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Biondi M, Marino M, Mantini D, Spironelli C. Unveiling altered connectivity between cognitive networks and cerebellum in schizophrenia. Schizophr Res 2024; 271:47-58. [PMID: 39013344 DOI: 10.1016/j.schres.2024.06.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/12/2024] [Accepted: 06/23/2024] [Indexed: 07/18/2024]
Abstract
Cognitive functioning is a crucial aspect in schizophrenia (SZ), and when altered it has devastating effects on patients' quality of life and treatment outcomes. Several studies suggested that they could result from altered communication between the cortex and cerebellum. However, the neural correlates underlying these impairments have not been identified. In this study, we investigated resting state functional connectivity (rsFC) in SZ patients, by considering the interactions between cortical networks supporting cognition and cerebellum. In addition, we investigated the relationship between SZ patients' rsFC and their symptoms. We used fMRI data from 74 SZ patients and 74 matched healthy controls (HC) downloaded from the publicly available database SchizConnect. We implemented a seed-based connectivity approach to identify altered functional connections between specific cortical networks and cerebellum. We considered ten commonly studied resting state networks, whose functioning encompasses specific cognitive functions, and the cerebellum, whose involvement in supporting cognition has been recently identified. We then explored the relationship between altered rsFC values and Positive and Negative Syndrome Scale (PANSS) scores. The SZ group showed increased connectivity values compared with HC group for cortical networks involved in attentive processes, which were also linked to PANSS items describing attention and language-related processing. We also showed decreased connectivity between cerebellar regions, and increased connectivity between them and attentive networks, suggesting the contribution of cerebellum to attentive and affective deficits. In conclusion, our findings highlighted the link between negative symptoms in SZ and altered connectivity within the cerebellum and between the same and cortical networks supporting cognition.
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Affiliation(s)
| | - Marco Marino
- Department of General Psychology, University of Padova, Italy; Movement Control and Neuroplasticity Research Group, KU, Leuven, Belgium
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU, Leuven, Belgium.
| | - Chiara Spironelli
- Padova Neuroscience Center, University of Padova, Italy; Department of General Psychology, University of Padova, Italy
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2
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Low TA, Chilvers MJ, Zhu H, Carlson HL, Harris AD, Goodyear BG, Dukelow SP. Structural network topology associated with naming improvements following intensive aphasia therapy in post-stroke aphasia. J Neurol Sci 2024; 462:123065. [PMID: 38820737 DOI: 10.1016/j.jns.2024.123065] [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: 03/05/2024] [Revised: 05/09/2024] [Accepted: 05/27/2024] [Indexed: 06/02/2024]
Abstract
A stroke can disrupt the finely tuned language network resulting in aphasia, a language impairment. Though many stroke survivors with aphasia recover within the first 6 months, a significant proportion have lasting deficits. The factors contributing to optimal treatment response remain unclear. Some evidence suggests that increased modularity or fragmentation of brain networks may underlie post-stroke aphasia severity and the extent of recovery. We examined associations between network organization and aphasia recovery in sixteen chronic stroke survivors with non-fluent aphasia following 35 h of Multi-Modality Aphasia Therapy over 10 days and 20 healthy controls who underwent imaging at a single timepoint. Using diffusion-weighted scans obtained before and after treatment, we constructed whole-brain structural connectomes representing the number of probabilistic streamlines between brain regions. Graph theory metrics were quantified for each connectome using the Brain Connectivity Toolbox. Correlations were examined between graph metrics and speech performance measured using the Boston Naming Test (BNT) at pre-, post- and 3-months post-intervention. Compared to controls, participants with stroke demonstrated higher whole-brain modularity at pre-treatment. Modularity did not differ between pre- and post-treatment. In individuals who responded to therapy, higher pre-treatment modularity was associated with worse performance on the BNT. Moreover, higher pre-treatment participation coefficients (i.e., how well a region is connected outside its own module) for the left IFG, planum temporale, and posterior temporal gyri were associated with greater improvements at post-treatment. These results suggest that pre-treatment network topology may impact therapeutic gains, highlighting the influence of network organization on post-stroke aphasia recovery.
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Affiliation(s)
- Trevor A Low
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Matthew J Chilvers
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Harold Zhu
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Helen L Carlson
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Ashley D Harris
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Bradley G Goodyear
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Sean P Dukelow
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Division of Physical Medicine and Rehabilitation, University of Calgary, Calgary, Alberta, Canada.
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3
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Obrig H, Regenbrecht F, Pino D, Krause CD. Verbal short term memory contribution to sentence comprehension decreases with increasing syntactic complexity in people with aphasia. Neuroimage 2024:120730. [PMID: 39009249 DOI: 10.1016/j.neuroimage.2024.120730] [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: 03/20/2024] [Revised: 06/20/2024] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
Sentence comprehension requires the integration of linguistic units presented in a temporal sequence based on a non-linear underlying syntactic structure. While it is uncontroversial that storage is mandatory for this process, there are opposing views regarding the relevance of general short-term-/working-memory capacities (STM/WM) versus language specific resources. Here we report results from 43 participants with an acquired brain lesion in the extended left hemispheric language network and resulting language deficits, who performed a sentence-to-picture matching task and an experimental task assessing phonological short-term memory. The sentence task systematically varied syntactic complexity (embedding depth and argument order) while lengths, number of propositions and plausibility were kept constant. Clinical data including digit-/ block-spans and lesion size and site were additionally used in the analyses. Correlational analyses confirm that performance on STM/WM-tasks (experimental task and digit-span) are the only two relevant predictors for correct sentence-picture-matching, while reaction times only depended on age and lesion size. Notably increasing syntactic complexity reduced the correlational strength speaking for the additional recruitment of language specific resources independent of more general verbal STM/WM capacities, when resolving complex syntactic structure. The complementary lesion-behaviour analysis yielded different lesion volumes correlating with either the sentence-task or the STM-task. Factoring out STM measures lesions in the anterior temporal lobe correlated a larger decrease in accuracy with increasing syntactic complexity. We conclude that overall sentence comprehension depends on STM/WM capacity, while increases in syntactic complexity tax another independent cognitive resource.
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Affiliation(s)
- Hellmuth Obrig
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology & Department of Neurology, 04103 Leipzig, Germany; Clinic for Cognitive Neurology, University Hospital & Faculty of Medicine, 04103 Leipzig, Germany.
| | - Frank Regenbrecht
- Clinic for Cognitive Neurology, University Hospital & Faculty of Medicine, 04103 Leipzig, Germany
| | - Danièle Pino
- Clinic for Cognitive Neurology, University Hospital & Faculty of Medicine, 04103 Leipzig, Germany
| | - Carina D Krause
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology & Department of Neurology, 04103 Leipzig, Germany; International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity IMPRS NeuroComm https://imprs-neurocom.mpg.de/home
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4
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Wu W, Hoffman P. Functional integration and segregation during semantic cognition: Evidence across age groups. Cortex 2024; 178:157-173. [PMID: 39013249 DOI: 10.1016/j.cortex.2024.06.015] [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: 11/02/2023] [Revised: 04/05/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024]
Abstract
Semantic cognition is underpinned by ventral anterior temporal lobe (vATL) which encodes knowledge representations and inferior frontal gyrus (IFG), which controls activation of knowledge based on the needs of the current context. This core semantic network has been validated in substantial empirical findings in the past. However, it remains unclear how these core semantic areas dynamically communicate with each other, and with other neural networks, to achieve successful semantic processing. Here, we investigated this question by testing functional connectivity in the core semantic network during semantic tasks and whether these connections were affected by cognitive ageing. Compared to a non-semantic task, semantic tasks increased the connectivity between left and right IFGs, indicating a bilateral semantic control system. Strengthened connectivity was also found between left IFG and left vATL, and this effect was stronger in the young group. At a whole-brain scale, IFG and vATL increased their coupling with multiple-demand regions during semantic tasks, even though these areas were deactivated relative to non-semantic tasks. This suggests that the domain-general executive network contributes to semantic processing. In contrast, IFG and vATL decreased their interaction with default mode network (DMN) areas during semantic tasks, even though these areas were positively activated by the task. This suggests that DMN areas do not contribute to all semantic tasks: their activation may sometimes reflect automatic retrieval of task-irrelevant memories and associations. Taken together, our study characterizes a dynamic connectivity mechanism supporting semantic cognition within and beyond core semantic regions.
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Affiliation(s)
- Wei Wu
- School of Philosophy, Psychology & Language Sciences, University of Edinburgh, Edinburgh, UK; Department of Music, Durham University, Durham, UK.
| | - Paul Hoffman
- School of Philosophy, Psychology & Language Sciences, University of Edinburgh, Edinburgh, UK.
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5
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Ihalainen R, Annen J, Gosseries O, Cardone P, Panda R, Martial C, Thibaut A, Laureys S, Chennu S. Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in a cohort of patients with traumatic disorders of consciousness. PLoS One 2024; 19:e0298110. [PMID: 38968195 PMCID: PMC11226086 DOI: 10.1371/journal.pone.0298110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/13/2024] [Indexed: 07/07/2024] Open
Abstract
Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.
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Affiliation(s)
- Riku Ihalainen
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
- School of Computing, University of Kent, Canterbury, United Kingdom
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
- Department of Data Analysis, University of Ghent, Ghent, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Paolo Cardone
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- CERVO Brain Research Centre, de la Canardière, Québec, Canada
- Consciousness Science Institute, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Srivas Chennu
- School of Computing, University of Kent, Canterbury, United Kingdom
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Combrisson E, Basanisi R, Gueguen MCM, Rheims S, Kahane P, Bastin J, Brovelli A. Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife 2024; 12:RP92938. [PMID: 38941238 PMCID: PMC11213568 DOI: 10.7554/elife.92938] [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: 06/30/2024] Open
Abstract
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Maelle CM Gueguen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of LyonLyonFrance
| | - Philippe Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut NeurosciencesGrenobleFrance
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
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Arrigo IV, da Silva PHR, Leoni RF. Functional and Effective Connectivity Underlying Semantic Verbal Fluency. Brain Topogr 2024:10.1007/s10548-024-01059-x. [PMID: 38839695 DOI: 10.1007/s10548-024-01059-x] [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: 02/17/2023] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
Semantic verbal fluency (SVF) impairment is present in several neurological disorders. Although activation in SVF-related areas has been reported, how these regions are connected and their functional roles in the network remain divergent. We assessed SVF static and dynamic functional connectivity (FC) and effective connectivity in healthy participants using functional magnetic resonance imaging. We observed activation in the inferior frontal (IFG), middle temporal (pMTG) and angular gyri (AG), anterior cingulate (AC), insular cortex, and regions of the superior, middle, and medial frontal gyri (SFG, MFG, MidFG). Our static FC analysis showed a highly interconnected task and resting state network. Increased connectivity of AC with the pMTG and AG was observed for the task. The dynamic FC analysis provided circuits with connections similarly modulated across time and regions related to category identification, language comprehension, word selection and recovery, word generation, inhibition of speaking, speech planning, and articulatory planning of orofacial movements. Finally, the effective connectivity analysis provided a network that best explained our data, starting at the AG and going to the pMTG, from which there was a division between the ventral and dorsal streams. The SFG and MFG regions were connected and modulated by the MidFG, while the inferior regions formed the ventral stream. Therefore, we successfully assessed the SVF network, exploring regions associated with the entire processing, from category identification to word generation. The methodological approach can be helpful for further investigation of the SVF network in neurological disorders.
