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Popescu M, Popescu EA, DeGraba TJ, Hughes JD. Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes. Clin Neurophysiol 2024; 163:22-36. [PMID: 38669765 DOI: 10.1016/j.clinph.2024.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/27/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024]
Abstract
OBJECTIVE Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD). METHODS Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns. RESULTS PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity. CONCLUSIONS Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology. SIGNIFICANCE Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD.
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Affiliation(s)
- Mihai Popescu
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Elena-Anda Popescu
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Thomas J DeGraba
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - John D Hughes
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA; Behavioral Biology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
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2
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da Silva Castanheira J, Wiesman AI, Hansen JY, Misic B, Baillet S. The neurophysiological brain-fingerprint of Parkinson's disease. EBioMedicine 2024; 105:105201. [PMID: 38908100 DOI: 10.1016/j.ebiom.2024.105201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/24/2024] Open
Abstract
BACKGROUND Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). METHODS We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. FINDINGS The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. INTERPRETATION The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. FUNDING Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).
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Affiliation(s)
| | - Alex I Wiesman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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3
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Vogel JW, Alexander-Bloch AF, Wagstyl K, Bertolero MA, Markello RD, Pines A, Sydnor VJ, Diaz-Papkovich A, Hansen JY, Evans AC, Bernhardt B, Misic B, Satterthwaite TD, Seidlitz J. Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain. Proc Natl Acad Sci U S A 2024; 121:e2219137121. [PMID: 38861593 PMCID: PMC11194492 DOI: 10.1073/pnas.2219137121] [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: 11/08/2022] [Accepted: 04/27/2024] [Indexed: 06/13/2024] Open
Abstract
Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, whether similar gradients are maintained in the adult brain remains unknown. Here, we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral, and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately reconstructs the position of brain tissue samples, ii) delineates known functional territories, and iii) can model the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical axes differentiating the primary sensory cortex from the association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two nonhuman primate datasets and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well-known transcription factors (e.g., PAX6 and SIX3), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.
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Affiliation(s)
- Jacob W. Vogel
- Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden202 13
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA19104
- Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA19104
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, LondonWC1N 3AR, United Kingdom
| | - Maxwell A. Bertolero
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
| | - Ross D. Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Adam Pines
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA94305
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
| | - Alex Diaz-Papkovich
- Quantitative Life Sciences, McGill University, Montreal, QCH3A 1E3, Canada
- McGill Genome Centre, McGill University, Montreal, QCH3A 0G1, Canada
| | - Justine Y. Hansen
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Alan C. Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA19104
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA19104
- Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA19104
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Luo Z, Yin E, Zeng LL, Shen H, Su J, Peng L, Yan Y, Hu D. Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas. iScience 2024; 27:109206. [PMID: 38439977 PMCID: PMC10910261 DOI: 10.1016/j.isci.2024.109206] [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: 07/31/2023] [Revised: 11/24/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.
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Affiliation(s)
- Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
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5
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Betzel R, Puxeddu MG, Seguin C, Bazinet V, Luppi A, Podschun A, Singleton SP, Faskowitz J, Parakkattu V, Misic B, Markett S, Kuceyeski A, Parkes L. Controlling the human connectome with spatially diffuse input signals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.581006. [PMID: 38463980 PMCID: PMC10925126 DOI: 10.1101/2024.02.27.581006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.
