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Ivanova AA, Mineroff Z, Zimmerer V, Kanwisher N, Varley R, Fedorenko E. The Language Network Is Recruited but Not Required for Nonverbal Event Semantics. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2021; 2:176-201. [PMID: 37216147 PMCID: PMC10158592 DOI: 10.1162/nol_a_00030] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 01/07/2021] [Indexed: 05/24/2023]
Abstract
The ability to combine individual concepts of objects, properties, and actions into complex representations of the world is often associated with language. Yet combinatorial event-level representations can also be constructed from nonverbal input, such as visual scenes. Here, we test whether the language network in the human brain is involved in and necessary for semantic processing of events presented nonverbally. In Experiment 1, we scanned participants with fMRI while they performed a semantic plausibility judgment task versus a difficult perceptual control task on sentences and line drawings that describe/depict simple agent-patient interactions. We found that the language network responded robustly during the semantic task performed on both sentences and pictures (although its response to sentences was stronger). Thus, language regions in healthy adults are engaged during a semantic task performed on pictorial depictions of events. But is this engagement necessary? In Experiment 2, we tested two individuals with global aphasia, who have sustained massive damage to perisylvian language areas and display severe language difficulties, against a group of age-matched control participants. Individuals with aphasia were severely impaired on the task of matching sentences to pictures. However, they performed close to controls in assessing the plausibility of pictorial depictions of agent-patient interactions. Overall, our results indicate that the left frontotemporal language network is recruited but not necessary for semantic processing of nonverbally presented events.
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Affiliation(s)
- Anna A. Ivanova
- 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
| | - Zachary Mineroff
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vitor Zimmerer
- Division of Psychology and Language Sciences, University College London, London, UK
| | - Nancy Kanwisher
- 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
| | - Rosemary Varley
- Division of Psychology and Language Sciences, University College London, London, UK
| | - 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
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202
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Abend R, Bajaj MA, Harrewijn A, Matsumoto C, Michalska KJ, Necka E, Palacios-Barrios EE, Leibenluft E, Atlas LY, Pine DS. Threat-anticipatory psychophysiological response is enhanced in youth with anxiety disorders and correlates with prefrontal cortex neuroanatomy. J Psychiatry Neurosci 2021; 46:E212-E221. [PMID: 33703868 PMCID: PMC8061736 DOI: 10.1503/jpn.200110] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/27/2020] [Accepted: 09/14/2020] [Indexed: 12/27/2022] Open
Abstract
Background Threat anticipation engages neural circuitry that has evolved to promote defensive behaviours; perturbations in this circuitry could generate excessive threat-anticipation response, a key characteristic of pathological anxiety. Research into such mechanisms in youth faces ethical and practical limitations. Here, we use thermal stimulation to elicit pain-anticipatory psychophysiological response and map its correlates to brain structure among youth with anxiety and healthy youth. Methods Youth with anxiety (n = 25) and healthy youth (n = 25) completed an instructed threat-anticipation task in which cues predicted nonpainful or painful thermal stimulation; we indexed psychophysiological response during the anticipation and experience of pain using skin conductance response. High-resolution brain-structure imaging data collected in another visit were available for 41 participants. Analyses tested whether the 2 groups differed in their psychophysiological cue-based pain-anticipatory and pain-experience responses. Analyses then mapped psychophysiological response magnitude to brain structure. Results Youth with anxiety showed enhanced psychophysiological response specifically during anticipation of painful stimulation (b = 0.52, p = 0.003). Across the sample, the magnitude of psychophysiological anticipatory response correlated negatively with the thickness of the dorsolateral prefrontal cortex (pFWE < 0.05); psychophysiological response to the thermal stimulation correlated positively with the thickness of the posterior insula (pFWE < 0.05). Limitations Limitations included the modest sample size and the cross-sectional design. Conclusion These findings show that threat-anticipatory psychophysiological response differentiates youth with anxiety from healthy youth, and they link brain structure to psychophysiological response during pain anticipation and experience. A focus on threat anticipation in research on anxiety could delineate relevant neural circuitry.
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Affiliation(s)
- Rany Abend
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Mira A Bajaj
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Anita Harrewijn
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Chika Matsumoto
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Kalina J Michalska
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Elizabeth Necka
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Esther E Palacios-Barrios
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Ellen Leibenluft
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Lauren Y Atlas
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
| | - Daniel S Pine
- From the Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD (Abend, Bajaj, Harrewijn, Matsumoto, Leibenluft, Pine); the Department of Psychology, University of California Riverside, Riverside, CA (Michalaska); the 3 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD (Necka, Atlas); and the 1 Department of Psychology, University of Pittsburgh, Pittsburgh, PA (Palacios-Barrios)
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Numssen O, Bzdok D, Hartwigsen G. Functional specialization within the inferior parietal lobes across cognitive domains. eLife 2021; 10:63591. [PMID: 33650486 PMCID: PMC7946436 DOI: 10.7554/elife.63591] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 03/01/2021] [Indexed: 11/13/2022] Open
Abstract
The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social cognition, that define human interactions. Its putative domain-global role appears to tie into poorly understood differences between cognitive domains in both hemispheres. Across attentional, semantic, and social cognitive tasks, our study explored functional specialization within the IPL. The task specificity of IPL subregion activity was substantiated by distinct predictive signatures identified by multivariate pattern-learning algorithms. Moreover, the left and right IPL exerted domain-specific modulation of effective connectivity among their subregions. Task-evoked functional interactions of the anterior and posterior IPL subregions involved recruitment of distributed cortical partners. While anterior IPL subregions were engaged in strongly lateralized coupling links, both posterior subregions showed more symmetric coupling patterns across hemispheres. Our collective results shed light on how under-appreciated hemispheric specialization in the IPL supports some of the most distinctive human mental capacities.
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Affiliation(s)
- Ole Numssen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Leipzig, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Leipzig, Germany
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204
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Probing the structure-function relationship with neural networks constructed by solving a system of linear equations. Sci Rep 2021; 11:3808. [PMID: 33589672 PMCID: PMC7884791 DOI: 10.1038/s41598-021-82964-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 01/27/2021] [Indexed: 11/17/2022] Open
Abstract
Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.
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205
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Meng Y, Yang S, Chen H, Li J, Xu Q, Zhang Q, Lu G, Zhang Z, Liao W. Systematically disrupted functional gradient of the cortical connectome in generalized epilepsy: Initial discovery and independent sample replication. Neuroimage 2021; 230:117831. [PMID: 33549757 DOI: 10.1016/j.neuroimage.2021.117831] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 01/07/2021] [Accepted: 01/29/2021] [Indexed: 01/03/2023] Open
Abstract
Genetic generalized epilepsy is a network disorder typically involving distributed areas identified by classical neuroanatomy. However, the finer topological relationships in terms of continuous spatial arrangement between these systems are still ambiguous. Connectome gradients provide the topological representations of human macroscale hierarchy in an abstract low-dimensional space by embedding the functional connectome into a set of axes. Leveraging connectome gradients, we systematically scrutinized abnormalities of functional connectome gradient in patients with genetic generalized epilepsy with tonic-clonic seizure (GGE-GTCS, n = 78) compared to healthy controls (HC, n = 85), and further examined the reproducibility across multiple processing configurations and in an independent validation sample (patients with GGE-GTCS, n = 28; HC, n = 31). Our findings demonstrated an extended principal gradient at different spatial scales, network-level and vertex-level, in patients with GGE-GTCS. We found consistent results across processing parameters and in validation sample. The extended principal gradient revealed the excessive functional segregation between unimodal and transmodal systems associated with duration of epilepsy and age at seizure onset in patients. Furthermore, the connectivity profile of regions with abnormal principal gradients verified the disrupted functional hierarchy revealed by gradients. Together, our findings provided a novel view of functional system hierarchy alterations, which facilitated a continuous spatial arrangement of macroscale networks, to increase our understanding of the functional connectome hierarchy in generalized epilepsy.
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Affiliation(s)
- Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China.
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China
| | - Qirui Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China.
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206
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Prefrontal resting-state connectivity and antidepressant response: no associations in the ELECT-TDCS trial. Eur Arch Psychiatry Clin Neurosci 2021; 271:123-134. [PMID: 32880057 DOI: 10.1007/s00406-020-01187-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/20/2020] [Indexed: 12/24/2022]
Abstract
Functional and structural MRI of prefrontal cortex (PFC) may provide putative biomarkers for predicting the treatment response to transcranial direct current stimulation (tDCS) in depression. A recent MRI study from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Theror Depression Study) showed that depression improvement after tDCS was associated with gray matter volumes of PFC subregions. Based thereon, we investigated whether antidepressant effects of tDCS are similarly associated with baseline resting-state functional connectivity (rsFC). A subgroup of 51 patients underwent baseline rsFC-MRI. All patients of ELECT-TDCS were randomized to three treatment arms for 10 weeks (anodal-left, cathodal-right PFC tDCS plus placebo medication; escitalopram 10 mg/day for 3 weeks and 20 mg/day thereafter plus sham tDCS; and placebo medication plus sham tDCS). RsFC was calculated for various PFC regions and analyzed in relation to the individual antidepressant response. There was no significant association between baseline PFC connectivity of essential structural regions, nor any other PFC regions (after correction for multiple comparisons) and patients' individual antidepressant response. This study did not reveal an association between antidepressants effects of tDCS and baseline rsFC, unlike the gray matter volume findings. Thus, the antidepressant effects of tDCS may be differentially related to structural and functional MRI measurements.
