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Tang H, Zhu W, Jing J, Zhou Y, Liu H, Li S, Li Z, Liu Z, Liu C, Pan Y, Cai X, Meng X, Wang Y, Li H, Jiang Y, Wang S, Niu H, Wei T, Wang Y, Liu T. Disrupted structural network resilience in atherosclerosis: A large-scale cohort study. Brain Res 2025; 1859:149653. [PMID: 40252894 DOI: 10.1016/j.brainres.2025.149653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 02/22/2025] [Accepted: 04/17/2025] [Indexed: 04/21/2025]
Abstract
BACKGROUND Atherosclerosis is a major factor in cognitive decline among aging individuals and is frequently linked to the accumulation of white matter hyperintensities. Brain resilience, which represents the brain's capacity to withstand external disruptions, remains poorly understood in terms of how atherosclerosis impacts it and, in turn, influences cognition. Here, we investigated the relationship between atherosclerosis, white matter hyperintensities, and structural network resilience, along with their combined effects on cognitive performance. METHODS We utilized data from the large-scale community cohort Polyvascular Evaluation for Cognitive Impairment and Vascular Events (n = 2160). Whole-brain structural connections were constructed, and structural disconnections were simulated based on white matter hyperintensities. SNR, serving as a marker to quantify structural network resilience, is defined by the similarity of hub nodes between the original network and its disconnected counterpart. RESULTS SNR showed higher odds ratios compared to white matter hyperintensities in relation to arterial status. Additionally, chain mediation analysis indicated that cognitive decline associated with atherosclerosis was partially mediated by both white matter hyperintensities and structural network resilience. Atherosclerosis accelerates the degradation of brain structural network resilience as age increases. CONCLUSIONS These findings suggest that SNR could offer complementary insights into cognitive decline caused by atherosclerosis and serve as a potential biomarker of brain health in atherosclerotic conditions. Additionally, SNR may act as an indicator for guiding the selection of future therapies for atherosclerosis.
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Affiliation(s)
- Hui Tang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Shiping Li
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Zixiao Li
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ziyang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yuesong Pan
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China
| | - Xia Meng
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Tiemin Wei
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China
| | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
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2
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Milinkovic B, Barnett L, Carter O, Seth AK, Andrillon T. Capturing the emergent dynamical structure in biophysical neural models. PLoS Comput Biol 2025; 21:e1012572. [PMID: 40354301 PMCID: PMC12068601 DOI: 10.1371/journal.pcbi.1012572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2024] [Accepted: 03/31/2025] [Indexed: 05/14/2025] Open
Abstract
Complex neural systems can display structured emergent dynamics. Capturing this structure remains a significant scientific challenge. Using information theory, we apply Dynamical Independence (DI) to uncover the emergent dynamical structure in a minimal 5-node biophysical neural model, shaped by the interplay of two key aspects of brain organisation: integration and segregation. In our study, functional integration within the biophysical neural model is modulated by a global coupling parameter, while functional segregation is influenced by adding dynamical noise, which counteracts global coupling. Leveraging transfer entropy, DI defines a dimensionally-reduced macroscopic variable (e.g., a coarse-graining) as emergent to the extent that it behaves as an independent dynamical process, distinct from the micro-level dynamics. Dynamical dependence (a departure from dynamical independence) is measured by minimising the transfer entropy from microlevel variables to macroscopic variables across spatial scales. Our results indicate that the degree of emergence of macroscopic variables is relatively minimised at balanced points of integration and segregation and maximised at the extremes. Additionally, our method identifies to which degree the macroscopic dynamics are localised across microlevel nodes, thereby elucidating the emergent dynamical structure through the relationship between microscopic and macroscopic processes. We find that deviation from a balanced point between integration and segregation results in a less localised, more distributed emergent dynamical structure as identified by DI. This finding suggests that a balance of functional integration and segregation is associated with lower levels of emergence (higher dynamical dependence), which may be crucial for sustaining coherent, localised emergent macroscopic dynamical structures. This work also provides a complete computational implementation for the identification of emergent neural dynamics that could be applied both in silico and in vivo.
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Affiliation(s)
- Borjan Milinkovic
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
- Sussex Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
- Paris Brain Institute (ICM)/INSERM, Hôpital de la PitiÃ(c)-Salpêtrière, Paris, France
| | - Lionel Barnett
- Sussex Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
| | - Olivia Carter
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - Anil K. Seth
- Sussex Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
- Canadian Institute for Advanced Research, Program on Brain, Mind, and Consciousness, Toronto, Canada
| | - Thomas Andrillon
- Paris Brain Institute (ICM)/INSERM, Hôpital de la PitiÃ(c)-Salpêtrière, Paris, France
- Monash Centre for Consciousness & Contemplative Studies, Monash University, Melbourne, Australia
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3
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Luppi AI, Golkowski D, Ranft A, Ilg R, Jordan D, Bzdok D, Owen AM, Naci L, Stamatakis EA, Amico E, Misic B. General anaesthesia decreases the uniqueness of brain functional connectivity across individuals and species. Nat Hum Behav 2025; 9:987-1004. [PMID: 40128306 PMCID: PMC12106074 DOI: 10.1038/s41562-025-02121-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 01/16/2025] [Indexed: 03/26/2025]
Abstract
The human brain is characterized by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI scans acquired under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain, both with respect to the brains of other individuals and the brains of another species. Using functional connectivity, we report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organized: it co-localizes with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol and reversed upon recovery. Providing convergent evidence, we show that anaesthesia shifts the functional connectivity of the human brain closer to the functional connectivity of the macaque brain in a low-dimensional space. Finally, anaesthesia diminishes the match between spontaneous brain activity and cognitive brain patterns aggregated from the Neurosynth meta-analytic engine. Collectively, the present results reveal that anaesthetized human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.
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Affiliation(s)
- Andrea I Luppi
- Montréal Neurological Institute, McGill University, Montréal, Québec, Canada.
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Daniel Golkowski
- Department of Neurology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Andreas Ranft
- Department of Anesthesiology and Intensive Care, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Rudiger Ilg
- Department of Neurology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- Asklepios Clinic, Department of Neurology, Bad Tölz, Germany
| | - Denis Jordan
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Danilo Bzdok
- Montréal Neurological Institute, McGill University, Montréal, Québec, Canada
- Mila, Quebec Artificial Intelligence Institute, Montréal, Québec, Canada
| | - Adrian M Owen
- Western Institute for Neuroscience, Western University, London, Ontario, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Emmanuel A Stamatakis
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Enrico Amico
- School of Mathematics, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Québec, Canada
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4
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Taguchi T, Kitazono J, Sasai S, Oizumi M. Association of Bidirectional Network Cores in the Brain with Perceptual Awareness and Cognition. J Neurosci 2025; 45:e0802242025. [PMID: 40015987 PMCID: PMC12019110 DOI: 10.1523/jneurosci.0802-24.2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 01/07/2025] [Accepted: 02/20/2025] [Indexed: 03/01/2025] Open
Abstract
The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions. However, the link between such subnetworks and conscious perception remains unclear due to the complexity of brain networks. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions-termed the "cores" of a network-from brain activity. We applied this framework to resting-state and task-based human fMRI data from participants of both sexes to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. We found that the extracted central cores predominantly included cerebral cortical regions rather than subcortical regions. Additionally, regarding their relation to conscious perception, we demonstrated that the cores tend to include regions previously reported to be affected by electrical stimulation that altered conscious perception, although the results are not statistically robust due to the small sample size. Furthermore, in relation to cognitive functions, based on a meta-analysis and comparison of the core structure with a cortical functional connectivity gradient, we found that the central cores were related to unimodal sensorimotor functions. The proposed framework provides novel insights into the roles of network cores with strong bidirectional interactions in conscious perception and unimodal sensorimotor functions.
