1
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Fieldhouse JLP, van Paassen DN, van Engelen MPE, De Boer SCM, Hartog WL, Braak S, Schoonmade LJ, Schouws SNTM, Krudop WA, Oudega ML, Mutsaerts HJMM, Teunissen CE, Vijverberg EGB, Pijnenburg YAL. The pursuit for markers of disease progression in behavioral variant frontotemporal dementia: a scoping review to optimize outcome measures for clinical trials. Front Aging Neurosci 2024; 16:1382593. [PMID: 38784446 PMCID: PMC11112081 DOI: 10.3389/fnagi.2024.1382593] [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: 02/05/2024] [Accepted: 04/16/2024] [Indexed: 05/25/2024] Open
Abstract
Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative disorder characterized by diverse and prominent changes in behavior and personality. One of the greatest challenges in bvFTD is to capture, measure and predict its disease progression, due to clinical, pathological and genetic heterogeneity. Availability of reliable outcome measures is pivotal for future clinical trials and disease monitoring. Detection of change should be objective, clinically meaningful and easily assessed, preferably associated with a biological process. The purpose of this scoping review is to examine the status of longitudinal studies in bvFTD, evaluate current assessment tools and propose potential progression markers. A systematic literature search (in PubMed and Embase.com) was performed. Literature on disease trajectories and longitudinal validity of frequently-used measures was organized in five domains: global functioning, behavior, (social) cognition, neuroimaging and fluid biomarkers. Evaluating current longitudinal data, we propose an adaptive battery, combining a set of sensitive clinical, neuroimaging and fluid markers, adjusted for genetic and sporadic variants, for adequate detection of disease progression in bvFTD.
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Affiliation(s)
- Jay L. P. Fieldhouse
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Dirk N. van Paassen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Marie-Paule E. van Engelen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Sterre C. M. De Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Willem L. Hartog
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Simon Braak
- Department of Psychiatry, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, Netherlands
| | | | - Sigfried N. T. M. Schouws
- Department of Psychiatry, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- GGZ inGeest Mental Health Care, Amsterdam, Netherlands
| | - Welmoed A. Krudop
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- GGZ inGeest Mental Health Care, Amsterdam, Netherlands
| | - Mardien L. Oudega
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Department of Psychiatry, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, Netherlands
- GGZ inGeest Mental Health Care, Amsterdam, Netherlands
| | - Henk J. M. M. Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Charlotte E. Teunissen
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Everard G. B. Vijverberg
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Yolande A. L. Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
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2
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Jafarian A, Hughes LE, Adams NE, Lanskey JH, Naessens M, Rouse MA, Murley AG, Friston KJ, Rowe JB. Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy. Neuroimage 2023; 276:120193. [PMID: 37244323 DOI: 10.1016/j.neuroimage.2023.120193] [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: 10/24/2022] [Revised: 05/11/2023] [Accepted: 05/24/2023] [Indexed: 05/29/2023] Open
Abstract
We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters' concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals' neurophysiological observations. At the second level, individuals' 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.
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Affiliation(s)
- Amirhossein Jafarian
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Laura E Hughes
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Natalie E Adams
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom.
| | - Juliette H Lanskey
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Michelle Naessens
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Matthew A Rouse
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Alexander G Murley
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom.
| | - Karl J Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, United Kingdom.
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
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3
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Adams NE, Jafarian A, Perry A, Rouse MA, Shaw AD, Murley AG, Cope TE, Bevan-Jones WR, Passamonti L, Street D, Holland N, Nesbitt D, Hughes LE, Friston KJ, Rowe JB. Neurophysiological consequences of synapse loss in progressive supranuclear palsy. Brain 2023; 146:2584-2594. [PMID: 36514918 PMCID: PMC10232290 DOI: 10.1093/brain/awac471] [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: 06/23/2022] [Revised: 10/31/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from preclinical to clinical models of pathology and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations such as EEG and magnetoencephalography. Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by 11C-UCB-J PET, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson's syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild cortical atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using parametric empirical Bayesian inversion of a conductance-based canonical microcircuit model of magnetoencephalography data, we show that the inclusion of regional synaptic density-as a subject-specific prior on laminar-specific neuronal populations-markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology.
