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Amoruso L, García AM, Pusil S, Timofeeva P, Quiñones I, Carreiras M. Decoding bilingualism from resting-state oscillatory network organization. Ann N Y Acad Sci 2024; 1534:106-117. [PMID: 38419368 DOI: 10.1111/nyas.15113] [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] [Indexed: 03/02/2024]
Abstract
Can lifelong bilingualism be robustly decoded from intrinsic brain connectivity? Can we determine, using a spectrally resolved approach, the oscillatory networks that better predict dual-language experience? We recorded resting-state magnetoencephalographic activity in highly proficient Spanish-Basque bilinguals and Spanish monolinguals, calculated functional connectivity at canonical frequency bands, and derived topological network properties using graph analysis. These features were fed into a machine learning classifier to establish how robustly they discriminated between the groups. The model showed excellent classification (AUC: 0.91 ± 0.12) between individuals in each group. The key drivers of classification were network strength in beta (15-30 Hz) and delta (2-4 Hz) rhythms. Further characterization of these networks revealed the involvement of temporal, cingulate, and fronto-parietal hubs likely underpinning the language and default-mode networks (DMNs). Complementary evidence from a correlation analysis showed that the top-ranked features that better discriminated individuals during rest also explained interindividual variability in second language (L2) proficiency within bilinguals, further supporting the robustness of the machine learning model in capturing trait-like markers of bilingualism. Overall, our results show that long-term experience with an L2 can be "brain-read" at a fine-grained level from resting-state oscillatory network organization, highlighting its pervasive impact, particularly within language and DMN networks.
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Affiliation(s)
- Lucia Amoruso
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
| | - Adolfo M García
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Sandra Pusil
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Polina Timofeeva
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
| | - Ileana Quiñones
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Manuel Carreiras
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
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Binding LP, Dasgupta D, Taylor PN, Thompson PJ, O'Keeffe AG, de Tisi J, McEvoy AW, Miserocchi A, Winston GP, Duncan JS, Vos SB. Contribution of White Matter Fiber Bundle Damage to Language Change After Surgery for Temporal Lobe Epilepsy. Neurology 2023; 100:e1621-e1633. [PMID: 36750386 PMCID: PMC10103113 DOI: 10.1212/wnl.0000000000206862] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 12/12/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND AND OBJECTIVES In medically refractory temporal lobe epilepsy (TLE), 30%-50% of patients experience substantial language decline after resection in the language-dominant hemisphere. In this study, we investigated the contribution of white matter fiber bundle damage to language change at 3 and 12 months after surgery. METHODS We studied 127 patients who underwent TLE surgery from 2010 to 2019. Neuropsychological testing included picture naming, semantic fluency, and phonemic verbal fluency, performed preoperatively and 3 and 12 months postoperatively. Outcome was assessed using reliable change index (RCI; clinically significant decline) and change across timepoints (postoperative scores minus preoperative scores). Functional MRI was used to determine language lateralization. The arcuate fasciculus (AF), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus, middle longitudinal fasciculus (MLF), and uncinate fasciculus were mapped using diffusion MRI probabilistic tractography. Resection masks, drawn comparing coregistered preoperative and postoperative T1 MRI scans, were used as exclusion regions on preoperative tractography to estimate the percentage of preoperative tracts transected in surgery. Chi-squared assessments evaluated the occurrence of RCI-determined language decline. Independent sample t tests and MM-estimator robust regressions were used to assess the impact of clinical factors and fiber transection on RCI and change outcomes, respectively. RESULTS Language-dominant and language-nondominant resections were treated separately for picture naming because postoperative outcomes were significantly different between these groups. In language-dominant hemisphere resections, greater surgical damage to the AF and IFOF was related to RCI decline at 3 months. Damage to the inferior frontal subfasciculus of the IFOF was related to change at 3 months. In language-nondominant hemisphere resections, increased MLF resection was associated with RCI decline at 3 months, and damage to the anterior subfasciculus was related to change at 3 months. Language-dominant and language-nondominant resections were treated as 1 cohort for semantic and phonemic fluency because there were no significant differences in postoperative decline between these groups. Postoperative seizure freedom was associated with an absence of significant language decline 12 months after surgery for semantic fluency. DISCUSSION We demonstrate a relationship between fiber transection and naming decline after temporal lobe resection. Individualized surgical planning to spare white matter fiber bundles could help to preserve language function after surgery.