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Affiliation(s)
- Isabella Velloso Arrigo
- InBrain, Department of Physics, FFCLRP, University of Sao Paulo, Av. Bandeirantes, Ribeirao Preto, Sao Paulo, 3900, 14040-901, Brazil
| | - Pedro Henrique Rodrigues da Silva
- InBrain, Department of Physics, FFCLRP, University of Sao Paulo, Av. Bandeirantes, Ribeirao Preto, Sao Paulo, 3900, 14040-901, Brazil
| | - Renata Ferranti Leoni
- InBrain, Department of Physics, FFCLRP, University of Sao Paulo, Av. Bandeirantes, Ribeirao Preto, Sao Paulo, 3900, 14040-901, Brazil.
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8
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Zhang W, Jiang M, Teo KAC, Bhuvanakantham R, Fong L, Sim WKJ, Guo Z, Foo CHV, Chua RHJ, Padmanabhan P, Leong V, Lu J, Gulyás B, Guan C. Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study. Neuroimage 2024; 293:120629. [PMID: 38697588 DOI: 10.1016/j.neuroimage.2024.120629] [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: 12/05/2023] [Revised: 04/17/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024] Open
Abstract
Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.
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Affiliation(s)
- Wei Zhang
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Muyun Jiang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Kok Ann Colin Teo
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore; Division of Neurosurgery, National University Health System, Singapore
| | - Raghavan Bhuvanakantham
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - LaiGuan Fong
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore
| | - Wei Khang Jeremy Sim
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore
| | - Zhiwei Guo
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | | | | | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Victoria Leong
- Division of Psychology, Nanyang Technological University, Singapore; Department of Pediatrics, University of Cambridge, United Kingdom
| | - Jia Lu
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; DSO National Laboratories, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore.
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9
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Shain C, Kean H, Casto C, Lipkin B, Affourtit J, Siegelman M, Mollica F, Fedorenko E. Distributed Sensitivity to Syntax and Semantics throughout the Language Network. J Cogn Neurosci 2024; 36:1427-1471. [PMID: 38683732 DOI: 10.1162/jocn_a_02164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Human language is expressive because it is compositional: The meaning of a sentence (semantics) can be inferred from its structure (syntax). It is commonly believed that language syntax and semantics are processed by distinct brain regions. Here, we revisit this claim using precision fMRI methods to capture separation or overlap of function in the brains of individual participants. Contrary to prior claims, we find distributed sensitivity to both syntax and semantics throughout a broad frontotemporal brain network. Our results join a growing body of evidence for an integrated network for language in the human brain within which internal specialization is primarily a matter of degree rather than kind, in contrast with influential proposals that advocate distinct specialization of different brain areas for different types of linguistic functions.
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Affiliation(s)
| | - Hope Kean
- Massachusetts Institute of Technology
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10
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Li Q, Xing Y, Zhu Z, Fei X, Tang Y, Lu J. Effects of computerized cognitive training on functional brain networks in patients with vascular cognitive impairment and no dementia. CNS Neurosci Ther 2024; 30:e14779. [PMID: 38828650 PMCID: PMC11145123 DOI: 10.1111/cns.14779] [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: 01/14/2024] [Revised: 04/21/2024] [Accepted: 05/08/2024] [Indexed: 06/05/2024] Open
Abstract
AIMS Previous neuroimaging studies of vascular cognitive impairment, no dementia (VCIND), have reported functional alterations, but far less is known about the effects of cognitive training on functional connectivity (FC) of intrinsic connectivity networks (ICNs) and how they relate to intervention-related cognitive improvement. This study provides comprehensive research on the changes in intra- and inter-brain functional networks in patients with VCIND who received computerized cognitive training, with a focus on the underlying mechanisms and potential therapeutic strategies. METHODS We prospectively collected 60 patients with VCIND who were randomly divided into the training group (N = 30) receiving computerized cognitive training and the control group (N = 30) receiving fixed cognitive training. Functional MRI scans and cognitive assessments were performed at baseline, at the 7-week training, and at the 6-month follow-up. Utilizing templates for ICNs, the study employed a linear mixed model to compare intra- and inter-network FC changes between the two groups. Pearson correlation was applied to calculate the relationship between FC and cognitive function. RESULTS We found significantly decreased intra-network FC within the default mode network (DMN) following computerized cognitive training at Month 6 (p = 0.034), suggesting a potential loss of functional specialization. Computerized training led to increased functional coupling between the DMN and sensorimotor network (SMN) (p = 0.01) and between the language network (LN) and executive control network (ECN) at Month 6 (p < 0.001), indicating compensatory network adaptations in patients with VCIND. Notably, the intra-LN exhibited enhanced functional specialization after computerized cognitive training (p = 0.049), with significant FC increases among LN regions, which correlated with improvements in neuropsychological measures (p < 0.05), emphasizing the targeted impact of computerized cognitive training on language abilities. CONCLUSIONS This study provides insights into neuroplasticity and adaptive changes resulting from cognitive training in patients with VCIND, with implications for potential therapeutic strategies.
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Affiliation(s)
- Qiong‐Ge Li
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
| | - Yi Xing
- Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Zu‐De Zhu
- Collaborative Innovation Center for Language AbilityJiangsu Normal UniversityXuzhouChina
| | - Xiao‐Lu Fei
- Department of Information, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Yi Tang
- Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
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11
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Yildirim I, Paul LA. From task structures to world models: what do LLMs know? Trends Cogn Sci 2024; 28:404-415. [PMID: 38443199 DOI: 10.1016/j.tics.2024.02.008] [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: 07/12/2023] [Revised: 02/03/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024]
Abstract
In what sense does a large language model (LLM) have knowledge? We answer by granting LLMs 'instrumental knowledge': knowledge gained by using next-word generation as an instrument. We then ask how instrumental knowledge is related to the ordinary, 'worldly knowledge' exhibited by humans, and explore this question in terms of the degree to which instrumental knowledge can be said to incorporate the structured world models of cognitive science. We discuss ways LLMs could recover degrees of worldly knowledge and suggest that such recovery will be governed by an implicit, resource-rational tradeoff between world models and tasks. Our answer to this question extends beyond the capabilities of a particular AI system and challenges assumptions about the nature of knowledge and intelligence.
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Affiliation(s)
- Ilker Yildirim
- Department of Psychology, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Wu-Tsai Institute, Yale University, New Haven, CT, USA; Foundations of Data Science Institute, Yale University, New Haven, CT, USA.
| | - L A Paul
- Department of Philosophy, Yale University, New Haven, CT, USA; Wu-Tsai Institute, Yale University, New Haven, CT, USA; Munich Center for Mathematical Philosophy, Ludwig Maximilian University of Munich, Munich, Germany.
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12
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Sun C, Zhang J, Bu L, Lu J, Yao Y, Wu J. A speech fluency brain network derived from gliomas. Brain Commun 2024; 6:fcae153. [PMID: 38756538 PMCID: PMC11098038 DOI: 10.1093/braincomms/fcae153] [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: 01/12/2023] [Revised: 02/21/2024] [Accepted: 04/28/2024] [Indexed: 05/18/2024] Open
Abstract
The brain network of speech fluency has not yet been investigated via a study with a large and homogenous sample. This study analysed multimodal imaging data from 115 patients with low-grade glioma to explore the brain network of speech fluency. We applied voxel-based lesion-symptom mapping to identify domain-specific regions and white matter pathways associated with speech fluency. Direct cortical stimulation validated the domain-specific regions intra-operatively. We then performed connectivity-behaviour analysis with the aim of identifying connections that significantly correlated with speech fluency. Voxel-based lesion-symptom mapping analysis showed that damage to domain-specific regions (the middle frontal gyrus, the precentral gyrus, the orbital part of inferior frontal gyrus and the insula) and white matter pathways (corticospinal fasciculus, internal capsule, arcuate fasciculus, uncinate fasciculus, frontal aslant tract) are associated with reduced speech fluency. Furthermore, we identified connections emanating from these domain-specific regions that exhibited significant correlations with speech fluency. These findings illuminate the interaction between domain-specific regions and 17 domain-general regions-encompassing the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus and rolandic operculum, superior temporal gyrus, temporal pole, inferior temporal pole, middle cingulate gyrus, supramarginal gyrus, fusiform gyrus, inferior parietal lobe, as well as subcortical structures such as thalamus-implicating their collective role in supporting fluent speech. Our detailed mapping of the speech fluency network offers a strategic foundation for clinicians to safeguard language function during the surgical intervention for brain tumours.
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Affiliation(s)
- Cechen Sun
- Department of Biostatistics, School of Public Health & National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jie Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 201107, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200040, China
- Neurosurgical Institute of Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Linghao Bu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 201107, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200040, China
- Neurosurgical Institute of Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Junfeng Lu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 201107, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200040, China
- Neurosurgical Institute of Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Ye Yao
- Department of Biostatistics, School of Public Health & National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
- Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 201107, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200040, China
- Neurosurgical Institute of Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
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13
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Tilton-Bolowsky V, Stockbridge MD, Hillis AE. Remapping and Reconnecting the Language Network after Stroke. Brain Sci 2024; 14:419. [PMID: 38790398 PMCID: PMC11117613 DOI: 10.3390/brainsci14050419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
Here, we review the literature on neurotypical individuals and individuals with post-stroke aphasia showing that right-hemisphere regions homologous to language network and other regions, like the right cerebellum, are activated in language tasks and support language even in healthy people. We propose that language recovery in post-stroke aphasia occurs largely by potentiating the right hemisphere network homologous to the language network and other networks that previously supported language to a lesser degree and by modulating connection strength between nodes of the right-hemisphere language network and undamaged nodes of the left-hemisphere language network. Based on this premise (supported by evidence we review), we propose that interventions should be aimed at potentiating the right-hemisphere language network through Hebbian learning or by augmenting connections between network nodes through neuroplasticity, such as non-invasive brain stimulation and perhaps modulation of neurotransmitters involved in neuroplasticity. We review aphasia treatment studies that have taken this approach. We conclude that further aphasia rehabilitation with this aim is justified.