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Affiliation(s)
- Natalie E Adams
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Amirhossein Jafarian
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Alistair Perry
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Matthew A Rouse
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Alexander D Shaw
- Washington Singer Laboratories, University of Exeter, Exeter EX4 4QG, UK
| | - Alexander G Murley
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Thomas E Cope
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - W Richard Bevan-Jones
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Duncan Street
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Negin Holland
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - David Nesbitt
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Laura E Hughes
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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Friston K. Computational psychiatry: from synapses to sentience. Mol Psychiatry 2023; 28:256-268. [PMID: 36056173 PMCID: PMC7614021 DOI: 10.1038/s41380-022-01743-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 01/09/2023]
Abstract
This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, WC1N 3AR, UK.
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5
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Perry A, Hughes LE, Adams N, Naessens M, Murley AG, Rouse MA, Street D, Jones PS, Cope TE, Kocagoncu E, Rowe JB. The neurophysiological effect of NMDA-R antagonism of frontotemporal lobar degeneration is conditional on individual GABA concentration. Transl Psychiatry 2022; 12:348. [PMID: 36030249 PMCID: PMC9420128 DOI: 10.1038/s41398-022-02114-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 02/02/2023] Open
Abstract
There is a pressing need to accelerate therapeutic strategies against the syndromes caused by frontotemporal lobar degeneration, including symptomatic treatments. One approach is for experimental medicine, coupling neurophysiological studies of the mechanisms of disease with pharmacological interventions aimed at restoring neurochemical deficits. Here we consider the role of glutamatergic deficits and their potential as targets for treatment. We performed a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study in 20 people with symptomatic frontotemporal lobar degeneration (10 behavioural variant frontotemporal dementia, 10 progressive supranuclear palsy) and 19 healthy age- and gender-matched controls. Both magnetoencephalography sessions recorded a roving auditory oddball paradigm: on placebo or following 10 mg memantine, an uncompetitive NMDA-receptor antagonist. Ultra-high-field magnetic resonance spectroscopy confirmed lower concentrations of GABA in the right inferior frontal gyrus of people with frontotemporal lobar degeneration. While memantine showed a subtle effect on early-auditory processing in patients, there was no significant main effect of memantine on the magnitude of the mismatch negativity (MMN) response in the right frontotemporal cortex in patients or controls. However, the change in the right auditory cortex MMN response to memantine (vs. placebo) in patients correlated with individuals' prefrontal GABA concentration. There was no moderating effect of glutamate concentration or cortical atrophy. This proof-of-concept study demonstrates the potential for baseline dependency in the pharmacological restoration of neurotransmitter deficits to influence cognitive neurophysiology in neurodegenerative disease. With changes to multiple neurotransmitters in frontotemporal lobar degeneration, we suggest that individuals' balance of excitation and inhibition may determine drug efficacy, with implications for drug selection and patient stratification in future clinical trials.
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Affiliation(s)
- Alistair Perry
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK. .,Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ, UK.
| | - Laura E. Hughes
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF UK ,grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - Natalie Adams
- grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - Michelle Naessens
- grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - Alexander G. Murley
- grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - Matthew A. Rouse
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF UK
| | - Duncan Street
- grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - P. Simon Jones
- grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - Thomas E. Cope
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF UK ,grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - Ece Kocagoncu
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF UK ,grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
| | - James B. Rowe
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF UK ,grid.5335.00000000121885934Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0QQ UK
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6
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Adams RA, Pinotsis D, Tsirlis K, Unruh L, Mahajan A, Horas AM, Convertino L, Summerfelt A, Sampath H, Du XM, Kochunov P, Ji JL, Repovs G, Murray JD, Friston KJ, Hong LE, Anticevic A. Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia. Biol Psychiatry 2022; 91:202-215. [PMID: 34598786 PMCID: PMC8654393 DOI: 10.1016/j.biopsych.2021.07.024] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/29/2021] [Accepted: 07/29/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Diminished synaptic gain-the sensitivity of postsynaptic responses to neural inputs-may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. METHODS People with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms-resting, mismatch negativity, and 40-Hz auditory steady-state response-and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits. RESULTS Classic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all best explained by models with greater self-inhibition (decreased synaptic gain) in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in PScz in 3 paradigms. CONCLUSIONS First, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation.