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Affiliation(s)
- Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
- Program in Neuroscience, Indiana University, Bloomington IN 47401
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Andrea Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | | | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vibin Parakkattu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY
- Department of Computational Biology, Cornell University, Ithaca, NY
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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6
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Eisenhauer S, Gonzalez Alam TRDJ, Cornelissen PL, Smallwood J, Jefferies E. Individual word representations dissociate from linguistic context along a cortical unimodal to heteromodal gradient. Hum Brain Mapp 2024; 45:e26607. [PMID: 38339897 PMCID: PMC10836172 DOI: 10.1002/hbm.26607] [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: 04/24/2023] [Revised: 11/30/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024] Open
Abstract
Language comprehension involves multiple hierarchical processing stages across time, space, and levels of representation. When processing a word, the sensory input is transformed into increasingly abstract representations that need to be integrated with the linguistic context. Thus, language comprehension involves both input-driven as well as context-dependent processes. While neuroimaging research has traditionally focused on mapping individual brain regions to the distinct underlying processes, recent studies indicate that whole-brain distributed patterns of cortical activation might be highly relevant for cognitive functions, including language. One such pattern, based on resting-state connectivity, is the 'principal cortical gradient', which dissociates sensory from heteromodal brain regions. The present study investigated the extent to which this gradient provides an organizational principle underlying language function, using a multimodal neuroimaging dataset of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings from 102 participants during sentence reading. We found that the brain response to individual representations of a word (word length, orthographic distance, and word frequency), which reflect visual; orthographic; and lexical properties, gradually increases towards the sensory end of the gradient. Although these properties showed opposite effect directions in fMRI and MEG, their association with the sensory end of the gradient was consistent across both neuroimaging modalities. In contrast, MEG revealed that properties reflecting a word's relation to its linguistic context (semantic similarity and position within the sentence) involve the heteromodal end of the gradient to a stronger extent. This dissociation between individual word and contextual properties was stable across earlier and later time windows during word presentation, indicating interactive processing of word representations and linguistic context at opposing ends of the principal gradient. To conclude, our findings indicate that the principal gradient underlies the organization of a range of linguistic representations while supporting a gradual distinction between context-independent and context-dependent representations. Furthermore, the gradient reveals convergent patterns across neuroimaging modalities (similar location along the gradient) in the presence of divergent responses (opposite effect directions).
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Affiliation(s)
- Susanne Eisenhauer
- Department of PsychologyUniversity of YorkYorkUK
- York Neuroimaging Centre, Innovation WayYorkUK
| | | | | | | | - Elizabeth Jefferies
- Department of PsychologyUniversity of YorkYorkUK
- York Neuroimaging Centre, Innovation WayYorkUK
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7
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da Silva Castanheira J, Wiesman AI, Hansen JY, Misic B, Baillet S. The neurophysiological brain-fingerprint of Parkinson's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.03.23285441. [PMID: 36798232 PMCID: PMC9934726 DOI: 10.1101/2023.02.03.23285441] [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] [Indexed: 02/10/2023]
Abstract
In this study, we investigate the clinical potential of brain-fingerprints derived from electrophysiological brain activity for diagnostics and progression monitoring of Parkinson's disease (PD). We obtained brain-fingerprints from PD patients and age-matched healthy controls using short, task-free magnetoencephalographic recordings. The rhythmic components of the individual brain-fingerprint distinguished between patients and healthy participants with approximately 90% accuracy. The most prominent cortical features of the Parkinson's brain-fingerprint mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also show that Parkinson's disease stages can be decoded directly from cortical neurophysiological activity. Additionally, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. We further demonstrate that the arrhythmic components of cortical activity are more variable over short periods of time in patients with Parkinson's disease than in healthy controls, making individual differentiation between patients based on these features more challenging and explaining previous negative published results. Overall, we outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and clinical staging of Parkinson's disease. For this reason, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification and to the improved identification and testing of therapeutic neurostimulation targets.
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Affiliation(s)
| | - Alex I. Wiesman
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Justine Y. Hansen
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
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8
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Hansen JY, Cauzzo S, Singh K, García-Gomar MG, Shine JM, Bianciardi M, Misic B. Integrating brainstem and cortical functional architectures. RESEARCH SQUARE 2023:rs.3.rs-3569352. [PMID: 38076888 PMCID: PMC10705693 DOI: 10.21203/rs.3.rs-3569352/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
The brainstem is a fundamental component of the central nervous system yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. Here we use high-resolution 7 Tesla fMRI to derive a functional connectome encompassing cortex as well as 58 brainstem nuclei spanning the midbrain, pons and medulla. We identify a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as multiple emergent phenomena including neurophysiological oscillatory rhythms, patterns of cognitive functional specialization, and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicate all findings using 3 Tesla data from the same participants. Collectively, we find that multiple organizational features of cortical activity can be traced back to the brainstem.