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Affiliation(s)
| | | | - Argye E. Hillis
- Departments of Neurology, Physical Medicine & Rehabilitation, and Cognitive Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (V.T.-B.); (M.D.S.)
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14
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Gwilliams L, Marantz A, Poeppel D, King JR. Hierarchical dynamic coding coordinates speech comprehension in the brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590280. [PMID: 38659750 PMCID: PMC11042271 DOI: 10.1101/2024.04.19.590280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Speech comprehension requires the human brain to transform an acoustic waveform into meaning. To do so, the brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how these hierarchical features are generated and continuously coordinated. Here, we propose that each linguistic feature is dynamically represented in the brain to simultaneously represent successive events. To test this 'Hierarchical Dynamic Coding' (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and integration of a comprehensive hierarchy of language features spanning acoustic, phonetic, sub-lexical, lexical, syntactic and semantic representations. For this, we recorded 21 participants with magnetoencephalography (MEG), while they listened to two hours of short stories. Our analyses reveal three main findings. First, the brain incrementally represents and simultaneously maintains successive features. Second, the duration of these representations depend on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting the interference between successive features. Overall, HDC reveals how the human brain continuously builds and maintains a language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.
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Affiliation(s)
- Laura Gwilliams
- Department of Psychology, Stanford University
- Department of Psychology, New York University
| | - Alec Marantz
- Department of Psychology, New York University
- Department of Linguistics, New York University
| | - David Poeppel
- Department of Psychology, New York University
- Ernst Strungman Institute
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15
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Hert R, Järvikivi J, Arnhold A. The Importance of Linguistic Factors: He Likes Subject Referents. Cogn Sci 2024; 48:e13436. [PMID: 38564245 DOI: 10.1111/cogs.13436] [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: 03/23/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024]
Abstract
We report the results of one visual-world eye-tracking experiment and two referent selection tasks in which we investigated the effects of information structure in the form of prosody and word order manipulation on the processing of subject pronouns er and der in German. Factors such as subjecthood, focus, and topicality, as well as order of mention have been linked to an increased probability of certain referents being selected as the pronoun's antecedent and described as increasing this referent's prominence, salience, or accessibility. The goal of this study was to find out whether pronoun processing is primarily guided by linguistic factors (e.g., grammatical role) or nonlinguistic factors (e.g., first-mention), and whether pronoun interpretation can be described in terms of referents' "prominence" / "accessibility" / "salience." The results showed an overall subject preference for er, whereas der was affected by the object role and focus marking. While focus increases the attentional load and enhances memory representation for the focused referent making the focused referent more available, ultimately it did not affect the final interpretation of er, suggesting that "prominence" or the related concepts do not explain referent selection preferences. Overall, the results suggest a primacy of linguistic factors in determining pronoun resolution.
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Affiliation(s)
- Regina Hert
- Laboratoire de NeuroPsychoLinguistique (EA4156), Maison de la Recherche, Université de Toulouse - Jean-Jaurès
- Department of Linguistics, University of Alberta
| | | | - Anja Arnhold
- Department of Linguistics, University of Alberta
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16
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Kauf C, Tuckute G, Levy R, Andreas J, Fedorenko E. Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-Brain Similarity of fMRI Responses in the Language Network. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:7-42. [PMID: 38645614 PMCID: PMC11025651 DOI: 10.1162/nol_a_00116] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/11/2023] [Indexed: 04/23/2024]
Abstract
Representations from artificial neural network (ANN) language models have been shown to predict human brain activity in the language network. To understand what aspects of linguistic stimuli contribute to ANN-to-brain similarity, we used an fMRI data set of responses to n = 627 naturalistic English sentences (Pereira et al., 2018) and systematically manipulated the stimuli for which ANN representations were extracted. In particular, we (i) perturbed sentences' word order, (ii) removed different subsets of words, or (iii) replaced sentences with other sentences of varying semantic similarity. We found that the lexical-semantic content of the sentence (largely carried by content words) rather than the sentence's syntactic form (conveyed via word order or function words) is primarily responsible for the ANN-to-brain similarity. In follow-up analyses, we found that perturbation manipulations that adversely affect brain predictivity also lead to more divergent representations in the ANN's embedding space and decrease the ANN's ability to predict upcoming tokens in those stimuli. Further, results are robust as to whether the mapping model is trained on intact or perturbed stimuli and whether the ANN sentence representations are conditioned on the same linguistic context that humans saw. The critical result-that lexical-semantic content is the main contributor to the similarity between ANN representations and neural ones-aligns with the idea that the goal of the human language system is to extract meaning from linguistic strings. Finally, this work highlights the strength of systematic experimental manipulations for evaluating how close we are to accurate and generalizable models of the human language network.
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Affiliation(s)
- Carina Kauf
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Greta Tuckute
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Roger Levy
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jacob Andreas
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
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17
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Michaelov JA, Bardolph MD, Van Petten CK, Bergen BK, Coulson S. Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:107-135. [PMID: 38645623 PMCID: PMC11025652 DOI: 10.1162/nol_a_00105] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 03/24/2023] [Indexed: 04/23/2024]
Abstract
Theoretical accounts of the N400 are divided as to whether the amplitude of the N400 response to a stimulus reflects the extent to which the stimulus was predicted, the extent to which the stimulus is semantically similar to its preceding context, or both. We use state-of-the-art machine learning tools to investigate which of these three accounts is best supported by the evidence. GPT-3, a neural language model trained to compute the conditional probability of any word based on the words that precede it, was used to operationalize contextual predictability. In particular, we used an information-theoretic construct known as surprisal (the negative logarithm of the conditional probability). Contextual semantic similarity was operationalized by using two high-quality co-occurrence-derived vector-based meaning representations for words: GloVe and fastText. The cosine between the vector representation of the sentence frame and final word was used to derive contextual cosine similarity estimates. A series of regression models were constructed, where these variables, along with cloze probability and plausibility ratings, were used to predict single trial N400 amplitudes recorded from healthy adults as they read sentences whose final word varied in its predictability, plausibility, and semantic relationship to the likeliest sentence completion. Statistical model comparison indicated GPT-3 surprisal provided the best account of N400 amplitude and suggested that apparently disparate N400 effects of expectancy, plausibility, and contextual semantic similarity can be reduced to variation in the predictability of words. The results are argued to support predictive coding in the human language network.
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Affiliation(s)
- James A. Michaelov
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Megan D. Bardolph
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Cyma K. Van Petten
- Department of Psychology, Binghamton University, State University of New York, Binghamton, NY, USA
| | - Benjamin K. Bergen
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Seana Coulson
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
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18
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Schneider JM, Scott TL, Legault J, Qi Z. Limited but specific engagement of the mature language network during linguistic statistical learning. Cereb Cortex 2024; 34:bhae123. [PMID: 38566510 PMCID: PMC10987970 DOI: 10.1093/cercor/bhae123] [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: 06/26/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Statistical learning (SL) is the ability to detect and learn regularities from input and is foundational to language acquisition. Despite the dominant role of SL as a theoretical construct for language development, there is a lack of direct evidence supporting the shared neural substrates underlying language processing and SL. It is also not clear whether the similarities, if any, are related to linguistic processing, or statistical regularities in general. The current study tests whether the brain regions involved in natural language processing are similarly recruited during auditory, linguistic SL. Twenty-two adults performed an auditory linguistic SL task, an auditory nonlinguistic SL task, and a passive story listening task as their neural activation was monitored. Within the language network, the left posterior temporal gyrus showed sensitivity to embedded speech regularities during auditory, linguistic SL, but not auditory, nonlinguistic SL. Using a multivoxel pattern similarity analysis, we uncovered similarities between the neural representation of auditory, linguistic SL, and language processing within the left posterior temporal gyrus. No other brain regions showed similarities between linguistic SL and language comprehension, suggesting that a shared neurocomputational process for auditory SL and natural language processing within the left posterior temporal gyrus is specific to linguistic stimuli.
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Affiliation(s)
- Julie M Schneider
- Department of Communication Sciences and Disorders, Louisiana State University, 77 Hatcher Hall, Field House Dr., Baton Rouge, LA 70803, United States
- Department of Linguistics & Cognitive Science, University of Delaware, 125 E Main St, Newark, DE 19716, United States
| | - Terri L Scott
- School of Medicine, University of California San Francisco, 533 Parnassus Ave, San Francisco, CA 94143, United States
| | - Jennifer Legault
- Department of Psychology, Elizabethtown College, One Alpha Dr, Elizabethtown, PA 17022, United States
| | - Zhenghan Qi
- Department of Linguistics & Cognitive Science, University of Delaware, 125 E Main St, Newark, DE 19716, United States
- Bouvé College of Health Sciences, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States
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19
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Billot A, Kiran S. Disentangling neuroplasticity mechanisms in post-stroke language recovery. BRAIN AND LANGUAGE 2024; 251:105381. [PMID: 38401381 PMCID: PMC10981555 DOI: 10.1016/j.bandl.2024.105381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/28/2023] [Accepted: 01/12/2024] [Indexed: 02/26/2024]
Abstract
A major objective in post-stroke aphasia research is to gain a deeper understanding of neuroplastic mechanisms that drive language recovery, with the ultimate goal of enhancing treatment outcomes. Subsequent to recent advances in neuroimaging techniques, we now have the ability to examine more closely how neural activity patterns change after a stroke. However, the way these neural activity changes relate to language impairments and language recovery is still debated. The aim of this review is to provide a theoretical framework to better investigate and interpret neuroplasticity mechanisms underlying language recovery in post-stroke aphasia. We detail two sets of neuroplasticity mechanisms observed at the synaptic level that may explain functional neuroimaging findings in post-stroke aphasia recovery at the network level: feedback-based homeostatic plasticity and associative Hebbian plasticity. In conjunction with these plasticity mechanisms, higher-order cognitive control processes dynamically modulate neural activity in other regions to meet communication demands, despite reduced neural resources. This work provides a network-level neurobiological framework for understanding neural changes observed in post-stroke aphasia and can be used to define guidelines for personalized treatment development.
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Affiliation(s)
- Anne Billot
- Center for Brain Recovery, Boston University, Boston, USA; Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Swathi Kiran
- Center for Brain Recovery, Boston University, Boston, USA.
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20
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Tuckute G, Sathe A, Srikant S, Taliaferro M, Wang M, Schrimpf M, Kay K, Fedorenko E. Driving and suppressing the human language network using large language models. Nat Hum Behav 2024; 8:544-561. [PMID: 38172630 DOI: 10.1038/s41562-023-01783-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 11/10/2023] [Indexed: 01/05/2024]
Abstract
Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of the brain response associated with each sentence. We then use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress the activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also non-invasively control neural activity in higher-level cortical areas, such as the language network.
