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Seghier ML, Price CJ. Interpreting and validating complexity and causality in lesion-symptom prognoses. Brain Commun 2023; 5:fcad178. [PMID: 37346231 PMCID: PMC10279811 DOI: 10.1093/braincomms/fcad178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/08/2023] [Accepted: 06/04/2023] [Indexed: 06/23/2023] Open
Abstract
This paper considers the steps needed to generate pragmatic and interpretable lesion-symptom mappings that can be used for clinically reliable prognoses. The novel contributions are 3-fold. We first define and inter-relate five neurobiological and five methodological constraints that need to be accounted for when interpreting lesion-symptom associations and generating synthetic lesion data. The first implication is that, because of these constraints, lesion-symptom mapping needs to focus on probabilistic relationships between Lesion and Symptom, with Lesion as a multivariate spatial pattern, Symptom as a time-dependent behavioural profile and evidence that Lesion raises the probability of Symptom. The second implication is that in order to assess the strength of probabilistic causality, we need to distinguish between causal lesion sites, incidental lesion sites, spared but dysfunctional sites and intact sites, all of which might affect the accuracy of the predictions and prognoses generated. We then formulate lesion-symptom mappings in logical notations, including combinatorial rules, that are then used to evaluate and better understand complex brain-behaviour relationships. The logical and theoretical framework presented applies to any type of neurological disorder but is primarily discussed in relationship to stroke damage. Accommodating the identified constraints, we discuss how the 1965 Bradford Hill criteria for inferring probabilistic causality, post hoc, from observed correlations in epidemiology-can be applied to lesion-symptom mapping in stroke survivors. Finally, we propose that rather than rely on post hoc evaluation of how well the causality criteria have been met, the neurobiological and methodological constraints should be addressed, a priori, by changing the experimental design of lesion-symptom mappings and setting up an open platform to share and validate the discovery of reliable and accurate lesion rules that are clinically useful.
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Affiliation(s)
- Mohamed L Seghier
- Correspondence to: Mohamed Seghier Department of Biomedical Engineering Khalifa University of Science and Technology PO BOX: 127788, Abu Dhabi, UAE E-mail:
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
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2
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Sperber C, Gallucci L, Umarova R. The low dimensionality of post-stroke cognitive deficits: it's the lesion anatomy! Brain 2023; 146:2443-2452. [PMID: 36408903 DOI: 10.1093/brain/awac443] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/01/2022] [Accepted: 11/10/2022] [Indexed: 10/06/2023] Open
Abstract
For years, dissociation studies on neurological single-case patients with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value. Still, associational computational methods for dimensionality reduction-such as principal component analysis or factor analysis-became popular for the identification of fundamental cognitive functions and to understand human cognitive brain architecture from post-stroke neuropsychological profiles. In the present in silico study with lesion imaging of 300 stroke patients, we investigated the dimensionality of artificial simulated neuropsychological profiles that exclusively contained independent fundamental cognitive functions without any underlying low-dimensional cognitive architecture. Still, the anatomy of stroke lesions alone was sufficient to create a dependence between variables that allowed a low-dimensional description of the data with principal component analysis. All criteria that we used to estimate the dimensionality of data, including the Kaiser criterion, were strongly affected by lesion anatomy, while the Joliffe criterion provided the least affected estimates. The dimensionality of profiles was reduced by 62-70% for the Kaiser criterion, up to the degree that is commonly found in neuropsychological studies on actual cognitive measures. The interpretability of such low-dimensional factors as deficits of fundamental cognitive functions and their provided insights into human cognitive architecture thus seem to be severely limited, and the heavy focus of current cognitive neuroscience on group studies and associations calls for improvements. We suggest that qualitative criteria and dissociation patterns could be used to refine estimates for the dimensionality of the cognitive architecture behind post-stroke deficits. Further, given the strong impact of lesion anatomy on the associational structure of data, we see the need for further optimization of interpretation strategies of computational factors in post-stroke lesion studies of cognitive deficits.
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Affiliation(s)
- Christoph Sperber
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Laura Gallucci
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Roza Umarova
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
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Yoo JH, Chong B, Barber PA, Stinear C, Wang A. Predicting Motor Outcomes Using Atlas-Based Voxel Features of Post-Stroke Neuroimaging: A Scoping Review. Neurorehabil Neural Repair 2023:15459683231173668. [PMID: 37191349 DOI: 10.1177/15459683231173668] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
BACKGROUND Atlas-based voxel features have the potential to aid motor outcome prognostication after stroke, but are seldom used in clinically feasible prediction models. This could be because neuroimaging feature development is a non-standardized, complex, multistep process. This is a barrier to entry for researchers and poses issues for reproducibility and validation in a field of research where sample sizes are typically small. OBJECTIVES The primary aim of this review is to describe the methodologies currently used in motor outcome prediction studies using atlas-based voxel neuroimaging features. Another aim is to identify neuroanatomical regions commonly used for motor outcome prediction. METHODS A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol was constructed and OVID Medline and Scopus databases were searched for relevant studies. The studies were then screened and details about imaging modality, image acquisition, image normalization, lesion segmentation, region of interest determination, and imaging measures were extracted. RESULTS Seventeen studies were included and examined. Common limitations were a lack of detailed reporting on image acquisition and the specific brain templates used for normalization and a lack of clear reasoning behind the atlas or imaging measure selection. A wide variety of sensorimotor regions relate to motor outcomes and there is no consensus use of one single sensorimotor atlas for motor outcome prediction. CONCLUSION There is an ongoing need to validate imaging predictors and further improve methodological techniques and reporting standards in neuroimaging feature development for motor outcome prediction post-stroke.