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Affiliation(s)
- Rick A Adams
- Centre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom; Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
| | - Dimitris Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City University of London, London, United Kingdom; Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Konstantinos Tsirlis
- Centre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom
| | - Leonhardt Unruh
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Aashna Mahajan
- Centre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom
| | - Ana Montero Horas
- Centre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom
| | - Laura Convertino
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Ann Summerfelt
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Hemalatha Sampath
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Xiaoming Michael Du
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Grega Repovs
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
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7
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Benhamou E, Zhao S, Sivasathiaseelan H, Johnson JCS, Requena-Komuro MC, Bond RL, van Leeuwen JEP, Russell LL, Greaves CV, Nelson A, Nicholas JM, Hardy CJD, Rohrer JD, Warren JD. Decoding expectation and surprise in dementia: the paradigm of music. Brain Commun 2021; 3:fcab173. [PMID: 34423301 PMCID: PMC8376684 DOI: 10.1093/braincomms/fcab173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2021] [Indexed: 01/08/2023] Open
Abstract
Making predictions about the world and responding appropriately to unexpected events are essential functions of the healthy brain. In neurodegenerative disorders, such as frontotemporal dementia and Alzheimer's disease, impaired processing of 'surprise' may underpin a diverse array of symptoms, particularly abnormalities of social and emotional behaviour, but is challenging to characterize. Here, we addressed this issue using a novel paradigm: music. We studied 62 patients (24 female; aged 53-88) representing major syndromes of frontotemporal dementia (behavioural variant, semantic variant primary progressive aphasia, non-fluent-agrammatic variant primary progressive aphasia) and typical amnestic Alzheimer's disease, in relation to 33 healthy controls (18 female; aged 54-78). Participants heard famous melodies containing no deviants or one of three types of deviant note-acoustic (white-noise burst), syntactic (key-violating pitch change) or semantic (key-preserving pitch change). Using a regression model that took elementary perceptual, executive and musical competence into account, we assessed accuracy detecting melodic deviants and simultaneously recorded pupillary responses and related these to deviant surprise value (information-content) and carrier melody predictability (entropy), calculated using an unsupervised machine learning model of music. Neuroanatomical associations of deviant detection accuracy and coupling of detection to deviant surprise value were assessed using voxel-based morphometry of patients' brain MRI. Whereas Alzheimer's disease was associated with normal deviant detection accuracy, behavioural and semantic variant frontotemporal dementia syndromes were associated with strikingly similar profiles of impaired syntactic and semantic deviant detection accuracy and impaired behavioural and autonomic sensitivity to deviant information-content (all P < 0.05). On the other hand, non-fluent-agrammatic primary progressive aphasia was associated with generalized impairment of deviant discriminability (P < 0.05) due to excessive false-alarms, despite retained behavioural and autonomic sensitivity to deviant information-content and melody predictability. Across the patient cohort, grey matter correlates of acoustic deviant detection accuracy were identified in precuneus, mid and mesial temporal regions; correlates of syntactic deviant detection accuracy and information-content processing, in inferior frontal and anterior temporal cortices, putamen and nucleus accumbens; and a common correlate of musical salience coding in supplementary motor area (all P < 0.05, corrected for multiple comparisons in pre-specified regions of interest). Our findings suggest that major dementias have distinct profiles of sensory 'surprise' processing, as instantiated in music. Music may be a useful and informative paradigm for probing the predictive decoding of complex sensory environments in neurodegenerative proteinopathies, with implications for understanding and measuring the core pathophysiology of these diseases.