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207
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Abstract
Tractography is an important technique that allows the in vivo reconstruction of structural connections in the brain using diffusion MRI. Although tracking algorithms have improved during the last two decades, results of validation studies and international challenges warn about the reliability of tractography and point out the need for improved algorithms. In propagation-based tracking, connections have traditionally been modeled as piece-wise linear segments. In this work, we propose a novel propagation-based tracker that is capable of generating geometrically smooth ( C1 ) curves using parallel transport frames. Notably, our approach does not increase the complexity of the propagation problem that remains two-dimensional. Moreover, our tracker has a novel mechanism to reduce noise related propagation errors by incorporating topographic regularity of connections, a neuroanatomic property of many brain pathways. We ran extensive experiments and compared our approach against deterministic and other probabilistic algorithms. Our experiments on FiberCup and ISMRM 2015 challenge datasets as well as on 56 subjects of the Human Connectome Project show highly promising results both visually and quantitatively. Open-source implementations of the algorithm are shared publicly.
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208
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Jolly E, Chang LJ. Multivariate spatial feature selection in fMRI. Soc Cogn Affect Neurosci 2021; 16:795-806. [PMID: 33501987 PMCID: PMC8343556 DOI: 10.1093/scan/nsab010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/16/2020] [Accepted: 01/25/2021] [Indexed: 01/20/2023] Open
Abstract
Multivariate neuroimaging analyses constitute a powerful class of techniques to identify psychological representations. However, not all psychological processes are represented at the same spatial scale throughout the brain. This heterogeneity is apparent when comparing hierarchically organized local representations of perceptual processes to flexible transmodal representations of more abstract cognitive processes such as social and affective operations. An open question is how the spatial scale of analytic approaches interacts with the spatial scale of the representations under investigation. In this article, we describe how multivariate analyses can be viewed as existing on a spatial spectrum, anchored by searchlights used to identify locally distributed patterns of information on one end, whole brain approach used to identify diffuse neural representations at the other and region-based approaches in between. We describe how these distinctions are an important and often overlooked analytic consideration and provide heuristics to compare these different techniques to choose based on the analyst’s inferential goals.
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Affiliation(s)
- E Jolly
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, Hanover, NH 03755, USA
| | - L J Chang
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, Hanover, NH 03755, USA
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209
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Overlooked Tertiary Sulci Serve as a Meso-Scale Link between Microstructural and Functional Properties of Human Lateral Prefrontal Cortex. J Neurosci 2021; 41:2229-2244. [PMID: 33478989 DOI: 10.1523/jneurosci.2362-20.2021] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 12/22/2020] [Accepted: 01/05/2021] [Indexed: 11/21/2022] Open
Abstract
Understanding the relationship between neuroanatomy and function in portions of cortex that perform functions largely specific to humans such as lateral prefrontal cortex (LPFC) is of major interest in systems and cognitive neuroscience. When considering neuroanatomical-functional relationships in LPFC, shallow indentations in cortex known as tertiary sulci have been largely unexplored. Here, by implementing a multimodal approach and manually defining 936 neuroanatomical structures in 72 hemispheres (in both males and females), we show that a subset of these overlooked tertiary sulci serve as a meso-scale link between microstructural (myelin content) and functional (network connectivity) properties of human LPFC in individual participants. For example, the posterior middle frontal sulcus (pmfs) is a tertiary sulcus with three components that differ in their myelin content, resting-state connectivity profiles, and engagement across meta-analyses of 83 cognitive tasks. Further, generating microstructural profiles of myelin content across cortical depths for each pmfs component and the surrounding middle frontal gyrus (MFG) shows that both gyral and sulcal components of the MFG have greater myelin content in deeper compared with superficial layers and that the myelin content in superficial layers of the gyral components is greater than sulcal components. These findings support a classic, yet largely unconsidered theory that tertiary sulci may serve as landmarks in association cortices, as well as a modern cognitive neuroscience theory proposing a functional hierarchy in LPFC. As there is a growing need for computational tools that automatically define tertiary sulci throughout cortex, we share pmfs probabilistic sulcal maps with the field.SIGNIFICANCE STATEMENT Lateral prefrontal cortex (LPFC) is critical for functions that are thought to be specific to humans compared with other mammals. However, relationships between fine-scale neuroanatomical structures largely specific to hominoid cortex and functional properties of LPFC remain elusive. Here, we show that these structures, which have been largely unexplored throughout history, surprisingly serve as markers for anatomical and functional organization in human LPFC. These findings have theoretical, methodological, developmental, and evolutionary implications for improved understanding of neuroanatomical-functional relationships not only in LPFC, but also in association cortices more broadly. Finally, these findings ignite new questions regarding how morphological features of these neglected neuroanatomical structures contribute to functions of association cortices that are critical for human-specific aspects of cognition.
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210
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Zarkali A, McColgan P, Leyland LA, Lees AJ, Rees G, Weil RS. Organisational and neuromodulatory underpinnings of structural-functional connectivity decoupling in patients with Parkinson's disease. Commun Biol 2021; 4:86. [PMID: 33469150 PMCID: PMC7815846 DOI: 10.1038/s42003-020-01622-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/18/2020] [Indexed: 01/01/2023] Open
Abstract
Parkinson's dementia is characterised by changes in perception and thought, and preceded by visual dysfunction, making this a useful surrogate for dementia risk. Structural and functional connectivity changes are seen in humans with Parkinson's disease, but the organisational principles are not known. We used resting-state fMRI and diffusion-weighted imaging to examine changes in structural-functional connectivity coupling in patients with Parkinson's disease, and those at risk of dementia. We identified two organisational gradients to structural-functional connectivity decoupling: anterior-to-posterior and unimodal-to-transmodal, with stronger structural-functional connectivity coupling in anterior, unimodal areas and weakened towards posterior, transmodal regions. Next, we related spatial patterns of decoupling to expression of neurotransmitter receptors. We found that dopaminergic and serotonergic transmission relates to decoupling in Parkinson's overall, but instead, serotonergic, cholinergic and noradrenergic transmission relates to decoupling in patients with visual dysfunction. Our findings provide a framework to explain the specific disorders of consciousness in Parkinson's dementia, and the neurotransmitter systems that underlie these.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK.
| | - Peter McColgan
- Huntington's Disease Centre, University College London, Russell Square House, London, WC1B 5EH, UK
| | - Louise-Ann Leyland
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Andrew J Lees
- Reta Lila Weston Institute of Neurological Studies, 1 Wakefield Street, London, WC1N 1PJ, UK
| | - Geraint Rees
- Institute of Cognitive Neuroscience, University College London, 17-19 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Rimona S Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
- Movement Disorders Consortium, University College London, London, WC1N 3BG, UK
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Liang X, Zhao C, Jin X, Jiang Y, Yang L, Chen Y, Gong G. Sex-related human brain asymmetry in hemispheric functional gradients. Neuroimage 2021; 229:117761. [PMID: 33454413 DOI: 10.1016/j.neuroimage.2021.117761] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/16/2020] [Accepted: 01/07/2021] [Indexed: 01/25/2023] Open
Abstract
The left and right hemispheres of the human brain are two connected but relatively independent functional modules; they show multidimensional asymmetries ranging from particular local brain unit properties to entire hemispheric connectome topology. To date, however, it remains largely unknown whether and how hemispheric functional hierarchical structures differ between hemispheres. In the present study, we adopted a newly developed resting-state (rs) functional connectivity (FC)-based gradient approach to evaluate hemispheric functional hierarchical structures and their asymmetries in right-handed healthy young adults. Our results showed an overall mirrored principal functional gradient between hemispheres, with the sensory cortex and the default-mode network (DMN) anchored at the two opposite ends of the gradient. Interestingly, the left hemisphere showed a significantly larger full range of the principal gradient in both males and females, with males exhibiting greater leftward asymmetry. Similarly, the principal gradient component scores of two regions around the middle temporal gyrus and posterior orbitofrontal cortex exhibited similar hemisphere × sex interaction effects: a greater degree of leftward asymmetry in males than in females. Moreover, we observed significant main hemisphere and sex effects in distributed regions across the entire hemisphere. All these results are reproducible and robust between test-retest rs-fMRI sessions. Our findings provide evidence of functional gradients that enhance the present understanding of human brain asymmetries in functional organization and highlight the impact of sex on hemispheric functional gradients and their asymmetries.
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Affiliation(s)
- Xinyu Liang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; School of Systems Science, Beijing Normal University, Beijing, China
| | - Xinhu Jin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yaya Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yijun Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & 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.
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212
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High-resolution connectomic fingerprints: Mapping neural identity and behavior. Neuroimage 2021; 229:117695. [PMID: 33422711 DOI: 10.1016/j.neuroimage.2020.117695] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/16/2020] [Accepted: 12/23/2020] [Indexed: 01/30/2023] Open
Abstract
Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.
