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Batsikadze G, Pakusch J, Klein M, Ernst TM, Thieme A, Nicksirat SA, Steiner KM, Nio E, Genc E, Maderwald S, Deuschl C, Merz CJ, Quick HH, Mark MD, Timmann D. Mild Deficits in Fear Learning: Evidence from Humans and Mice with Cerebellar Cortical Degeneration. eNeuro 2024; 11:ENEURO.0365-23.2023. [PMID: 38176906 PMCID: PMC10897646 DOI: 10.1523/eneuro.0365-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/21/2023] [Accepted: 12/08/2023] [Indexed: 01/06/2024] Open
Abstract
Functional brain imaging studies in humans suggest involvement of the cerebellum in fear conditioning but do not allow conclusions about the functional significance. The main aim of the present study was to examine whether patients with cerebellar degeneration show impaired fear conditioning and whether this is accompanied by alterations in cerebellar cortical activations. To this end, a 2 d differential fear conditioning study was conducted in 20 cerebellar patients and 21 control subjects using a 7 tesla (7 T) MRI system. Fear acquisition and extinction training were performed on day 1, followed by recall on day 2. Cerebellar patients learned to differentiate between the CS+ and CS-. Acquisition and consolidation of learned fear, however, was slowed. Additionally, extinction learning appeared to be delayed. The fMRI signal was reduced in relation to the prediction of the aversive stimulus and altered in relation to its unexpected omission. Similarly, mice with cerebellar cortical degeneration (spinocerebellar ataxia type 6, SCA6) were able to learn the fear association, but retrieval of fear memory was reduced. In sum, cerebellar cortical degeneration led to mild abnormalities in the acquisition of learned fear responses in both humans and mice, particularly manifesting postacquisition training. Future research is warranted to investigate the basis of altered fMRI signals related to fear learning.
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Affiliation(s)
- Giorgi Batsikadze
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, 45141 Essen, Germany
| | - Johanna Pakusch
- Behavioral Neuroscience, Ruhr University Bochum, 44801 Bochum, Germany
| | - Michael Klein
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
| | - Thomas Michael Ernst
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, 45141 Essen, Germany
| | - Andreas Thieme
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
| | - Seyed Ali Nicksirat
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
| | - Katharina Marie Steiner
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
| | - Enzo Nio
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, 45141 Essen, Germany
| | - Erhan Genc
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), 44139 Dortmund, Germany
| | - Stefan Maderwald
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, 45141 Essen, Germany
| | - Cornelius Deuschl
- Institute of Diagnostic and Interventional Radiology and Neuroradiology and C-TNBS, Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
| | - Christian Josef Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, 44801 Bochum, Germany
| | - Harald H Quick
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, 45141 Essen, Germany
- High-Field and Hybrid MR Imaging, Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
| | - Melanie D Mark
- Behavioral Neuroscience, Ruhr University Bochum, 44801 Bochum, Germany
| | - Dagmar Timmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, 45147 Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, 45141 Essen, Germany
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Ding L, Liu H, Jing J, Jiang Y, Meng X, Chen Y, Zhao X, Niu H, Liu T, Wang Y, Li Z. Lesion Network Mapping for Neurological Deficit in Acute Ischemic Stroke. Ann Neurol 2023; 94:572-584. [PMID: 37314250 DOI: 10.1002/ana.26721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To create a comprehensive map of strategic lesion network localizations for neurological deficits, and identify prognostic neuroimaging biomarkers to facilitate the early detection of patients with a high risk of poor functional outcomes in acute ischemic stroke (AIS). METHODS In a large-scale multicenter study of 7,807 patients with AIS, we performed voxel-based lesion-symptom mapping, functional disconnection mapping (FDC), and structural disconnection mapping (SDC) to identify distinct lesion and network localizations for National Institutes of Health Stroke Scale (NIHSS) score. Impact scores were calculated based on the odds ratios or t-values of voxels from voxel-based lesion-symptom mapping, FDC, and SDC results. Ordinal regression models were used to investigate the predictive value of the impact scores on functional outcome (defined as the modified Rankin score at 3 months). RESULTS We constructed lesion, FDC, and SDC maps for each item of the NIHSS score, which provided insights into the neuroanatomical substrate and network localization of neurological function deficits after AIS. The lesion impact score of limb ataxia, the SDC impact score of limb deficit, and FDC impact score of sensation and dysarthria were significantly associated with modified Rankin Scale at 3 months. Adding the SDC impact score, FDC impact score, and lesion impact score to the NIHSS total score improved the performance in predicting functional outcomes, as compared with using the NIHSS score alone. INTERPRETATION We constructed comprehensive maps of strategic lesion network localizations for neurological deficits that were predictive of functional outcomes in AIS. These results may provide specifically localized targets for future neuromodulation therapies. ANN NEUROL 2023;94:572-584.