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Affiliation(s)
- Justine Y. Hansen
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Simone Cauzzo
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Parkinson’s Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Kavita Singh
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - María Guadalupe García-Gomar
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Escuela Nacional de Estudios Superiores, Unidad Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | - James M. Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Marta Bianciardi
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Harvard University, Boston, MA, USA
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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9
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Betzel RF, Faskowitz J, Sporns O. Living on the edge: network neuroscience beyond nodes. Trends Cogn Sci 2023; 27:1068-1084. [PMID: 37716895 PMCID: PMC10592364 DOI: 10.1016/j.tics.2023.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/14/2023] [Accepted: 08/10/2023] [Indexed: 09/18/2023]
Abstract
Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.
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Affiliation(s)
- Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
| | - Joshua Faskowitz
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
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10
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Hansen JY, Cauzzo S, Singh K, García-Gomar MG, Shine JM, Bianciardi M, Misic B. Integrating brainstem and cortical functional architectures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.564245. [PMID: 37961347 PMCID: PMC10634864 DOI: 10.1101/2023.10.26.564245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The brainstem is a fundamental component of the central nervous system yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. Here we use high-resolution 7 Tesla fMRI to derive a functional connectome encompassing cortex as well as 58 brainstem nuclei spanning the midbrain, pons and medulla. We identify a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as multiple emergent phenomena including neurophysiological oscillatory rhythms, patterns of cognitive functional specialization, and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicate all findings using 3 Tesla data from the same participants. Collectively, we find that multiple organizational features of cortical activity can be traced back to the brainstem.
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Affiliation(s)
- Justine Y. Hansen
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Simone Cauzzo
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Parkinson’s Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Kavita Singh
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - María Guadalupe García-Gomar
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Escuela Nacional de Estudios Superiores, Unidad Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | - James M. Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Marta Bianciardi
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Harvard University, Boston, MA, USA
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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11
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Vo A, Tremblay C, Rahayel S, Shafiei G, Hansen JY, Yau Y, Misic B, Dagher A. Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson's disease. Neuroimage Clin 2023; 40:103523. [PMID: 38016407 PMCID: PMC10687705 DOI: 10.1016/j.nicl.2023.103523] [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: 06/15/2023] [Revised: 09/30/2023] [Accepted: 10/05/2023] [Indexed: 11/30/2023]
Abstract
Parkinson's disease pathology is hypothesized to spread through the brain via axonal connections between regions and is further modulated by local vulnerabilities within those regions. The resulting changes to brain morphology have previously been demonstrated in both prodromal and de novo Parkinson's disease patients. However, it remains unclear whether the pattern of atrophy progression in Parkinson's disease over time is similarly explained by network-based spreading and local vulnerability. We address this gap by mapping the trajectory of cortical atrophy rates in a large, multi-centre cohort of Parkinson's disease patients and relate this atrophy progression pattern to network architecture and gene expression profiles. Across 4-year follow-up visits, increased atrophy rates were observed in posterior, temporal, and superior frontal cortices. We demonstrated that this progression pattern was shaped by network connectivity. Regional atrophy rates were strongly related to atrophy rates across structurally and functionally connected regions. We also found that atrophy progression was associated with specific gene expression profiles. The genes whose spatial distribution in the brain was most related to atrophy rate were those enriched for mitochondrial and metabolic function. Taken together, our findings demonstrate that both global and local brain features influence vulnerability to neurodegeneration in Parkinson's disease.