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213
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Shafiei G, Markello RD, Vos de Wael R, Bernhardt BC, Fulcher BD, Misic B. Topographic gradients of intrinsic dynamics across neocortex. eLife 2020; 9:e62116. [PMID: 33331819 PMCID: PMC7771969 DOI: 10.7554/elife.62116] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/16/2020] [Indexed: 12/16/2022] Open
Abstract
The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6000 statistical properties of individual brain regions' time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Ben D Fulcher
- School of Physics, The University of SydneySydneyAustralia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
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214
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Ivanova AA, Srikant S, Sueoka Y, Kean HH, Dhamala R, O'Reilly UM, Bers MU, Fedorenko E. Comprehension of computer code relies primarily on domain-general executive brain regions. eLife 2020; 9:e58906. [PMID: 33319744 PMCID: PMC7738192 DOI: 10.7554/elife.58906] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 11/06/2020] [Indexed: 12/22/2022] Open
Abstract
Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.
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Affiliation(s)
- Anna A Ivanova
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Shashank Srikant
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Yotaro Sueoka
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Hope H Kean
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Riva Dhamala
- Eliot-Pearson Department of Child Study and Human Development, Tufts UniversityMedfordUnited States
| | - Una-May O'Reilly
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Marina U Bers
- Eliot-Pearson Department of Child Study and Human Development, Tufts UniversityMedfordUnited States
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
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215
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Karapanagiotidis T, Vidaurre D, Quinn AJ, Vatansever D, Poerio GL, Turnbull A, Ho NSP, Leech R, Bernhardt BC, Jefferies E, Margulies DS, Nichols TE, Woolrich MW, Smallwood J. The psychological correlates of distinct neural states occurring during wakeful rest. Sci Rep 2020; 10:21121. [PMID: 33273566 PMCID: PMC7712889 DOI: 10.1038/s41598-020-77336-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/27/2020] [Indexed: 12/22/2022] Open
Abstract
When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants' experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain's response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.
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Affiliation(s)
| | - Diego Vidaurre
- Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, 8000, Aarhus, Denmark
| | - Andrew J Quinn
- Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China
| | - Giulia L Poerio
- Department of Psychology, University of Essex, Colchester, Essex, CO4 3SQ, UK
| | - Adam Turnbull
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
| | - Nerissa Siu Ping Ho
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
| | - Robert Leech
- Centre for Neuroimaging Science, Kings College, London, SE5 8AF, UK
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, H3A 2B4, Canada
| | - Elizabeth Jefferies
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
| | - Daniel S Margulies
- Brain and Spine Institute (ICM), National Center for Scientific Research, Paris, 75013, France
| | - Thomas E Nichols
- Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Mark W Woolrich
- Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
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216
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Ziaeemehr A, Zarei M, Valizadeh A, Mirasso CR. Frequency-dependent organization of the brain’s functional network through delayed-interactions. Neural Netw 2020; 132:155-165. [DOI: 10.1016/j.neunet.2020.08.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/19/2020] [Accepted: 08/06/2020] [Indexed: 01/29/2023]
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217
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Madden DJ, Jain S, Monge ZA, Cook AD, Lee A, Huang H, Howard CM, Cohen JR. Influence of structural and functional brain connectivity on age-related differences in fluid cognition. Neurobiol Aging 2020; 96:205-222. [PMID: 33038808 PMCID: PMC7722190 DOI: 10.1016/j.neurobiolaging.2020.09.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 08/08/2020] [Accepted: 09/05/2020] [Indexed: 01/01/2023]
Abstract
We used graph theoretical measures to investigate the hypothesis that structural brain connectivity constrains the influence of functional connectivity on the relation between age and fluid cognition. Across 143 healthy, community-dwelling adults 19-79 years of age, we estimated structural network properties from diffusion-weighted imaging and functional network properties from resting-state functional magnetic resonance imaging. We confirmed previous reports of age-related decline in the strength and efficiency of structural networks, as well as in the connectivity strength within and between structural network modules. Functional networks, in contrast, exhibited age-related decline only in system segregation, a measure of the distinctiveness among network modules. Aging was associated with decline in a composite measure of fluid cognition, particularly tests of executive function. Functional system segregation was a significant mediator of age-related decline in executive function. Structural network properties did not directly influence the age-related decline in functional system segregation. The raw correlational data underlying the graph theoretical measures indicated that structural connectivity exerts a limited constraint on age-related decline in functional connectivity.
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Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
| | - Shivangi Jain
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Zachary A Monge
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Angela D Cook
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Alexander Lee
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Hua Huang
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Cortney M Howard
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Jessica R Cohen
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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218
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Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A. Structure-function coupling in the human connectome: A machine learning approach. Neuroimage 2020; 226:117609. [PMID: 33271268 DOI: 10.1016/j.neuroimage.2020.117609] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 02/03/2023] Open
Abstract
While the function of most biological systems is tightly constrained by their structure, current evidence suggests that coupling between the structure and function of brain networks is relatively modest. We aimed to investigate whether the modest coupling between connectome structure and function is a fundamental property of nervous systems or a limitation of current brain network models. We developed a new deep learning framework to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models (group: R=0.9±0.1, individual: R=0.55±0.1). Crucially, brain function predicted from an individual's structural connectome explained significant inter-individual variation in cognitive performance. Our results suggest that structure-function coupling in human brain networks is substantially tighter than previously suggested. We establish the margin by which current brain network models can be improved and demonstrate how deep learning can facilitate investigation of relations between brain function and behavior.
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Affiliation(s)
- T Sarwar
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - Y Tian
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
| | - B T T Yeo
- Department of Electrical and Computer Engineering, Center for Sleep & Cognition & N.1 Institute for Health, National University of Singapore, 15 119077, Singapore
| | - K Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia; Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia.
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219
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Ball G, Seidlitz J, O’Muircheartaigh J, Dimitrova R, Fenchel D, Makropoulos A, Christiaens D, Schuh A, Passerat-Palmbach J, Hutter J, Cordero-Grande L, Hughes E, Price A, Hajnal JV, Rueckert D, Robinson EC, Edwards AD. Cortical morphology at birth reflects spatiotemporal patterns of gene expression in the fetal human brain. PLoS Biol 2020; 18:e3000976. [PMID: 33226978 PMCID: PMC7721147 DOI: 10.1371/journal.pbio.3000976] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 12/07/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023] Open
Abstract
Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. We compared cortical morphology captured by high-resolution, multimodal magnetic resonance imaging (MRI) in n = 292 healthy newborn infants (mean age at birth = 39.9 weeks) with regional patterns of gene expression in the fetal cortex across gestation (n = 156 samples from 16 brains, aged 12 to 37 postconceptional weeks [pcw]). We tested the hypothesis that noninvasive measures of cortical structure at birth mirror areal differences in cortical gene expression across gestation, and in a cohort of n = 64 preterm infants (mean age at birth = 32.0 weeks), we tested whether cortical alterations observed after preterm birth were associated with altered gene expression in specific developmental cell populations. Neonatal cortical structure was aligned to differential patterns of cell-specific gene expression in the fetal cortex. Principal component analysis (PCA) of 6 measures of cortical morphology and microstructure showed that cortical regions were ordered along a principal axis, with primary cortex clearly separated from heteromodal cortex. This axis was correlated with estimated tissue maturity, indexed by differential expression of genes expressed by progenitor cells and neurons, and engaged in stem cell differentiation, neuron migration, and forebrain development. Preterm birth was associated with altered regional MRI metrics and patterns of differential gene expression in glial cell populations. The spatial patterning of gene expression in the developing cortex was thus mirrored by regional variation in cortical morphology and microstructure at term, and this was disrupted by preterm birth. This work provides a framework to link molecular mechanisms to noninvasive measures of cortical development in early life and highlights novel pathways to injury in neonatal populations at increased risk of neurodevelopmental disorder. Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. A large neuroimaging study of newborn infants reveals how their cortical structure at birth is associated with patterns of gene expression in the fetal cortex and how this relationship is affected by preterm birth.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
- * E-mail:
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, United States of America
- Department of Psychiatry, University of Cambridge, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daphna Fenchel
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | | | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Jo V. Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
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220
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Ehlers MR, Nold J, Kuhn M, Klingelhöfer-Jens M, Lonsdorf TB. Revisiting potential associations between brain morphology, fear acquisition and extinction through new data and a literature review. Sci Rep 2020; 10:19894. [PMID: 33199738 PMCID: PMC7670460 DOI: 10.1038/s41598-020-76683-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 11/02/2020] [Indexed: 01/16/2023] Open
Abstract
Inter-individual differences in defensive responding are widely established but their morphological correlates in humans have not been investigated exhaustively. Previous studies reported associations with cortical thickness of the dorsal anterior cingulate cortex, insula and medial orbitofrontal cortex as well as amygdala volume in fear conditioning studies. However, these associations are partly inconsistent and often derived from small samples. The current study aimed to replicate previously reported associations between physiological and subjective measures of fear acquisition and extinction and brain morphology. Structural magnetic resonance imaging was performed on 107 healthy adults who completed a differential cued fear conditioning paradigm with 24 h delayed extinction while skin conductance response (SCR) and fear ratings were recorded. Cortical thickness and subcortical volume were obtained using the software Freesurfer. Results obtained by traditional null hypothesis significance testing and Bayesians statistics do not support structural brain-behavior relationships: Neither differential SCR nor fear ratings during fear acquisition or extinction training could be predicted by cortical thickness or subcortical volume in regions previously reported. In summary, the current pre-registered study does not corroborate associations between brain morphology and inter-individual differences in defensive responding but differences in experimental design and analyses approaches compared to previous work should be acknowledged.