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Affiliation(s)
- Ji-Hun Yoo
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Benjamin Chong
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Medicine, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Peter Alan Barber
- Department of Medicine, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Cathy Stinear
- Department of Medicine, The University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Medicine, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
- Centre for Medical Imaging, The University of Auckland, Auckland, New Zealand
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Kernbach JM, Hartwigsen G, Lim JS, Bae HJ, Yu KH, Schlaug G, Bonkhoff A, Rost NS, Bzdok D. Bayesian stroke modeling details sex biases in the white matter substrates of aphasia. Commun Biol 2023; 6:354. [PMID: 37002267 PMCID: PMC10066402 DOI: 10.1038/s42003-023-04733-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
Ischemic cerebrovascular events often lead to aphasia. Previous work provided hints that such strokes may affect women and men in distinct ways. Women tend to suffer strokes with more disabling language impairment, even if the lesion size is comparable to men. In 1401 patients, we isolate data-led representations of anatomical lesion patterns and hand-tailor a Bayesian analytical solution to carefully model the degree of sex divergence in predicting language outcomes ~3 months after stroke. We locate lesion-outcome effects in the left-dominant language network that highlight the ventral pathway as a core lesion focus across different tests of language performance. We provide detailed evidence for sex-specific brain-behavior associations in the domain-general networks associated with cortico-subcortical pathways, with unique contributions of the fornix in women and cingular fiber bundles in men. Our collective findings suggest diverging white matter substrates in how stroke causes language deficits in women and men. Clinically acknowledging such sex disparities has the potential to improve personalized treatment for stroke patients worldwide.
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Affiliation(s)
- Julius M Kernbach
- Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), RWTH Aachen University Hospital, Aachen, Germany
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Music, Neuroimaging, and Stroke Recovery Laboratory, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Gesa Hartwigsen
- Max Planck Institute for Human Cognitive and Brain Sciences, Lise Meitner Research Group Cognition and Plasticity, Leipzig, Germany
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee-Joon Bae
- Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Kyung-Ho Yu
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Gottfried Schlaug
- Music, Neuroimaging, and Stroke Recovery Laboratory, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Anna Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, Montreal, QC, Canada.
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada.
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5
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Seghier ML. The elusive metric of lesion load. Brain Struct Funct 2023; 228:703-716. [PMID: 36947181 DOI: 10.1007/s00429-023-02630-1] [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: 12/19/2022] [Accepted: 03/15/2023] [Indexed: 03/23/2023]
Abstract
One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.
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Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
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Sperber C, Gallucci L, Smaczny S, Umarova R. Bayesian lesion-deficit inference with Bayes factor mapping: Key advantages, limitations, and a toolbox. Neuroimage 2023; 271:120008. [PMID: 36914109 DOI: 10.1016/j.neuroimage.2023.120008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/15/2023] Open
Abstract
Statistical lesion-symptom mapping is largely dominated by frequentist approaches with null hypothesis significance testing. They are popular for mapping functional brain anatomy but are accompanied by some challenges and limitations. The typical analysis design and the structure of clinical lesion data are linked to the multiple comparison problem, an association problem, limitations to statistical power, and a lack of insights into evidence for the null hypothesis. Bayesian lesion deficit inference (BLDI) could be an improvement as it collects evidence for the null hypothesis, i.e. the absence of effects, and does not accumulate α-errors with repeated testing. We implemented BLDI by Bayes factor mapping with Bayesian t-tests and general linear models and evaluated its performance in comparison to frequentist lesion-symptom mapping with a permutation-based family-wise error correction. We mapped the voxel-wise neural correlates of simulated deficits in an in-silico-study with 300 stroke patients, and the voxel-wise and disconnection-wise neural correlates of phonemic verbal fluency and constructive ability in 137 stroke patients. Both the performance of frequentist and Bayesian lesion-deficit inference varied largely across analyses. In general, BLDI could find areas with evidence for the null hypothesis and was statistically more liberal in providing evidence for the alternative hypothesis, i.e. the identification of lesion-deficit associations. BLDI performed better in situations in which the frequentist method is typically strongly limited, for example with on average small lesions and in situations with low power, where BLDI also provided unprecedented transparency in terms of the informative value of the data. On the other hand, BLDI suffered more from the association problem, which led to a pronounced overshoot of lesion-deficit associations in analyses with high statistical power. We further implemented a new approach to lesion size control, adaptive lesion size control, that, in many situations, was able to counter the limitations imposed by the association problem, and increased true evidence both for the null and the alternative hypothesis. In summary, our results suggest that BLDI is a valuable addition to the method portfolio of lesion-deficit inference with some specific and exclusive advantages: it deals better with smaller lesions and low statistical power (i.e. small samples and effect sizes) and identifies regions with absent lesion-deficit associations. However, it is not superior to established frequentist approaches in all respects and therefore not to be seen as a general replacement. To make Bayesian lesion-deficit inference widely accessible, we published an R toolkit for the analysis of voxel-wise and disconnection-wise data.