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Affiliation(s)
- Tomoya Taguchi
- Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Jun Kitazono
- Graduate School of Data Science, Yokohama City University, Kanagawa 236-0027, Japan
| | | | - Masafumi Oizumi
- Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
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5
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Zheng Y, Yang Y, Zhen Y, Wang X, Liu L, Zheng H, Tang S. Altered integrated and segregated states in cocaine use disorder. Front Neurosci 2025; 19:1572463. [PMID: 40270764 PMCID: PMC12014740 DOI: 10.3389/fnins.2025.1572463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2025] [Accepted: 03/19/2025] [Indexed: 04/25/2025] Open
Abstract
Introduction Cocaine use disorder (CUD) is a chronic brain condition that severely impairs cognitive function and behavioral control. The neural mechanisms underlying CUD, particularly its impact on brain integration-segregation dynamics, remain unclear. Methods In this study, we integrate dynamic functional connectivity and graph theory to compare the brain state properties of healthy controls and CUD patients. Results We find that CUD influences both integrated and segregated states, leading to distinct alterations in connectivity patterns and network properties. CUD disrupts connectivity involving the default mode network, frontoparietal network, and subcortical structures. In addition, integrated states show distinct sensorimotor connectivity alterations, while segregated states exhibit significant alterations in frontoparietal-subcortical connectivity. Regional connectivity alterations among both states are significantly associated with MOR and H3 receptor distributions, with integrated states showing more receptor-connectivity couplings. Furthermore, CUD alters the positive-negative correlation balance, increases functional complexity at threshold 0, and reduces mean betweenness centrality and modularity in the critical subnetworks. Segregated states in CUD exhibit lower normalized clustering coefficients and functional complexity at a threshold of 0.3. We also identify network properties in integrated states that are reliably correlated with cocaine consumption patterns. Discussion Our findings reveal temporal effects of CUD on brain integration and segregation, providing novel insights into the dynamic neural mechanisms underlying cocaine addiction.
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Affiliation(s)
- Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
| | - Yaqian Yang
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
| | - Xin Wang
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing, China
| | - Longzhao Liu
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing, Beijing, China
| | - Shaoting Tang
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing, China
- Hangzhou International Innovation Institute, Beihang University, Hangzhou, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
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6
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Luppi AI, Uhrig L, Tasserie J, Shafiei G, Muta K, Hata J, Okano H, Golkowski D, Ranft A, Ilg R, Jordan D, Gini S, Liu ZQ, Yee Y, Signorelli CM, Cofre R, Destexhe A, Menon DK, Stamatakis EA, Connor CW, Gozzi A, Fulcher BD, Jarraya B, Misic B. Comprehensive profiling of anaesthetised brain dynamics across phylogeny. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.22.644729. [PMID: 40196621 PMCID: PMC11974681 DOI: 10.1101/2025.03.22.644729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
The intrinsic dynamics of neuronal circuits shape information processing and cognitive function. Combining non-invasive neuroimaging with anaesthetic-induced suppression of information processing provides a unique opportunity to understand how local dynamics mediate the link between neurobiology and the organism's functional repertoire. To address this question, we compile a unique dataset of multi-scale neural activity during wakefulness and anesthesia encompassing human, macaque, marmoset, mouse and nematode. We then apply massive feature extraction to comprehensively characterize local neural dynamics across > 6 000 time-series features. Using dynamics as a common space for comparison across species, we identify a phylogenetically conserved dynamical profile of anaesthesia that encompasses multiple features, including reductions in intrinsic timescales. This dynamical signature has an evolutionarily conserved spatial layout, covarying with transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque and mouse cortex. At the network level, anesthetic-induced changes in local dynamics manifest as reductions in inter-regional synchrony. This relationship between local dynamics and global connectivity can be recapitulated in silico using a connectome-based computational model. Finally, this dynamical regime of anaesthesia is experimentally reversed in vivo by deep-brain stimulation of the centromedian thalamus in the macaque, resulting in restored arousal and behavioural responsiveness. Altogether, comprehensive dynamical phenotyping reveals that spatiotemporal isolation of local neural activity during anesthesia is conserved across species and anesthetics.
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Affiliation(s)
- Andrea I. Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Centre for Eudaimonia and Human Flourishing, Department of Psychiatry, University of Oxford, Oxford, UK
- St John’s College, University of Cambridge, Cambridge, UK
| | - Lynn Uhrig
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
- Department of Anesthesiology and Critical Care, Necker Hospital, Université de Paris Cité, Paris, France
| | - Jordy Tasserie
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Golia Shafiei
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kanako Muta
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama Japan
| | - Junichi Hata
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama Japan
- Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama Japan
- Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
| | - Daniel Golkowski
- Department of Neurology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Andreas Ranft
- Department of Anesthesiology and Intensive Care, Technical University of Munich, Munich, Germany
| | - Rudiger Ilg
- Department of Neurology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
- Asklepios Clinic, Department of Neurology, Bad Tolz, Germany
| | - Denis Jordan
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
- University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Silvia Gini
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Centre for Mind/Brain Sciences, University of Trento, Italy
| | - Zhen-Qi Liu
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Yohan Yee
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Camilo M. Signorelli
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
- Center for Philosophy of Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark
| | - Rodrigo Cofre
- Paris-Saclay University, CNRS, Paris-Saclay Institute for Neuroscience (NeuroPSI), Saclay, France
| | - Alain Destexhe
- Paris-Saclay University, CNRS, Paris-Saclay Institute for Neuroscience (NeuroPSI), Saclay, France
| | - David K. Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Emmanuel A. Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Christopher W. Connor
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Biomedical Engineering, Physiology and Biophysics, Boston University, Boston, Massachusetts
| | - Alessandro Gozzi
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Sydney, Australia
| | - Bechir Jarraya
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
- Department of Neurology, Foch Hospital, Suresnes, France
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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7
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Tang H, Zhao H, Liu H, Jiang J, Kochan N, Jing J, Brodaty H, Wen W, Sachdev PS, Liu T. Structural damage-driven brain compensation among near-centenarians and centenarians without dementia. Neuroimage 2025; 308:121065. [PMID: 39889810 DOI: 10.1016/j.neuroimage.2025.121065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 11/13/2024] [Accepted: 01/28/2025] [Indexed: 02/03/2025] Open
Abstract
Compensation has been proposed as a mechanism to explain how individuals in very old age remain able to maintain normal cognitive functioning. Previous studies have provided evidence on the role of increasing functional connectivity as a compensatory mechanism for age-related white matter damage. However, we lack direct investigation into how these mechanisms contribute to the preservation of cognition in the very old population. We examined a cohort of near-centenarians and centenarians without dementia (aged 95-103 years, n=44). We constructed a structural disconnection matrix based on the disruption of white matter pathways caused by white matter hyperintensities (WMHs), aiming to explore the relationship between functional connections, cognitive preservation and white matter damage. Our results revealed that structural damage can reliably explain the variations of functional connections or cognitive maintenance. Notably, we found significant correlations between the weights in the functional connectivity model and the weights in the cognition model. We observed positive correlations between models for brain disconnections and cognitive function in near-centenarians and centenarians. The strongest effects were found between attention and somatomotor network (SMN) (r=0.397, p<0.001), memory and SMN (r=0.333 p<0.001), fluency and visual network (VIS) - control network (CN) (r=0.406, p<0.001), language and VIS (r=0.309, p<0.001), visuospatial ability and VIS-default mode network (DMN) (r=0.464, p<0.001), as well as global cognition and VIS-DMN (r=0.335, p<0.001). These findings suggest that enhancement of functional connectivity may serve as a compensatory mechanism, such that it mitigates the effects of white matter damage and contributes to preserved cognitive performance in very old age.
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Affiliation(s)
- Hui Tang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Haichao Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China; Faculty of Psychology, MOE Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Prince of Wales Hospital, Barker Street, Randwick, NSW 2031, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Prince of Wales Hospital, Barker Street, Randwick, NSW 2031, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Prince of Wales Hospital, Barker Street, Randwick, NSW 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Prince of Wales Hospital, Barker Street, Randwick, NSW 2031, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Prince of Wales Hospital, Barker Street, Randwick, NSW 2031, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia.
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China.
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8
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Manickam T, Ramasamy V, Doraisamy N. Comparison of data-driven thresholding methods using directed functional brain networks. Rev Neurosci 2025; 36:119-138. [PMID: 39217451 DOI: 10.1515/revneuro-2024-0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 07/24/2024] [Indexed: 09/04/2024]
Abstract
Over the past two centuries, intensive empirical research has been conducted on the human brain. As an electroencephalogram (EEG) records millisecond-to-millisecond changes in the electrical potentials of the brain, it has enormous potential for identifying useful information about neuronal transactions. The EEG data can be modelled as graphs by considering the electrode sites as nodes and the linear and nonlinear statistical dependencies among them as edges (with weights). The graph theoretical modelling of EEG data results in functional brain networks (FBNs), which are fully connected (complete) weighted undirected/directed networks. Since various brain regions are interconnected via sparse anatomical connections, the weak links can be filtered out from the fully connected networks using a process called thresholding. Multiple researchers in the past decades proposed many thresholding methods to gather more insights about the influential neuronal connections of FBNs. This paper reviews various thresholding methods used in the literature for FBN analysis. The analysis showed that data-driven methods are unbiased since no arbitrary user-specified threshold is required. The efficacy of four data-driven thresholding methods, namely minimum spanning tree (MST), minimum connected component (MCC), union of shortest path trees (USPT), and orthogonal minimum spanning tree (OMST), in characterizing cognitive behavior of the normal human brain is analysed using directed FBNs constructed from EEG data of different cognitive load states. The experimental results indicate that both MCC and OMST thresholding methods can detect cognitive load-induced changes in the directed functional brain networks.