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Affiliation(s)
- Lingling Ding
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xingquan Zhao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- Beijing Engineering Research Center of Digital Healthcare for Neurological Diseases, Beijing, China
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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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Billot A, Thiebaut de Schotten M, Parrish TB, Thompson CK, Rapp B, Caplan D, Kiran S. Structural disconnections associated with language impairments in chronic post-stroke aphasia using disconnectome maps. Cortex 2022; 155:90-106. [PMID: 35985126 PMCID: PMC9623824 DOI: 10.1016/j.cortex.2022.06.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/14/2021] [Accepted: 06/10/2022] [Indexed: 11/16/2022]
Abstract
Inconsistent findings have been reported about the impact of structural disconnections on language function in post-stroke aphasia. This study investigated patterns of structural disconnections associated with chronic language impairments using disconnectome maps. Seventy-six individuals with post-stroke aphasia underwent a battery of language assessments and a structural MRI scan. Support-vector regression disconnectome-symptom mapping analyses were performed to examine the correlations between disconnectome maps, representing the probability of disconnection at each white matter voxel and different language scores. To further understand whether significant disconnections were primarily representing focal damage or a more extended network of seemingly preserved but disconnected areas beyond the lesion site, results were qualitatively compared to support-vector regression lesion-symptom mapping analyses. Part of the left white matter perisylvian network was similarly disconnected in 90% of the individuals with aphasia. Surrounding this common left perisylvian disconnectome, specific structural disconnections in the left fronto-temporo-parietal network were significantly associated with aphasia severity and with lower performance in auditory comprehension, syntactic comprehension, syntactic production, repetition and naming tasks. Auditory comprehension, repetition and syntactic processing deficits were related to disconnections in areas that overlapped with and extended beyond lesion sites significant in SVR-LSM analyses. In contrast, overall language abilities as measured by aphasia severity and naming seemed to be mostly explained by focal damage at the level of the insular and central opercular cortices, given the high overlap between SVR-DSM and SVR-LSM results for these scores. While focal damage seems to be sufficient to explain broad measures of language performance, the structural disconnections between language areas provide additional information on the neural basis of specific and persistent language impairments at the chronic stage beyond lesion volume. Leveraging routinely available clinical data, disconnectome mapping furthers our understanding of anatomical connectivity constraints that may limit the recovery of some language abilities in chronic post-stroke aphasia.
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Affiliation(s)
- Anne Billot
- Sargent College of Health & Rehabilitation Sciences, Boston University, Boston, MA, USA; School of Medicine, Boston University, Boston, MA, USA.
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Todd B Parrish
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Cynthia K Thompson
- Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA
| | - David Caplan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Swathi Kiran
- Sargent College of Health & Rehabilitation Sciences, Boston University, Boston, MA, USA
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Pflugshaupt T, Bauer D, Frey J, Vanbellingen T, Kaufmann BC, Bohlhalter S, Nyffeler T. The right anterior temporal lobe critically contributes to magnitude knowledge. Brain Commun 2020; 2:fcaa157. [PMID: 33225278 PMCID: PMC7667527 DOI: 10.1093/braincomms/fcaa157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/20/2020] [Accepted: 08/26/2020] [Indexed: 11/14/2022] Open
Abstract
Cognitive estimation is a mental ability applied to solve numerical problems when precise facts are unknown, unavailable or impractical to calculate. It has been associated with several underlying cognitive components, most often with executive functions and semantic memory. Little is known about the neural correlates of cognitive estimation. To address this issue, the present cross-sectional study applied lesion-symptom mapping in a group of 55 patients with left hemineglect due to right-hemisphere stroke. Previous evidence suggests a high prevalence of cognitive estimation impairment in these patients, as they might show a general bias towards large magnitudes. Compared to 55 age- and gender-matched healthy controls, the patient group demonstrated impaired cognitive estimation. However, the expected large magnitude bias was not found. Lesion-symptom mapping related their general estimation impairment predominantly to brain damage in the right anterior temporal lobe. Also critically involved were the right uncinate fasciculus, the anterior commissure and the right inferior frontal gyrus. The main findings of this study emphasize the role of semantic memory in cognitive estimation, with reference to a growing body of neuroscientific literature postulating a transmodal hub for semantic cognition situated in the bilateral anterior temporal lobe. That such semantic hub function may also apply to numerical knowledge is not undisputed. We here propose a critical contribution of the right anterior temporal lobe to at least one aspect of number processing, i.e. the knowledge about real-world numerical magnitudes.
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Affiliation(s)
| | - Daniel Bauer
- Neurocenter, Luzerner Kantonsspital, Luzern, Switzerland
| | - Julia Frey
- Neurocenter, Luzerner Kantonsspital, Luzern, Switzerland
| | - Tim Vanbellingen
- Neurocenter, Luzerner Kantonsspital, Luzern, Switzerland
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering, University of Bern, Bern, Switzerland
| | - Brigitte C Kaufmann
- Neurocenter, Luzerner Kantonsspital, Luzern, Switzerland
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering, University of Bern, Bern, Switzerland
| | | | - Thomas Nyffeler
- Neurocenter, Luzerner Kantonsspital, Luzern, Switzerland
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering, University of Bern, Bern, Switzerland
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Dagher A, Lehéricy S, Rowe JB, Siebner HR. Disease-informed brain mapping teaches important lessons about the human brain. Neuroimage 2019; 190:1-3. [PMID: 30798013 DOI: 10.1016/j.neuroimage.2019.02.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Affiliation(s)
- Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Stéphane Lehéricy
- Institut Du Cerveau et de La Moelle épinière, Centre for NeuroImaging Research, Team Movement Investigation and Therapeutics, Sorbonne Université, UPMC - Inserm U1127, CNRS UMR, 7225, Paris, France.
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, UK; Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK.
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Hvidovre Hospital, University of Copenhagen, Denmark.
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