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Affiliation(s)
- Christoph Sperber
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland.
| | - Laura Gallucci
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Stefan Smaczny
- Centre of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Roza Umarova
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
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van Grinsven EE, Smits AR, van Kessel E, Raemaekers MAH, de Haan EHF, Huenges Wajer IMC, Ruijters VJ, Philippens MEP, Verhoeff JJC, Ramsey NF, Robe PAJT, Snijders TJ, van Zandvoort MJE. The impact of etiology in lesion-symptom mapping - A direct comparison between tumor and stroke. Neuroimage Clin 2022; 37:103305. [PMID: 36610310 PMCID: PMC9850191 DOI: 10.1016/j.nicl.2022.103305] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/25/2022]
Abstract
INTRODUCTION Lesion-symptom mapping is a key tool in understanding the relationship between brain structures and behavior. However, the behavioral consequences of lesions from different etiologies may vary because of how they affect brain tissue and how they are distributed. The inclusion of different etiologies would increase the statistical power but has been critically debated. Meanwhile, findings from lesion studies are a valuable resource for clinicians and used across different etiologies. Therefore, the main objective of the present study was to directly compare lesion-symptom maps for memory and language functions from two populations, a tumor versus a stroke population. METHODS Data from two different studies were combined. Both the brain tumor (N = 196) and stroke (N = 147) patient populations underwent neuropsychological testing and an MRI, pre-operatively for the tumor population and within three months after stroke. For this study, we selected two internationally widely used standardized cognitive tasks, the Rey Auditory Verbal Learning Test and the Verbal Fluency Test. We used a state-of-the-art machine learning-based, multivariate voxel-wise approach to produce lesion-symptom maps for these cognitive tasks for both populations separately and combined. RESULTS Our lesion-symptom mapping results for the separate patient populations largely followed the expected neuroanatomical pattern based on previous literature. Substantial differences in lesion distribution hindered direct comparison. Still, in brain areas with adequate coverage in both groups, considerable LSM differences between the two populations were present for both memory and fluency tasks. Post-hoc analyses of these locations confirmed that the cognitive consequences of focal brain damage varied between etiologies. CONCLUSION The differences in the lesion-symptom maps between the stroke and tumor population could partly be explained by differences in lesion volume and topography. Despite these methodological limitations, both the lesion-symptom mapping results and the post-hoc analyses confirmed that etiology matters when investigating the cognitive consequences of lesions with lesion-symptom mapping. Therefore, caution is advised with generalizing lesion-symptom results across etiologies.
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Affiliation(s)
- E E van Grinsven
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands.
| | - A R Smits
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - E van Kessel
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - M A H Raemaekers
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - E H F de Haan
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; St. Hugh's College, Oxford University, UK
| | - I M C Huenges Wajer
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Department of Experimental Psychology and Helmholtz Institute, Utrecht University, the Netherlands
| | - V J Ruijters
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - M E P Philippens
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - J J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - N F Ramsey
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - P A J T Robe
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - T J Snijders
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - M J E van Zandvoort
- Department of Neurology & Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Department of Experimental Psychology and Helmholtz Institute, Utrecht University, the Netherlands
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Ren W, Jia C, Zhou Y, Zhao J, Wang B, Yu W, Li S, Hu Y, Zhang H. A precise language network revealed by the independent component-based lesion mapping in post-stroke aphasia. Front Neurol 2022; 13:981653. [PMID: 36247758 PMCID: PMC9561861 DOI: 10.3389/fneur.2022.981653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022] Open
Abstract
Brain lesion mapping studies have provided the strongest evidence regarding the neural basis of cognition. However, it remained a problem to identify symptom-specific brain networks accounting for observed clinical and neuroanatomical heterogeneity. Independent component analysis (ICA) is a statistical method that decomposes mixed signals into multiple independent components. We aimed to solve this issue by proposing an independent component-based lesion mapping (ICLM) method to identify the language network in patients with moderate to severe post-stroke aphasia. Lesions were first extracted from 49 patients with post-stroke aphasia as masks applied to fMRI data in a cohort of healthy participants to calculate the functional connectivity (FC) within the masks and non-mask brain voxels. ICA was further performed on a reformatted FC matrix to extract multiple independent networks. Specifically, we found that one of the lesion-related independent components (ICs) highly resembled classical language networks. Moreover, the damaged level within the language-related lesioned network is strongly associated with language deficits, including aphasia quotient, naming, and auditory comprehension scores. In comparison, none of the other two traditional lesion mapping methods found any regions responsible for language dysfunction. The language-related lesioned network extracted with the ICLM method showed high specificity in detecting aphasia symptoms compared with the performance of resting ICs and classical language networks. In total, we detected a precise language network in patients with aphasia and proved its efficiency in the relationship with language symptoms. In general, our ICLM could successfully identify multiple lesion-related networks from complicated brain diseases, and be used as an effective tool to study brain-behavior relationships and provide potential biomarkers of particular clinical behavioral deficits.
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Affiliation(s)
- Weijing Ren
- School of Rehabilitation, Capital Medical University, Beijing, China
- Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing Bo'ai Hospital, Beijing, China
- University of Health and Rehabilitation Sciences, Qingdao, China
| | - Chunying Jia
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Ying Zhou
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Jingdu Zhao
- School of Rehabilitation, Capital Medical University, Beijing, China
- Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing Bo'ai Hospital, Beijing, China
| | - Bo Wang
- Department of Hearing and Language Rehabilitation, China Rehabilitation Research Center, Beijing Bo'ai Hospital, Beijing, China
| | - Weiyong Yu
- Department of Radiology, China Rehabilitation Research Center, Beijing Bo'ai Hospital, Beijing, China
| | - Shiyi Li
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Yiru Hu
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Hao Zhang
- School of Rehabilitation, Capital Medical University, Beijing, China
- Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing Bo'ai Hospital, Beijing, China
- University of Health and Rehabilitation Sciences, Qingdao, China
- *Correspondence: Hao Zhang
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9
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Sperber C, Griffis J, Kasties V. Indirect structural disconnection-symptom mapping. Brain Struct Funct 2022; 227:3129-3144. [PMID: 36048282 DOI: 10.1007/s00429-022-02559-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/24/2022] [Indexed: 01/01/2023]
Abstract
In vivo tracking of white matter fibres catalysed a modern perspective on the pivotal role of brain connectome disruption in neuropsychological deficits. However, the examination of white matter integrity in neurological patients by diffusion-weighted magnetic resonance imaging bears conceptual limitations and is not widely applicable, as it requires imaging-compatible patients and resources beyond the capabilities of many researchers. The indirect estimation of structural disconnection offers an elegant and economical alternative. For this approach, a patient's structural lesion information and normative connectome data are combined to estimate different measures of lesion-induced structural disconnection. Using one of several toolboxes, this method is relatively easy to implement and is even available to scientists without expertise in fibre tracking analyses. Nevertheless, the anatomo-behavioural statistical mapping of structural brain disconnection requires analysis steps that are not covered by these toolboxes. In this paper, we first review the current state of indirect lesion disconnection estimation, the different existing measures, and the available software. Second, we aim to fill the remaining methodological gap in statistical disconnection-symptom mapping by providing an overview and guide to disconnection data and the statistical mapping of their relationship to behavioural measurements using either univariate or multivariate statistical modelling. To assist in the practical implementation of statistical analyses, we have included software tutorials and analysis scripts.