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Affiliation(s)
- Thilaga Manickam
- Department of Mathematics, Amrita School of Physical Sciences, 77649 Amrita Vishwa Vidyapeetham , Coimbatore, Tamilnadu 641112, India
| | - Vijayalakshmi Ramasamy
- College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30458, USA
| | - Nandagopal Doraisamy
- Cognitive Neuroengineering Laboratory, School of Information Technology and Mathematical Sciences, Division of IT, Engineering and the Environments, University of South Australia, Adelaide 5000, Australia
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9
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Hu H, Coppola P, Stamatakis EA, Naci L. Typical and disrupted small-world architecture and regional communication in full-term and preterm infants. PNAS NEXUS 2025; 4:pgaf015. [PMID: 39931103 PMCID: PMC11809590 DOI: 10.1093/pnasnexus/pgaf015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 01/03/2025] [Indexed: 02/13/2025]
Abstract
Understanding the emergence of complex cognition in the neonate is one of the great frontiers of cognitive neuroscience. In the adult brain, small-world organization enables efficient information segregation and integration and dynamic adaptability to cognitive demands. It remains unknown, however, when functional small-world architecture emerges in development, whether it is present by birth and how prematurity affects it. We leveraged the world's largest fMRI neonatal dataset-Developing Human Connectome Project-to include full-term neonates (n = 278), and preterm neonates scanned at term-equivalent age (TEA; n = 72), or before TEA (n = 70), and the Human Connectome Project for a reference adult group (n = 176). Although different from adults', the small-world architecture was developed in full-term neonates at birth. The key novel finding was that premature neonates before TEA showed dramatic underdevelopment of small-world organization and regional communication in 9/11 networks, with disruption in 32% of brain nodes. The somatomotor and dorsal attention networks carry the largest spatial effect, and visual network the smallest. Significant prematurity-related disruption of small-world architecture and reduced efficiency of regional communication in networks related to high-order cognition, including language, persisted at TEA. Critically, at full-term birth or by TEA, infants exhibited functional small-world architecture, which facilitates differentiated and integrated neural processes that support complex cognition. Conversely, this brain infrastructure is significantly underdeveloped before infants reach TEA. These findings improve understanding of the ontogeny of functional small-world architecture and efficiency of neural communication, and of their disruption by premature birth.
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Affiliation(s)
- Huiqing Hu
- Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, No. 152 Luoyu Road, Hongshan District, Wuhan 430079, Hubei, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, No. 152 Luoyu Road, Hongshan District, Wuhan 430079, Hubei, China
| | - Peter Coppola
- Division of Anaesthesia, Addenbrookes Hospital, University of Cambridge, Hills Rd, Cambridge CB2 0QQ, United Kingdom
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, United Kingdom
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, Addenbrookes Hospital, University of Cambridge, Hills Rd, Cambridge CB2 0QQ, United Kingdom
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, United Kingdom
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, 42a Pearse St, Dublin D02 X9W9, Ireland
- Global Brain Health Institute, Trinity College Dublin, 42a Pearse St, Dublin D02 X9W9, Ireland
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10
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Taguchi T, Kitazono J, Sasai S, Oizumi M. Association of bidirectional network cores in the brain with perceptual awareness and cognition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.04.30.591001. [PMID: 38746271 PMCID: PMC11092575 DOI: 10.1101/2024.04.30.591001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions. However, the link between such subnetworks and conscious perception remains unclear due to the complexity of brain networks. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions-termed the "cores" of a network-from brain activity. We applied this framework to resting-state and task-based human fMRI data from participants of both sexes to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. We found that the extracted central cores predominantly included cerebral cortical regions rather than subcortical regions. Additionally, regarding their relation to conscious perception, we demonstrated that the cores tend to include regions previously reported to be affected by electrical stimulation that altered conscious perception, although the results are not statistically robust due to the small sample size. Furthermore, in relation to cognitive functions, based on a meta-analysis and comparison of the core structure with a cortical functional connectivity gradient, we found that the central cores were related to unimodal sensorimotor functions. The proposed framework provides novel insights into the roles of network cores with strong bidirectional interactions in conscious perception and unimodal sensorimotor functions.
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Affiliation(s)
- Tomoya Taguchi
- Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Jun Kitazono
- Graduate School of Data Science, Yokohama City University, Kanagawa, Japan
| | | | - Masafumi Oizumi
- Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
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11
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Ben Messaoud R, Le Du V, Bousfiha C, Corsi MC, Gonzalez-Astudillo J, Kaufmann BC, Venot T, Couvy-Duchesne B, Migliaccio L, Rosso C, Bartolomeo P, Chavez M, De Vico Fallani F. Low-dimensional controllability of brain networks. PLoS Comput Biol 2025; 21:e1012691. [PMID: 39775065 PMCID: PMC11706394 DOI: 10.1371/journal.pcbi.1012691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 12/02/2024] [Indexed: 01/11/2025] Open
Abstract
Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain.
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Affiliation(s)
- Remy Ben Messaoud
- Inria Paris, Paris, France
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Vincent Le Du
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Camile Bousfiha
- Inria Paris, Paris, France
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Marie-Constance Corsi
- Inria Paris, Paris, France
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Juliana Gonzalez-Astudillo
- Inria Paris, Paris, France
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Brigitte Charlotte Kaufmann
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Tristan Venot
- Inria Paris, Paris, France
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Baptiste Couvy-Duchesne
- Inria Paris, Paris, France
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
- Institute for Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Lara Migliaccio
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
- Department of Neurology, Institute of Memory and Alzheimer’s Disease, Centre of Excellence of Neurodegenerative Disease, Hôpital Pitié-Salpêtrière, Paris, France
| | - Charlotte Rosso
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
- Urgences Cérébro-Vasculaires, DMU Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Paolo Bartolomeo
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Mario Chavez
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Fabrizio De Vico Fallani
- Inria Paris, Paris, France
- Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
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12
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Luppi AI, Sanz Perl Y, Vohryzek J, Mediano PAM, Rosas FE, Milisav F, Suarez LE, Gini S, Gutierrez-Barragan D, Gozzi A, Misic B, Deco G, Kringelbach ML. Competitive interactions shape brain dynamics and computation across species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.19.619194. [PMID: 39484469 PMCID: PMC11526968 DOI: 10.1101/2024.10.19.619194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2024]
Abstract
Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.
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Affiliation(s)
- Andrea I. Luppi
- University of Oxford, Oxford, UK
- St John’s College, Cambridge, UK
- Montreal Neurological Institute, Montreal, Canada
| | | | | | | | | | | | | | - Silvia Gini
- Italian Institute of Technology, Rovereto, Italy
- Centre for Mind/Brain Sciences, University of Trento, Italy
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13
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Siddiqui A, Abu Hasan R, Saad Azhar Ali S, Elamvazuthi I, Lu CK, Tang TB. Detection of Low Resilience Using Data-Driven Effective Connectivity Measures. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3657-3668. [PMID: 39302782 DOI: 10.1109/tnsre.2024.3465269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approaches may not always be reliable as conventional thresholding approaches are subjected to human biases. Using a mental resilience study, we investigate if data-driven thresholding techniques such as Global Cost Efficiency (GCE-abs) and Orthogonal Minimum Spanning Trees (OMSTs) could provide equivalent results, whilst eliminating human biases. We implemented Phase Slope Index (PSI) to compute effective brain connectivity, and applied data-driven thresholding approaches to filter the brain networks in order to identify key features of low resilience within a cohort of healthy individuals. Our dataset encompassed resting-state EEG recordings gathered from a total of 36 participants (31 females and 5 males). Relevant features were extracted to train and validate a classifier model (Support Vector Machine, SVM). The detection of low stress resilience among healthy individuals using the SVM model scores an accuracy of 80.6% with GCE-abs, and 75% with OMSTs, respectively.