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Affiliation(s)
- Mana R Ehlers
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, W34, 20246, Hamburg, Germany.
| | - Janne Nold
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, W34, 20246, Hamburg, Germany
| | - Manuel Kuhn
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, W34, 20246, Hamburg, Germany
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, 02478, USA
| | - Maren Klingelhöfer-Jens
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, W34, 20246, Hamburg, Germany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, W34, 20246, Hamburg, Germany
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221
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Cross N, Paquola C, Pomares FB, Perrault AA, Jegou A, Nguyen A, Aydin U, Bernhardt BC, Grova C, Dang-Vu TT. Cortical gradients of functional connectivity are robust to state-dependent changes following sleep deprivation. Neuroimage 2020; 226:117547. [PMID: 33186718 DOI: 10.1016/j.neuroimage.2020.117547] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/19/2020] [Accepted: 11/04/2020] [Indexed: 12/26/2022] Open
Abstract
Sleep deprivation leads to significant impairments in cognitive performance and changes to the interactions between large scale cortical networks, yet the hierarchical organization of cortical activity across states is still being explored. We used functional magnetic resonance imaging to assess activations and connectivity during cognitive tasks in 20 healthy young adults, during three states: (i) following a normal night of sleep, (ii) following 24hr of total sleep deprivation, and (iii) after a morning recovery nap. Situating cortical activity during cognitive tasks along hierarchical organizing gradients based upon similarity of functional connectivity patterns, we found that regional variations in task-activations were captured by an axis differentiating areas involved in executive control from default mode regions and paralimbic cortex. After global signal regression, the range of functional differentiation along this axis at baseline was significantly related to decline in working memory performance (2-back task) following sleep deprivation, as well as the extent of recovery in performance following a nap. The relative positions of cortical regions within gradients did not significantly change across states, except for a lesser differentiation of the visual system and increased coupling of the posterior cingulate cortex with executive control areas after sleep deprivation. This was despite a widespread increase in the magnitude of functional connectivity across the cortex following sleep deprivation. Cortical gradients of functional differentiation thus appear relatively insensitive to state-dependent changes following sleep deprivation and recovery, suggesting that there are no large-scale changes in cortical functional organization across vigilance states. Certain features of particular gradient axes may be informative for the extent of decline in performance on more complex tasks following sleep deprivation, and could be beneficial over traditional voxel- or parcel-based approaches in identifying realtionships between state-dependent brain activity and behavior.
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Affiliation(s)
- Nathan Cross
- PERFORM Centre, Concordia University, Montreal, Canada; Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montreal, Canada; Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Florence B Pomares
- PERFORM Centre, Concordia University, Montreal, Canada; Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montreal, Canada; Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Aurore A Perrault
- PERFORM Centre, Concordia University, Montreal, Canada; Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montreal, Canada; Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Aude Jegou
- PERFORM Centre, Concordia University, Montreal, Canada; Multimodal Functional Imaging lab, Department of Physics, Concordia University, Montreal, Canada
| | - Alex Nguyen
- PERFORM Centre, Concordia University, Montreal, Canada; Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montreal, Canada
| | - Umit Aydin
- PERFORM Centre, Concordia University, Montreal, Canada; Multimodal Functional Imaging lab, Department of Physics, Concordia University, Montreal, Canada; Multimodal Funational Imaging Lab, Biomedical Engineering Dpt, Neurology and Neurosurgery Dpt, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Christophe Grova
- PERFORM Centre, Concordia University, Montreal, Canada; Multimodal Functional Imaging lab, Department of Physics, Concordia University, Montreal, Canada; Multimodal Funational Imaging Lab, Biomedical Engineering Dpt, Neurology and Neurosurgery Dpt, McGill University, Montreal, Quebec, Canada.
| | - Thien Thanh Dang-Vu
- PERFORM Centre, Concordia University, Montreal, Canada; Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montreal, Canada; Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
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222
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Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients. Neuroimage 2020; 222:117224. [DOI: 10.1016/j.neuroimage.2020.117224] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 01/24/2023] Open
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223
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Seguin C, Tian Y, Zalesky A. Network communication models improve the behavioral and functional predictive utility of the human structural connectome. Netw Neurosci 2020; 4:980-1006. [PMID: 33195945 PMCID: PMC7655041 DOI: 10.1162/netn_a_00161] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/03/2020] [Indexed: 12/11/2022] Open
Abstract
The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia
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224
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Abstract
Communication models describe the flow of signals among nodes of a network. In neural systems, communication models are increasingly applied to investigate network dynamics across the whole brain, with the ultimate aim to understand how signal flow gives rise to brain function. Communication models range from diffusion-like processes to those related to infectious disease transmission and those inspired by engineered communication systems like the internet. This Focus Feature brings together novel investigations of a diverse range of mechanisms and strategies that could shape communication in mammal whole-brain networks.
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Affiliation(s)
- Daniel Graham
- Department of Psychology, Hobart and William Smith Colleges, Geneva, NY, USA
| | | | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
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225
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Ito T, Hearne LJ, Cole MW. A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales. Neuroimage 2020; 221:117141. [PMID: 32663642 PMCID: PMC7779074 DOI: 10.1016/j.neuroimage.2020.117141] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/21/2020] [Accepted: 07/02/2020] [Indexed: 11/24/2022] Open
Abstract
Many studies have identified the role of localized and distributed cognitive functionality by mapping either local task-related activity or distributed functional connectivity (FC). However, few studies have directly explored the relationship between a brain region's localized task activity and its distributed task FC. Here we systematically evaluated the differential contributions of task-related activity and FC changes to identify a relationship between localized and distributed processes across the cortical hierarchy. We found that across multiple tasks, the magnitude of regional task-evoked activity was high in unimodal areas, but low in transmodal areas. In contrast, we found that task-state FC was significantly reduced in unimodal areas relative to transmodal areas. This revealed a strong negative relationship between localized task activity and distributed FC across cortical regions that was associated with the previously reported principal gradient of macroscale organization. Moreover, this dissociation corresponded to hierarchical cortical differences in the intrinsic timescale estimated from resting-state fMRI and region myelin content estimated from structural MRI. Together, our results contribute to a growing literature illustrating the differential contributions of a hierarchical cortical gradient representing localized and distributed cognitive processes.
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Affiliation(s)
- Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA.
| | - Luke J Hearne
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
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226
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Paquola C, Seidlitz J, Benkarim O, Royer J, Klimes P, Bethlehem RAI, Larivière S, Vos de Wael R, Rodríguez-Cruces R, Hall JA, Frauscher B, Smallwood J, Bernhardt BC. A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain. PLoS Biol 2020; 18:e3000979. [PMID: 33253185 PMCID: PMC7728398 DOI: 10.1371/journal.pbio.3000979] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 12/10/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022] Open
Abstract
The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico-cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico-cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior-posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals.
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Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Petr Klimes
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jeffery A. Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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227
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Srivastava P, Nozari E, Kim JZ, Ju H, Zhou D, Becker C, Pasqualetti F, Pappas GJ, Bassett DS. Models of communication and control for brain networks: distinctions, convergence, and future outlook. Netw Neurosci 2020; 4:1122-1159. [PMID: 33195951 PMCID: PMC7655113 DOI: 10.1162/netn_a_00158] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 07/21/2020] [Indexed: 12/13/2022] Open
Abstract
Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Erfan Nozari
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Jason Z. Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Harang Ju
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Dale Zhou
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Cassiano Becker
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA USA
| | - George J. Pappas
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Santa Fe Institute, Santa Fe, NM USA
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228
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Vézquez-Rodríguez B, Liu ZQ, Hagmann P, Misic B. Signal propagation via cortical hierarchies. Netw Neurosci 2020; 4:1072-1090. [PMID: 33195949 PMCID: PMC7657265 DOI: 10.1162/netn_a_00153] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 06/15/2020] [Indexed: 12/16/2022] Open
Abstract
The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting-state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were then labeled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is rerouted. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviorally relevant communication patterns in brain networks. In the present report we asked how signals travel on brain networks and what types of nodes they potentially visit en route. We traced individual path motifs to investigate the propensity of communication paths to explore the putative unimodal-transmodal cortical hierarchy. We find that the architecture of the network promotes signaling via the hierarchy, suggesting a link between the structure and function of the network. Importantly, we also find instances where detours are promoted, particularly as paths traverse attention-related networks. Finally, information about hierarchical position aids navigation in some parts of the network, over and above spatial location. Altogether, the present results touch on several emerging themes in network neuroscience, including the nature of structure-function relationships, network communication and the role of cortical hierarchies.
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Affiliation(s)
- Bertha Vézquez-Rodríguez
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
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229
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Wengler K, Goldberg AT, Chahine G, Horga G. Distinct hierarchical alterations of intrinsic neural timescales account for different manifestations of psychosis. eLife 2020; 9:e56151. [PMID: 33107431 PMCID: PMC7591251 DOI: 10.7554/elife.56151] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 10/05/2020] [Indexed: 12/14/2022] Open
Abstract
Hierarchical perceptual-inference models of psychosis may provide a holistic framework for understanding psychosis in schizophrenia including heterogeneity in clinical presentations. Particularly, hypothesized alterations at distinct levels of the perceptual-inference hierarchy may explain why hallucinations and delusions tend to cluster together yet sometimes manifest in isolation. To test this, we used a recently developed resting-state fMRI measure of intrinsic neural timescale (INT), which reflects the time window of neural integration and captures hierarchical brain gradients. In analyses examining extended sensory hierarchies that we first validated, we found distinct hierarchical INT alterations for hallucinations versus delusions in the auditory and somatosensory systems, thus providing support for hierarchical perceptual-inference models of psychosis. Simulations using a large-scale biophysical model suggested local elevations of excitation-inhibition ratio at different hierarchical levels as a potential mechanism. More generally, our work highlights the robustness and utility of INT for studying hierarchical processes relevant to basic and clinical neuroscience.