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Affiliation(s)
- Greta Tuckute
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Aalok Sathe
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shashank Srikant
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- MIT-IBM Watson AI Lab, Cambridge, MA, USA
| | - Maya Taliaferro
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mingye Wang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin Schrimpf
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Quest for Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA.
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21
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Lei VLC, Leong TI, Leong CT, Liu L, Choi CU, Sereno MI, Li D, Huang R. Phase-encoded fMRI tracks down brainstorms of natural language processing with subsecond precision. Hum Brain Mapp 2024; 45:e26617. [PMID: 38339788 PMCID: PMC10858339 DOI: 10.1002/hbm.26617] [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: 07/14/2023] [Revised: 12/04/2023] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Natural language processing unfolds information overtime as spatially separated, multimodal, and interconnected neural processes. Existing noninvasive subtraction-based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed rapid phase-encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging data, as well as overcoming scanner noise and head-motion challenges during overt language tasks. We captured real-time information flows as coherent hemodynamic waves traveling over the cortical surface during listening, reading aloud, reciting, and oral cross-language interpreting tasks. We were able to observe the timing, location, direction, and surge of traveling waves in all language tasks, which were visualized as "brainstorms" on brain "weather" maps. The paths of hemodynamic traveling waves provide direct evidence for dual-stream models of the visual and auditory systems as well as logistics models for crossmodal and cross-language processing. Specifically, we have tracked down the step-by-step processing of written or spoken sentences first being received and processed by the visual or auditory streams, carried across language and domain-general cognitive regions, and finally delivered as overt speeches monitored through the auditory cortex, which gives a complete picture of information flows across the brain during natural language functioning. PRACTITIONER POINTS: Phase-encoded fMRI enables simultaneous imaging of high spatial and temporal resolution, capturing continuous spatiotemporal dynamics of the entire brain during real-time overt natural language tasks. Spatiotemporal traveling wave patterns provide direct evidence for constructing comprehensive and explicit models of human information processing. This study unlocks the potential of applying rapid phase-encoded fMRI to indirectly track the underlying neural information flows of sequential sensory, motor, and high-order cognitive processes.
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Affiliation(s)
- Victoria Lai Cheng Lei
- Centre for Cognitive and Brain SciencesUniversity of MacauTaipaChina
- Faculty of Arts and HumanitiesUniversity of MacauTaipaChina
| | - Teng Ieng Leong
- Centre for Cognitive and Brain SciencesUniversity of MacauTaipaChina
- Faculty of Arts and HumanitiesUniversity of MacauTaipaChina
| | - Cheok Teng Leong
- Centre for Cognitive and Brain SciencesUniversity of MacauTaipaChina
- Faculty of Science and TechnologyUniversity of MacauTaipaChina
| | - Lili Liu
- Centre for Cognitive and Brain SciencesUniversity of MacauTaipaChina
- Faculty of Science and TechnologyUniversity of MacauTaipaChina
| | - Chi Un Choi
- Centre for Cognitive and Brain SciencesUniversity of MacauTaipaChina
| | - Martin I. Sereno
- Department of PsychologySan Diego State UniversitySan DiegoCaliforniaUSA
| | - Defeng Li
- Centre for Cognitive and Brain SciencesUniversity of MacauTaipaChina
- Faculty of Arts and HumanitiesUniversity of MacauTaipaChina
| | - Ruey‐Song Huang
- Centre for Cognitive and Brain SciencesUniversity of MacauTaipaChina
- Faculty of Science and TechnologyUniversity of MacauTaipaChina
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22
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Waraich SA, Victor JD. The Geometry of Low- and High-Level Perceptual Spaces. J Neurosci 2024; 44:e1460232023. [PMID: 38267235 PMCID: PMC10860617 DOI: 10.1523/jneurosci.1460-23.2023] [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: 08/01/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 01/26/2024] Open
Abstract
Low-level features are typically continuous (e.g., the gamut between two colors), but semantic information is often categorical (there is no corresponding gradient between dog and turtle) and hierarchical (animals live in land, water, or air). To determine the impact of these differences on cognitive representations, we characterized the geometry of perceptual spaces of five domains: a domain dominated by semantic information (animal names presented as words), a domain dominated by low-level features (colored textures), and three intermediate domains (animal images, lightly texturized animal images that were easy to recognize, and heavily texturized animal images that were difficult to recognize). Each domain had 37 stimuli derived from the same animal names. From 13 participants (9F), we gathered similarity judgments in each domain via an efficient psychophysical ranking paradigm. We then built geometric models of each domain for each participant, in which distances between stimuli accounted for participants' similarity judgments and intrinsic uncertainty. Remarkably, the five domains had similar global properties: each required 5-7 dimensions, and a modest amount of spherical curvature provided the best fit. However, the arrangement of the stimuli within these embeddings depended on the level of semantic information: dendrograms derived from semantic domains (word, image, and lightly texturized images) were more "tree-like" than those from feature-dominated domains (heavily texturized images and textures). Thus, the perceptual spaces of domains along this feature-dominated to semantic-dominated gradient shift to a tree-like organization when semantic information dominates, while retaining a similar global geometry.
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Affiliation(s)
| | - Jonathan D Victor
- Division of Systems Neurology and Neuroscience, Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York 10065, New York
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23
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Wolna A, Szewczyk J, Diaz M, Domagalik A, Szwed M, Wodniecka Z. Domain-general and language-specific contributions to speech production in a second language: an fMRI study using functional localizers. Sci Rep 2024; 14:57. [PMID: 38168139 PMCID: PMC10761726 DOI: 10.1038/s41598-023-49375-9] [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: 07/03/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
For bilinguals, speaking in a second language (L2) compared to the native language (L1) is usually more difficult. In this study we asked whether the difficulty in L2 production reflects increased demands imposed on domain-general or core language mechanisms. We compared the brain response to speech production in L1 and L2 within two functionally-defined networks in the brain: the Multiple Demand (MD) network and the language network. We found that speech production in L2 was linked to a widespread increase of brain activity in the domain-general MD network. The language network did not show a similarly robust differences in processing speech in the two languages, however, we found increased response to L2 production in the language-specific portion of the left inferior frontal gyrus (IFG). To further explore our results, we have looked at domain-general and language-specific response within the brain structures postulated to form a Bilingual Language Control (BLC) network. Within this network, we found a robust increase in response to L2 in the domain-general, but also in some language-specific voxels including in the left IFG. Our findings show that L2 production strongly engages domain-general mechanisms, but only affects language sensitive portions of the left IFG. These results put constraints on the current model of bilingual language control by precisely disentangling the domain-general and language-specific contributions to the difficulty in speech production in L2.
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Affiliation(s)
- Agata Wolna
- Institute of Psychology, Jagiellonian University, Ul. Ingardena 6, 30-060, Kraków, Poland.
| | - Jakub Szewczyk
- Institute of Psychology, Jagiellonian University, Ul. Ingardena 6, 30-060, Kraków, Poland
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Michele Diaz
- Social, Life, and Engineering Sciences Imaging Center, The Pennsylvania State University, Pennsylvania, USA
| | | | - Marcin Szwed
- Institute of Psychology, Jagiellonian University, Ul. Ingardena 6, 30-060, Kraków, Poland
| | - Zofia Wodniecka
- Institute of Psychology, Jagiellonian University, Ul. Ingardena 6, 30-060, Kraków, Poland.
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Olson HA, Chen EM, Lydic KO, Saxe RR. Left-Hemisphere Cortical Language Regions Respond Equally to Observed Dialogue and Monologue. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2023; 4:575-610. [PMID: 38144236 PMCID: PMC10745132 DOI: 10.1162/nol_a_00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 09/20/2023] [Indexed: 12/26/2023]
Abstract
Much of the language we encounter in our everyday lives comes in the form of conversation, yet the majority of research on the neural basis of language comprehension has used input from only one speaker at a time. Twenty adults were scanned while passively observing audiovisual conversations using functional magnetic resonance imaging. In a block-design task, participants watched 20 s videos of puppets speaking either to another puppet (the dialogue condition) or directly to the viewer (the monologue condition), while the audio was either comprehensible (played forward) or incomprehensible (played backward). Individually functionally localized left-hemisphere language regions responded more to comprehensible than incomprehensible speech but did not respond differently to dialogue than monologue. In a second task, participants watched videos (1-3 min each) of two puppets conversing with each other, in which one puppet was comprehensible while the other's speech was reversed. All participants saw the same visual input but were randomly assigned which character's speech was comprehensible. In left-hemisphere cortical language regions, the time course of activity was correlated only among participants who heard the same character speaking comprehensibly, despite identical visual input across all participants. For comparison, some individually localized theory of mind regions and right-hemisphere homologues of language regions responded more to dialogue than monologue in the first task, and in the second task, activity in some regions was correlated across all participants regardless of which character was speaking comprehensibly. Together, these results suggest that canonical left-hemisphere cortical language regions are not sensitive to differences between observed dialogue and monologue.
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25
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DiNicola LM, Sun W, Buckner RL. Side-by-side regions in dorsolateral prefrontal cortex estimated within the individual respond differentially to domain-specific and domain-flexible processes. J Neurophysiol 2023; 130:1602-1615. [PMID: 37937340 PMCID: PMC11068361 DOI: 10.1152/jn.00277.2023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/06/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023] Open
Abstract
A recurring debate concerns whether regions of primate prefrontal cortex (PFC) support domain-flexible or domain-specific processes. Here we tested the hypothesis with functional MRI (fMRI) that side-by-side PFC regions, within distinct parallel association networks, differentially support domain-flexible and domain-specialized processing. Individuals (N = 9) were intensively sampled, and all effects were estimated within their own idiosyncratic anatomy. Within each individual, we identified PFC regions linked to distinct networks, including a dorsolateral PFC (DLPFC) region coupled to the medial temporal lobe (MTL) and an extended region associated with the canonical multiple-demand network. We further identified an inferior PFC region coupled to the language network. Exploration in separate task data, collected within the same individuals, revealed a robust functional triple dissociation. The DLPFC region linked to the MTL was recruited during remembering and imagining the future, distinct from juxtaposed regions that were modulated in a domain-flexible manner during working memory. The inferior PFC region linked to the language network was recruited during sentence processing. Detailed analysis of the trial-level responses further revealed that the DLPFC region linked to the MTL specifically tracked processes associated with scene construction. These results suggest that the DLPFC possesses a domain-specialized region that is small and easily confused with nearby (larger) regions associated with cognitive control. The newly described region is domain specialized for functions traditionally associated with the MTL. We discuss the implications of these findings in relation to convergent anatomical analysis in the monkey.NEW & NOTEWORTHY Competing hypotheses link regions of prefrontal cortex (PFC) to domain-flexible or domain-specific processes. Here, using a precision neuroimaging approach, we identify a domain-specialized region in dorsolateral PFC, coupled to the medial temporal lobe and recruited for scene construction. This region is juxtaposed to, but distinct from, broader PFC regions recruited flexibly for cognitive control. Region distinctions align with broader network differences, suggesting that PFC regions gain dissociable processing properties via segregated anatomical projections.