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14
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Pourmotabbed H, Clarke DF, Chang C, Babajani-Feremi A. Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology. Commun Biol 2024; 7:1221. [PMID: 39349968 PMCID: PMC11443053 DOI: 10.1038/s42003-024-06807-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 08/29/2024] [Indexed: 10/04/2024] Open
Abstract
Cognitive, behavioral, and disease traits are influenced by both genetic and environmental factors. Individual differences in these traits have been associated with graph theoretical properties of resting-state networks, indicating that variations in connectome topology may be driven by genetics. In this study, we establish the heritability of global and local graph properties of resting-state networks derived from functional MRI (fMRI) and magnetoencephalography (MEG) using a large sample of twins and non-twin siblings from the Human Connectome Project. We examine the heritability of MEG in the source space, providing a more accurate estimate of genetic influences on electrophysiological networks. Our findings show that most graph measures are more heritable for MEG compared to fMRI and the heritability for MEG is greater for amplitude compared to phase synchrony in the delta, high beta, and gamma frequency bands. This suggests that the fast neuronal dynamics in MEG offer unique insights into the genetic basis of brain network organization. Furthermore, we demonstrate that brain network features can serve as genetic fingerprints to accurately identify pairs of identical twins within a cohort. These results highlight novel opportunities to relate individual connectome signatures to genetic mechanisms underlying brain function.
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Affiliation(s)
- Haatef Pourmotabbed
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Dave F Clarke
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Abbas Babajani-Feremi
- Magnetoencephalography (MEG) Lab, The Norman Fixel Institute of Neurological Diseases, Gainesville, FL, USA.
- Department of Neurology, University of Florida, Gainesville, FL, USA.
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15
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Vandewouw MM, Ye Y(J, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Arnold PD, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions. Hum Brain Mapp 2024; 45:e26815. [PMID: 39254138 PMCID: PMC11386318 DOI: 10.1002/hbm.26815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 07/08/2024] [Accepted: 07/29/2024] [Indexed: 09/11/2024] Open
Abstract
With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.
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Affiliation(s)
- Marlee M. Vandewouw
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Institute of Biomedical EngineeringUniversity of TorontoTorontoCanada
| | - Yifan (Julia) Ye
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Division of Engineering ScienceUniversity of TorontoTorontoCanada
| | - Jennifer Crosbie
- Department of PsychiatryUniversity of TorontoTorontoCanada
- Department of PsychiatryThe Hospital for Sick ChildrenTorontoOntarioCanada
| | - Russell J. Schachar
- Department of PsychiatryUniversity of TorontoTorontoCanada
- Department of PsychiatryThe Hospital for Sick ChildrenTorontoOntarioCanada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural NeurosciencesMcMaster UniversityHamiltonCanada
| | | | - Elizabeth Kelley
- Department of PsychologyQueen's UniversityKingstonCanada
- Centre for Neuroscience StudiesQueen's UniversityKingstonCanada
- Department of PsychiatryQueen's UniversityKingstonCanada
| | - Muhammad Ayub
- Department of PsychiatryQueen's UniversityKingstonCanada
- Division of PsychiatryUniversity of College LondonLondonUK
| | - Jessica Jones
- Department of PsychologyQueen's UniversityKingstonCanada
- Centre for Neuroscience StudiesQueen's UniversityKingstonCanada
- Department of PsychiatryQueen's UniversityKingstonCanada
| | - Paul D. Arnold
- The Mathison Centre for Mental Health Research & Education, Cumming School of MedicineUniversity of CalgaryCalgaryCanada
| | - Margot J. Taylor
- Department of Diagnostic and Interventional RadiologyThe Hospital for Sick ChildrenTorontoCanada
- Program in Neurosciences and Mental HealthThe Hospital for Sick ChildrenTorontoCanada
- Department of PsychologyUniversity of TorontoTorontoCanada
- Department of Medical ImagingUniversity of TorontoTorontoCanada
| | - Jason P. Lerch
- Program in Neurosciences and Mental HealthThe Hospital for Sick ChildrenTorontoCanada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Department of Medical BiophysicsUniversity of TorontoTorontoCanada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Program in Neurosciences and Mental HealthThe Hospital for Sick ChildrenTorontoCanada
- Institute of Medical ScienceUniversity of TorontoTorontoCanada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Institute of Biomedical EngineeringUniversity of TorontoTorontoCanada
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16
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Luppi AI, Singleton SP, Hansen JY, Jamison KW, Bzdok D, Kuceyeski A, Betzel RF, Misic B. Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies. Nat Biomed Eng 2024; 8:1142-1161. [PMID: 39103509 PMCID: PMC11410673 DOI: 10.1038/s41551-024-01242-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/02/2024] [Indexed: 08/07/2024]
Abstract
The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.
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Affiliation(s)
- Andrea I Luppi
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - S Parker Singleton
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- MILA, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Richard F Betzel
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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17
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard J, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. A synergistic workspace for human consciousness revealed by Integrated Information Decomposition. eLife 2024; 12:RP88173. [PMID: 39022924 PMCID: PMC11257694 DOI: 10.7554/elife.88173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Pedro AM Mediano
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Center for Complexity Science, Imperial College LondonLondonUnited Kingdom
- Data Science Institute, Imperial College LondonLondonUnited Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - John Pickard
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Psychedelics Division - Neuroscape, Department of Neurology, University of CaliforniaSan FranciscoUnited States
| | - Guy B Williams
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Michael M Craig
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Paola Finoia
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
| | - Adrian M Owen
- Department of Psychology and Department of Physiology and Pharmacology, The Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity CollegeDublinIreland
| | - David K Menon
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Daniel Bor
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
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18
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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19
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Vohryzek J, Luppi AI, Atasoy S, Deco G, Carhart-Harris RL, Timmermann C, Kringelbach ML. Time-resolved coupling between connectome harmonics and subjective experience under the psychedelic DMT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.30.596410. [PMID: 38853985 PMCID: PMC11160714 DOI: 10.1101/2024.05.30.596410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Exploring the intricate relationship between brain's structure and function, and how this affects subjective experience is a fundamental pursuit in neuroscience. Psychedelic substances offer a unique insight into the influences of specific neurotransmitter systems on perception, cognition and consciousness. Specifically, their impact on brain function propagates across the structural connectome - a network of white matter pathways linking different regions. To comprehensively grasp the effects of psychedelic compounds on brain function, we used a theoretically rigorous framework known as connectome harmonic decomposition. This framework provides a robust method to characterize how brain function intricately depends on the organized network structure of the human connectome. We show that the connectome harmonic repertoire under DMT is reshaped in line with other reported psychedelic compounds - psilocybin, LSD and ketamine. Furthermore, we show that the repertoire entropy of connectome harmonics increases under DMT, as with those other psychedelics. Importantly, we demonstrate for the first time that measures of energy spectrum difference and repertoire entropy of connectome harmonics indexes the intensity of subjective experience of the participants in a time-resolved manner reflecting close coupling between connectome harmonics and subjective experience.
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Affiliation(s)
- Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Andrea I. Luppi
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- St John’s College, University of Cambridge, Cambridge, United Kingdom
- Division of Information Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Selen Atasoy
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Robin L. Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, United Kingdom
- Departments of Neurology and Psychiatry, University of California San Francisco, San Francisco, USA
| | - Christopher Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
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20
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Luppi AI, Olbrich E, Finn C, Suárez LE, Rosas FE, Mediano PA, Jost J. Quantifying synergy and redundancy between networks. CELL REPORTS. PHYSICAL SCIENCE 2024; 5:101892. [PMID: 38720789 PMCID: PMC11077508 DOI: 10.1016/j.xcrp.2024.101892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/18/2024] [Accepted: 03/01/2024] [Indexed: 05/12/2024]
Abstract
Understanding how different networks relate to each other is key for understanding complex systems. We introduce an intuitive yet powerful framework to disentangle different ways in which networks can be (dis)similar and complementary to each other. We decompose the shortest paths between nodes as uniquely contributed by one source network, or redundantly by either, or synergistically by both together. Our approach considers the networks' full topology, providing insights at multiple levels of resolution: from global statistics to individual paths. Our framework is widely applicable across scientific domains, from public transport to brain networks. In humans and 124 other species, we demonstrate the prevalence of unique contributions by long-range white-matter fibers in structural brain networks. Across species, efficient communication also relies on significantly greater synergy between long-range and short-range fibers than expected by chance. Our framework could find applications for designing network systems or evaluating existing ones.