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Affiliation(s)
- Andrew Vo
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Christina Tremblay
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Shady Rahayel
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada; Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montréal, Canada
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yvonne Yau
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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12
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Milisav F, Bazinet V, Iturria-Medina Y, Misic B. Resolving inter-regional communication capacity in the human connectome. Netw Neurosci 2023; 7:1051-1079. [PMID: 37781139 PMCID: PMC10473316 DOI: 10.1162/netn_a_00318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/03/2023] [Indexed: 10/03/2023] Open
Abstract
Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.
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Affiliation(s)
- Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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13
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Hansen JY, Shafiei G, Voigt K, Liang EX, Cox SML, Leyton M, Jamadar SD, Misic B. Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease. PLoS Biol 2023; 21:e3002314. [PMID: 37747886 PMCID: PMC10553842 DOI: 10.1371/journal.pbio.3002314] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 10/05/2023] [Accepted: 08/28/2023] [Indexed: 09/27/2023] Open
Abstract
The brain is composed of disparate neural populations that communicate and interact with one another. Although fiber bundles, similarities in molecular architecture, and synchronized neural activity all reflect how brain regions potentially interact with one another, a comprehensive study of how all these interregional relationships jointly reflect brain structure and function remains missing. Here, we systematically integrate 7 multimodal, multiscale types of interregional similarity ("connectivity modes") derived from gene expression, neurotransmitter receptor density, cellular morphology, glucose metabolism, haemodynamic activity, and electrophysiology in humans. We first show that for all connectivity modes, feature similarity decreases with distance and increases when regions are structurally connected. Next, we show that connectivity modes exhibit unique and diverse connection patterns, hub profiles, spatial gradients, and modular organization. Throughout, we observe a consistent primacy of molecular connectivity modes-namely correlated gene expression and receptor similarity-that map onto multiple phenomena, including the rich club and patterns of abnormal cortical thickness across 13 neurological, psychiatric, and neurodevelopmental disorders. Finally, to construct a single multimodal wiring map of the human cortex, we fuse all 7 connectivity modes and show that the fused network maps onto major organizational features of the cortex including structural connectivity, intrinsic functional networks, and cytoarchitectonic classes. Altogether, this work contributes to the integrative study of interregional relationships in the human cerebral cortex.
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Affiliation(s)
- Justine Y. Hansen
- Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Katharina Voigt
- Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Australia
| | - Emma X. Liang
- Monash Biomedical Imaging, Monash University, Clayton, Australia
| | | | - Marco Leyton
- Montréal Neurological Institute, McGill University, Montréal, Canada
- Department of Psychiatry, McGill University, Montréal, Canada
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Australia
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Canada
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14
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [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: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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15
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Stier C, Braun C, Focke NK. Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness. Neuroimage 2023; 278:120275. [PMID: 37451375 PMCID: PMC10443236 DOI: 10.1016/j.neuroimage.2023.120275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Oscillatory power and phase synchronization map neuronal dynamics and are commonly studied to differentiate the healthy and diseased brain. Yet, little is known about the course and spatial variability of these features from early adulthood into old age. Leveraging magnetoencephalography (MEG) resting-state data in a cross-sectional adult sample (n = 350), we probed lifespan differences (18-88 years) in connectivity and power and interaction effects with sex. Building upon recent attempts to link brain structure and function, we tested the spatial correspondence between age effects on cortical thickness and those on functional networks. We further probed a direct structure-function relationship at the level of the study sample. We found MEG frequency-specific patterns with age and divergence between sexes in low frequencies. Connectivity and power exhibited distinct linear trajectories or turning points at midlife that might reflect different physiological processes. In the delta and beta bands, these age effects corresponded to those on cortical thickness, pointing to co-variation between the modalities across the lifespan. Structure-function coupling was frequency-dependent and observed in unimodal or multimodal regions. Altogether, we provide a comprehensive overview of the topographic functional profile of adulthood that can form a basis for neurocognitive and clinical investigations. This study further sheds new light on how the brain's structural architecture relates to fast oscillatory activity.