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Affiliation(s)
- Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Wendy Sun
- Division of Medical Sciences, Harvard Medical School, Boston, Massachusetts, United States
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
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26
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Pitcher D, Sliwinska MW, Kaiser D. TMS disruption of the lateral prefrontal cortex increases neural activity in the default mode network when naming facial expressions. Soc Cogn Affect Neurosci 2023; 18:nsad072. [PMID: 38048419 PMCID: PMC10695328 DOI: 10.1093/scan/nsad072] [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: 03/12/2023] [Revised: 10/17/2023] [Accepted: 11/15/2023] [Indexed: 12/06/2023] Open
Abstract
Recognizing facial expressions is dependent on multiple brain networks specialized for different cognitive functions. In the current study, participants (N = 20) were scanned using functional magnetic resonance imaging (fMRI), while they performed a covert facial expression naming task. Immediately prior to scanning thetaburst transcranial magnetic stimulation (TMS) was delivered over the right lateral prefrontal cortex (PFC), or the vertex control site. A group whole-brain analysis revealed that TMS induced opposite effects in the neural responses across different brain networks. Stimulation of the right PFC (compared to stimulation of the vertex) decreased neural activity in the left lateral PFC but increased neural activity in three nodes of the default mode network (DMN): the right superior frontal gyrus, right angular gyrus and the bilateral middle cingulate gyrus. A region of interest analysis showed that TMS delivered over the right PFC reduced neural activity across all functionally localised face areas (including in the PFC) compared to TMS delivered over the vertex. These results suggest that visually recognizing facial expressions is dependent on the dynamic interaction of the face-processing network and the DMN. Our study also demonstrates the utility of combined TMS/fMRI studies for revealing the dynamic interactions between different functional brain networks.
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Affiliation(s)
- David Pitcher
- Department of Psychology, University of York, Heslington, York YO105DD, UK
| | | | - Daniel Kaiser
- Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus-Liebig-Universität Gießen, Gießen 35392, Germany
- Center for Mind, Brain and Behaviour, Philipps-Universität Marburg, and Justus-Liebig-Universität Gießen, Marburg 35032, Germany
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27
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Zhang Y, Ding R, Frassinelli D, Tuomainen J, Klavinskis-Whiting S, Vigliocco G. The role of multimodal cues in second language comprehension. Sci Rep 2023; 13:20824. [PMID: 38012193 PMCID: PMC10682458 DOI: 10.1038/s41598-023-47643-2] [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: 05/22/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
In face-to-face communication, multimodal cues such as prosody, gestures, and mouth movements can play a crucial role in language processing. While several studies have addressed how these cues contribute to native (L1) language processing, their impact on non-native (L2) comprehension is largely unknown. Comprehension of naturalistic language by L2 comprehenders may be supported by the presence of (at least some) multimodal cues, as these provide correlated and convergent information that may aid linguistic processing. However, it is also the case that multimodal cues may be less used by L2 comprehenders because linguistic processing is more demanding than for L1 comprehenders, leaving more limited resources for the processing of multimodal cues. In this study, we investigated how L2 comprehenders use multimodal cues in naturalistic stimuli (while participants watched videos of a speaker), as measured by electrophysiological responses (N400) to words, and whether there are differences between L1 and L2 comprehenders. We found that prosody, gestures, and informative mouth movements each reduced the N400 in L2, indexing easier comprehension. Nevertheless, L2 participants showed weaker effects for each cue compared to L1 comprehenders, with the exception of meaningful gestures and informative mouth movements. These results show that L2 comprehenders focus on specific multimodal cues - meaningful gestures that support meaningful interpretation and mouth movements that enhance the acoustic signal - while using multimodal cues to a lesser extent than L1 comprehenders overall.
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Affiliation(s)
- Ye Zhang
- Experimental Psychology, University College London, London, UK
| | - Rong Ding
- Language and Computation in Neural Systems, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Diego Frassinelli
- Department of Linguistics, University of Konstanz, Konstanz, Germany
| | - Jyrki Tuomainen
- Speech, Hearing and Phonetic Sciences, University College London, London, UK
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28
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Abbott N, Love T. Bridging the Divide: Brain and Behavior in Developmental Language Disorder. Brain Sci 2023; 13:1606. [PMID: 38002565 PMCID: PMC10670267 DOI: 10.3390/brainsci13111606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023] Open
Abstract
Developmental language disorder (DLD) is a heterogenous neurodevelopmental disorder that affects a child's ability to comprehend and/or produce spoken and/or written language, yet it cannot be attributed to hearing loss or overt neurological damage. It is widely believed that some combination of genetic, biological, and environmental factors influences brain and language development in this population, but it has been difficult to bridge theoretical accounts of DLD with neuroimaging findings, due to heterogeneity in language impairment profiles across individuals and inconsistent neuroimaging findings. Therefore, the purpose of this overview is two-fold: (1) to summarize the neuroimaging literature (while drawing on findings from other language-impaired populations, where appropriate); and (2) to briefly review the theoretical accounts of language impairment patterns in DLD, with the goal of bridging the disparate findings. As will be demonstrated with this overview, the current state of the field suggests that children with DLD have atypical brain volume, laterality, and activation/connectivity patterns in key language regions that likely contribute to language difficulties. However, the precise nature of these differences and the underlying neural mechanisms contributing to them remain an open area of investigation.
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Affiliation(s)
- Noelle Abbott
- School of Speech, Language, and Hearing Sciences, San Diego State University, San Diego, CA 92182, USA;
- San Diego State University/University of California San Diego Joint Doctoral Program in Language and Communicative Disorders, San Diego, CA 92182, USA
| | - Tracy Love
- School of Speech, Language, and Hearing Sciences, San Diego State University, San Diego, CA 92182, USA;
- San Diego State University/University of California San Diego Joint Doctoral Program in Language and Communicative Disorders, San Diego, CA 92182, USA
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29
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Kang K, Xiao Y, Yu H, Diaz MT, Zhang H. Multilingual Language Diversity Protects Native Language Production under Different Control Demands. Brain Sci 2023; 13:1587. [PMID: 38002547 PMCID: PMC10670415 DOI: 10.3390/brainsci13111587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/06/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
The use of multiple languages has been found to influence individuals' cognitive abilities. Although some studies have also investigated the effect of multilingualism on non-native language proficiency, fewer studies have focused on how multilingual experience affects native language production. This study investigated the effect of multilingualism on native language production, specifically examining control demands through a semantic Go/No-Go picture naming task. The multilingual experience was quantified using language entropy, which measures the uncertainty and diversity of language use. Control demands were achieved by manipulating the proportion of Go (i.e., naming) trials in different conditions. Results showed that as control demands increased, multilingual individuals exhibited poorer behavioral performance and greater brain activation throughout the brain. Moreover, more diverse language use was associated with higher accuracy in naming and more interconnected brain networks with greater involvement of domain-general neural resources and less domain-specific neural resources. Notably, the varied and balanced use of multiple languages enabled multilingual individuals to respond more efficiently to increased task demands during native language production.
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Affiliation(s)
- Keyi Kang
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Psychology, University of Macau, Taipa, Macau SAR, China
| | - Yumeng Xiao
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Hanxiang Yu
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Michele T. Diaz
- Department of Psychology, The Pennsylvania State University, State College, PA 16801, USA
| | - Haoyun Zhang
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Psychology, University of Macau, Taipa, Macau SAR, China
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30
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Eldaief MC, Brickhouse M, Katsumi Y, Rosen H, Carvalho N, Touroutoglou A, Dickerson BC. Atrophy in behavioural variant frontotemporal dementia spans multiple large-scale prefrontal and temporal networks. Brain 2023; 146:4476-4485. [PMID: 37201288 PMCID: PMC10629759 DOI: 10.1093/brain/awad167] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/10/2023] [Accepted: 04/16/2023] [Indexed: 05/20/2023] Open
Abstract
The identification of a neurodegenerative disorder's distributed pattern of atrophy-or atrophy 'signature'-can lend insights into the cortical networks that degenerate in individuals with specific constellations of symptoms. In addition, this signature can be used as a biomarker to support early diagnoses and to potentially reveal pathological changes associated with said disorder. Here, we characterized the cortical atrophy signature of behavioural variant frontotemporal dementia (bvFTD). We used a data-driven approach to estimate cortical thickness using surface-based analyses in two independent, sporadic bvFTD samples (n = 30 and n = 71, total n = 101), using age- and gender-matched cognitively and behaviourally normal individuals. We found highly similar patterns of cortical atrophy across the two independent samples, supporting the reliability of our bvFTD signature. Next, we investigated whether our bvFTD signature targets specific large-scale cortical networks, as is the case for other neurodegenerative disorders. We specifically asked whether the bvFTD signature topographically overlaps with the salience network, as previous reports have suggested. We hypothesized that because phenotypic presentations of bvFTD are diverse, this would not be the case, and that the signature would cross canonical network boundaries. Consistent with our hypothesis, the bvFTD signature spanned rostral portions of multiple networks, including the default mode, limbic, frontoparietal control and salience networks. We then tested whether the signature comprised multiple anatomical subtypes, which themselves overlapped with specific networks. To explore this, we performed a hierarchical clustering analysis. This yielded three clusters, only one of which extensively overlapped with a canonical network (the limbic network). Taken together, these findings argue against the hypothesis that the salience network is preferentially affected in bvFTD, but rather suggest that-at least in patients who meet diagnostic criteria for the full-blown syndrome-neurodegeneration in bvFTD encompasses a distributed set of prefrontal, insular and anterior temporal nodes of multiple large-scale brain networks, in keeping with the phenotypic diversity of this disorder.