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Affiliation(s)
- Andrea I. Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- St John’s College, University of Cambridge, Cambridge, UK
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eckehard Olbrich
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
| | - Conor Finn
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
| | - Laura E. Suárez
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E. Rosas
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Department of Informatics, University of Sussex, Brighton, UK
- Centre for Complexity Science, Imperial College London, London, UK
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | | | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- ScaDS.AI, Leipzig University, Leipzig, Germany
- Santa Fe Institute, Santa Fe, NM, USA
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21
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Tolle HM, Farah JC, Mallaroni P, Mason NL, Ramaekers JG, Amico E. The unique neural signature of your trip: Functional connectome fingerprints of subjective psilocybin experience. Netw Neurosci 2024; 8:203-225. [PMID: 38562294 PMCID: PMC10898784 DOI: 10.1162/netn_a_00349] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/23/2023] [Indexed: 04/04/2024] Open
Abstract
The emerging neuroscientific frontier of brain fingerprinting has recently established that human functional connectomes (FCs) exhibit fingerprint-like idiosyncratic features, which map onto heterogeneously distributed behavioral traits. Here, we harness brain-fingerprinting tools to extract FC features that predict subjective drug experience induced by the psychedelic psilocybin. Specifically, in neuroimaging data of healthy volunteers under the acute influence of psilocybin or a placebo, we show that, post psilocybin administration, FCs become more idiosyncratic owing to greater intersubject dissimilarity. Moreover, whereas in placebo subjects idiosyncratic features are primarily found in the frontoparietal network, in psilocybin subjects they concentrate in the default mode network (DMN). Crucially, isolating the latter revealed an FC pattern that predicts subjective psilocybin experience and is characterized by reduced within-DMN and DMN-limbic connectivity, as well as increased connectivity between the DMN and attentional systems. Overall, these results contribute to bridging the gap between psilocybin-mediated effects on brain and behavior, while demonstrating the value of a brain-fingerprinting approach to pharmacological neuroimaging.
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Affiliation(s)
- Hanna M. Tolle
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Juan Carlos Farah
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Natasha L. Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Johannes G. Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Enrico Amico
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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22
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Huang RR, Wu JJ, Shen J, Xing XX, Hua XY, Zheng MX, Xiao LB, Xu JG. Limbic system plasticity after electroacupuncture intervention in knee osteoarthritis rats. Neurosci Lett 2024; 820:137580. [PMID: 38072028 DOI: 10.1016/j.neulet.2023.137580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/16/2023] [Accepted: 12/05/2023] [Indexed: 12/25/2023]
Abstract
Knee osteoarthritis (KOA) is characterized by debilitating pain. Electroacupuncture (EA), a traditional Chinese medical therapy, has shown promise in KOA pain management. This study investigated the therapeutic potential of EA in KOA and its impact on limbic system neural plasticity. Sixteen rats were randomly assigned into two groups: EA group and sham-EA group. EA or sham-EA interventions were administered at acupoints ST32 (Futu) and ST36 (Zusanli) for three weeks. Post-intervention resting-state fMRI was scanned, assessing parameters including Amplitude of low frequency fluctuations (ALFF), regional homogeneity (ReHo), functional connectivity (FC) and nodal characterizations of network within limbic system. The results showed that EA was strategically directed towards the limbic system, resulting in discernible alterations in neural activity, FC, and network characteristics. Our findings demonstrate that EA had a significant impact on the limbic system neural plasticity in rats with KOA, presenting a novel nonpharmacological approach for KOA treatment.
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Affiliation(s)
- Rong-Rong Huang
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Jun Shen
- Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Arthritis Institute of Integrated Traditional Chinese and Western Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Xiang-Xin Xing
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Lian-Bo Xiao
- Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Arthritis Institute of Integrated Traditional Chinese and Western Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Jian-Guang Xu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
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23
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Luppi AI, Golkowski D, Ranft A, Ilg R, Jordan D, Bzdok D, Owen AM, Naci L, Stamatakis EA, Amico E, Misic B. General anaesthesia reduces the uniqueness of brain connectivity across individuals and across species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.08.566332. [PMID: 38014199 PMCID: PMC10680788 DOI: 10.1101/2023.11.08.566332] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The human brain is characterised by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain: both with respect to the brains of other individuals, and the brains of another species. We report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organised: it co-localises with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol, and reversed upon recovery. Providing convergent evidence, we show that under anaesthesia the functional connectivity of the human brain becomes more similar to the macaque brain. Finally, anaesthesia diminishes the match between spontaneous brain activity and meta-analytic brain patterns aggregated from the NeuroSynth engine. Collectively, the present results reveal that anaesthetised human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.
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Affiliation(s)
- Andrea I Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Daniel Golkowski
- Department of Neurology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Andreas Ranft
- Department of Anesthesiology and Intensive Care, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Rudiger Ilg
- Department of Neurology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
- Asklepios Clinic, Department of Neurology, Bad Tolz, Germany
| | - Denis Jordan
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Danilo Bzdok
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- MILA, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Adrian M Owen
- Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Emmanuel A Stamatakis
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Enrico Amico
- Neuro-X Institute, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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24
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Liu Q, Zhou B, Zhang X, Qing P, Zhou X, Zhou F, Xu X, Zhu S, Dai J, Huang Y, Wang J, Zou Z, Kendrick KM, Becker B, Zhao W. Abnormal multi-layered dynamic cortico-subcortical functional connectivity in major depressive disorder and generalized anxiety disorder. J Psychiatr Res 2023; 167:23-31. [PMID: 37820447 DOI: 10.1016/j.jpsychires.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/16/2023] [Accepted: 10/05/2023] [Indexed: 10/13/2023]
Abstract
Comorbidity has been frequently observed between generalized anxiety disorder (GAD) and major depressive disorder (MDD), however, common and distinguishable alterations in the topological organization of functional brain networks remain poorly understood. We sought to determine a robust and sensitive functional connectivity marker for diagnostic classification and symptom severity prediction. Multi-layered dynamic functional connectivity including whole brain, network-node and node-node layers via graph theory and gradient analyses were applied to functional MRI resting-state data obtained from 31 unmedicated GAD and 34 unmedicated MDD patients as well as 33 age and education matched healthy controls (HC). GAD and MDD symptoms were assessed using Penn State Worry Questionnaire and Beck Depression Inventory II, respectively. Three network measures including global properties (i.e., global efficiency, characteristic path length), regional nodal property (i.e., degree) and connectivity gradients were computed. Results showed that both patient groups exhibited abnormal dynamic cortico-subcortical topological organization compared to healthy controls, with MDD > GAD > HC in degree of randomization. Furthermore, our multi-layered dynamic functional connectivity network model reached 77% diagnostic accuracy between GAD and MDD and was highly predictive of symptom severity, respectively. Gradients of functional connectivity for superior frontal cortex-subcortical regions, middle temporal gyrus-subcortical regions and amygdala-cortical regions contributed more in this model compared to other gradients. We found shared and distinct cortico-subcortical connectivity features in dynamic functional brain networks between GAD and MDD, which together can promote the understanding of common and disorder-specific topological organization dysregulations and facilitate early neuroimaging-based diagnosis.
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Affiliation(s)
- Qi Liu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Bo Zhou
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Xiaodong Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Peng Qing
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Xinqi Zhou
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, 610066, China
| | - Feng Zhou
- Faculty of Psychology, Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, 400715, China
| | - Xiaolei Xu
- School of Psychology, Shandong Normal University, Jinan, 250014, China
| | - Siyu Zhu
- School of Sport Training, Chengdu Sport University, Chengdu, 610041, China
| | - Jing Dai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yulan Huang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Jinyu Wang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Zhili Zou
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Pokfulam, Hong Kong; Department of Psychology, The University of Hong Kong, Hong Kong, Pokfulam, Hong Kong; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Weihua Zhao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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25
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Yadav Y, Elumalai P, Williams N, Jost J, Samal A. Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks. Front Aging Neurosci 2023; 15:1120846. [PMID: 37293668 PMCID: PMC10244515 DOI: 10.3389/fnagi.2023.1120846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored. Methods We apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225). Results We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes-movement, affective processing, and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults. Discussion Our results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organization of functional connectivity networks, both in health and disease.
