defined a priori contributions of brain areas to assumed brain function. Particularly, we analysed single-region models, with only single areas presumed to contribute functionally, and multiple-region models, with two contributing regions that interacted in a synergistic, redundant or mutually inhibitory mode. The functional contributions could vary in proportion to the lesion damage or in a binary way. The analyses showed a considerably better performance of the tested multivariate than univariate methods in terms of accuracy and mis-inference error. Specifically, the univariate approaches of Lesion Symptom Mapping as well as Lesion Symptom Correlation mis-inferred substantial contributions from several areas even in the single-region models, and also after accounting for lesion size. By contrast, the multivariate approaches of Multi-Area Pattern Prediction, which is based on machine learning, and Multi-perturbation Shapley value Analysis, based on coalitional game theory, delivered consistently higher accuracy and specificity. Our findings suggest that the tested multivariate approaches produce largely reliable lesion inferences, without requiring lesion size consideration, while the application of the univariate methods may yield substantial mis-localizations that limit the reliability of functional attributions.
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Affiliation(s)
- Melissa Zavaglia
- University Medical Center Hamburg-Eppendorf, Institute of Computational Neuroscience, 20246 Hamburg, Germany
- Jacobs University, Focus Area Health, 28759 Bremen, Germany
- Technical University Munich, MIRMI-Munich Institute of Robotics and Machine Intelligence, 80992 Munich, Germany
| | - Caroline Malherbe
- University Medical Center Hamburg-Eppendorf, Institute of Computational Neuroscience, 20246 Hamburg, Germany
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Sebastian Schlaadt
- University Medical Center Hamburg-Eppendorf, Institute of Computational Neuroscience, 20246 Hamburg, Germany
| | - Parashkev Nachev
- Institute of Neurology, University College London, WC1E 6BT London, United Kingdom
| | - Claus C Hilgetag
- University Medical Center Hamburg-Eppendorf, Institute of Computational Neuroscience, 20246 Hamburg, Germany
- Department of Health Sciences, Boston University, MA 02215 Boston, USA
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Ofir‐Geva S, Meilijson I, Frenkel‐Toledo S, Soroker N. Use of multi-perturbation Shapley analysis in lesion studies of functional networks: The case of upper limb paresis. Hum Brain Mapp 2023; 44:1320-1343. [PMID: 36206326 PMCID: PMC9921264 DOI: 10.1002/hbm.26105] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 09/07/2022] [Accepted: 09/19/2022] [Indexed: 11/07/2022] Open
Abstract
Understanding the impact of variation in lesion topography on the expression of functional impairments following stroke is important, as it may pave the way to modeling structure-function relations in statistical terms while pointing to constraints for adaptive remapping and functional recovery. Multi-perturbation Shapley-value analysis (MSA) is a relatively novel game-theoretical approach for multivariate lesion-symptom mapping. In this methodological paper, we provide a comprehensive explanation of MSA. We use synthetic data to assess the method's accuracy and perform parameter optimization. We then demonstrate its application using a cohort of 107 first-event subacute stroke patients, assessed for upper limb (UL) motor impairment (Fugl-Meyer Assessment scale). Under the conditions tested, MSA could correctly detect simulated ground-truth lesion-symptom relationships with a sensitivity of 75% and specificity of ~90%. For real behavioral data, MSA disclosed a strong hemispheric effect in the relative contribution of specific regions-of-interest (ROIs): poststroke UL motor function was mostly contributed by damage to ROIs associated with movement planning (supplementary motor cortex and superior frontal gyrus) following left-hemispheric damage (LHD) and by ROIs associated with movement execution (primary motor and somatosensory cortices and the ventral brainstem) following right-hemispheric damage (RHD). Residual UL motor ability following LHD was found to depend on a wider array of brain structures compared to the residual motor ability of RHD patients. The results demonstrate that MSA can provide a unique insight into the relative importance of different hubs in neural networks, which is difficult to obtain using standard univariate methods.
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Affiliation(s)
- Shay Ofir‐Geva
- Department of Neurological RehabilitationLoewenstein Rehabilitation Medical CenterRaananaIsrael
- Department of Rehabilitation Medicine, Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Isaac Meilijson
- School of Mathematical SciencesTel Aviv UniversityTel AvivIsrael
| | | | - Nachum Soroker
- Department of Neurological RehabilitationLoewenstein Rehabilitation Medical CenterRaananaIsrael
- Department of Rehabilitation Medicine, Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
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3
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Fakhar K, Hilgetag CC. Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. PLoS Comput Biol 2022; 18:e1010250. [PMID: 35714139 PMCID: PMC9246164 DOI: 10.1371/journal.pcbi.1010250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 06/30/2022] [Accepted: 05/25/2022] [Indexed: 11/24/2022] Open
Abstract
Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach has gained new prominence through the arrival of modern perturbation techniques with unprecedented levels of spatiotemporal precision. While inferences drawn from brain perturbations are conceptually powerful, they face methodological difficulties. Particularly, they are challenged to disentangle the true causal contributions of the involved elements, since often functions arise from coalitions of distributed, interacting elements, and localized perturbations have unknown global consequences. To elucidate these limitations, we systematically and exhaustively lesioned a small artificial neural network (ANN) playing a classic arcade game. We determined the functional contributions of all nodes and links, contrasting results from sequential single-element perturbations with simultaneous perturbations of multiple elements. We found that lesioning individual elements, one at a time, produced biased results. By contrast, multi-site lesion analysis captured crucial details that were missed by single-site lesions. We conclude that even small and seemingly simple ANNs show surprising complexity that needs to be addressed by multi-lesioning for a coherent causal characterization.