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Affiliation(s)
- Elia Benhamou
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Sijia Zhao
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK
| | - Harri Sivasathiaseelan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Jeremy C S Johnson
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Maï-Carmen Requena-Komuro
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Rebecca L Bond
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Janneke E P van Leeuwen
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Lucy L Russell
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Caroline V Greaves
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Annabel Nelson
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Jennifer M Nicholas
- Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Chris J D Hardy
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Jason D Warren
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
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8
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Kocagoncu E, Klimovich-Gray A, Hughes LE, Rowe JB. Evidence and implications of abnormal predictive coding in dementia. Brain 2021; 144:3311-3321. [PMID: 34240109 PMCID: PMC8677549 DOI: 10.1093/brain/awab254] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 03/15/2021] [Accepted: 06/17/2021] [Indexed: 11/14/2022] Open
Abstract
The diversity of cognitive deficits and neuropathological processes associated with dementias has encouraged divergence in pathophysiological explanations of disease. Here, we review an alternative framework that emphasizes convergent critical features of cognitive pathophysiology. Rather than the loss of ‘memory centres’ or ‘language centres’, or singular neurotransmitter systems, cognitive deficits are interpreted in terms of aberrant predictive coding in hierarchical neural networks. This builds on advances in normative accounts of brain function, specifically the Bayesian integration of beliefs and sensory evidence in which hierarchical predictions and prediction errors underlie memory, perception, speech and behaviour. We describe how analogous impairments in predictive coding in parallel neurocognitive systems can generate diverse clinical phenomena, including the characteristics of dementias. The review presents evidence from behavioural and neurophysiological studies of perception, language, memory and decision-making. The reformulation of cognitive deficits in terms of predictive coding has several advantages. It brings diverse clinical phenomena into a common framework; it aligns cognitive and movement disorders; and it makes specific predictions on cognitive physiology that support translational and experimental medicine studies. The insights into complex human cognitive disorders from the predictive coding framework may therefore also inform future therapeutic strategies.
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Affiliation(s)
- Ece Kocagoncu
- Cambridge Centre for Frontotemporal Dementia, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Laura E Hughes
- Cambridge Centre for Frontotemporal Dementia, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - James B Rowe
- Cambridge Centre for Frontotemporal Dementia, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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9
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Jiang J, Benhamou E, Waters S, Johnson JCS, Volkmer A, Weil RS, Marshall CR, Warren JD, Hardy CJD. Processing of Degraded Speech in Brain Disorders. Brain Sci 2021; 11:394. [PMID: 33804653 PMCID: PMC8003678 DOI: 10.3390/brainsci11030394] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/15/2021] [Accepted: 03/18/2021] [Indexed: 11/30/2022] Open
Abstract
The speech we hear every day is typically "degraded" by competing sounds and the idiosyncratic vocal characteristics of individual speakers. While the comprehension of "degraded" speech is normally automatic, it depends on dynamic and adaptive processing across distributed neural networks. This presents the brain with an immense computational challenge, making degraded speech processing vulnerable to a range of brain disorders. Therefore, it is likely to be a sensitive marker of neural circuit dysfunction and an index of retained neural plasticity. Considering experimental methods for studying degraded speech and factors that affect its processing in healthy individuals, we review the evidence for altered degraded speech processing in major neurodegenerative diseases, traumatic brain injury and stroke. We develop a predictive coding framework for understanding deficits of degraded speech processing in these disorders, focussing on the "language-led dementias"-the primary progressive aphasias. We conclude by considering prospects for using degraded speech as a probe of language network pathophysiology, a diagnostic tool and a target for therapeutic intervention.
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Affiliation(s)
- Jessica Jiang
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; (J.J.); (E.B.); (J.C.S.J.); (R.S.W.); (C.R.M.); (J.D.W.)
| | - Elia Benhamou
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; (J.J.); (E.B.); (J.C.S.J.); (R.S.W.); (C.R.M.); (J.D.W.)
| | - Sheena Waters
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London EC1M 6BQ, UK;
| | - Jeremy C. S. Johnson
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; (J.J.); (E.B.); (J.C.S.J.); (R.S.W.); (C.R.M.); (J.D.W.)
| | - Anna Volkmer
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK;
| | - Rimona S. Weil
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; (J.J.); (E.B.); (J.C.S.J.); (R.S.W.); (C.R.M.); (J.D.W.)
| | - Charles R. Marshall
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; (J.J.); (E.B.); (J.C.S.J.); (R.S.W.); (C.R.M.); (J.D.W.)
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London EC1M 6BQ, UK;
| | - Jason D. Warren
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; (J.J.); (E.B.); (J.C.S.J.); (R.S.W.); (C.R.M.); (J.D.W.)
| | - Chris J. D. Hardy
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; (J.J.); (E.B.); (J.C.S.J.); (R.S.W.); (C.R.M.); (J.D.W.)