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Affiliation(s)
- Christoph Sperber
- University of Tubingen: Eberhard Karls Universitat Tubingen, Tubingen, Germany.
| | - Joseph Griffis
- University of Tubingen: Eberhard Karls Universitat Tubingen, Tubingen, Germany
| | - Vanessa Kasties
- Centre of Neurology, Hertie-Institute for Clinical Brain Research, University of Tubingen, Tubingen, Germany
- Child Development Center, University Childrens Hospital Zurich, University of Zurich, Zurich, Switzerland
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10
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Preserved anatomical bypasses predict variance in language functions after stroke. Cortex 2022; 155:46-61. [DOI: 10.1016/j.cortex.2022.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/11/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022]
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Abstract
Mapping human brain function is a long-standing goal of neuroscience that promises to inform the development of new treatments for brain disorders. Early maps of human brain function were based on locations of brain damage or brain stimulation that caused a functional change. Over time, this approach was largely replaced by technologies such as functional neuroimaging, which identify brain regions in which activity is correlated with behaviours or symptoms. Despite their advantages, these technologies reveal correlations, not causation. This creates challenges for interpreting the data generated from these tools and using them to develop treatments for brain disorders. A return to causal mapping of human brain function based on brain lesions and brain stimulation is underway. New approaches can combine these causal sources of information with modern neuroimaging and electrophysiology techniques to gain new insights into the functions of specific brain areas. In this Review, we provide a definition of causality for translational research, propose a continuum along which to assess the relative strength of causal information from human brain mapping studies and discuss recent advances in causal brain mapping and their relevance for developing treatments.
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Rosenzopf H, Wiesen D, Basilakos A, Yourganov G, Bonilha L, Rorden C, Fridriksson J, Karnath HO, Sperber C. Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia of gesture production. Brain Commun 2022; 4:fcac004. [PMID: 35169709 PMCID: PMC8833454 DOI: 10.1093/braincomms/fcac004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/19/2021] [Accepted: 01/07/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Left hemispheric cerebral stroke can cause apraxia, a motor-cognitive disorder characterised by deficits of higher-order motor skills such as the failure to accurately produce meaningful gestures. This disorder provides unique insights into the anatomical and cognitive architecture of the human praxis system. The present study aimed to map the structural brain network that is damaged in apraxia. We assessed the ability to perform meaningful gestures with the hand in 101 patients with chronic left hemisphere stroke. Structural white matter fibre damage was directly assessed by diffusion tensor imaging and fractional anisotropy mapping. We used multivariate topographical inference on tract-based fractional anisotropy topographies to identify white matter disconnection associated with apraxia. We found relevant pathological white matter alterations in a densely connected fronto-temporo-parietal network of short and long association fibres. Hence, the findings suggest that heterogeneous topographical results in previous lesion mapping studies might not only result from differences in study design, but also from the general methodological limitations of univariate topographical mapping in uncovering the structural praxis network. A striking role of middle and superior temporal lobe disconnection, including temporo-temporal short association fibres, was found, suggesting strong involvement of the temporal lobe in the praxis network. Further, the results stressed the importance of subcortical disconnections for the emergence of apractic symptoms. Our study provides a fine-grain view into the structural connectivity of the human praxis network and suggests a potential value of disconnection measures in the clinical prediction of behavioural post-stroke outcome.
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Affiliation(s)
- Hannah Rosenzopf
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Daniel Wiesen
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Alexandra Basilakos
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Grigori Yourganov
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | - Christopher Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Hans-Otto Karnath
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Christoph Sperber
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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13
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Tackling the Complexity of Lesion-Symptoms Mapping: How to Bridge the Gap Between Data Scientists and Clinicians? ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:195-203. [PMID: 34862543 DOI: 10.1007/978-3-030-85292-4_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Accurate and predictive lesion-symptoms mapping is a major goal in the field of clinical neurosciences. Recent studies have called for a reappraisal of the results given by the standard univariate voxel-based lesion-symptom mapping technique, emphasizing the need of developing multivariate methods. While the organization of large datasets and their analysis with machine learning (ML) approaches represents an opportunity to increase prediction accuracy, the complexity and dimensionality of the problem remain a major obstacle. Acknowledging the difficulty of inferring individual outcomes from the observation of spatial patterns of lesions, we propose here to base prediction on new individuals on models of brain connectivity, whereby the disruption of a given network predicts the occurrence of selective deficits. Well-suited ML tools are necessary to capture the relevant information from limited datasets and perform reliable inference.