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Affiliation(s)
- Mark C Eldaief
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Center for Brain Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Michael Brickhouse
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Yuta Katsumi
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Howard Rosen
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nicole Carvalho
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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31
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Molloy MF, Osher DE. A personalized cortical atlas for functional regions of interest. J Neurophysiol 2023; 130:1067-1080. [PMID: 37727907 PMCID: PMC10994647 DOI: 10.1152/jn.00108.2023] [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: 03/14/2023] [Revised: 09/18/2023] [Accepted: 09/18/2023] [Indexed: 09/21/2023] Open
Abstract
Advances in functional MRI (fMRI) allow mapping an individual's brain function in vivo. Task fMRI can localize domain-specific regions of cognitive processing or functional regions of interest (fROIs) within an individual. Moreover, data from resting state (no task) fMRI can be used to define an individual's connectome, which can characterize that individual's functional organization via connectivity-based parcellations. However, can connectivity-based parcellations alone predict an individual's fROIs? Here, we describe an approach to compute individualized rs-fROIs (i.e., regions that correspond to given fROI constructed using only resting state data) for motor control, working memory, high-level vision, and language comprehension. The rs-fROIs were computed and validated using a large sample of young adults (n = 1,018) with resting state and task fMRI from the Human Connectome Project. First, resting state parcellations were defined across a sequence of resolutions from broadscale to fine-grained networks in a training group of 500 individuals. Second, 21 rs-fROIs were defined from the training group by identifying the rs network that most closely matched task-defined fROIs across all individuals. Third, the selectivity of rs-fROIs was investigated in a training set of the remaining 518 individuals. All computed rs-fROIs were indeed selective for their preferred category. Critically, the rs-fROIs had higher selectivity than probabilistic atlas parcels for nearly all fROIs. In conclusion, we present a potential approach to define selective fROIs on an individual-level circumventing the need for multiple task-based localizers.NEW & NOTEWORTHY We compute individualized resting state parcels that identify an individual's own functional regions of interest (fROIs) for high-level vision, language comprehension, motor control, and working memory, using only their functional connectome. This approach demonstrates a rapid and powerful alternative for finding a large set of fROIs in an individual, using only their unique connectivity pattern, which does not require the costly acquisition of multiple fMRI localizer tasks.
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Affiliation(s)
- M. Fiona Molloy
- Department of Psychology, The Ohio State University, Columbus, Ohio, United States
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States
| | - David E. Osher
- Department of Psychology, The Ohio State University, Columbus, Ohio, United States
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32
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Pulvermüller F. Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks. Prog Neurobiol 2023; 230:102511. [PMID: 37482195 PMCID: PMC10518464 DOI: 10.1016/j.pneurobio.2023.102511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 05/02/2023] [Accepted: 07/18/2023] [Indexed: 07/25/2023]
Abstract
Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles.
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Affiliation(s)
- Friedemann Pulvermüller
- Brain Language Laboratory, Department of Philosophy and Humanities, WE4, Freie Universität Berlin, 14195 Berlin, Germany; Berlin School of Mind and Brain, Humboldt Universität zu Berlin, 10099 Berlin, Germany; Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany; Cluster of Excellence 'Matters of Activity', Humboldt Universität zu Berlin, 10099 Berlin, Germany.
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33
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Zhang G, Xu Y, Wang X, Li J, Shi W, Bi Y, Lin N. A social-semantic working-memory account for two canonical language areas. Nat Hum Behav 2023; 7:1980-1997. [PMID: 37735521 DOI: 10.1038/s41562-023-01704-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023]
Abstract
Language and social cognition are traditionally studied as separate cognitive domains, yet accumulative studies reveal overlapping neural correlates at the left ventral temporoparietal junction (vTPJ) and the left lateral anterior temporal lobe (lATL), which have been attributed to sentence processing and social concept activation. We propose a common cognitive component underlying both effects: social-semantic working memory. We confirmed two key predictions of our hypothesis using functional MRI. First, the left vTPJ and lATL showed sensitivity to sentences only when the sentences conveyed social meaning; second, these regions showed persistent social-semantic-selective activity after the linguistic stimuli disappeared. We additionally found that both regions were sensitive to the socialness of non-linguistic stimuli and were more tightly connected with the social-semantic-processing areas than with the sentence-processing areas. The converging evidence indicates the social-semantic working-memory function of the left vTPJ and lATL and challenges the general-semantic and/or syntactic accounts for the neural activity of these regions.
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Affiliation(s)
- Guangyao Zhang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Yangwen Xu
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jixing Li
- Department of Linguistics and Translation, City University of Hong Kong, Hong Kong SAR, China
| | - Weiting Shi
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Nan Lin
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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Tuckute G, Sathe A, Srikant S, Taliaferro M, Wang M, Schrimpf M, Kay K, Fedorenko E. Driving and suppressing the human language network using large language models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.16.537080. [PMID: 37090673 PMCID: PMC10120732 DOI: 10.1101/2023.04.16.537080] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Transformer models such as GPT generate human-like language and are highly predictive of human brain responses to language. Here, using fMRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of brain response associated with each sentence. Then, we use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also noninvasively control neural activity in higher-level cortical areas, like the language network.
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Affiliation(s)
- Greta Tuckute
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Aalok Sathe
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Shashank Srikant
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- MIT-IBM Watson AI Lab, Cambridge, MA 02142, USA
| | - Maya Taliaferro
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Mingye Wang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Martin Schrimpf
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- Quest for Intelligence, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455 USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138 USA
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35
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Ness T, Langlois VJ, Kim AE, Novick JM. The State of Cognitive Control in Language Processing. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2023:17456916231197122. [PMID: 37819251 DOI: 10.1177/17456916231197122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Understanding language requires readers and listeners to cull meaning from fast-unfolding messages that often contain conflicting cues pointing to incompatible ways of interpreting the input (e.g., "The cat was chased by the mouse"). This article reviews mounting evidence from multiple methods demonstrating that cognitive control plays an essential role in resolving conflict during language comprehension. How does cognitive control accomplish this task? Psycholinguistic proposals have conspicuously failed to address this question. We introduce an account in which cognitive control aids language processing when cues conflict by sending top-down biasing signals that strengthen the interpretation supported by the most reliable evidence available. We also provide a computationally plausible model that solves the critical problem of how cognitive control "knows" which way to direct its biasing signal by allowing linguistic knowledge itself to issue crucial guidance. Such a mental architecture can explain a range of experimental findings, including how moment-to-moment shifts in cognitive-control state-its level of activity within a person-directly impact how quickly and successfully language comprehension is achieved.
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Affiliation(s)
- Tal Ness
- Department of Hearing and Speech Sciences and Program in Neuroscience and Cognitive Science, University of Maryland, College Park
| | - Valerie J Langlois
- Institute for Cognitive Science and Department of Psychology and Neuroscience, University of Colorado, Boulder
| | - Albert E Kim
- Institute for Cognitive Science and Department of Psychology and Neuroscience, University of Colorado, Boulder
| | - Jared M Novick
- Department of Hearing and Speech Sciences and Program in Neuroscience and Cognitive Science, University of Maryland, College Park
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Karpychev V, Malyutina S, Zhuravleva A, Bronov O, Kuzin V, Marinets A, Dragoy O. Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis. Epilepsy Behav 2023; 147:109407. [PMID: 37688840 DOI: 10.1016/j.yebeh.2023.109407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/03/2023] [Accepted: 08/19/2023] [Indexed: 09/11/2023]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE. METHODS We collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs-regions with high connectivity. RESULTS The language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE. SIGNIFICANCE Our study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.
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Affiliation(s)
- Victor Karpychev
- Center for Language and Brain, HSE University, Moscow, Russian Federation.
| | - Svetlana Malyutina
- Center for Language and Brain, HSE University, Moscow, Russian Federation
| | - Anna Zhuravleva
- Center for Language and Brain, HSE University, Moscow, Russian Federation
| | - Oleg Bronov
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Vasiliy Kuzin
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Aleksei Marinets
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russian Federation; Institute of Linguistics, Russian Academy of Sciences, Moscow, Russian Federation
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Friederici AD. Evolutionary neuroanatomical expansion of Broca's region serving a human-specific function. Trends Neurosci 2023; 46:786-796. [PMID: 37596132 DOI: 10.1016/j.tins.2023.07.004] [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: 03/20/2023] [Revised: 06/23/2023] [Accepted: 07/20/2023] [Indexed: 08/20/2023]
Abstract
The question concerning the evolution of language is directly linked to the debate on whether language and action are dependent or not and to what extent Broca's region serves as a common neural basis. The debate resulted in two opposing views, one arguing for and one against the dependence of language and action mainly based on neuroscientific data. This article presents an evolutionary neuroanatomical framework which may offer a solution to this dispute. It is proposed that in humans, Broca's region houses language and action independently in spatially separated subregions. This became possible due to an evolutionary expansion of Broca's region in the human brain, which was not paralleled by a similar expansion in the chimpanzee's brain, providing additional space needed for the neural representation of language in humans.
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Affiliation(s)
- Angela D Friederici
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Stephanstraße 1A, 04103 Leipzig, Germany.
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Zhao Y, Chen Y, Cheng K, Huang W. Artificial intelligence based multimodal language decoding from brain activity: A review. Brain Res Bull 2023; 201:110713. [PMID: 37487829 DOI: 10.1016/j.brainresbull.2023.110713] [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: 04/27/2023] [Revised: 06/26/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023]
Abstract
Decoding brain activity is conducive to the breakthrough of brain-computer interface (BCI) technology. The development of artificial intelligence (AI) continually promotes the progress of brain language decoding technology. Existent research has mainly focused on a single modality and paid insufficient attention to AI methods. Therefore, our objective is to provide an overview of relevant decoding research from the perspective of different modalities and methodologies. The modalities involve text, speech, image, and video, whereas the core method is using AI-built decoders to translate brain signals induced by multimodal stimuli into text or vocal language. The semantic information of brain activity can be successfully decoded into a language at various levels, ranging from words through sentences to discourses. However, the decoding effect is affected by various factors, such as the decoding model, vector representation model, and brain regions. Challenges and future directions are also discussed. The advances in brain language decoding and BCI technology will potentially assist patients with clinical aphasia in regaining the ability to communicate.