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10
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Adams NE, Hughes LE, Rouse MA, Phillips HN, Shaw AD, Murley AG, Cope TE, Bevan-Jones WR, Passamonti L, Street D, Holland N, Nesbitt D, Friston K, Rowe JB. GABAergic cortical network physiology in frontotemporal lobar degeneration. Brain 2021; 144:2135-2145. [PMID: 33710299 PMCID: PMC8370432 DOI: 10.1093/brain/awab097] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 12/31/2020] [Accepted: 01/03/2021] [Indexed: 11/23/2022] Open
Abstract
The clinical syndromes caused by frontotemporal lobar degeneration are heterogeneous, including the behavioural variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy. Although pathologically distinct, they share many behavioural, cognitive and physiological features, which may in part arise from common deficits of major neurotransmitters such as γ-aminobutyric acid (GABA). Here, we quantify the GABAergic impairment and its restoration with dynamic causal modelling of a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study. We analysed 17 patients with bvFTD, 15 patients with progressive supranuclear palsy, and 20 healthy age- and gender-matched controls. In addition to neuropsychological assessment and structural MRI, participants undertook two magnetoencephalography sessions using a roving auditory oddball paradigm: once on placebo and once on 10 mg of the oral GABA reuptake inhibitor tiagabine. A subgroup underwent ultrahigh-field magnetic resonance spectroscopy measurement of GABA concentration, which was reduced among patients. We identified deficits in frontotemporal processing using conductance-based biophysical models of local and global neuronal networks. The clinical relevance of this physiological deficit is indicated by the correlation between top-down connectivity from frontal to temporal cortex and clinical measures of cognitive and behavioural change. A critical validation of the biophysical modelling approach was evidence from parametric empirical Bayes analysis that GABA levels in patients, measured by spectroscopy, were related to posterior estimates of patients’ GABAergic synaptic connectivity. Further evidence for the role of GABA in frontotemporal lobar degeneration came from confirmation that the effects of tiagabine on local circuits depended not only on participant group, but also on individual baseline GABA levels. Specifically, the phasic inhibition of deep cortico-cortical pyramidal neurons following tiagabine, but not placebo, was a function of GABA concentration. The study provides proof-of-concept for the potential of dynamic causal modelling to elucidate mechanisms of human neurodegenerative disease, and explains the variation in response to candidate therapies among patients. The laminar- and neurotransmitter-specific features of the modelling framework, can be used to study other treatment approaches and disorders. In the context of frontotemporal lobar degeneration, we suggest that neurophysiological restoration in selected patients, by targeting neurotransmitter deficits, could be used to bridge between clinical and preclinical models of disease, and inform the personalized selection of drugs and stratification of patients for future clinical trials.
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Affiliation(s)
- Natalie E Adams
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Laura E Hughes
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,MMRC Cognition and Brain Sciences Unit, Cambridge CB2 7EF, UK
| | - Matthew A Rouse
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Holly N Phillips
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK
| | | | - Alexander G Murley
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - Thomas E Cope
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,MMRC Cognition and Brain Sciences Unit, Cambridge CB2 7EF, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - W Richard Bevan-Jones
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - Duncan Street
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - Negin Holland
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - David Nesbitt
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,MMRC Cognition and Brain Sciences Unit, Cambridge CB2 7EF, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - James B Rowe
- Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK.,MMRC Cognition and Brain Sciences Unit, Cambridge CB2 7EF, UK.,Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
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11
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Sumner RL, Spriggs MJ, Shaw AD. Modelling thalamocortical circuitry shows that visually induced LTP changes laminar connectivity in human visual cortex. PLoS Comput Biol 2021; 17:e1008414. [PMID: 33476341 PMCID: PMC7853500 DOI: 10.1371/journal.pcbi.1008414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 02/02/2021] [Accepted: 10/05/2020] [Indexed: 11/19/2022] Open
Abstract