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Affiliation(s)
- Yasharth Yadav
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Indian Institute of Science Education and Research (IISER), Pune, India
| | | | - Nitin Williams
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Espoo, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- The Santa Fe Institute, Santa Fe, NM, United States
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Homi Bhabha National Institute (HBNI), Mumbai, India
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26
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Sassenberg TA, Burton PC, Mwilambwe-Tshilobo L, Jung RE, Rustichini A, Spreng RN, DeYoung CG. Conscientiousness associated with efficiency of the salience/ventral attention network: Replication in three samples using individualized parcellation. Neuroimage 2023; 272:120081. [PMID: 37011715 PMCID: PMC10132286 DOI: 10.1016/j.neuroimage.2023.120081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 03/21/2023] [Accepted: 03/31/2023] [Indexed: 04/03/2023] Open
Abstract
Conscientiousness, and related constructs impulsivity and self-control, have been related to structural and functional properties of regions in the prefrontal cortex (PFC) and anterior insula. Network-based conceptions of brain function suggest that these regions belong to a single large-scale network, labeled the salience/ventral attention network (SVAN). The current study tested associations between conscientiousness and resting-state functional connectivity in this network using two community samples (N's = 244 and 239) and data from the Human Connectome Project (N = 1000). Individualized parcellation was used to improve functional localization accuracy and facilitate replication. Functional connectivity was measured using an index of network efficiency, a graph theoretical measure quantifying the capacity for parallel information transfer within a network. Efficiency of a set of parcels in the SVAN was significantly associated with conscientiousness in all samples. Findings are consistent with a theory of conscientiousness as a function of variation in neural networks underlying effective prioritization of goals.
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Affiliation(s)
- Tyler A Sassenberg
- Department of Psychology, University of Minnesota, N616 Elliott Hall, 75 East River Parkway, Minneapolis, MN 55455, USA.
| | - Philip C Burton
- Department of Psychology, University of Minnesota, N616 Elliott Hall, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Laetitia Mwilambwe-Tshilobo
- Department of Psychology, Princeton University, USA; University of Pennsylvania, Annenberg School for Communication, USA
| | - Rex E Jung
- Department of Neurosurgery, University of New Mexico, USA
| | | | - R Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Canada
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, N616 Elliott Hall, 75 East River Parkway, Minneapolis, MN 55455, USA
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27
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Martin S, Williams KA, Saur D, Hartwigsen G. Age-related reorganization of functional network architecture in semantic cognition. Cereb Cortex 2023; 33:4886-4903. [PMID: 36190445 PMCID: PMC10110455 DOI: 10.1093/cercor/bhac387] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/02/2022] [Accepted: 09/03/2022] [Indexed: 11/15/2022] Open
Abstract
Cognitive aging is associated with widespread neural reorganization processes in the human brain. However, the behavioral impact of such reorganization is not well understood. The current neuroimaging study investigated age differences in the functional network architecture during semantic word retrieval in young and older adults. Combining task-based functional connectivity, graph theory and cognitive measures of fluid and crystallized intelligence, our findings show age-accompanied large-scale network reorganization even when older adults have intact word retrieval abilities. In particular, functional networks of older adults were characterized by reduced decoupling between systems, reduced segregation and efficiency, and a larger number of hub regions relative to young adults. Exploring the predictive utility of these age-related changes in network topology revealed high, albeit less efficient, performance for older adults whose brain graphs showed stronger dedifferentiation and reduced distinctiveness. Our results extend theoretical accounts on neurocognitive aging by revealing the compensational potential of the commonly reported pattern of network dedifferentiation when older adults can rely on their prior knowledge for successful task processing. However, we also demonstrate the limitations of such compensatory reorganization and show that a youth-like network architecture in terms of balanced integration and segregation is associated with more economical processing.
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Affiliation(s)
- Sandra Martin
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Language & Aphasia Laboratory, Department of Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Kathleen A Williams
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Dorothee Saur
- Language & Aphasia Laboratory, Department of Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
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28
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Menon DK, Bor D, Stamatakis EA. Reduced emergent character of neural dynamics in patients with a disrupted connectome. Neuroimage 2023; 269:119926. [PMID: 36740030 PMCID: PMC9989666 DOI: 10.1016/j.neuroimage.2023.119926] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Leverhulme Centre for the Future of Intelligence, Cambridge, UK; The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Brain Science, Center for Psychedelic Research, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Center for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK; Department of Psychology, Queen Mary University of London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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29
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Xu S, Zhang Z, Li L, Zhou Y, Lin D, Zhang M, Zhang L, Huang G, Liu X, Becker B, Liang Z. Functional connectivity profiles of the default mode and visual networks reflect temporal accumulative effects of sustained naturalistic emotional experience. Neuroimage 2023; 269:119941. [PMID: 36791897 DOI: 10.1016/j.neuroimage.2023.119941] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/30/2023] [Accepted: 02/11/2023] [Indexed: 02/15/2023] Open
Abstract
Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.
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Affiliation(s)
- Shuyue Xu
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhiguo Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China; Peng Cheng Laboratory, Shenzhen 518055, China; Marshall Laboratory of Biomedical Engineering, Shenzhen 518060, China
| | - Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Yongjie Zhou
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, Shenzhen, China
| | - Danyi Lin
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Min Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China
| | - Li Zhang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Gan Huang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Xiqin Liu
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, MOE Key Laboratory for Neuroinformation, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Benjamin Becker
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, MOE Key Laboratory for Neuroinformation, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Zhen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China; Marshall Laboratory of Biomedical Engineering, Shenzhen 518060, China.
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30
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Wu H, Xie Q, Pan J, Liang Q, Lan Y, Guo Y, Han J, Xie M, Liu Y, Jiang L, Wu X, Li Y, Qin P. Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability. Neuroimage 2023; 272:120050. [PMID: 36963740 DOI: 10.1016/j.neuroimage.2023.120050] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/04/2023] [Accepted: 03/21/2023] [Indexed: 03/26/2023] Open
Abstract
Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.
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Affiliation(s)
- Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Qiuyou Xie
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China; Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China
| | - Jiahui Pan
- School of Software, South China Normal University, Foshan, 528225, China; Pazhou Lab, Guangzhou, 510330, China
| | - Qimei Liang
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yue Lan
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yequn Guo
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Musi Xie
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Yueyao Liu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Liubei Jiang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Xuehai Wu
- Pazhou Lab, Guangzhou, 510330, China; Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200433, China.
| | - Yuanqing Li
- Pazhou Lab, Guangzhou, 510330, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China; Pazhou Lab, Guangzhou, 510330, China.
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31
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Luppi AI, Singleton SP, Hansen JY, Bzdok D, Kuceyeski A, Betzel RF, Misic B. Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532981. [PMID: 36993597 PMCID: PMC10055141 DOI: 10.1101/2023.03.16.532981] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain's network architecture. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies.
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Affiliation(s)
- Andrea I. Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Justine Y. Hansen
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Danilo Bzdok
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- MILA, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, U.S.A
| | - Richard F. Betzel
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, U.S.A
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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32
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Vandewouw MM, Brian J, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data. JAMA Netw Open 2023; 6:e232066. [PMID: 36912839 PMCID: PMC10011941 DOI: 10.1001/jamanetworkopen.2023.2066] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/22/2023] [Indexed: 03/14/2023] Open
Abstract
Importance Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings. Objective To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets. Design, Setting, and Participants This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study. Main Outcomes and Measures The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested. Results Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set. Conclusions and Relevance The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.
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Affiliation(s)
- Marlee M. Vandewouw
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Brian
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell J. Schachar
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Robert Nicolson
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Muhammad Ayub
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Margot J. Taylor
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Jason P. Lerch
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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Dimitriadis SI. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths. Neuroinformatics 2023; 21:71-88. [PMID: 36372844 DOI: 10.1007/s12021-022-09610-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/15/2022]
Abstract
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de PonentPasseig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Integrative Neuroimaging Lab, 55133, Thessaloniki, Greece.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Wales, CF24 4HQ, Cardiff, UK.
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University Brain Research Imaging Centre (CUBRIC), CF24 4HQ, Cardiff, Wales, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, CF24 4HQ, Cardiff, Wales, UK.