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Affiliation(s)
- Lawrence Peter Binding
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia.
| | - Debayan Dasgupta
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Peter Neal Taylor
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Pamela Jane Thompson
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Aidan G O'Keeffe
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Jane de Tisi
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Andrew William McEvoy
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Anna Miserocchi
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Gavin P Winston
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - John S Duncan
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Sjoerd B Vos
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
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Harrington DL, Shen Q, Wei X, Litvan I, Huang M, Lee RR. Functional topologies of spatial cognition predict cognitive and motor progression in Parkinson’s. Front Aging Neurosci 2022; 14:987225. [PMID: 36299614 PMCID: PMC9589098 DOI: 10.3389/fnagi.2022.987225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/12/2022] [Indexed: 11/23/2022] Open
Abstract
Background Spatial cognition deteriorates in Parkinson’s disease (PD), but the neural substrates are not understood, despite the risk for future dementia. It is also unclear whether deteriorating spatial cognition relates to changes in other cognitive domains or contributes to motor dysfunction. Objective This study aimed to identify functional connectivity abnormalities in cognitively normal PD (PDCN) in regions that support spatial cognition to determine their relationship to interfacing cognitive functions and motor disability, and to determine if they predict cognitive and motor progression 2 years later in a PDCN subsample. Methods Sixty-three PDCN and 43 controls underwent functional MRI while judging whether pictures, rotated at various angles, depicted the left or right hand. The task activates systems that respond to increases in rotation angle, a proxy for visuospatial difficulty. Angle-modulated functional connectivity was analyzed for frontal cortex, posterior cortex, and basal ganglia regions. Results Two aberrant connectivity patterns were found in PDCN, which were condensed into principal components that characterized the strength and topology of angle-modulated connectivity. One topology related to a marked failure to amplify frontal, posterior, and basal ganglia connectivity with other brain areas as visuospatial demands increased, unlike the control group (control features). Another topology related to functional reorganization whereby regional connectivity was strengthened with brain areas not recruited by the control group (PDCN features). Functional topologies correlated with diverse cognitive domains at baseline, underscoring their influences on spatial cognition. In PDCN, expression of topologies that were control features predicted greater cognitive progression longitudinally, suggesting inefficient communications within circuitry normally recruited to handle spatial demands. Conversely, stronger expression of topologies that were PDCN features predicted less longitudinal cognitive decline, suggesting functional reorganization was compensatory. Parieto-occipital topologies (control features) had different prognostic implications for longitudinal changes in motor disability. Expression of one topology predicted less motor decline, whereas expression of another predicted increased postural instability and gait disturbance (PIGD) feature severity. Concurrently, greater longitudinal decline in spatial cognition predicted greater motor and PIGD feature progression, suggesting deterioration in shared substrates. Conclusion These novel discoveries elucidate functional mechanisms of visuospatial cognition in PDCN, which foreshadow future cognitive and motor disability.