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Affiliation(s)
- Kenneth Wengler
- Department of Psychiatry, Columbia UniversityNew YorkUnited States
- New York State Psychiatric InstituteNew YorkUnited States
| | | | - George Chahine
- Department of Psychiatry, Yale UniversityNew HavenUnited States
| | - Guillermo Horga
- Department of Psychiatry, Columbia UniversityNew YorkUnited States
- New York State Psychiatric InstituteNew YorkUnited States
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230
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Mckeown B, Strawson WH, Wang HT, Karapanagiotidis T, Vos de Wael R, Benkarim O, Turnbull A, Margulies D, Jefferies E, McCall C, Bernhardt B, Smallwood J. The relationship between individual variation in macroscale functional gradients and distinct aspects of ongoing thought. Neuroimage 2020; 220:117072. [PMID: 32585346 PMCID: PMC7573534 DOI: 10.1016/j.neuroimage.2020.117072] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/15/2020] [Accepted: 06/17/2020] [Indexed: 12/12/2022] Open
Abstract
Contemporary accounts of ongoing thought recognise it as a heterogeneous and multidimensional construct, varying in both form and content. An emerging body of evidence demonstrates that distinct types of experience are associated with unique neurocognitive profiles, that can be described at the whole-brain level as interactions between multiple large-scale networks. The current study sought to explore the possibility that whole-brain functional connectivity patterns at rest may be meaningfully related to patterns of ongoing thought that occurred over this period. Participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) followed by a questionnaire retrospectively assessing the content and form of their ongoing thoughts during the scan. A non-linear dimension reduction algorithm was applied to the rs-fMRI data to identify components explaining the greatest variance in whole-brain connectivity patterns. Using these data, we examined whether specific types of thought measured at the end of the scan were predictive of individual variation along the first three low-dimensional components of functional connectivity at rest. Multivariate analyses revealed that individuals for whom the connectivity of the sensorimotor system was maximally distinct from the visual system were most likely to report thoughts related to finding solutions to problems or goals and least likely to report thoughts related to the past. These results add to an emerging literature that suggests that unique patterns of experience are associated with distinct distributed neurocognitive profiles and highlight that unimodal systems may play an important role in this process.
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Affiliation(s)
- Brontë Mckeown
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom.
| | - Will H Strawson
- Neuroscience, Brighton and Sussex Medical School, University of Sussex, United Kingdom
| | - Hao-Ting Wang
- Sackler Centre for Consciousness Studies, University of Sussex, United Kingdom
| | | | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Adam Turnbull
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
| | - Daniel Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR, 7225, Paris, France
| | - Elizabeth Jefferies
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
| | - Cade McCall
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
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231
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Burt JB, Helmer M, Shinn M, Anticevic A, Murray JD. Generative modeling of brain maps with spatial autocorrelation. Neuroimage 2020; 220:117038. [PMID: 32585343 DOI: 10.1016/j.neuroimage.2020.117038] [Citation(s) in RCA: 181] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/02/2020] [Accepted: 06/05/2020] [Indexed: 01/02/2023] Open
Abstract
Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene set enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.
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Affiliation(s)
| | | | - Maxwell Shinn
- Yale University, Interdepartmental Neuroscience Program, USA
| | - Alan Anticevic
- Yale University, Department of Psychiatry, USA; Yale University, Interdepartmental Neuroscience Program, USA
| | - John D Murray
- Yale University, Department of Physics, USA; Yale University, Department of Psychiatry, USA; Yale University, Interdepartmental Neuroscience Program, USA.
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232
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Park BY, Vos de Wael R, Paquola C, Larivière S, Benkarim O, Royer J, Tavakol S, Cruces RR, Li Q, Valk SL, Margulies DS, Mišić B, Bzdok D, Smallwood J, Bernhardt BC. Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function. Neuroimage 2020; 224:117429. [PMID: 33038538 DOI: 10.1016/j.neuroimage.2020.117429] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/13/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.
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Affiliation(s)
- Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Qiongling Li
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Bratislav Mišić
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Danilo Bzdok
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, New York, United Kingdom
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
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233
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Fedorenko E, Blank IA, Siegelman M, Mineroff Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 2020; 203:104348. [PMID: 32569894 DOI: 10.1101/477851] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 05/14/2020] [Accepted: 05/31/2020] [Indexed: 05/25/2023]
Abstract
To understand what you are reading now, your mind retrieves the meanings of words and constructions from a linguistic knowledge store (lexico-semantic processing) and identifies the relationships among them to construct a complex meaning (syntactic or combinatorial processing). Do these two sets of processes rely on distinct, specialized mechanisms or, rather, share a common pool of resources? Linguistic theorizing, empirical evidence from language acquisition and processing, and computational modeling have jointly painted a picture whereby lexico-semantic and syntactic processing are deeply inter-connected and perhaps not separable. In contrast, many current proposals of the neural architecture of language continue to endorse a view whereby certain brain regions selectively support syntactic/combinatorial processing, although the locus of such "syntactic hub", and its nature, vary across proposals. Here, we searched for selectivity for syntactic over lexico-semantic processing using a powerful individual-subjects fMRI approach across three sentence comprehension paradigms that have been used in prior work to argue for such selectivity: responses to lexico-semantic vs. morpho-syntactic violations (Experiment 1); recovery from neural suppression across pairs of sentences differing in only lexical items vs. only syntactic structure (Experiment 2); and same/different meaning judgments on such sentence pairs (Experiment 3). Across experiments, both lexico-semantic and syntactic conditions elicited robust responses throughout the left fronto-temporal language network. Critically, however, no regions were more strongly engaged by syntactic than lexico-semantic processing, although some regions showed the opposite pattern. Thus, contra many current proposals of the neural architecture of language, syntactic/combinatorial processing is not separable from lexico-semantic processing at the level of brain regions-or even voxel subsets-within the language network, in line with strong integration between these two processes that has been consistently observed in behavioral and computational language research. The results further suggest that the language network may be generally more strongly concerned with meaning than syntactic form, in line with the primary function of language-to share meanings across minds.
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Affiliation(s)
- Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA.
| | - Idan Asher Blank
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Department of Psychology, UCLA, Los Angeles, CA 90095, USA
| | - Matthew Siegelman
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Department of Psychology, Columbia University, New York, NY 10027, USA
| | - Zachary Mineroff
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Eberly Center for Teaching Excellence & Educational Innovation, CMU, Pittsburgh, PA 15213, USA
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234
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Fedorenko E, Blank IA, Siegelman M, Mineroff Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 2020; 203:104348. [PMID: 32569894 PMCID: PMC7483589 DOI: 10.1016/j.cognition.2020.104348] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 05/14/2020] [Accepted: 05/31/2020] [Indexed: 12/31/2022]
Abstract
To understand what you are reading now, your mind retrieves the meanings of words and constructions from a linguistic knowledge store (lexico-semantic processing) and identifies the relationships among them to construct a complex meaning (syntactic or combinatorial processing). Do these two sets of processes rely on distinct, specialized mechanisms or, rather, share a common pool of resources? Linguistic theorizing, empirical evidence from language acquisition and processing, and computational modeling have jointly painted a picture whereby lexico-semantic and syntactic processing are deeply inter-connected and perhaps not separable. In contrast, many current proposals of the neural architecture of language continue to endorse a view whereby certain brain regions selectively support syntactic/combinatorial processing, although the locus of such "syntactic hub", and its nature, vary across proposals. Here, we searched for selectivity for syntactic over lexico-semantic processing using a powerful individual-subjects fMRI approach across three sentence comprehension paradigms that have been used in prior work to argue for such selectivity: responses to lexico-semantic vs. morpho-syntactic violations (Experiment 1); recovery from neural suppression across pairs of sentences differing in only lexical items vs. only syntactic structure (Experiment 2); and same/different meaning judgments on such sentence pairs (Experiment 3). Across experiments, both lexico-semantic and syntactic conditions elicited robust responses throughout the left fronto-temporal language network. Critically, however, no regions were more strongly engaged by syntactic than lexico-semantic processing, although some regions showed the opposite pattern. Thus, contra many current proposals of the neural architecture of language, syntactic/combinatorial processing is not separable from lexico-semantic processing at the level of brain regions-or even voxel subsets-within the language network, in line with strong integration between these two processes that has been consistently observed in behavioral and computational language research. The results further suggest that the language network may be generally more strongly concerned with meaning than syntactic form, in line with the primary function of language-to share meanings across minds.