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Affiliation(s)
- Christina Stier
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany.
| | - Christoph Braun
- MEG-Center, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Niels K Focke
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany
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16
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Castaldo F, Páscoa Dos Santos F, Timms RC, Cabral J, Vohryzek J, Deco G, Woolrich M, Friston K, Verschure P, Litvak V. Multi-modal and multi-model interrogation of large-scale functional brain networks. Neuroimage 2023; 277:120236. [PMID: 37355200 PMCID: PMC10958139 DOI: 10.1016/j.neuroimage.2023.120236] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/26/2023] Open
Abstract
Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.
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Affiliation(s)
- Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Francisco Páscoa Dos Santos
- Eodyne Systems SL, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom
| | - Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom; Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Paul Verschure
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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17
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Johnson EL, Lin JJ, King-Stephens D, Weber PB, Laxer KD, Saez I, Girgis F, D'Esposito M, Knight RT, Badre D. A rapid theta network mechanism for flexible information encoding. Nat Commun 2023; 14:2872. [PMID: 37208373 DOI: 10.1038/s41467-023-38574-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum.
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Affiliation(s)
- Elizabeth L Johnson
- Departments of Medical Social Sciences and Pediatrics, Northwestern University, Chicago, IL, USA.
| | - Jack J Lin
- Department of Neurology and Center for Mind and Brain, University of California, Davis, CA, USA
| | - David King-Stephens
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Peter B Weber
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
| | - Kenneth D Laxer
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
| | - Ignacio Saez
- Department of Neurological Surgery, University of California, Davis, CA, USA
- Departments of Neuroscience, Neurosurgery, and Neurology, Ichan School of Medicine at Mt. Sinai, New York, NY, USA
| | - Fady Girgis
- Department of Neurological Surgery, University of California, Davis, CA, USA
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA, USA
| | - David Badre
- Department of Cognitive, Linguistic, and Psychological Sciences, and Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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18
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Williams N, Wang S, Arnulfo G, Nobili L, Palva S, Palva J. Modules in connectomes of phase-synchronization comprise anatomically contiguous, functionally related regions. Neuroimage 2023; 272:120036. [PMID: 36966852 DOI: 10.1016/j.neuroimage.2023.120036] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/14/2023] [Indexed: 04/05/2023] Open
Abstract
Modules in brain functional connectomes are essential to balancing segregation and integration of neuronal activity. Connectomes are the complete set of pairwise connections between brain regions. Non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) have been used to identify modules in connectomes of phase-synchronization. However, their resolution is suboptimal because of spurious phase-synchronization due to EEG volume conduction or MEG field spread. Here, we used invasive, intracerebral recordings from stereo-electroencephalography (SEEG, N = 67), to identify modules in connectomes of phase-synchronization. To generate SEEG-based group-level connectomes affected only minimally by volume conduction, we used submillimeter accurate localization of SEEG contacts and referenced electrode contacts in cortical gray matter to their closest contacts in white matter. Combining community detection methods with consensus clustering, we found that the connectomes of phase-synchronization were characterized by distinct and stable modules at multiple spatial scales, across frequencies from 3 to 320 Hz. These modules were highly similar within canonical frequency bands. Unlike the distributed brain systems identified with functional Magnetic Resonance Imaging (fMRI), modules up to the high-gamma frequency band comprised only anatomically contiguous regions. Notably, the identified modules comprised cortical regions involved in shared repertoires of sensorimotor and cognitive functions including memory, language and attention. These results suggest that the identified modules represent functionally specialised brain systems, which only partially overlap with the brain systems reported with fMRI. Hence, these modules might regulate the balance between functional segregation and functional integration through phase-synchronization.