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14
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Sperber C. The strange role of brain lesion size in cognitive neuropsychology. Cortex 2021; 146:216-226. [PMID: 34902680 DOI: 10.1016/j.cortex.2021.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 07/11/2021] [Accepted: 11/03/2021] [Indexed: 11/18/2022]
Abstract
The size of brain lesions is a variable that is frequently considered in cognitive neuropsychology. In particular, lesion-deficit inference studies often control for lesion size, and the association of lesion size with post-stroke cognitive deficits and its predictive value are studied. In the present article, the role of lesion size in cognitive deficits and its computational or design-wise consideration is discussed and questioned. First, I argue that the commonly discussed role or effect of lesion size in cognitive deficits eludes us. A generally valid understanding of the causal relation of lesion size, lesion location, and cognitive deficits is unachievable. Second, founded on the theory of causal inference, I argue that lesion size control is no generally appropriate covariate control. Instead, it is identified as a procedure with only situational benefits, which is supported by empirical data. This theoretical background is used to suggest possible research practices in lesion-deficit inference, post-stroke outcome prediction, and behavioural studies. Last, control for lesion size is put into a bigger historical context - it is identified to relate to a long-known association problem in neuropsychology, which was previously discussed from the perspectives of a mislocalisation in lesion-deficit mapping and the symptom complex approach.
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Affiliation(s)
- Christoph Sperber
- Centre of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
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15
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Abstract
Cognitive and behavioural outcomes in stroke reflect the interaction between two complex anatomically-distributed patterns: the functional organization of the brain and the structural distribution of ischaemic injury. Conventional outcome models—for individual prediction or population-level inference—commonly ignore this complexity, discarding anatomical variation beyond simple characteristics such as lesion volume. This sets a hard limit on the maximum fidelity such models can achieve. High-dimensional methods can overcome this problem, but only at prohibitively large data scales. Drawing on one of the largest published collections of anatomically-registered imaging of acute stroke—N = 1333—here we use non-linear dimensionality reduction to derive a succinct latent representation of the anatomical patterns of ischaemic injury, agglomerated into 21 distinct intuitive categories. We compare the maximal predictive performance it enables against both simpler low-dimensional and more complex high-dimensional representations, employing multiple empirically-informed ground truth models of distributed structure–outcome relationships. We show our representation sets a substantially higher ceiling on predictive fidelity than conventional low-dimensional approaches, but lower than that achievable within a high-dimensional framework. Where descriptive simplicity is a necessity, such as within clinical care or research trials of modest size, the representation we propose arguably offers a favourable compromise of compactness and fidelity.
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16
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Kasties V, Karnath H, Sperber C. Strategies for feature extraction from structural brain imaging in lesion-deficit modelling. Hum Brain Mapp 2021; 42:5409-5422. [PMID: 34415093 PMCID: PMC8519857 DOI: 10.1002/hbm.25629] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 06/30/2021] [Accepted: 08/07/2021] [Indexed: 12/25/2022] Open
Abstract
High‐dimensional modelling of post‐stroke deficits from structural brain imaging is highly relevant to basic cognitive neuroscience and bears the potential to be translationally used to guide individual rehabilitation measures. One strategy to optimise model performance is well‐informed feature selection and representation. However, different feature representation strategies were so far used, and it is not known what strategy is best for modelling purposes. The present study compared the three common main strategies: voxel‐wise representation, lesion‐anatomical componential feature reduction and region‐wise atlas‐based feature representation. We used multivariate, machine‐learning‐based lesion‐deficit models to predict post‐stroke deficits based on structural lesion data. Support vector regression was tuned by nested cross‐validation techniques and tested on held‐out validation data to estimate model performance. While we consistently found the numerically best models for lower‐dimensional, featurised data and almost always for principal components extracted from lesion maps, our results indicate only minor, non‐significant differences between different feature representation styles. Hence, our findings demonstrate the general suitability of all three commonly applied feature representations in lesion‐deficit modelling. Likewise, model performance between qualitatively different popular brain atlases was not significantly different. Our findings also highlight potential minor benefits in individual fine‐tuning of feature representations and the challenge posed by the high, multifaceted complexity of lesion data, where lesion‐anatomical and functional criteria might suggest opposing solutions to feature reduction.
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Affiliation(s)
- Vanessa Kasties
- Centre of Neurology, Division of NeuropsychologyHertie‐Institute for Clinical Brain Research, University of TübingenTübingenGermany
| | - Hans‐Otto Karnath
- Centre of Neurology, Division of NeuropsychologyHertie‐Institute for Clinical Brain Research, University of TübingenTübingenGermany
| | - Christoph Sperber
- Centre of Neurology, Division of NeuropsychologyHertie‐Institute for Clinical Brain Research, University of TübingenTübingenGermany
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17
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Bonkhoff AK, Schirmer MD, Bretzner M, Hong S, Regenhardt RW, Brudfors M, Donahue KL, Nardin MJ, Dalca AV, Giese AK, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Benavente OR, Bevan S, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah CL, Rolfs A, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Söderholm M, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Bzdok D, Wu O, Rost NS. Outcome after acute ischemic stroke is linked to sex-specific lesion patterns. Nat Commun 2021; 12:3289. [PMID: 34078897 PMCID: PMC8172535 DOI: 10.1038/s41467-021-23492-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 04/30/2021] [Indexed: 01/31/2023] Open
Abstract
Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.