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Affiliation(s)
- Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA.
| | - Idan Asher Blank
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Department of Psychology, UCLA, Los Angeles, CA 90095, USA
| | - Matthew Siegelman
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Department of Psychology, Columbia University, New York, NY 10027, USA
| | - Zachary Mineroff
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Eberly Center for Teaching Excellence & Educational Innovation, CMU, Pittsburgh, PA 15213, USA
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235
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Cona G, Wiener M, Scarpazza C. From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of neuroimaging studies. Neuroimage 2020; 224:117407. [PMID: 32992001 DOI: 10.1016/j.neuroimage.2020.117407] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 08/31/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
According to the ATOM (A Theory Of Magnitude), formulated by Walsh more than fifteen years ago, there is a general system of magnitude in the brain that comprises regions, such as the parietal cortex, shared by space, time and other magnitudes. The present meta-analysis of neuroimaging studies used the Activation Likelihood Estimation (ALE) method in order to determine the set of regions commonly activated in space and time processing and to establish the neural activations specific to each magnitude domain. Following PRISMA guidelines, we included in the analysis a total of 112 and 114 experiments, exploring space and time processing, respectively. We clearly identified the presence of a system of brain regions commonly recruited in both space and time that includes: bilateral insula, the pre-supplementary motor area (pre-SMA), the right frontal operculum and the intraparietal sulci. These regions might be the best candidates to form the core magnitude neural system. Surprisingly, along each of these regions but the insula, ALE values progressed in a cortical gradient from time to space. The SMA exhibited an anterior-posterior gradient, with space activating more-anterior regions (i.e., pre-SMA) and time activating more-posterior regions (i.e., SMA-proper). Frontal and parietal regions showed a dorsal-ventral gradient: space is mediated by dorsal frontal and parietal regions, and time recruits ventral frontal and parietal regions. Our study supports but also expands the ATOM theory. Therefore, we here re-named it the 'GradiATOM' theory (Gradient Theory of Magnitude), proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space- and time-related neural populations to interact with each other over minimal distances.
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Affiliation(s)
- Giorgia Cona
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy; Padova Neuroscience Center, University of Padua, Italy.
| | - Martin Wiener
- Department of Psychology, George Mason University, Fairfax, VA.
| | - Cristina Scarpazza
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy.
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236
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Jouravlev O, Kell AJE, Mineroff Z, Haskins AJ, Ayyash D, Kanwisher N, Fedorenko E. Reduced Language Lateralization in Autism and the Broader Autism Phenotype as Assessed with Robust Individual-Subjects Analyses. Autism Res 2020; 13:1746-1761. [PMID: 32935455 DOI: 10.1002/aur.2393] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/28/2020] [Accepted: 08/25/2020] [Indexed: 12/13/2022]
Abstract
One of the few replicated functional brain differences between individuals with autism spectrum disorders (ASD) and neurotypical (NT) controls is reduced language lateralization. However, most prior reports relied on comparisons of group-level activation maps or functional markers that had not been validated at the individual-subject level, and/or used tasks that do not isolate language processing from other cognitive processes, complicating interpretation. Furthermore, few prior studies have examined functional responses in other brain networks, as needed to determine the spatial selectivity of the effect. Using functional magnetic resonance imaging (fMRI), we compared language lateralization between 28 adult ASD participants and carefully pairwise-matched controls, with the language regions defined individually using a well-validated language "localizer" task. Across two language comprehension paradigms, ASD participants showed less lateralized responses due to stronger right hemisphere activity. Furthermore, this effect did not stem from a ubiquitous reduction in lateralization of function across the brain: ASD participants did not differ from controls in the lateralization of two other large-scale networks-the Theory of Mind network and the Multiple Demand network. Finally, in an exploratory study, we tested whether reduced language lateralization may also be present in NT individuals with high autism-like traits. Indeed, autistic trait load in a large set of NT participants (n = 189) was associated with less lateralized language responses. These results suggest that reduced language lateralization is robustly associated with autism and, to some extent, with autism-like traits in the general population, and this lateralization reduction appears to be restricted to the language system. LAY SUMMARY: How do brains of individuals with autism spectrum disorders (ASD) differ from those of neurotypical (NT) controls? One of the most consistently reported differences is the reduction of lateralization during language processing in individuals with ASD. However, most prior studies have used methods that made this finding difficult to interpret, and perhaps even artifactual. Using robust individual-level markers of lateralization, we found that indeed, ASD individuals show reduced lateralization for language due to stronger right-hemisphere activity. We further show that this reduction is not due to a general reduction of lateralization of function across the brain. Finally, we show that greater autistic trait load is associated with less lateralized language responses in the NT population. These results suggest that reduced language lateralization is robustly associated with autism and, to some extent, with autism-like traits in the general population. Autism Res 2020, 13: 1746-1761. © 2020 International Society for Autism Research and Wiley Periodicals LLC.
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Affiliation(s)
- Olessia Jouravlev
- Brain and Cognitive Sciences Department, MIT, Cambridge, Massachusetts, USA.,Department of Cognitive Science, Carleton University, Ottawa, Ontario, Canada
| | - Alexander J E Kell
- Brain and Cognitive Sciences Department, MIT, Cambridge, Massachusetts, USA.,Zuckerman Institute, Columbia University, New York, New York, USA
| | - Zachary Mineroff
- Brain and Cognitive Sciences Department, MIT, Cambridge, Massachusetts, USA.,Eberly Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Amanda J Haskins
- Brain and Cognitive Sciences Department, MIT, Cambridge, Massachusetts, USA.,Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, USA
| | - Dima Ayyash
- Brain and Cognitive Sciences Department, MIT, Cambridge, Massachusetts, USA.,McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts, USA
| | - Nancy Kanwisher
- Brain and Cognitive Sciences Department, MIT, Cambridge, Massachusetts, USA.,McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts, USA
| | - Evelina Fedorenko
- Brain and Cognitive Sciences Department, MIT, Cambridge, Massachusetts, USA.,McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts, USA
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237
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Papadopoulos L, Lynn CW, Battaglia D, Bassett DS. Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state. PLoS Comput Biol 2020; 16:e1008144. [PMID: 32886673 PMCID: PMC7537889 DOI: 10.1371/journal.pcbi.1008144] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 10/06/2020] [Accepted: 07/12/2020] [Indexed: 01/09/2023] Open
Abstract
At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity—in concert with network structure—affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions’ baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations. Stimulation can be used to alter brain activity and is a therapeutic option for certain neurological conditions. However, predicting the distributed effects of local perturbations is difficult. Previous studies show that responses to stimulation depend on anatomical (or structural) coupling. In addition to structure, here we consider how stimulation effects also depend on the brain’s collective dynamical (or functional) state, arising from the coordination of rhythmic activity across large-scale networks. In a whole-brain computational model, we show that global responses to regional stimulation can indeed be contingent upon and differ across various dynamical working points. Notably, depending on the network’s oscillatory regime, stimulation can accelerate the activity of the stimulated site, and lead to widespread effects at both the new, excited frequency, as well as in a much broader frequency range including areas’ baseline frequencies. While structural connectivity is a good predictor of “excited band” changes, in some states “baseline band” effects can be better predicted by functional connectivity, which depends upon the system’s oscillatory regime. By integrating and extending past efforts, our results thus indicate that dynamical—in additional to structural—brain organization plays a role in governing how focal stimulation modulates interactions between distributed network elements.
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Affiliation(s)
- Lia Papadopoulos
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Christopher W. Lynn
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France
| | - Danielle S. Bassett
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail:
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238
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Luo N, Sui J, Abrol A, Chen J, Turner JA, Damaraju E, Fu Z, Fan L, Lin D, Zhuo C, Xu Y, Glahn DC, Rodrigue AL, Banich MT, Pearlson GD, Calhoun VD. Structural Brain Architectures Match Intrinsic Functional Networks and Vary across Domains: A Study from 15 000+ Individuals. Cereb Cortex 2020; 30:5460-5470. [PMID: 32488253 DOI: 10.1093/cercor/bhaa127] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across nine functional domains, in both a discovery cohort (n = 6005) and a replication cohort (UK Biobank, n = 9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g., somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared with unimodal cortex, which may lead to higher interindividual variability and lower S-F coherence.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Jessica A Turner
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health Center, Nankai University Affiliated Anding Hospital, Tianjin, 300222, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College/First Hospital of Shanxi Medical University, Taiyuan 030000, China
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Amanda L Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA.,Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Hartford Hospital/Institute of Living, Hartford, CT 06114, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.,Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06519, US
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.,Departments of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, GA 30302, USA.,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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239
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Yang S, Meng Y, Li J, Li B, Fan YS, Chen H, Liao W. The thalamic functional gradient and its relationship to structural basis and cognitive relevance. Neuroimage 2020; 218:116960. [DOI: 10.1016/j.neuroimage.2020.116960] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/04/2020] [Accepted: 05/14/2020] [Indexed: 12/30/2022] Open
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240
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MacCormack JK, Stein AG, Kang J, Giovanello KS, Satpute AB, Lindquist KA. Affect in the Aging Brain: A Neuroimaging Meta-Analysis of Older Vs. Younger Adult Affective Experience and Perception. AFFECTIVE SCIENCE 2020; 1:128-154. [PMID: 36043210 PMCID: PMC9382982 DOI: 10.1007/s42761-020-00016-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 08/20/2020] [Indexed: 05/15/2023]
Abstract
We report the first functional neuroimaging meta-analysis on age-related differences in adult neural activity during affect. We identified and coded experimental contrasts from 27 studies (published 1997-2018) with 490 older adults (55-87 years, M age = 69 years) and 470 younger adults (18-39 years, M age = 24 years). Using multilevel kernel density analysis, we assessed functional brain activation contrasts for older vs. younger adult affect across in-scanner tasks (i.e., affect induction and perception). Relative to older adults, younger adults showed more reliable activation in subcortical structures (e.g., amygdala, thalamus, caudate) and in relatively more posterior aspects of specific brain structures (e.g., posterior insula, mid- and posterior cingulate). In contrast, older adults exhibited more reliable activation in the prefrontal cortex and more anterior aspects of specific brain structures (e.g., anterior insula, anterior cingulate). Meta-analytic coactivation network analyses further revealed that in younger adults, the amygdala and mid-cingulate were more central, locally efficient network nodes, whereas in older adults, regions in the superior and medial prefrontal cortex were more central, locally efficient network nodes. Collectively, these findings help characterize age differences in the brain basis of affect and provide insights for future investigations into the neural mechanisms underlying affective aging.