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Affiliation(s)
- Yuhao Zhao
- College of Language Intelligence, Sichuan International Studies University, Chongqing 400031, PR China
| | - Yu Chen
- Technical College for the Deaf, Tianjin University of Technology, Tianjin 300384, PR China
| | - Kaiwen Cheng
- College of Language Intelligence, Sichuan International Studies University, Chongqing 400031, PR China.
| | - Wei Huang
- Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
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Le Stanc L, Youssov K, Giavazzi M, Sliwinski A, Bachoud-Lévi AC, Jacquemot C. Language disorders in patients with striatal lesions: Deciphering the role of the striatum in language performance. Cortex 2023; 166:91-106. [PMID: 37354871 DOI: 10.1016/j.cortex.2023.04.016] [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: 01/09/2023] [Revised: 03/22/2023] [Accepted: 04/13/2023] [Indexed: 06/26/2023]
Abstract
The classical neural model of language refers to a cortical network involving frontal, parietal and temporal regions. However, patients with subcortical lesions of the striatum have language difficulties. We investigated whether the striatum is directly involved in language or whether its role in decision-making has an indirect effect on language performance, by testing carriers of Huntington's disease (HD) mutations and controls. HD is a genetic neurodegenerative disease primarily affecting the striatum and causing language disorders. We asked carriers of the HD mutation in the premanifest (before clinical diagnosis) and early disease stages, and controls to perform two discrimination tasks, one involving linguistic and the other non-linguistic stimuli. We used the hierarchical drift diffusion model (HDDM) to analyze the participants' responses and to assess the decision and non-decision parameters separately. We hypothesized that any language deficits related to decision-making impairments would be reflected in the decision parameters of linguistic and non-linguistic tasks. We also assessed the relative contributions of both HDDM decision and non-decision parameters to the participants' behavioral data (response time and discriminability). Finally, we investigated whether the decision and non-decision parameters of the HDDM were correlated with brain atrophy. The HDDM analysis showed that patients with early HD have impaired decision parameters relative to controls, regardless of the task. In both tasks, decision parameters better explained the variance of response time and discriminability performance than non-decision parameters. In the linguistic task, decision parameters were positively correlated with gray matter volume in the ventral striatum and putamen, whereas non-decision parameters were not. Language impairment in patients with striatal atrophy is better explained by a deficit of decision-making than by a deficit of core linguistic processing. These results suggest that the striatum is involved in language through the modulation of decision-making, presumably by regulating the process of choice between linguistic alternatives.
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Affiliation(s)
- Lorna Le Stanc
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France; Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France; Université Paris-Est Créteil, Faculté de Médecine, Créteil, France; Université Paris Cité, LaPsyDÉ, CNRS, Paris, France
| | - Katia Youssov
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France; Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France; Université Paris-Est Créteil, Faculté de Médecine, Créteil, France; AP-HP, Centre de Référence Maladie de Huntington, Service de Neurologie, Hôpital Henri Mondor-Albert Chenevier, Créteil, France
| | - Maria Giavazzi
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France; Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France; Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
| | - Agnès Sliwinski
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France; Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France; Université Paris-Est Créteil, Faculté de Médecine, Créteil, France; AP-HP, Centre de Référence Maladie de Huntington, Service de Neurologie, Hôpital Henri Mondor-Albert Chenevier, Créteil, France
| | - Anne-Catherine Bachoud-Lévi
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France; Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France; Université Paris-Est Créteil, Faculté de Médecine, Créteil, France; AP-HP, Centre de Référence Maladie de Huntington, Service de Neurologie, Hôpital Henri Mondor-Albert Chenevier, Créteil, France
| | - Charlotte Jacquemot
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France; Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France; Université Paris-Est Créteil, Faculté de Médecine, Créteil, France.
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Samra K, MacDougall AM, Bouzigues A, Bocchetta M, Cash DM, Greaves CV, Convery RS, van Swieten JC, Jiskoot L, Seelaar H, Moreno F, Sanchez-Valle R, Laforce R, Graff C, Masellis M, Tartaglia MC, Rowe JB, Borroni B, Finger E, Synofzik M, Galimberti D, Vandenberghe R, de Mendonça A, Butler CR, Gerhard A, Ducharme S, Le Ber I, Tiraboschi P, Santana I, Pasquier F, Levin J, Otto M, Sorbi S, Rohrer JD, Russell LL. Prodromal language impairment in genetic frontotemporal dementia within the GENFI cohort. J Neurol Sci 2023; 451:120711. [PMID: 37348248 DOI: 10.1016/j.jns.2023.120711] [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: 10/30/2022] [Revised: 06/01/2023] [Accepted: 06/08/2023] [Indexed: 06/24/2023]
Abstract
OBJECTIVE To identify whether language impairment exists presymptomatically in genetic frontotemporal dementia (FTD), and if so, the key differences between the main genetic mutation groups. METHODS 682 participants from the international multicentre Genetic FTD Initiative (GENFI) study were recruited: 290 asymptomatic and 82 prodromal mutation carriers (with C9orf72, GRN, and MAPT mutations) as well as 310 mutation-negative controls. Language was assessed using items from the Progressive Aphasia Severity Scale, as well as the Boston Naming Test (BNT), modified Camel and Cactus Test (mCCT) and a category fluency task. Participants also underwent a 3 T volumetric T1-weighted MRI from which regional brain volumes within the language network were derived and compared between the groups. RESULTS 3% of asymptomatic (4% C9orf72, 4% GRN, 2% MAPT) and 48% of prodromal (46% C9orf72, 42% GRN, 64% MAPT) mutation carriers had impairment in at least one language symptom compared with 13% of controls. In prodromal mutation carriers significantly impaired word retrieval was seen in all three genetic groups whilst significantly impaired grammar/syntax and decreased fluency was seen only in C9orf72 and GRN mutation carriers, and impaired articulation only in the C9orf72 group. Prodromal MAPT mutation carriers had significant impairment on the category fluency task and the BNT whilst prodromal C9orf72 mutation carriers were impaired on the category fluency task only. Atrophy in the dominant perisylvian language regions differed between groups, with earlier, more widespread volume loss in C9orf72, and later focal atrophy in the temporal lobe in MAPT mutation carriers. CONCLUSIONS Language deficits exist in the prodromal but not asymptomatic stages of genetic FTD across all three genetic groups. Improved understanding of the language phenotype prior to phenoconversion to fully symptomatic FTD will help develop outcome measures for future presymptomatic trials.
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Affiliation(s)
- Kiran Samra
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Amy M MacDougall
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Arabella Bouzigues
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Caroline V Greaves
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Rhian S Convery
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | | | - Lize Jiskoot
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Harro Seelaar
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia Universitary Hospital, San Sebastian, Spain; Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain
| | - Raquel Sanchez-Valle
- Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, QC, Canada
| | - Caroline Graff
- Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden; Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, UK
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany; Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Daniela Galimberti
- Fondazione Ca' Granda, IRCCS Ospedale Policlinico, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy
| | - 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
| | - Alexandre de Mendonça
- Laboratory of Neurosciences, Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Chris R Butler
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK; Department of Brain Sciences, Imperial College London, UK
| | - Alex Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK; Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Germany
| | - Simon Ducharme
- Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Québec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Isabelle Le Ber
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Centre de Référence des Démences rares ou Précoces, IM2A, 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)
| | | | - Isabel Santana
- University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Florence Pasquier
- Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians Universität München, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Markus Otto
- Department of Neurology, University of Ulm, Germany
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Lucy L Russell
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK.
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Chen X, Tan W, Cheng Y, Huang D, Liu D, Zhang J, Li J, Liu Z, Pan Y, Palaniyappan L. Polygenic risk for schizophrenia and the language network: Putative compensatory reorganization in unaffected siblings. Psychiatry Res 2023; 326:115319. [PMID: 37352748 DOI: 10.1016/j.psychres.2023.115319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/11/2023] [Accepted: 06/18/2023] [Indexed: 06/25/2023]
Abstract
Language-related symptoms, such as disorganized, impoverished speech and communicative behaviors, are one of the core features of schizophrenia. These features most strongly correlate with cognitive deficits and polygenic risk among various symptom dimensions of schizophrenia. Nevertheless, unaffected siblings with genetic high-risk fail to show consistent deficits in language network (LN), indicating that either (1) polygenic risk has no notable effect on LN and/or (2) siblings show compensatory changes in opposing direction to patients. To answer this question, we related polygenic risk scores (PRS) to the region-level, tract-level, and systems-level structure (cortical thickness and fiber connectivity) of LN in 182 patients, 48 unaffected siblings and 135 healthy controls. We also studied the relationships between symptoms, language-related cognition, social functioning and LN structure. We observed a significantly lower thickness in LN (especially the Broca's, Wernicke's area and their right homologues) in patients. Siblings had a distinctly higher thickness in parts of the LN and a more pronounced small-world-like structural integration within the LN. Patients with reduced LN thickness had higher PRS, more disorganization and impoverished speech with lower language-related cognition and social functioning. We conclude that the genetic susceptibility and putative compensatory changes for schizophrenia operate, in part, via key regions in the Language Network.
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Affiliation(s)
- Xudong Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Wenjian Tan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yixin Cheng
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Danqing Huang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Dayi Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jiamei Zhang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jinyue Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yunzhi Pan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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Asaridou SS, Cler GJ, Wiedemann A, Krishnan S, Smith HJ, Willis HE, Healy MP, Watkins KE. Microstructural Properties of the Cerebellar Peduncles in Children with Developmental Language Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548858. [PMID: 37503009 PMCID: PMC10370025 DOI: 10.1101/2023.07.13.548858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of cortico-striatal systems, little is known about the role of the cerebellum in DLD. Cortico-cerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination in this tract, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not cortico-cerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech acquisition and development.
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Affiliation(s)
- Salomi S. Asaridou
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gabriel J. Cler
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Speech & Hearing Sciences, University of Washington, Seattle, USA
| | - Anna Wiedemann
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Saloni Krishnan
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Surrey, UK
| | - Harriet J. Smith
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Hanna E. Willis
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Máiréad P. Healy
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kate E. Watkins
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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Chen X, Affourtit J, Ryskin R, Regev TI, Norman-Haignere S, Jouravlev O, Malik-Moraleda S, Kean H, Varley R, Fedorenko E. The human language system, including its inferior frontal component in "Broca's area," does not support music perception. Cereb Cortex 2023; 33:7904-7929. [PMID: 37005063 PMCID: PMC10505454 DOI: 10.1093/cercor/bhad087] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 04/04/2023] Open
Abstract
Language and music are two human-unique capacities whose relationship remains debated. Some have argued for overlap in processing mechanisms, especially for structure processing. Such claims often concern the inferior frontal component of the language system located within "Broca's area." However, others have failed to find overlap. Using a robust individual-subject fMRI approach, we examined the responses of language brain regions to music stimuli, and probed the musical abilities of individuals with severe aphasia. Across 4 experiments, we obtained a clear answer: music perception does not engage the language system, and judgments about music structure are possible even in the presence of severe damage to the language network. In particular, the language regions' responses to music are generally low, often below the fixation baseline, and never exceed responses elicited by nonmusic auditory conditions, like animal sounds. Furthermore, the language regions are not sensitive to music structure: they show low responses to both intact and structure-scrambled music, and to melodies with vs. without structural violations. Finally, in line with past patient investigations, individuals with aphasia, who cannot judge sentence grammaticality, perform well on melody well-formedness judgments. Thus, the mechanisms that process structure in language do not appear to process music, including music syntax.