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Affiliation(s)
- Deborah L. Harrington
- Research Service, VA San Diego Healthcare System, San Diego, CA, United States
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- *Correspondence: Deborah L. Harrington,
| | - Qian Shen
- Research Service, VA San Diego Healthcare System, San Diego, CA, United States
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Xiangyu Wei
- Research Service, VA San Diego Healthcare System, San Diego, CA, United States
- Revelle College, University of California, San Diego, La Jolla, CA, United States
| | - Irene Litvan
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Mingxiong Huang
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Radiology Service, VA San Diego Healthcare System, San Diego, CA, United States
| | - Roland R. Lee
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Radiology Service, VA San Diego Healthcare System, San Diego, CA, United States
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Martin S, Williams KA, Saur D, Hartwigsen G. Age-related reorganization of functional network architecture in semantic cognition. Cereb Cortex 2022; 33:4886-4903. [PMID: 36190445 PMCID: PMC10110455 DOI: 10.1093/cercor/bhac387] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/02/2022] [Accepted: 09/03/2022] [Indexed: 11/15/2022] Open
Abstract
Cognitive aging is associated with widespread neural reorganization processes in the human brain. However, the behavioral impact of such reorganization is not well understood. The current neuroimaging study investigated age differences in the functional network architecture during semantic word retrieval in young and older adults. Combining task-based functional connectivity, graph theory and cognitive measures of fluid and crystallized intelligence, our findings show age-accompanied large-scale network reorganization even when older adults have intact word retrieval abilities. In particular, functional networks of older adults were characterized by reduced decoupling between systems, reduced segregation and efficiency, and a larger number of hub regions relative to young adults. Exploring the predictive utility of these age-related changes in network topology revealed high, albeit less efficient, performance for older adults whose brain graphs showed stronger dedifferentiation and reduced distinctiveness. Our results extend theoretical accounts on neurocognitive aging by revealing the compensational potential of the commonly reported pattern of network dedifferentiation when older adults can rely on their prior knowledge for successful task processing. However, we also demonstrate the limitations of such compensatory reorganization and show that a youth-like network architecture in terms of balanced integration and segregation is associated with more economical processing.
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Affiliation(s)
- Sandra Martin
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,Language & Aphasia Laboratory, Department of Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Kathleen A Williams
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Dorothee Saur
- Language & Aphasia Laboratory, Department of Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
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Gazzina S, Grassi M, Premi E, Alberici A, Benussi A, Archetti S, Gasparotti R, Bocchetta M, Cash DM, Todd EG, Peakman G, Convery RS, van Swieten JC, Jiskoot LC, Seelaar H, Sanchez-Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Rowe JB, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Butler CR, Santana I, Gerhard A, Ber IL, Pasquier F, Ducharme S, Levin J, Danek A, Sorbi S, Otto M, Rohrer JD, Borroni B. Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia. Neurobiol Aging 2022; 114:94-104. [PMID: 35339292 DOI: 10.1016/j.neurobiolaging.2022.02.009] [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: 10/06/2021] [Revised: 02/17/2022] [Accepted: 02/19/2022] [Indexed: 10/19/2022]
Abstract
Frontotemporal dementia associated with granulin (GRN) mutations presents asymmetric brain atrophy. We applied a Minimum Spanning Tree plus an Efficiency Cost Optimization approach to cortical thickness data in order to test whether graph theory measures could identify global or local impairment of connectivity in the presymptomatic phase of pathology, where other techniques failed in demonstrating changes. We included 52 symptomatic GRN mutation carriers (SC), 161 presymptomatic GRN mutation carriers (PSC) and 341 non-carriers relatives from the Genetic Frontotemporal dementia research Initiative cohort. Group differences of global, nodal and edge connectivity in (Minimum Spanning Tree plus an Efficiency Cost Optimization) graph were tested via Structural Equation Models. Global graph perturbation was selectively impaired in SC compared to non-carriers, with no changes in PSC. At the local level, only SC exhibited perturbation of frontotemporal nodes, but edge connectivity revealed a characteristic pattern of interhemispheric disconnection, involving homologous parietal regions, in PSC. Our results suggest that GRN-related frontotemporal dementia resembles a disconnection syndrome, with interhemispheric disconnection between parietal regions in presymptomatic phases that progresses to frontotemporal areas as symptoms emerge.