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Affiliation(s)
- Jennifer K. MacCormack
- Department of Psychiatry, University of Pittsburgh, 506 Old Engineering Hall, 3943 O’Hara St, Pittsburgh, PA 15213 USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Andrea G. Stein
- Department of Psychology, University of Wisconsin-Madison, Madison, WI USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI USA
| | - Kelly S. Giovanello
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Ajay B. Satpute
- Department of Psychology, Northeastern University, Boston, MA USA
| | - Kristen A. Lindquist
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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241
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Avelar-Pereira B, Bäckman L, Wåhlin A, Nyberg L, Salami A. Increased functional homotopy of the prefrontal cortex is associated with corpus callosum degeneration and working memory decline. Neurobiol Aging 2020; 96:68-78. [PMID: 32949903 DOI: 10.1016/j.neurobiolaging.2020.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 06/29/2020] [Accepted: 08/10/2020] [Indexed: 11/18/2022]
Abstract
Functional homotopy reflects the link between spontaneous activity in a voxel and its counterpart in the opposite hemisphere. Alterations in homotopic functional connectivity (FC) are seen in normal aging, with highest and lowest homotopy being present in sensory-motor and higher-order regions, respectively. Homotopic FC relates to underlying structural connections, but its neurobiological underpinnings remain unclear. The genu of the corpus callosum joins symmetrical parts of the prefrontal cortex (PFC) and is susceptible to age-related degeneration, suggesting that PFC homotopic connectivity is linked to changes in white-matter integrity. We investigated homotopic connectivity changes and whether these were associated with white-matter integrity in 338 individuals. In addition, we examined whether PFC homotopic FC was related to changes in the genu over 10 years and working memory over 5 years. There were increases and decreases in functional homotopy, with the former being prevalent in subcortical and frontal regions. Increased PFC homotopic FC was partially driven by structural degeneration and negatively associated with working memory, suggesting that it reflects detrimental age-related changes.
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Affiliation(s)
- Bárbara Avelar-Pereira
- Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden; Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.
| | - Lars Bäckman
- Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Anders Wåhlin
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Alireza Salami
- Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden; Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
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242
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Kirschner M, Shafiei G, Markello RD, Makowski C, Talpalaru A, Hodzic-Santor B, Devenyi GA, Paquola C, Bernhardt BC, Lepage M, Chakravarty MM, Dagher A, Mišić B. Latent Clinical-Anatomical Dimensions of Schizophrenia. Schizophr Bull 2020; 46:1426-1438. [PMID: 32744604 PMCID: PMC8496914 DOI: 10.1093/schbul/sbaa097] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Widespread structural brain abnormalities have been consistently reported in schizophrenia, but their relation to the heterogeneous clinical manifestations remains unknown. In particular, it is unclear whether anatomical abnormalities in discrete regions give rise to discrete symptoms or whether distributed abnormalities give rise to the broad clinical profile associated with schizophrenia. Here, we apply a multivariate data-driven approach to investigate covariance patterns between multiple-symptom domains and distributed brain abnormalities in schizophrenia. Structural magnetic resonance imaging and clinical data were derived from one discovery sample (133 patients and 113 controls) and one independent validation sample (108 patients and 69 controls). Disease-related voxel-wise brain abnormalities were estimated using deformation-based morphometry. Partial least-squares analysis was used to comprehensively map clinical, neuropsychological, and demographic data onto distributed deformation in a single multivariate model. The analysis identified 3 latent clinical-anatomical dimensions that collectively accounted for 55% of the covariance between clinical data and brain deformation. The first latent clinical-anatomical dimension was replicated in an independent sample, encompassing cognitive impairments, negative symptom severity, and brain abnormalities within the default mode and visual networks. This cognitive-negative dimension was associated with low socioeconomic status and was represented across multiple races. Altogether, we identified a continuous cognitive-negative dimension of schizophrenia, centered on 2 intrinsic networks. By simultaneously taking into account both clinical manifestations and neuroanatomical abnormalities, the present results open new avenues for multi-omic stratification and biotyping of individuals with schizophrenia.
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Affiliation(s)
- Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland,McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Carolina Makowski
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Alexandra Talpalaru
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | - Benazir Hodzic-Santor
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Gabriel A Devenyi
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Psychiatry, McGill University, Montréal, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Martin Lepage
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Psychiatry, McGill University, Montréal, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Biological and Biomedical Engineering, McGill University, Montréal, Canada,Department of Psychiatry, McGill University, Montréal, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada,To whom correspondence should be addressed; tel: 514-398-1857, fax: 514-398-1857, e-mail:
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243
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Abstract
The development and persistence of laterality is a key feature of human motor behavior, with the asymmetry of hand use being the most prominent. The idea that asymmetrical functions of the hands reflect asymmetries in terms of structural and functional brain organization has been tested many times. However, despite advances in laterality research and increased understanding of this population-level bias, the neural basis of handedness remains elusive. Recent developments in diffusion magnetic resonance imaging enabled the exploration of lateralized motor behavior also in terms of white matter and connectional neuroanatomy. Despite incomplete and partly inconsistent evidence, structural connectivity of both intrahemispheric and interhemispheric white matter seems to differ between left and right-handers. Handedness was related to asymmetry of intrahemispheric pathways important for visuomotor and visuospatial processing (superior longitudinal fasciculus), but not to projection tracts supporting motor execution (corticospinal tract). Moreover, the interindividual variability of the main commissural pathway corpus callosum seems to be associated with handedness. The review highlights the importance of exploring new avenues for the study of handedness and presents the latest state of knowledge that can be used to guide future neuroscientific and genetic research.
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Affiliation(s)
- Sanja Budisavljevic
- Department of General Psychology, University of Padova, Padova, Italy.,The School of Medicine, University of St. Andrews, St. Andrews, UK
| | - Umberto Castiello
- Department of General Psychology, University of Padova, Padova, Italy
| | - Chiara Begliomini
- Department of General Psychology, University of Padova, Padova, Italy
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Bajada CJ, Costa Campos LQ, Caspers S, Muscat R, Parker GJM, Lambon Ralph MA, Cloutman LL, Trujillo-Barreto NJ. A tutorial and tool for exploring feature similarity gradients with MRI data. Neuroimage 2020; 221:117140. [PMID: 32650053 PMCID: PMC7116330 DOI: 10.1016/j.neuroimage.2020.117140] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 06/09/2020] [Accepted: 07/02/2020] [Indexed: 11/15/2022] Open
Abstract
There has been an increasing interest in examining organisational principles of the cerebral cortex (and subcortical regions) using different MRI features such as structural or functional connectivity. Despite the widespread interest, introductory tutorials on the underlying technique targeted for the novice neuroimager are sparse in the literature. Articles that investigate various "neural gradients" (for example based on region studied "cortical gradients," "cerebellar gradients," "hippocampal gradients" etc … or feature of interest "functional gradients," "cytoarchitectural gradients," "myeloarchitectural gradients" etc …) have increased in popularity. Thus, we believe that it is opportune to discuss what is generally meant by "gradient analysis". We introduce basics concepts in graph theory, such as graphs themselves, the degree matrix, and the adjacency matrix. We discuss how one can think about gradients of feature similarity (the similarity between timeseries in fMRI, or streamline in tractography) using graph theory and we extend this to explore such gradients across the whole MRI scale; from the voxel level to the whole brain level. We proceed to introduce a measure for quantifying the level of similarity in regions of interest. We propose the term "the Vogt-Bailey index" for such quantification to pay homage to our history as a brain mapping community. We run through the techniques on sample datasets including a brain MRI as an example of the application of the techniques on real data and we provide several appendices that expand upon details. To maximise intuition, the appendices contain a didactic example describing how one could use these techniques to solve a particularly pernicious problem that one may encounter at a wedding. Accompanying the article is a tool, available in both MATLAB and Python, that enables readers to perform the analysis described in this article on their own data. We refer readers to the graphical abstract as an overview of the analysis pipeline presented in this work.