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Affiliation(s)
- Kayson Fakhar
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
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4
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Malherbe C, Cheng B, Königsberg A, Cho TH, Ebinger M, Endres M, Fiebach JB, Fiehler J, Galinovic I, Puig J, Thijs V, Lemmens R, Muir KW, Nighoghossian N, Pedraza S, Simonsen CZ, Wouters A, Gerloff C, Hilgetag CC, Thomalla G. Game-theoretical mapping of fundamental brain functions based on lesion deficits in acute stroke. Brain Commun 2021; 3:fcab204. [PMID: 34585140 PMCID: PMC8473841 DOI: 10.1093/braincomms/fcab204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/21/2021] [Accepted: 07/01/2021] [Indexed: 11/12/2022] Open
Abstract
Lesion analysis is a fundamental and classical approach for inferring the causal contributions of brain regions to brain function. However, many studies have been limited by the shortcomings of methodology or clinical data. Aiming to overcome these limitations, we here use an objective multivariate approach based on game theory, Multi-perturbation Shapley value Analysis, in conjunction with data from a large cohort of 394 acute stroke patients, to derive causal contributions of brain regions to four principal functional components of the widely used National Institutes of Health Stroke Score measure. The analysis was based on a high-resolution parcellation of the brain into 294 grey and white matter regions. Through initial lesion symptom mapping for identifying all potential candidate regions and repeated iterations of the game-theoretical approach to remove non-significant contributions, the analysis derived the smallest sets of regions contributing to each of the four principal functional components as well as functional interactions among the regions. Specifically, the factor 'language and consciousness' was related to contributions of cortical regions in the left hemisphere, including the prefrontal gyrus, the middle frontal gyrus, the ventromedial putamen and the inferior frontal gyrus. Right and left motor functions were associated with contributions of the left and right dorsolateral putamen and the posterior limb of the internal capsule, correspondingly. Moreover, the superior corona radiata and the paracentral lobe of the right hemisphere as well as the right caudal area 23 of the cingulate gyrus were mainly related to left motor function, while the prefrontal gyrus, the external capsule and the sagittal stratum fasciculi of the left hemisphere contributed to right motor function. Our approach demonstrates a practically feasible strategy for applying an objective lesion inference method to a high-resolution map of the human brain and distilling a small, characteristic set of grey and white matter structures contributing to fundamental brain functions. In addition, we present novel findings of synergistic interactions between brain regions that provide insight into the functional organization of brain networks.
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Affiliation(s)
- Caroline Malherbe
- University Medical Center Hamburg-Eppendorf, Institute of Computational Neuroscience, Hamburg, Germany.,Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alina Königsberg
- Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tae-Hee Cho
- Neurology, Université Claude Bernard Lyon 1, Lyon, France
| | - Martin Ebinger
- Centrum für Schlaganfallforschung Berlin (CSB), Charité, Universitätsmedizin Berlin, Berlin, Germany.,Medical Park Berlin Humboldtmühle, 13507 Berlin, Germany
| | - Matthias Endres
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jochen B Fiebach
- Centrum für Schlaganfallforschung Berlin (CSB), Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ivana Galinovic
- Centrum für Schlaganfallforschung Berlin (CSB), Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Josep Puig
- Department of Radiology, Institut de Diagnostic per la Image (IDI), Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI), Girona, Spain
| | - Vincent Thijs
- Stroke, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Robin Lemmens
- Neurology, UZ Leuven, Leuven, Belgium.,VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, Leuven, Belgium
| | - Keith W Muir
- Institute of Neuroscience & Psychology, University of Glasgow, Glasgow, UK
| | | | - Salvador Pedraza
- Department of Radiology, Institut de Diagnostic per la Image (IDI), Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI), Girona, Spain
| | - Claus Z Simonsen
- Department of Neurology, Aarhus University Hospital, Aarhus N, Denmark
| | - Anke Wouters
- Neurology, UZ Leuven, Leuven, Belgium.,VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, Leuven, Belgium
| | - Christian Gerloff
- Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claus C Hilgetag
- University Medical Center Hamburg-Eppendorf, Institute of Computational Neuroscience, Hamburg, Germany.,Department of Health Sciences, Boston University, Boston, MA, USA
| | - Götz Thomalla
- Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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6
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Toba MN, Zavaglia M, Rastelli F, Valabrégue R, Pradat‐Diehl P, Valero‐Cabré A, Hilgetag CC. Game theoretical mapping of causal interactions underlying visuo-spatial attention in the human brain based on stroke lesions. Hum Brain Mapp 2017; 38:3454-3471. [PMID: 28419682 PMCID: PMC5645205 DOI: 10.1002/hbm.23601] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 03/22/2017] [Accepted: 03/23/2017] [Indexed: 01/08/2023] Open
Abstract
Anatomical studies conducted in neurological conditions have developed our understanding of the causal relationships between brain lesions and their clinical consequences. The analysis of lesion patterns extended across brain networks has been particularly useful in offering new insights on brain-behavior relationships. Here we applied multiperturbation Shapley value Analysis (MSA), a multivariate method based on coalitional game theory inferring causal regional contributions to specific behavioral outcomes from the characteristic functional deficits after stroke lesions. We established the causal patterns of contributions and interactions of nodes of the attentional orienting network on the basis of lesion and behavioral data from 25 right hemisphere stroke patients tested in visuo-spatial attention tasks. We calculated the percentage of damaged voxels for five right hemisphere cortical regions contributing to attentional orienting, involving seven specific Brodmann Areas (BA): Frontal Eye Fields, (FEF-BA6), Intraparietal Sulcus (IPS-BA7), Inferior Frontal Gyrus (IFG-BA44/BA45), Temporo-Parietal Junction (TPJ-BA39/BA40), and Inferior Occipital Gyrus (IOG-BA19). We computed the MSA contributions of these seven BAs to three behavioral clinical tests (line bisection, bells cancellation, and letter cancelation). Our analyses indicated IPS as the main contributor to the attentional orienting and also revealed synergistic influences among IPS, TPJ, and IOG (for bells cancellation and line bisection) and between TPJ and IFG (for bells and letter cancellation tasks). The findings demonstrate the ability of the MSA approach to infer plausible causal contributions of relevant right hemisphere sites in poststroke visuo-spatial attention and awareness disorders. Hum Brain Mapp 38:3454-3471, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Monica N. Toba
- Cerebral Dynamics, Plasticity and Rehabilitation Team, Frontlab, Brain and Spine Institute, ICMParisFrance
- Sorbonne Universités, UPMC Paris 06, Inserm UMR S 1127, CNRS UMR 7225, F‐75013, & IHU‐A‐ICMParisFrance
- Laboratory of Functional Neurosciences (EA 4559)University Hospital of Amiens and University of Picardy Jules VerneAmiensFrance
| | - Melissa Zavaglia
- Department of Computational NeuroscienceUniversity Medical Center Hamburg‐EppendorfHamburgGermany
- School of Engineering and ScienceJacobs University BremenGermany
| | - Federica Rastelli
- Cerebral Dynamics, Plasticity and Rehabilitation Team, Frontlab, Brain and Spine Institute, ICMParisFrance
- Sorbonne Universités, UPMC Paris 06, Inserm UMR S 1127, CNRS UMR 7225, F‐75013, & IHU‐A‐ICMParisFrance
- AP‐HP, HxU Pitié‐Salpêtrière‐Charles‐Foix, service de Médecine Physique et de Réadaptation & PHRC Regional NEGLECTParisFrance
| | - Romain Valabrégue
- Sorbonne Universités, UPMC Paris 06, Inserm UMR S 1127, CNRS UMR 7225, F‐75013, & IHU‐A‐ICMParisFrance
- Centre for NeuroImaging Research ‐ CENIR, Brain and Spine Institute, ICM, Paris, France
| | - Pascale Pradat‐Diehl
- AP‐HP, HxU Pitié‐Salpêtrière‐Charles‐Foix, service de Médecine Physique et de Réadaptation & PHRC Regional NEGLECTParisFrance
- GRC‐UPMC n° 18‐ Handicap cognitif et réadaptationParisFrance
| | - Antoni Valero‐Cabré
- Cerebral Dynamics, Plasticity and Rehabilitation Team, Frontlab, Brain and Spine Institute, ICMParisFrance
- Sorbonne Universités, UPMC Paris 06, Inserm UMR S 1127, CNRS UMR 7225, F‐75013, & IHU‐A‐ICMParisFrance
- Laboratory for Cerebral Dynamics, Plasticity & Rehabilitation, Boston University School of MedicineBostonMassachusetts
- Cognitive Neuroscience and Information Technology Research Program, Open University of Catalonia (UOC)Barcelona08035Spain
| | - Claus C. Hilgetag
- Department of Computational NeuroscienceUniversity Medical Center Hamburg‐EppendorfHamburgGermany
- Department of Health SciencesBoston UniversityBostonMassachusetts
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