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19
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Colenbier N, Sareen E, Del-Aguila Puntas T, Griffa A, Pellegrino G, Mantini D, Marinazzo D, Arcara G, Amico E. Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states. Neuroimage 2023; 271:120021. [PMID: 36918139 DOI: 10.1016/j.neuroimage.2023.120021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/21/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.
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Affiliation(s)
| | - Ekansh Sareen
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Tamara Del-Aguila Puntas
- Laboratorio de Psicobiologia, Departmento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Spain
| | - Alessandra Griffa
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | | | - Enrico Amico
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland.
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20
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Yang H, Wu G, Li Y, Ma Y, Chen R, Pines A, Xu T, Sydnor VJ, Satterthwaite TD, Cui Z. Connectional Hierarchy in Human Brain Revealed by Individual Variability of Functional Network Edges. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.08.531800. [PMID: 36945479 PMCID: PMC10028904 DOI: 10.1101/2023.03.08.531800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The human cerebral cortex is connected by intricate inter-areal wiring at the macroscale. The cortical hierarchy from primary sensorimotor to higher-order association areas is a unifying organizational principle across various neurobiological properties; however, previous studies have not clarified whether the connections between cortical regions exhibit a similar hierarchical pattern. Here, we identify a connectional hierarchy indexed by inter-individual variability of functional connectivity edges, which continuously progresses along a hierarchical gradient from within-network connections to between-network edges connecting sensorimotor and association networks. We found that this connectional hierarchy of variability aligns with both hemodynamic and electromagnetic connectivity strength and is constrained by structural connectivity strength. Moreover, the patterning of connectional hierarchy is related to inter-regional similarity in transcriptional and neurotransmitter receptor profiles. Using the Neurosynth cognitive atlas and cortical vulnerability maps in 13 brain disorders, we found that the connectional hierarchy of variability is associated with similarity networks of cognitive relevance and that of disorder vulnerability. Finally, we found that the prominence of this hierarchical gradient of connectivity variability declines during youth. Together, our results reveal a novel hierarchal organizational principle at the connectional level that links multimodal and multiscale human connectomes to individual variability in functional connectivity.
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Affiliation(s)
- Hang Yang
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Guowei Wu
- Chinese Institute for Brain Research, Beijing, 102206, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yaoxin Li
- Chinese Institute for Brain Research, Beijing, 102206, China
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yiyao Ma
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Runsen Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Adam Pines
- Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Valerie J. Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, 102206, China
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21
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Correspondence between gene expression and neurotransmitter receptor and transporter density in the human brain. Neuroimage 2022; 264:119671. [PMID: 36209794 DOI: 10.1016/j.neuroimage.2022.119671] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/29/2022] [Accepted: 10/05/2022] [Indexed: 11/07/2022] Open
Abstract
Neurotransmitter receptors modulate signaling between neurons. Thus, neurotransmitter receptors and transporters play a key role in shaping brain function. Due to the lack of comprehensive neurotransmitter receptor/transporter density datasets, microarray gene expression measuring mRNA transcripts is often used as a proxy for receptor densities. In the present report, we comprehensively test the spatial correlation between gene expression and protein density for a total of 27 neurotransmitter receptors, receptor binding-sites, and transporters across 9 different neurotransmitter systems, using both PET and autoradiography radioligand-based imaging modalities. We find poor spatial correspondences between gene expression and density for all neurotransmitter receptors and transporters except four single-protein metabotropic receptors (5-HT1A, CB1, D2, and MOR). These expression-density associations are related to gene differential stability and can vary between cortical and subcortical structures. Altogether, we recommend using direct measures of receptor and transporter density when relating neurotransmitter systems to brain structure and function.