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Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Univ. Lille, Inserm, CHU Lille, U1171 - LilNCog (JPARC) - Lille Neurosciences & Cognition, F-59000, Lille, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert W Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mikael Brudfors
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco J Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brandon L Hancock
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Steven J T Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Elissa C McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - John Attia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Oscar R Benavente
- Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Stephen Bevan
- School of Life Sciences, University of Lincoln, Lincoln, UK
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Amanda Donatti
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Sao Paulo, Brazil
| | - Christoph J Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, USA
- Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | - Lukas Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Robin Lemmens
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), Leuven, Belgium
- VIB, Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Christopher R Levi
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
| | - Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | | | - Chia-Ling Phuah
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | | | - Stefan Ropele
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ralph L Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, UK
- St Peter's and Ashford Hospitals, Egham, UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Martin Söderholm
- Department of clinical sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund and Malmö, Sweden
| | - Alessandro Sousa
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Sao Paulo, Brazil
| | - Tara M Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
- Department of Neurology, Austin Health, Heidelberg, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ramin Zand
- Department of Neurology, Geisinger, Danville, PA, USA
| | - Patrick F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bradford B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Arne G Lindgren
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Jane Maguire
- University of Technology Sydney, Sydney, NSW, Australia
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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18
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Bonkhoff AK, Lim JS, Bae HJ, Weaver NA, Kuijf HJ, Biesbroek JM, Rost NS, Bzdok D. Generative lesion pattern decomposition of cognitive impairment after stroke. Brain Commun 2021; 3:fcab110. [PMID: 34189457 PMCID: PMC8233115 DOI: 10.1093/braincomms/fcab110] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/21/2021] [Accepted: 05/20/2021] [Indexed: 01/28/2023] Open
Abstract
Cognitive impairment is a frequent and disabling sequela of stroke. There is however incomplete understanding of how lesion topographies in the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modelling to enable a hemisphere-aware analysis of 1080 acute ischaemic stroke patients with deep profiling ∼3 months after stroke. We show the relevance of the left hemisphere in the prediction of language and memory assessments and relevance of the right hemisphere in the prediction of visuospatial functioning. Global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left was particularly informative about lost naming and memory functions, while damage to these regions on the right was linked to lost visuospatial functioning. Global cognitive impairment was predominantly linked to lesioned tissue in the supramarginal and angular gyrus, the post-central gyrus as well as the lateral occipital and opercular cortices of the left hemisphere. Hence, our analysis strategy uncovered that lesion patterns with unique hemispheric distributions are characteristic of how cognitive capacity is lost due to ischaemic brain tissue damage.
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Affiliation(s)
- Anna K Bonkhoff
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, MA, Boston, USA
| | - Jae-Sung Lim
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Hee-Joon Bae
- Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Nick A Weaver
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - J Matthijs Biesbroek
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Natalia S Rost
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, MA, Boston, USA
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada.,Mila-Quebec Artificial Intelligence Institute, Montreal, Canada
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19
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D'Imperio D, Romeo Z, Maistrello L, Durgoni E, Della Pietà C, De Filippo De Grazia M, Meneghello F, Turolla A, Zorzi M. Sensorimotor, Attentional, and Neuroanatomical Predictors of Upper Limb Motor Deficits and Rehabilitation Outcome after Stroke. Neural Plast 2021; 2021:8845685. [PMID: 33868400 PMCID: PMC8035034 DOI: 10.1155/2021/8845685] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/28/2021] [Accepted: 03/01/2021] [Indexed: 11/23/2022] Open
Abstract
The rehabilitation of motor deficits following stroke relies on both sensorimotor and cognitive abilities, thereby involving large-scale brain networks. However, few studies have investigated the integration between motor and cognitive domains, as well as its neuroanatomical basis. In this retrospective study, upper limb motor responsiveness to technology-based rehabilitation was examined in a sample of 29 stroke patients (18 with right and 11 with left brain damage). Pretreatment sensorimotor and attentional abilities were found to influence motor recovery. Training responsiveness increased as a function of the severity of motor deficits, whereas spared attentional abilities, especially visuospatial attention, supported motor improvements. Neuroanatomical analysis of structural lesions and white matter disconnections showed that the poststroke motor performance was associated with putamen, insula, corticospinal tract, and frontoparietal connectivity. Motor rehabilitation outcome was mainly associated with the superior longitudinal fasciculus and partial involvement of the corpus callosum. The latter findings support the hypothesis that motor recovery engages large-scale brain networks that involve cognitive abilities and provides insight into stroke rehabilitation strategies.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Marco Zorzi
- IRCCS San Camillo Hospital, Venice, Italy
- Department of General Psychology and Padova Neuroscience Center, University of Padova, Italy
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20
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Griffis JC, Metcalf NV, Corbetta M, Shulman GL. Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions. Neuroimage Clin 2021; 30:102639. [PMID: 33813262 PMCID: PMC8053805 DOI: 10.1016/j.nicl.2021.102639] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 12/19/2022]
Abstract
Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.
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Affiliation(s)
- Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neuroscience, University of Padua, Padua, Italy; Padua Neuroscience Center, Padua, Italy
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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21
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Martinez Oeckel A, Rijntjes M, Glauche V, Kümmerer D, Kaller CP, Egger K, Weiller C. The extreme capsule and aphasia: proof-of-concept of a new way relating structure to neurological symptoms. Brain Commun 2021; 3:fcab040. [PMID: 33870191 PMCID: PMC8042249 DOI: 10.1093/braincomms/fcab040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/15/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
We present anatomy-based symptom-lesion mapping to assess the association between lesions of tracts in the extreme capsule and aphasia. The study cohort consisted of 123 patients with acute left-hemispheric stroke without a lesion of language-related cortical areas of the Stanford atlas of functional regions of interest. On templates generated through global fibre tractography, lesions of the extreme capsule and of the arcuate fascicle were quantified and correlated with the occurrence of aphasia (n = 18) as defined by the Token Test. More than 15% damage of the slice plane through the extreme capsule was a strong independent predictor of aphasia in stroke patients, odds ratio 16.37, 95% confidence interval: 3.11–86.16, P < 0.01. In contrast, stroke lesions of >15% in the arcuate fascicle were not associated with aphasia. Our results support the relevance of a ventral pathway in the language network running through the extreme capsule.