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Affiliation(s)
- Claude J Bajada
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, The University of Malta, Malta; Division of Neuroscience & Experimental Psychology, School of Biological Sciences, The University of Manchester, UK; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany.
| | - Lucas Q Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany; Institute of Complex Systems and Institute for Advanced Simulation (ICS-2/IAS-2), Research Centre Jülich, 52425, Jülich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany; Institute for Anatomy I, Medical Faculty, Heinrich-Heine-University Duesseldorf, 40221, Duesseldorf, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, 52425, Jülich, Germany
| | - Richard Muscat
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, The University of Malta, Malta
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Computer Science, and Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, UK; Bioxydyn Limited, Manchester, UK
| | | | - Lauren L Cloutman
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, The University of Manchester, UK
| | - Nelson J Trujillo-Barreto
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, The University of Manchester, UK
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245
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Dynamic representations in networked neural systems. Nat Neurosci 2020; 23:908-917. [PMID: 32541963 DOI: 10.1038/s41593-020-0653-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 05/12/2020] [Indexed: 11/08/2022]
Abstract
A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types of approaches. Here we briefly review the two separate bodies of literature; we then review the recent strides made to address this gap. We continue with a discussion of how patterns of activity evolve from one representation to another, forming dynamic representations that unfold on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission while revealing exciting frontiers for future research.
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246
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Abend R, Gold AL, Britton JC, Michalska KJ, Shechner T, Sachs JF, Winkler AM, Leibenluft E, Averbeck BB, Pine DS. Anticipatory Threat Responding: Associations With Anxiety, Development, and Brain Structure. Biol Psychiatry 2020; 87:916-925. [PMID: 31955915 PMCID: PMC7211142 DOI: 10.1016/j.biopsych.2019.11.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/16/2019] [Accepted: 11/07/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND While translational theories link neurodevelopmental changes in threat learning to pathological anxiety, findings from studies in patients inconsistently support these theories. This inconsistency may reflect difficulties in studying large patient samples with wide age ranges using consistent methods. A dearth of imaging data in patients further limits translational advances. We address these gaps through a psychophysiology and structural brain imaging study in a large sample of patients across the lifespan. METHODS A total of 351 participants (8-50 years of age; 209 female subjects; 195 healthy participants and 156 medication-free, treatment-seeking patients with anxiety) completed a differential threat conditioning and extinction paradigm that has been validated in pediatric and adult populations. Skin conductance response indexed psychophysiological response to conditioned (CS+, CS-) and unconditioned threat stimuli. Structural magnetic resonance imaging data were available for 250 participants. Analyses tested anxiety and age associations with psychophysiological response in addition to associations between psychophysiology and brain structure. RESULTS Regardless of age, patients and healthy comparison subjects demonstrated comparable differential threat conditioning and extinction. The magnitude of skin conductance response to both conditioned stimulus types differentiated patients from comparison subjects and covaried with dorsal prefrontal cortical thickness; structure-response associations were moderated by anxiety and age in several regions. Unconditioned responding was unrelated to anxiety and brain structure. CONCLUSIONS Rather than impaired threat learning, pathological anxiety involves heightened skin conductance response to potential but not immediately present threats; this anxiety-related potentiation of anticipatory responding also relates to variation in brain structure. These findings inform theoretical considerations by highlighting anticipatory response to potential threat in anxiety.
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Affiliation(s)
- Rany Abend
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.
| | - Andrea L. Gold
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI; Pediatric Anxiety Research Center, Bradley Hospital, Riverside, RI
| | | | | | - Tomer Shechner
- Psychology Department, University of Haifa, Haifa, Israel
| | | | - Anderson M. Winkler
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Ellen Leibenluft
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Bruno B. Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of
Health, Bethesda, MD
| | - Daniel S. Pine
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
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247
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Blank IA, Fedorenko E. No evidence for differences among language regions in their temporal receptive windows. Neuroimage 2020; 219:116925. [PMID: 32407994 DOI: 10.1016/j.neuroimage.2020.116925] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 03/20/2020] [Accepted: 05/06/2020] [Indexed: 10/24/2022] Open
Abstract
The "core language network" consists of left frontal and temporal regions that are selectively engaged in linguistic processing. Whereas functional differences among these regions have long been debated, many accounts propose distinctions in terms of representational grain-size-e.g., words vs. phrases/sentences-or processing time-scale, i.e., operating on local linguistic features vs. larger spans of input. Indeed, the topography of language regions appears to overlap with a cortical hierarchy reported by Lerner et al. (2011) wherein mid-posterior temporal regions are sensitive to low-level features of speech, surrounding areas-to word-level information, and inferior frontal areas-to sentence-level information and beyond. However, the correspondence between the language network and this hierarchy of "temporal receptive windows" (TRWs) is difficult to establish because the precise anatomical locations of language regions vary across individuals. To directly test this correspondence, we first identified language regions in each participant with a well-validated task-based localizer, which confers high functional resolution to the study of TRWs (traditionally based on stereotactic coordinates); then, we characterized regional TRWs with the naturalistic story listening paradigm of Lerner et al. (2011), which augments task-based characterizations of the language network by more closely resembling comprehension "in the wild". We find no region-by-TRW interactions across temporal and inferior frontal regions, which are all sensitive to both word-level and sentence-level information. Therefore, the language network as a whole constitutes a unique stage of information integration within a broader cortical hierarchy.
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Affiliation(s)
- Idan A Blank
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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248
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Neuroimaging contrast across the cortical hierarchy is the feature maximally linked to behavior and demographics. Neuroimage 2020; 215:116853. [PMID: 32302765 PMCID: PMC7311192 DOI: 10.1016/j.neuroimage.2020.116853] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 04/10/2020] [Indexed: 01/21/2023] Open
Abstract
An essential task of neuroscience is to elucidate the relationship between brain activity, brain structure, and human behavior. This study aims to understand this 3-way relationship by studying the population covariance of resting-state functional connectivity, cortical thickness, and behavioral/demographic measures in a large cohort of individuals. Using a data-driven canonical correlation analysis, we found that maximal pairwise correlations between the three modalities are approximately along the same direction across subjects, which is characterized by the change of the overall positive-negative trait of human behavior. More importantly, this behavioral change is associated with a divergent modulation of both resting-state connectivity and cortical thickness across cortical hierarchies between the higher-order cognitive networks and lower-order sensory/motor regions. The findings suggest that the cross-hierarchy contrast of structural and functional brain measures is tightly linked to the overall positive-negative trait of human behavior/demographics.
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249
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Canini M, Cavoretto P, Scifo P, Pozzoni M, Petrini A, Iadanza A, Pontesilli S, Scotti R, Candiani M, Falini A, Baldoli C, Della Rosa PA. Subcortico-Cortical Functional Connectivity in the Fetal Brain: A Cognitive Development Blueprint. Cereb Cortex Commun 2020; 1:tgaa008. [PMID: 34296089 PMCID: PMC8152909 DOI: 10.1093/texcom/tgaa008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 02/24/2020] [Accepted: 03/25/2020] [Indexed: 12/19/2022] Open
Abstract
Recent evidence has shown that patterns of cortico-cortical functional synchronization are consistently traceable by the end of the third trimester of pregnancy. The involvement of subcortical structures in early functional and cognitive development has never been explicitly investigated, notwithstanding their pivotal role in different cognitive processes. We address this issue by exploring subcortico-cortical functional connectivity at rest in a group of normally developing fetuses between the 25th and 32nd weeks of gestation. Results show significant functional coupling between subcortical nuclei and cortical networks related to: (i) sensorimotor processing, (ii) decision making, and (iii) learning capabilities. This functional maturation framework unearths a Cognitive Development Blueprint, according to which grounding cognitive skills are planned to develop with higher ontogenetic priority. Specifically, our evidence suggests that a newborn already possesses the ability to: (i) perceive the world and interact with it, (ii) create salient representations for the selection of adaptive behaviors, and (iii) store, retrieve, and evaluate the outcomes of interactions, in order to gradually improve adaptation to the extrauterine environment.
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Affiliation(s)
- Matteo Canini
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Paolo Cavoretto
- Department of Gynecology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Paola Scifo
- Department of Nuclear Medicine, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Mirko Pozzoni
- Department of Gynecology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Alessandro Petrini
- Department of Computer Science, Università degli Studi Milano, 20122 Milan, Italy
| | - Antonella Iadanza
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Silvia Pontesilli
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Roberta Scotti
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Massimo Candiani
- Department of Gynecology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Andrea Falini
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Cristina Baldoli
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
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250
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Fedorenko E, Blank IA. Broca's Area Is Not a Natural Kind. Trends Cogn Sci 2020; 24:270-284. [PMID: 32160565 PMCID: PMC7211504 DOI: 10.1016/j.tics.2020.01.001] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 12/21/2019] [Accepted: 01/09/2020] [Indexed: 01/09/2023]
Abstract
Theories of human cognition prominently feature 'Broca's area', which causally contributes to a myriad of mental functions. However, Broca's area is not a monolithic, multipurpose unit - it is structurally and functionally heterogeneous. Some functions engaging (subsets of) this area share neurocognitive resources, whereas others rely on separable circuits. A decade of converging evidence has now illuminated a fundamental distinction between two subregions of Broca's area that likely play computationally distinct roles in cognition: one belongs to the domain-specific 'language network', the other to the domain-general 'multiple-demand (MD) network'. Claims about Broca's area should be (re)cast in terms of these (and other, as yet undetermined) functional components, to establish a cumulative research enterprise where empirical findings can be replicated and theoretical proposals can be meaningfully compared and falsified.
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Affiliation(s)
- Evelina Fedorenko
- Brain and Cognitive Sciences Department, and McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
| | - Idan A Blank
- Department of Psychology, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA.
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