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22
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Hansen JY, Shafiei G, Markello RD, Smart K, Cox SML, Nørgaard M, Beliveau V, Wu Y, Gallezot JD, Aumont É, Servaes S, Scala SG, DuBois JM, Wainstein G, Bezgin G, Funck T, Schmitz TW, Spreng RN, Galovic M, Koepp MJ, Duncan JS, Coles JP, Fryer TD, Aigbirhio FI, McGinnity CJ, Hammers A, Soucy JP, Baillet S, Guimond S, Hietala J, Bedard MA, Leyton M, Kobayashi E, Rosa-Neto P, Ganz M, Knudsen GM, Palomero-Gallagher N, Shine JM, Carson RE, Tuominen L, Dagher A, Misic B. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci 2022; 25:1569-1581. [PMID: 36303070 PMCID: PMC9630096 DOI: 10.1038/s41593-022-01186-3] [Citation(s) in RCA: 115] [Impact Index Per Article: 57.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 09/20/2022] [Indexed: 01/13/2023]
Abstract
Neurotransmitter receptors support the propagation of signals in the human brain. How receptor systems are situated within macro-scale neuroanatomy and how they shape emergent function remain poorly understood, and there exists no comprehensive atlas of receptors. Here we collate positron emission tomography data from more than 1,200 healthy individuals to construct a whole-brain three-dimensional normative atlas of 19 receptors and transporters across nine different neurotransmitter systems. We found that receptor profiles align with structural connectivity and mediate function, including neurophysiological oscillatory dynamics and resting-state hemodynamic functional connectivity. Using the Neurosynth cognitive atlas, we uncovered a topographic gradient of overlapping receptor distributions that separates extrinsic and intrinsic psychological processes. Finally, we found both expected and novel associations between receptor distributions and cortical abnormality patterns across 13 disorders. We replicated all findings in an independently collected autoradiography dataset. This work demonstrates how chemoarchitecture shapes brain structure and function, providing a new direction for studying multi-scale brain organization.
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Affiliation(s)
- Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Golia Shafiei
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Ross D Markello
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Kelly Smart
- Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Sylvia M L Cox
- Department of Psychiatry, McGill University, Montréal, QC, Canada
| | - Martin Nørgaard
- Department of Psychology, Center for Reproducible Neuroscience, Stanford University, Stanford, CA, USA
- Neurobiology Research Unit, Cimbi & OpenNeuroPET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vincent Beliveau
- Neurobiology Research Unit, Cimbi & OpenNeuroPET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Yanjun Wu
- Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jean-Dominique Gallezot
- Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Étienne Aumont
- Cognitive Pharmacology Research Unit, UQAM, Montréal, QC, Canada
| | - Stijn Servaes
- McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
| | | | | | | | - Gleb Bezgin
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
| | - Thomas Funck
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Taylor W Schmitz
- Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada
| | - R Nathan Spreng
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Marian Galovic
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont Saint Peter, UK
| | - Matthias J Koepp
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont Saint Peter, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont Saint Peter, UK
| | - Jonathan P Coles
- Department of Medicine, Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Tim D Fryer
- Department of Clinical Neurosciences, Wolfson Brain Imaging Centre, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Franklin I Aigbirhio
- Department of Clinical Neurosciences, Wolfson Brain Imaging Centre, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Colm J McGinnity
- King's College London and Guy's and St. Thomas' PET Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Alexander Hammers
- King's College London and Guy's and St. Thomas' PET Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jean-Paul Soucy
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Sylvain Baillet
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Synthia Guimond
- Department of Psychiatry, Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
- Department of Psychoeducation and Psychology, University of Quebec in Outaouais, Gatineau, QC, Canada
| | - Jarmo Hietala
- Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Marc-André Bedard
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Cognitive Pharmacology Research Unit, UQAM, Montréal, QC, Canada
| | - Marco Leyton
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Department of Psychiatry, McGill University, Montréal, QC, Canada
| | - Eliane Kobayashi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Pedro Rosa-Neto
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
| | - Melanie Ganz
- Neurobiology Research Unit, Cimbi & OpenNeuroPET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit, Cimbi & OpenNeuroPET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - James M Shine
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Richard E Carson
- Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lauri Tuominen
- Department of Psychiatry, Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Alain Dagher
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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