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Affiliation(s)
- Stefano Gazzina
- Neurophysiology Unit, ASST Spedali Civili Hospital, Brescia, Italy
| | - Mario Grassi
- Department of Brain and Behavioral Science, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
| | - Enrico Premi
- Stroke Unit, Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy
| | | | - Alberto Benussi
- Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy; Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Silvana Archetti
- Biotechnology Laboratory, Department of Diagnostics, Spedali Civili Hospital, Brescia, Italy
| | | | - Martina Bocchetta
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - David M Cash
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Emily G Todd
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Georgia Peakman
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Rhian S Convery
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | | | - Lize C Jiskoot
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Harro Seelaar
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Facultéde Médecine, Université Laval, Quebec City, Québec, Canada
| | - Caroline Graff
- Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tubingen, Tubingen, Germany
| | - Daniela Galimberti
- Fondazione Ca' Granda, IRCCS Ospedale Policlinico, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium; Neurology Service, University Hospitals Leuven, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | | | | | - Chris R Butler
- Nueld Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Isabel Santana
- University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Alexander Gerhard
- Division of Neuroscience & Experimental Psychology, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK; Departments of Geriatric Medicine and Nuclear Medicine, Essen University Hospital, Essen, Germany
| | - Isabelle Le Ber
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Centre de référence des démences rares ou précoces, Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND), Paris, France
| | | | - Simon Ducharme
- Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Adrian Danek
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Markus Otto
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Jonathan D Rohrer
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Barbara Borroni
- Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy.
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6
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Adezati E, Thye M, Edmondson-Stait AJ, Szaflarski JP, Mirman D. Lesion correlates of auditory sentence comprehension deficits in post-stroke aphasia. NEUROIMAGE. REPORTS 2022; 2:None. [PMID: 35243477 PMCID: PMC8843825 DOI: 10.1016/j.ynirp.2021.100076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/27/2021] [Accepted: 12/28/2021] [Indexed: 11/19/2022]
Abstract
Auditory sentence comprehension requires coordination of multiple levels of processing: auditory-phonological perception, lexical-semantic comprehension, syntactic parsing and discourse construction, as well as executive functions such as verbal working memory (WM) and cognitive control. This study examined the lesion correlates of sentence comprehension deficits in post-stroke aphasia, building on prior work on this topic by using a different and clinically-relevant measure of sentence comprehension (the Token Test) and multivariate (SCCAN) and connectome-based lesion-symptom mapping methods. The key findings were that lesions in the posterior superior temporal lobe and inferior frontal gyrus (pars triangularis) were associated with sentence comprehension deficits, which was observed in both mass univariate and multivariate lesion-symptom mapping. Graph theoretic measures of connectome disruption were not statistically significantly associated with sentence comprehension deficits after accounting for overall lesion size.
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Affiliation(s)
- Erica Adezati
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Melissa Thye
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | | | - Jerzy P. Szaflarski
- Department of Neurology and the University of Alabama at Birmingham Epilepsy Center, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Daniel Mirman
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Corresponding author.
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7
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Garcia-Cabello E, Gonzalez-Burgos L, Pereira JB, Hernández-Cabrera JA, Westman E, Volpe G, Barroso J, Ferreira D. The Cognitive Connectome in Healthy Aging. Front Aging Neurosci 2021; 13:694254. [PMID: 34489673 PMCID: PMC8416612 DOI: 10.3389/fnagi.2021.694254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/23/2021] [Indexed: 11/17/2022] Open
Abstract
Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups. Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures. Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups. Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.
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Affiliation(s)
- Eloy Garcia-Cabello
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain
| | - Lissett Gonzalez-Burgos
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Joana B Pereira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Juan Andres Hernández-Cabrera
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - José Barroso
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain
| | - Daniel Ferreira
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Mayo Clinic, Rochester, MN, United States
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8
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Cotosck KR, Meltzer JA, Nucci MP, Lukasova K, Mansur LL, Amaro E. Engagement of Language and Domain General Networks during Word Monitoring in a Native and Unknown Language. Brain Sci 2021; 11:brainsci11081063. [PMID: 34439682 PMCID: PMC8393423 DOI: 10.3390/brainsci11081063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/06/2021] [Accepted: 08/09/2021] [Indexed: 12/27/2022] Open
Abstract
Functional neuroimaging studies have highlighted the roles of three networks in processing language, all of which are typically left-lateralized: a ventral stream involved in semantics, a dorsal stream involved in phonology and speech production, and a more dorsal "multiple demand" network involved in many effortful tasks. As lateralization in all networks may be affected by life factors such as age, literacy, education, and brain pathology, we sought to develop a task paradigm with which to investigate the engagement of these networks, including manipulations to selectively emphasize semantic and phonological processing within a single task performable by almost anyone regardless of literacy status. In young healthy participants, we administered an auditory word monitoring task, in which participants had to note the occurrence of a target word within a continuous story presented in either their native language, Portuguese, or the unknown language, Japanese. Native language task performance activated ventral stream language networks, left lateralized but bilateral in the anterior temporal lobe. Unfamiliar language performance, being more difficult, activated left hemisphere dorsal stream structures and the multiple demand network bilaterally, but predominantly in the right hemisphere. These findings suggest that increased demands on phonological processing to accomplish word monitoring in the absence of semantic support may result in the bilateral recruitment of networks involved in speech perception under more challenging conditions.