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Affiliation(s)
- Xuanyi Chen
- Department of Cognitive Sciences, Rice University, TX 77005, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Josef Affourtit
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rachel Ryskin
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive & Information Sciences, University of California, Merced, Merced, CA 95343, United States
| | - Tamar I Regev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Samuel Norman-Haignere
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States
| | - Olessia Jouravlev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive Science, Carleton University, Ottawa, ON, Canada
| | - Saima Malik-Moraleda
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
| | - Hope Kean
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rosemary Varley
- Psychology & Language Sciences, UCL, London, WCN1 1PF, United Kingdom
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
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44
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Lei VLC, Leong TI, Leong CT, Liu L, Choi CU, Sereno MI, Li D, Huang RS. Phase-encoded fMRI tracks down brainstorms of natural language processing with sub-second precision. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.29.542546. [PMID: 37398177 PMCID: PMC10312422 DOI: 10.1101/2023.05.29.542546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The human language system interacts with cognitive and sensorimotor regions during natural language processing. However, where, when, and how these processes occur remain unclear. Existing noninvasive subtraction-based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed phase-encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging (fMRI) data, as well as overcoming scanner noise and head-motion challenges during overt language tasks. We captured neural information flows as coherent waves traveling over the cortical surface during listening, reciting, and oral cross-language interpreting. The timing, location, direction, and surge of traveling waves, visualized as 'brainstorms' on brain 'weather' maps, reveal the functional and effective connectivity of the brain in action. These maps uncover the functional neuroanatomy of language perception and production and motivate the construction of finer-grained models of human information processing.
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Affiliation(s)
| | - Teng Ieng Leong
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Cheok Teng Leong
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Lili Liu
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Chi Un Choi
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Martin I. Sereno
- Department of Psychology, San Diego State University, San Diego, CA, United States
| | - Defeng Li
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ruey-Song Huang
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
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45
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Shain C, Paunov A, Chen X, Lipkin B, Fedorenko E. No evidence of theory of mind reasoning in the human language network. Cereb Cortex 2023; 33:6299-6319. [PMID: 36585774 PMCID: PMC10183748 DOI: 10.1093/cercor/bhac505] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 01/01/2023] Open
Abstract
Language comprehension and the ability to infer others' thoughts (theory of mind [ToM]) are interrelated during development and language use. However, neural evidence that bears on the relationship between language and ToM mechanisms is mixed. Although robust dissociations have been reported in brain disorders, brain activations for contrasts that target language and ToM bear similarities, and some have reported overlap. We take another look at the language-ToM relationship by evaluating the response of the language network, as measured with fMRI, to verbal and nonverbal ToM across 151 participants. Individual-participant analyses reveal that all core language regions respond more strongly when participants read vignettes about false beliefs compared to the control vignettes. However, we show that these differences are largely due to linguistic confounds, and no such effects appear in a nonverbal ToM task. These results argue against cognitive and neural overlap between language processing and ToM. In exploratory analyses, we find responses to social processing in the "periphery" of the language network-right-hemisphere homotopes of core language areas and areas in bilateral angular gyri-but these responses are not selectively ToM-related and may reflect general visual semantic processing.
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Affiliation(s)
- Cory Shain
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, MIT Bldg 46-316077 Massachusetts Avenue, Cambridge, MA 02139, United States
| | - Alexander Paunov
- INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, Gif sur Yvette 91191, France
| | - Xuanyi Chen
- Department of Cognitive Sciences, Rice University, 6100 Main Street, Houston, TX 77005, United States
| | - Benjamin Lipkin
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, MIT Bldg 46-316077 Massachusetts Avenue, Cambridge, MA 02139, United States
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, MIT Bldg 46-316077 Massachusetts Avenue, Cambridge, MA 02139, United States
- Program in Speech Hearing in Bioscience and Technology, Harvard Medical School, 260 Longwood Avenue, TMEC 333, Boston, MA 02115, United States
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46
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Zhao Y, Cox CR, Lambon Ralph MA, Halai AD. Using in vivo functional and structural connectivity to predict chronic stroke aphasia deficits. Brain 2023; 146:1950-1962. [PMID: 36346107 PMCID: PMC10151190 DOI: 10.1093/brain/awac388] [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: 12/21/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 11/10/2022] Open
Abstract
Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain-behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone. These neural measurements were used to construct models to predict four key language-cognitive factors: (i) phonology; (ii) semantics; (iii) executive function; and (iv) fluency. Our results showed that each factor (except executive ability) could be significantly related to each neural measurement alone; however, structural and functional connectivity models did not explain additional variance above the lesion models. We did find evidence that the structural and functional predictors may be linked to the core lesion sites. First, the predictive functional connectivity features were found to be located within functional resting-state networks identified in healthy controls, suggesting that the result might reflect functionally specific reorganization (damage to a node within a network can result in disruption to the entire network). Second, predictive structural connectivity features were located within core lesion sites, suggesting that multimodal information may be redundant in prediction modelling. In addition, we observed that the optimum sparsity within the regularized regression models differed for each behavioural component and across different imaging features, suggesting that future studies should consider optimizing hyperparameters related to sparsity per target. Together, the results indicate that the observed network-level disruption was predicted by the lesion alone and does not significantly improve model performance in predicting the profile of language impairment.
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Affiliation(s)
- Ying Zhao
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Christopher R Cox
- Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
| | | | - Ajay D Halai
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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47
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Tang J, LeBel A, Jain S, Huth AG. Semantic reconstruction of continuous language from non-invasive brain recordings. Nat Neurosci 2023; 26:858-866. [PMID: 37127759 DOI: 10.1038/s41593-023-01304-9] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 03/15/2023] [Indexed: 05/03/2023]
Abstract
A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain-computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain-computer interfaces.
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Affiliation(s)
- Jerry Tang
- Department of Computer Science, The University of Texas at Austin, Austin, TX, USA
| | - Amanda LeBel
- Department of Neuroscience, The University of Texas at Austin, Austin, TX, USA
| | - Shailee Jain
- Department of Computer Science, The University of Texas at Austin, Austin, TX, USA
| | - Alexander G Huth
- Department of Computer Science, The University of Texas at Austin, Austin, TX, USA.
- Department of Neuroscience, The University of Texas at Austin, Austin, TX, USA.
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48
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Hauptman M, Blank I, Fedorenko E. Non-literal language processing is jointly supported by the language and theory of mind networks: Evidence from a novel meta-analytic fMRI approach. Cortex 2023; 162:96-114. [PMID: 37023480 PMCID: PMC10210011 DOI: 10.1016/j.cortex.2023.01.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/08/2022] [Accepted: 01/11/2023] [Indexed: 03/12/2023]
Abstract
Going beyond the literal meaning of language is key to communicative success. However, the mechanisms that support non-literal inferences remain debated. Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. We identified 74 fMRI experiments (n = 1,430 participants) from 2001 to 2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. anatomical locations, as is typically done) for three candidate brain networks-the language-selective network (Fedorenko, Behr, & Kanwisher, 2011), which supports language processing, the Theory of Mind (ToM) network (Saxe & Kanwisher, 2003), which supports social inferences, and the domain-general Multiple-Demand (MD) network (Duncan, 2010), which supports executive control. These atlases were created by overlaying individual activation maps of participants who performed robust and extensively validated 'localizer' tasks that selectively target each network in question (n = 806 for language; n = 198 for ToM; n = 691 for MD). We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. These results suggest that non-literal processing is supported by both i) mechanisms that process literal linguistic meaning, and ii) mechanisms that support general social inference. They thus undermine a strong divide between literal and non-literal aspects of language and challenge the claim that non-literal processing requires additional executive resources.
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Affiliation(s)
- Miriam Hauptman
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Idan Blank
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychology, UCLA, Los Angeles, CA 90095, USA; Department of Linguistics, UCLA, Los Angeles, CA 90095, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Program in Speech and Hearing in Bioscience and Technology, Harvard University, Boston, MA 02114, USA.
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49
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Yuan B, Xie H, Wang Z, Xu Y, Zhang H, Liu J, Chen L, Li C, Tan S, Lin Z, Hu X, Gu T, Lu J, Liu D, Wu J. The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing. Neuroimage 2023; 274:120132. [PMID: 37105337 DOI: 10.1016/j.neuroimage.2023.120132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/05/2023] [Accepted: 04/21/2023] [Indexed: 04/29/2023] Open
Abstract
Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.
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Affiliation(s)
- Binke Yuan
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China.
| | - Hui Xie
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Department of Psychology, The University of Hong Kong, Hong Kong, China
| | - Zhihao Wang
- CNRS - Centre d'Economie de la Sorbonne, Panthéon-Sorbonne University, France
| | - Yangwen Xu
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento 38123, Italy
| | - Hanqing Zhang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Jiaxuan Liu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Lifeng Chen
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Chaoqun Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Shiyao Tan
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Zonghui Lin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Xin Hu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Tianyi Gu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Junfeng Lu
- Glioma Surgery Division, Neurologic Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China; Brain Function Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Dongqiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China; Key Laboratory of Brain and Cognitive Neuroscience, Liaoning Province, Dalian, PR China.
| | - Jinsong Wu
- Glioma Surgery Division, Neurologic Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China; Brain Function Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
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50
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Xie X, Hu P, Tian Y, Qiu B, Wang K, Bai T. Abnormal resting-state function within language network and its improvement among post-stroke aphasia. Behav Brain Res 2023; 443:114344. [PMID: 36781021 DOI: 10.1016/j.bbr.2023.114344] [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: 10/26/2022] [Revised: 01/30/2023] [Accepted: 02/10/2023] [Indexed: 02/13/2023]
Abstract
Several studies with resting-state magnetic resonance imaging (rs-fMRI) have examined functional impairments and plasticity within language network in patients with post-stroke aphasia (PSA). However, there is still ubiquitous inconsistency across these studies, partly due to restricted to very small sample size and the absence of validation with follow-up data. In the current study, we aimed at providing relatively strong evidence to support functional impairments and its reorganization in PSA. Here, the amplitude of low frequency fluctuations (ALFF) and functional connectivity were used to assess functional alterations of PSA with moderate sample size at baseline (thirty-five PSA patients and thirty-five healthy controls). Functional abnormalities at baseline were observed whether improved among sixteen follow-up patients. Compared with controls, PSA at baseline presented decreased ALFF in the left inferior frontal gyrus (IFG) and decreased functional connectivity of the left IFG with the bilateral supplementary motor area (SMA) and right superior temporal gyrus (STG). The decreased ALFF in IFG, decreased IFG-SMA and IFG-STG connectivity were enhanced among follow-up patients and was synchronized with language-performance improvement. Our results revealed reduced intrinsic neural activity and inter-connections within language network in PSA, which would be normalized synchronously as the improvement of language performance.
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Affiliation(s)
- Xiaohui Xie
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yanghua Tian
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Bensheng Qiu
- Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
| | - Tongjian Bai
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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