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Affiliation(s)
- Ariane Martinez Oeckel
- Department of Neurology and Clinical Neurosciences, Faculty of Medicine, University of Freiburg, Freiburg 79106, Germany
| | - Michel Rijntjes
- Department of Neurology and Clinical Neurosciences, Faculty of Medicine, University of Freiburg, Freiburg 79106, Germany
| | - Volkmar Glauche
- Department of Neurology and Clinical Neurosciences, Faculty of Medicine, University of Freiburg, Freiburg 79106, Germany
| | - Dorothee Kümmerer
- Department of Neurology and Clinical Neurosciences, Faculty of Medicine, University of Freiburg, Freiburg 79106, Germany
| | - Christoph P Kaller
- Department of Neuroradiology, Faculty of Medicine, University of Freiburg, Freiburg 79106, Germany
| | - Karl Egger
- Department of Neuroradiology, Faculty of Medicine, University of Freiburg, Freiburg 79106, Germany
| | - Cornelius Weiller
- Department of Neurology and Clinical Neurosciences, Faculty of Medicine, University of Freiburg, Freiburg 79106, Germany
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22
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Hula WD, Panesar S, Gravier ML, Yeh FC, Dresang HC, Dickey MW, Fernandez-Miranda JC. Structural white matter connectometry of word production in aphasia: an observational study. Brain 2020; 143:2532-2544. [PMID: 32705146 DOI: 10.1093/brain/awaa193] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/12/2020] [Accepted: 04/22/2020] [Indexed: 11/15/2022] Open
Abstract
While current dual-steam neurocognitive models of language function have coalesced around the view that distinct neuroanatomical networks subserve semantic and phonological processing, respectively, the specific white matter components of these networks remain a matter of debate. To inform this debate, we investigated relationships between structural white matter connectivity and word production in a cross-sectional study of 42 participants with aphasia due to unilateral left hemisphere stroke. Specifically, we reconstructed a local connectome matrix for each participant from diffusion spectrum imaging data and regressed these matrices on indices of semantic and phonological ability derived from their responses to a picture-naming test and a computational model of word production. These connectometry analyses indicated that both dorsally located (arcuate fasciculus) and ventrally located (inferior frontal-occipital, uncinate, and middle longitudinal fasciculi) tracts were associated with semantic ability, while associations with phonological ability were more dorsally situated, including the arcuate and middle longitudinal fasciculi. Associations with limbic pathways including the posterior cingulum bundle and the fornix were also found. All analyses controlled for total lesion volume and all results showing positive associations obtained false discovery rates < 0.05. These results challenge dual-stream accounts that deny a role for the arcuate fasciculus in semantic processing, and for ventral-stream pathways in language production. They also illuminate limbic contributions to both semantic and phonological processing for word production.
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Affiliation(s)
- William D Hula
- Geriatric Research, Education, and Clinical Center and Audiology and Speech Pathology Service, VA Pittsburgh Healthcare System, Pittsburgh PA, USA.,Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh PA, USA
| | - Sandip Panesar
- Department of Neurosurgery, Stanford University, Palo Alto, CA, USA
| | - Michelle L Gravier
- Department of Speech, Language, and Hearing Sciences, California State East Bay, Hayward, CA, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Haley C Dresang
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh PA, USA
| | - Michael Walsh Dickey
- Geriatric Research, Education, and Clinical Center and Audiology and Speech Pathology Service, VA Pittsburgh Healthcare System, Pittsburgh PA, USA.,Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh PA, USA
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23
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Toba MN, Malherbe C, Godefroy O, Rushmore RJ, Zavaglia M, Maatoug R, Mandonnet E, Valero-Cabré A, Hilgetag CC. Reply: Inhibition between human brain areas or methodological artefact? Brain 2020; 143:e39. [PMID: 32413896 DOI: 10.1093/brain/awaa093] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Monica N Toba
- Laboratory of Functional Neurosciences (EA 4559), University of Picardie Jules Verne, Amiens, France.,FRONTLAB Team, Cerebral Dynamics, Plasticity and Rehabilitation Group, Paris Brain Institute, ICM, Sorbonne Universités, UPMC Paris 06, Inserm UMR S 1127, CNRS UMR 7225, F-75013, and IHU-A-ICM, Paris, France
| | - Caroline Malherbe
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurology, Head and Neuro Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Olivier Godefroy
- Laboratory of Functional Neurosciences (EA 4559), University of Picardie Jules Verne, Amiens, France.,Department of Neurology, Amiens University Hospital, Amiens, France
| | - R Jarrett Rushmore
- Laboratory of Cerebral Dynamics, Plasticity and Rehabilitation, Boston University School of Medicine, Boston, MA 02118, USA
| | - Melissa Zavaglia
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Focus Area Health, Jacobs University Bremen, Germany
| | - Redwan Maatoug
- FRONTLAB Team, Cerebral Dynamics, Plasticity and Rehabilitation Group, Paris Brain Institute, ICM, Sorbonne Universités, UPMC Paris 06, Inserm UMR S 1127, CNRS UMR 7225, F-75013, and IHU-A-ICM, Paris, France
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisière Hospital, APHP, Paris, France, and University Paris 7, Paris, France
| | - Antoni Valero-Cabré
- FRONTLAB Team, Cerebral Dynamics, Plasticity and Rehabilitation Group, Paris Brain Institute, ICM, Sorbonne Universités, UPMC Paris 06, Inserm UMR S 1127, CNRS UMR 7225, F-75013, and IHU-A-ICM, Paris, France.,Laboratory of Cerebral Dynamics, Plasticity and Rehabilitation, Boston University School of Medicine, Boston, MA 02118, USA.,Cognitive Neuroscience and Information Technology Research Program, Open University of Catalonia (UOC), Barcelona, Catalunya, Spain
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Health Sciences Department, Boston University, 635 Commonwealth Ave. Boston, MA 02215, USA
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24
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Ivanova MV, Herron TJ, Dronkers NF, Baldo JV. An empirical comparison of univariate versus multivariate methods for the analysis of brain-behavior mapping. Hum Brain Mapp 2020; 42:1070-1101. [PMID: 33216425 PMCID: PMC7856656 DOI: 10.1002/hbm.25278] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 10/14/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023] Open
Abstract
Lesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. In the current study, we provide a much-needed systematic comparison of several ULSM and MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested the spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method's spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results.