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
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Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation. Neuroimage 2023; 265:119782. [PMID: 36464098 DOI: 10.1016/j.neuroimage.2022.119782] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/08/2022] [Accepted: 11/30/2022] [Indexed: 12/04/2022] Open
Abstract
Integration and segregation are two fundamental principles of brain organization. The brain manages the transitions and balance between different functional segregated or integrated states through neuromodulatory systems. Recently, computational and experimental studies suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology in healthy subjects during resting-state conditions and during an attentional task. Then, we employed a whole-brain neural mass model interconnected using a human connectome to simulate the effects of nicotine and investigate causal mechanisms for these changes. The drug effect was modeled decreasing both the global coupling and local feedback inhibition parameters, consistent with the known cellular effects of acetylcholine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during the task, but not in the resting state. In-task performance correlates with functional segregation, establishing a link between functional network topology and behavior. Furthermore, we found in the empirical data that the regional density of the nicotinic acetylcholine α4β2 correlates with the decrease in functional nodal strength by nicotine during the task. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks.
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Kozak S, Dezachyo O, Stanford W, Bar-Haim Y, Censor N, Dayan E. Elevated integration within the reward network underlies vulnerability to distress. Cereb Cortex 2022; 33:5797-5807. [PMID: 36453462 DOI: 10.1093/cercor/bhac460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 12/05/2022] Open
Abstract
Abstract
Distress tolerance (DT), the capability to persist under negative circumstances, underlies a range of psychopathologies. It has been proposed that DT may originate from the activity and connectivity in diverse neural networks integrated by the reward system. To test this hypothesis, we examined the link between DT and integration and segregation in the reward network as derived from resting-state functional connectivity data. DT was measured in 147 participants from a large community sample using the Behavioral Indicator of Resiliency to Distress task. Prior to DT evaluation, participants underwent a resting-state functional magnetic resonance imaging scan. For each participant, we constructed a whole-brain functional connectivity network and calculated the degree of reward network integration and segregation based on the extent to which reward network nodes showed functional connections within and outside their network. We found that distress-intolerant participants demonstrated heightened reward network integration relative to the distress-tolerant participants. In addition, these differences in integration were higher relative to the rest of the brain and, more specifically, the somatomotor network, which has been implicated in impulsive behavior. These findings support the notion that increased integration in large-scale brain networks may constitute a risk for distress intolerance and its psychopathological correlates.
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Affiliation(s)
- Stas Kozak
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Or Dezachyo
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - William Stanford
- Biological & Biomedical Sciences Program, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599 , United States
| | - Yair Bar-Haim
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Nitzan Censor
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599 , United States
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36
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Singleton SP, Luppi AI, Carhart-Harris RL, Cruzat J, Roseman L, Nutt DJ, Deco G, Kringelbach ML, Stamatakis EA, Kuceyeski A. Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain's control energy landscape. Nat Commun 2022; 13:5812. [PMID: 36192411 PMCID: PMC9530221 DOI: 10.1038/s41467-022-33578-1] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/22/2022] [Indexed: 11/09/2022] Open
Abstract
Psychedelics including lysergic acid diethylamide (LSD) and psilocybin temporarily alter subjective experience through their neurochemical effects. Serotonin 2a (5-HT2a) receptor agonism by these compounds is associated with more diverse (entropic) brain activity. We postulate that this increase in entropy may arise in part from a flattening of the brain's control energy landscape, which can be observed using network control theory to quantify the energy required to transition between recurrent brain states. Using brain states derived from existing functional magnetic resonance imaging (fMRI) datasets, we show that LSD and psilocybin reduce control energy required for brain state transitions compared to placebo. Furthermore, across individuals, reduction in control energy correlates with more frequent state transitions and increased entropy of brain state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors (obtained from publicly available positron emission tomography (PET) data under non-drug conditions), we demonstrate an association between the 5-HT2a receptor and reduced control energy. Our findings provide evidence that 5-HT2a receptor agonist compounds allow for more facile state transitions and more temporally diverse brain activity. More broadly, we demonstrate that receptor-informed network control theory can model the impact of neuropharmacological manipulation on brain activity dynamics.
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Affiliation(s)
- S Parker Singleton
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.
| | - Andrea I Luppi
- Division of Anesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
- Psychedelics Division, Neuroscape, University of California San Francisco, San Francisco, CA, USA
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, Spain
| | - Leor Roseman
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
| | - David J Nutt
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC, Australia
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center of Music in the Brain (MIB), Clinical Medicine, Aarhus University, Aarhus, Denmark
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Emmanuel A Stamatakis
- Division of Anesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Amy Kuceyeski
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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37
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A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01674-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2022]
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38
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Zhao H, Wen W, Cheng J, Jiang J, Kochan N, Niu H, Brodaty H, Sachdev P, Liu T. An accelerated degeneration of white matter microstructure and networks in the nondemented old-old. Cereb Cortex 2022; 33:4688-4698. [PMID: 36178117 DOI: 10.1093/cercor/bhac372] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 11/12/2022] Open
Abstract
The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.
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Affiliation(s)
- Haichao Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
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39
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Roffet F, Delrieux C, Patow G. Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory. Brain Sci 2022; 12:brainsci12091219. [PMID: 36138956 PMCID: PMC9496818 DOI: 10.3390/brainsci12091219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 11/17/2022] Open
Abstract
Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.
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Affiliation(s)
- Facundo Roffet
- Department of Electrical and Computer Engineering (DIEC), Universidad Nacional del Sur, Bahía Blanca AR-B8000, Argentina
| | - Claudio Delrieux
- Department of Electrical and Computer Engineering (DIEC), Universidad Nacional del Sur and National Council for Scientific and Technical Research (CONICET), Bahía Blanca AR-B8000, Argentina
| | - Gustavo Patow
- ViRVIG, University of Girona, 17003 Girona, Spain
- Correspondence:
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40
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Zarkali A, Luppi AI, Stamatakis EA, Reeves S, McColgan P, Leyland LA, Lees AJ, Weil RS. Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson's disease. Commun Biol 2022; 5:928. [PMID: 36075964 PMCID: PMC9458713 DOI: 10.1038/s42003-022-03903-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/25/2022] [Indexed: 11/09/2022] Open
Abstract
Hallucinations are a core feature of psychosis and common in Parkinson's. Their transient, unexpected nature suggests a change in dynamic brain states, but underlying causes are unknown. Here, we examine temporal dynamics and underlying structural connectivity in Parkinson's-hallucinations using a combination of functional and structural MRI, network control theory, neurotransmitter density and genetic analyses. We show that Parkinson's-hallucinators spent more time in a predominantly Segregated functional state with fewer between-state transitions. The transition from integrated-to-segregated state had lower energy cost in Parkinson's-hallucinators; and was therefore potentially preferable. The regional energy needed for this transition was correlated with regional neurotransmitter density and gene expression for serotoninergic, GABAergic, noradrenergic and cholinergic, but not dopaminergic, receptors. We show how the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations and highlight potential therapeutic targets by linking these changes to neurotransmitter systems involved in early sensory and complex visual processing.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK.