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Affiliation(s)
- Kelly R. Cotosck
- FNI–Functional Neuroimaging, LIM 4–Laboratório de Investigação Médica 44 (Laboratory of Medical Investigation 44), Department of Radiology, Universidade de São Paulo, São Paulo 05403-000, Brazil; (M.P.N.); (K.L.); (E.A.J.)
- Correspondence: or ; Tel.: +55-11-95131-2225
| | | | - Mariana P. Nucci
- FNI–Functional Neuroimaging, LIM 4–Laboratório de Investigação Médica 44 (Laboratory of Medical Investigation 44), Department of Radiology, Universidade de São Paulo, São Paulo 05403-000, Brazil; (M.P.N.); (K.L.); (E.A.J.)
| | - Katerina Lukasova
- FNI–Functional Neuroimaging, LIM 4–Laboratório de Investigação Médica 44 (Laboratory of Medical Investigation 44), Department of Radiology, Universidade de São Paulo, São Paulo 05403-000, Brazil; (M.P.N.); (K.L.); (E.A.J.)
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André 09210-580, Brazil
| | | | - Edson Amaro
- FNI–Functional Neuroimaging, LIM 4–Laboratório de Investigação Médica 44 (Laboratory of Medical Investigation 44), Department of Radiology, Universidade de São Paulo, São Paulo 05403-000, Brazil; (M.P.N.); (K.L.); (E.A.J.)
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9
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Cognitive dedifferentiation as a function of cognitive impairment in the ADNI and MemClin cohorts. Aging (Albany NY) 2021; 13:13430-13442. [PMID: 34038387 PMCID: PMC8202862 DOI: 10.18632/aging.203108] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/13/2021] [Indexed: 12/17/2022]
Abstract
The cause of cognitive dedifferentiation has been suggested as specific to late-life abnormal cognitive decline rather than a general feature of aging. This hypothesis was tested in two large cohorts with different characteristics. Individuals (n = 2710) were identified in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) research database (n = 1282) in North America, and in the naturalistic multi-site MemClin Project database (n = 1223), the latter recruiting from 9 out of 10 memory clinics in the greater Stockholm catchment area in Sweden. Comprehensive neuropsychological testing informed diagnosis of dementia, mild cognitive impairment (MCI), or subjective cognitive impairment (SCI). Diagnosis was further collapsed into cognitive impairment (CI: MCI or dementia) vs no cognitive impairment (NCI). After matching, loadings on the first principal component were higher in the CI vs NCI group in both ADNI (53.1% versus 38.3%) and MemClin (33.3% vs 30.8%). Correlations of all paired combinations of individual tests by diagnostic group were also stronger in the CI group in both ADNI (mean inter-test r = 0.51 vs r = 0.33, p < 0.001) and MemClin (r = 0.31 vs r = 0.27, p = 0.042). Dedifferentiation was explained by cognitive impairment when controlling for age, sex, and education. This finding replicated across two separate, large cohorts of older individuals. Knowledge that the structure of human cognition becomes less diversified and more dependent on general intelligence as a function of cognitive impairment should inform clinical assessment and care for these patients as their neurodegeneration progresses.
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