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Affiliation(s)
- Maria V Ivanova
- University of California, Berkeley, California, USA.,VA Northern California Health Care System, Martinez, California, USA
| | - Timothy J Herron
- VA Northern California Health Care System, Martinez, California, USA
| | - Nina F Dronkers
- University of California, Berkeley, California, USA.,VA Northern California Health Care System, Martinez, California, USA.,University of California, Davis, California, USA
| | - Juliana V Baldo
- VA Northern California Health Care System, Martinez, California, USA
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25
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Disconnection somewhere down the line: Multivariate lesion-symptom mapping of the line bisection error. Cortex 2020; 133:120-132. [PMID: 33120190 DOI: 10.1016/j.cortex.2020.09.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 06/17/2020] [Accepted: 09/04/2020] [Indexed: 11/23/2022]
Abstract
Line Bisection is a simple task frequently used in stroke patients to diagnose disorders of spatial perception characterized by a directional bisection bias to the ipsilesional side. However, previous anatomical and behavioural findings are contradictory, and the diagnostic validity of the line bisection task has been challenged. We hereby aimed to re-analyse the anatomical basis of pathological line bisection by using multivariate lesion-symptom mapping and disconnection-symptom mapping based on support vector regression in a sample of 163 right hemispheric acute stroke patients. In line with some previous studies, we observed that pathological line bisection was related to more than a single focal lesion location. Cortical damage primarily to right parietal areas, particularly the inferior parietal lobe, including the angular gyrus, as well as damage to the right basal ganglia contributed to the pathology. In contrast to some previous studies, an involvement of frontal cortical brain areas in the line bisection task was not observed. Subcortically, damage to the right superior longitudinal fasciculus (I, II and III) and arcuate fasciculus as well as the internal capsule was associated with line bisection errors. Moreover, white matter damage of interhemispheric fibre bundles, such as the anterior commissure and posterior parts of the corpus callosum projecting into the left hemisphere, was predictive of pathological deviation in the line bisection task.
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26
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Beume LA, Rijntjes M, Dressing A, Kaller CP, Hieber M, Martin M, Kirsch S, Kümmerer D, Urbach H, Umarova RM, Weiller C. Dissociation of visual extinction and neglect in the left hemisphere. Cortex 2020; 129:211-222. [PMID: 32505793 DOI: 10.1016/j.cortex.2020.04.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/05/2020] [Accepted: 04/06/2020] [Indexed: 11/27/2022]
Abstract
Visual neglect and extinction are two distinct visuospatial attention deficits that frequently occur after right hemisphere cerebral stroke. However, their different lesion profiles remain a matter of debate. In the left hemisphere, a domain-general dual-loop model with distinct computational abilities onto which several cognitive functions may project, has been proposed: a dorsal stream for sensori-motor mapping in time and space and a ventral stream for comprehension and representation of concepts. We wondered whether such a distinction may apply to visual extinction and neglect in left hemisphere lesions. Of 165 prospectively studied patients with acute left hemispheric ischemic stroke with a single lesion on MRI, 122 had no visuospatial attention deficit, 10 had extinction, 31 neglect and 2 had both, visual extinction and neglect. Voxel-based-lesion-symptom mapping (VLSM, FDR<.05) showed a clear anatomical dissociation. Extinction occurred after damage to the parietal cortex (anterior bank of the intraparietal sulcus, inferior parietal lobe, and supramarginal gyrus), while visual neglect occurred after damage mainly to the temporal lobe (superior and middle temporal lobe, anterior temporal pole), inferior ventral premotor cortex, frontal operculum, angular gyrus, and insula. Direct comparison of both conditions linked extinction to intraparietal sulcus and supramarginal gyrus (FDR<.05). Thus, in the left hemisphere extinction seems to be related to dorsal stream lesions, whereas neglect maps more on the ventral stream. These data cannot be generalized to the right hemisphere. However, a domain-general point-of-view may stimulate discussion on visuospatial attention processing also in the right hemisphere.
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Affiliation(s)
- Lena-Alexandra Beume
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany
| | - Michel Rijntjes
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany
| | - Andrea Dressing
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany
| | - Christoph P Kaller
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany
| | - Maren Hieber
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany
| | - Markus Martin
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany
| | - Simon Kirsch
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany
| | - Dorothee Kümmerer
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany
| | - Horst Urbach
- Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; Department of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany
| | - Roza M Umarova
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany; Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Cornelius Weiller
- Department of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany; Freiburg Brain Imaging Center, University Medical Center Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University Medical Center Freiburg, Freiburg, Germany.
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27
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Sperber C, Karnath HO. Inhibition between human brain areas or methodological artefact? Brain 2020; 143:e38. [PMID: 32413900 DOI: 10.1093/brain/awaa092] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Christoph Sperber
- Center of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Hans-Otto Karnath
- Center of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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