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Suzanne Reeves
- Division of Psychiatry, University College London, 149 Tottenham Court Rd, London, W1T 7BN, 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, University College London, 1 Wakefield Street, London, WC1N 1PJ, 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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41
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Luppi AI, Mediano PAM, Rosas FE, Holland N, Fryer TD, O'Brien JT, Rowe JB, Menon DK, Bor D, Stamatakis EA. A synergistic core for human brain evolution and cognition. Nat Neurosci 2022; 25:771-782. [PMID: 35618951 PMCID: PMC7614771 DOI: 10.1038/s41593-022-01070-0] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 03/30/2022] [Indexed: 12/11/2022]
Abstract
How does the organization of neural information processing enable humans' sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional and structural neuroimaging with meta-analytic results, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-order brain networks. The human brain leverages synergistic information to a greater extent than nonhuman primates, with high-synergy association cortices exhibiting the highest degree of evolutionary cortical expansion. Synaptic density mapping from positron emission tomography and convergent molecular and metabolic evidence demonstrate that synergistic interactions are supported by receptor diversity and human-accelerated genes underpinning synaptic function. This information-resolved approach provides analytic tools to disentangle information integration from coupling, enabling richer, more accurate interpretations of functional connectivity, and illuminating how the human neurocognitive architecture navigates the trade-off between robustness and integration.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
- Data Science Institute, Imperial College London, London, UK
- Center for Complexity Science, Imperial College London, London, UK
| | - Negin Holland
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Tim D Fryer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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42
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Al-Shargie F, Katmah R, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H. Stress management using fNIRS and binaural beats stimulation. BIOMEDICAL OPTICS EXPRESS 2022; 13:3552-3575. [PMID: 35781942 PMCID: PMC9208616 DOI: 10.1364/boe.455097] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/21/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs. We assessed the four cognitive conditions using salivary alpha-amylase, behavioral responses, and Functional Near-Infrared Spectroscopy (fNIRS). We quantified the vigilance and stress levels using the reaction time (RT) to stimuli, accuracy of detection, and the functional connectivity metrics of the fNIRS estimated by Phase Locking Values (PLV). We propose using the orthogonal minimum spanning tree (OMST) to determine the true connectivity network patterns of the PLV. Our results show that listening to 16-Hz BBs has significantly reduced the level of alpha amylase by 44%, reduced the RT to stimuli by 20% and increased the accuracy of target detection by 25%, (p < 0.001). The analysis of the connectivity network across the four different cognitive conditions revealed several statistically significant trends. Specifically, a significant increase in connectivity between the right and left dorsolateral prefrontal cortex (DLPFC) areas and left orbitofrontal cortex was found during the vigilance enhancement condition compared to the high vigilance. Likewise, similar patterns were found between the right and left DLPFC, orbitofrontal cortex, right ventrolateral prefrontal cortex (VLPFC) and right frontopolar PFC (prefrontal cortex) area during stress mitigation compared to mental stress. Furthermore, the connectivity network under stress condition alone showed significant connectivity increase between the VLPFC and DLPFC compared to other areas. The laterality index demonstrated left frontal laterality under high vigilance and VE conditions, and right DLPFC and left frontopolar PFC while under mental stress. Overall, our results showed that BBs can be used for vigilance enhancement and stress mitigation.
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Affiliation(s)
- Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Rateb Katmah
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Usman Tariq
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Fabio Babiloni
- Department Molecular Medicine, University of Sapienza Rome, 00185 Rome, Italy
| | - Fadwa Al-Mughairbi
- Department of Clinical Psychology, College of Medicines and Health Sciences, UAE University, Al Ain, United Arab Emirates
| | - Hasan Al-Nashash
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
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43
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Elumalai P, Yadav Y, Williams N, Saucan E, Jost J, Samal A. Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder. Sci Rep 2022; 12:8295. [PMID: 35585156 PMCID: PMC9117309 DOI: 10.1038/s41598-022-12171-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/04/2022] [Indexed: 11/20/2022] Open
Abstract
While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman-Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.
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Affiliation(s)
| | - Yasharth Yadav
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Indian Institute of Science Education and Research (IISER), Pune, India
| | - Nitin Williams
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Espoo, Finland.
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Emil Saucan
- Department of Applied Mathematics, ORT Braude College, Karmiel, Israel
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- The Santa Fe Institute, Santa Fe, NM, USA
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India.
- Homi Bhabha National Institute (HBNI), Mumbai, India.
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44
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness. Commun Biol 2022; 5:384. [PMID: 35444252 PMCID: PMC9021270 DOI: 10.1038/s42003-022-03330-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 03/30/2022] [Indexed: 12/02/2022] Open
Abstract
The human brain entertains rich spatiotemporal dynamics, which are drastically reconfigured when consciousness is lost due to anaesthesia or disorders of consciousness (DOC). Here, we sought to identify the neurobiological mechanisms that explain how transient pharmacological intervention and chronic neuroanatomical injury can lead to common reconfigurations of neural activity. We developed and systematically perturbed a neurobiologically realistic model of whole-brain haemodynamic signals. By incorporating PET data about the cortical distribution of GABA receptors, our computational model reveals a key role of spatially-specific local inhibition for reproducing the functional MRI activity observed during anaesthesia with the GABA-ergic agent propofol. Additionally, incorporating diffusion MRI data obtained from DOC patients reveals that the dynamics that characterise loss of consciousness can also emerge from randomised neuroanatomical connectivity. Our results generalise between anaesthesia and DOC datasets, demonstrating how increased inhibition and connectome perturbation represent distinct neurobiological paths towards the characteristic activity of the unconscious brain.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
- Data Science Institute, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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45
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Scharwächter L, Schmitt FJ, Pallast N, Fink GR, Aswendt M. Network analysis of neuroimaging in mice. Neuroimage 2022; 253:119110. [PMID: 35311664 DOI: 10.1016/j.neuroimage.2022.119110] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 10/18/2022] Open
Abstract
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
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Affiliation(s)
- Leon Scharwächter
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Felix J Schmitt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany
| | - Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
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46
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Hipólito I. Cognition Without Neural Representation: Dynamics of a Complex System. Front Psychol 2022; 12:643276. [PMID: 35095629 PMCID: PMC8789682 DOI: 10.3389/fpsyg.2021.643276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 10/31/2021] [Indexed: 12/26/2022] Open
Abstract
This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).
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Affiliation(s)
- Inês Hipólito
- Berlin School of Mind and Brain, Institut für Philosophie, Humboldt-Universität zu Berlin, Berlin, Germany
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Pourmotabbed H, de Jongh Curry AL, Clarke DF, Tyler-Kabara EC, Babajani-Feremi A. Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces. Hum Brain Mapp 2022; 43:1342-1357. [PMID: 35019189 PMCID: PMC8837594 DOI: 10.1002/hbm.25726] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/29/2021] [Accepted: 11/11/2021] [Indexed: 11/30/2022] Open
Abstract
Prior studies have used graph analysis of resting‐state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test–retest reliability and sensor versus source association of global graph measures. Atlas‐based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage‐corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.
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Affiliation(s)
- Haatef Pourmotabbed
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.,Magnetoencephalography Laboratory, Dell Children's Medical Center, Austin, Texas, USA.,Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA
| | - Amy L de Jongh Curry
- Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA
| | - Dave F Clarke
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
| | - Elizabeth C Tyler-Kabara
- Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
| | - Abbas Babajani-Feremi
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.,Magnetoencephalography Laboratory, Dell Children's Medical Center, Austin, Texas, USA.,Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
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48
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Luppi AI, Golkowski D, Ranft A, Ilg R, Jordan D, Menon DK, Stamatakis EA. Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane. Hum Brain Mapp 2021; 42:2802-2822. [PMID: 33738899 PMCID: PMC8127159 DOI: 10.1002/hbm.25405] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 02/10/2021] [Accepted: 02/27/2021] [Indexed: 12/22/2022] Open
Abstract
The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.
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Affiliation(s)
- Andrea I. Luppi
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Daniel Golkowski
- Department of Neurology, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - Andreas Ranft
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - Rüdiger Ilg
- Department of Neurology, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
- Department of NeurologyAsklepios ClinicBad TölzGermany
| | - Denis Jordan
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - David K. Menon
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Wolfon Brain Imaging CentreUniversity of CambridgeCambridgeUK
| | - Emmanuel A. Stamatakis
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
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Luppi AI, Craig MM, Coppola P, Peattie ARD, Finoia P, Williams GB, Allanson J, Pickard JD, Menon DK, Stamatakis EA. Preserved fractal character of structural brain networks is associated with covert consciousness after severe brain injury. Neuroimage Clin 2021; 30:102682. [PMID: 34215152 PMCID: PMC8102619 DOI: 10.1016/j.nicl.2021.102682] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 03/30/2021] [Accepted: 04/18/2021] [Indexed: 12/24/2022]
Abstract
Self-similarity is ubiquitous throughout natural phenomena, including the human brain. Recent evidence indicates that fractal dimension of functional brain networks, a measure of self-similarity, is diminished in patients diagnosed with disorders of consciousness arising from severe brain injury. Here, we set out to investigate whether loss of self-similarity is observed in the structural connectome of patients with disorders of consciousness. Using diffusion MRI tractography from N = 11 patients in a minimally conscious state (MCS), N = 10 patients diagnosed with unresponsive wakefulness syndrome (UWS), and N = 20 healthy controls, we show that fractal dimension of structural brain networks is diminished in DOC patients. Remarkably, we also show that fractal dimension of structural brain networks is preserved in patients who exhibit evidence of covert consciousness by performing mental imagery tasks during functional MRI scanning. These results demonstrate that differences in fractal dimension of structural brain networks are quantitatively associated with chronic loss of consciousness induced by severe brain injury, highlighting the close connection between structural organisation of the human brain and its ability to support cognitive function.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom.
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, United Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, United Kingdom
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, United Kingdom
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
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