Neuroplasticity is essential to learning and memory in the brain; it has therefore also been implicated in numerous neurological and psychiatric disorders, making measuring the state of neuroplasticity of foremost importance to clinical neuroscience. Long-term potentiation (LTP) is a key mechanism of neuroplasticity and has been studied extensively, and invasively in non-human animals. Translation to human application largely relies on the validation of non-invasive measures of LTP. The current study presents a generative thalamocortical computational model of visual cortex for investigating and replicating interlaminar connectivity changes using non-invasive EEG recording of humans. The model is combined with a commonly used visual sensory LTP paradigm and fit to the empirical EEG data using dynamic causal modelling. The thalamocortical model demonstrated remarkable accuracy recapitulating post-tetanus changes seen in invasive research, including increased excitatory connectivity from thalamus to layer IV and from layer IV to II/III, established major sites of LTP in visual cortex. These findings provide justification for the implementation of the presented thalamocortical model for ERP research, including to provide increased detail on the nature of changes that underlie LTP induced in visual cortex. Future applications include translating rodent findings to non-invasive research in humans concerning deficits to LTP that may underlie neurological and psychiatric disease. The brain’s ability to learn and form memories is governed by neuroplasticity. One of the major mechanisms of neuroplasticity is long-term potentiation (LTP). To study LTP in detail necessitates implanting electrodes in the brain of non-human animals. However, to translate this knowledge to humans requires a non-invasive method. Neural mass models use mathematical equations to describe the brain’s neural architecture and function over time. Fitting these models to real data, using methods such as dynamic causal modelling (DCM), helps to elucidate the connectivity and major channel changes that could have plausibly caused the observed effects in electroencephalography data recorded non-invasively from the scalp. The current study presents a thalamocortical model of the neural architecture of the visual system combined with a thalamic compartment. The model is able to represent the basic transfer of visual information to the cortex, mediated by major receptor types. We combined the thalamocortical model with a visual processing task that uses black and white grating images to induce and measure LTP in visual cortex. We hypothesised that the changes in the model would be consistent with what is seen in animal invasive recordings. The model demonstrated remarkable accuracy in recapitulating changes to neural architecture consistent with the induction of LTP in visual cortex. Additionally, the result demonstrated specificity to the visual input that induced LTP. Future applications include translating animal findings that are beginning to determine how disordered LTP may underlie neurological and psychiatric disease (for example depression, schizophrenia, autism, and dementia).
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Affiliation(s)
- Rachael L. Sumner
- School of Pharmacy, University of Auckland, Auckland, New Zealand
- * E-mail:
| | - Meg J. Spriggs
- Centre for Psychedelic Research, Department of Medicine, Imperial College London, London, United Kingdom
| | - Alexander D. Shaw
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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12
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McColgan P, Joubert J, Tabrizi SJ, Rees G. The human motor cortex microcircuit: insights for neurodegenerative disease. Nat Rev Neurosci 2020; 21:401-415. [PMID: 32555340 DOI: 10.1038/s41583-020-0315-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2020] [Indexed: 12/22/2022]
Abstract
The human motor cortex comprises a microcircuit of five interconnected layers with different cell types. In this Review, we use a layer-specific and cell-specific approach to integrate physiological accounts of this motor cortex microcircuit with the pathophysiology of neurodegenerative diseases affecting motor functions. In doing so we can begin to link motor microcircuit pathology to specific disease stages and clinical phenotypes. Based on microcircuit physiology, we can make future predictions of axonal loss and microcircuit dysfunction. With recent advances in high-resolution neuroimaging we can then test these predictions in humans in vivo, providing mechanistic insights into neurodegenerative disease.
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Affiliation(s)
- Peter McColgan
- Huntington's Disease Research Centre, UCL Institute of Neurology, University College London, London, UK.
| | - Julie Joubert
- Huntington's Disease Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Sarah J Tabrizi
- Huntington's Disease Research Centre, UCL Institute of Neurology, University College London, London, UK.,Dementia Research Institute at UCL, London, UK
| | - Geraint Rees
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK.,UCL Institute of Cognitive Neuroscience, University College London, London, UK
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13
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Wei H, Jafarian A, Zeidman P, Litvak V, Razi A, Hu D, Friston KJ. Bayesian fusion and multimodal DCM for EEG and fMRI. Neuroimage 2020; 211:116595. [DOI: 10.1016/j.neuroimage.2020.116595] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 01/07/2020] [Accepted: 01/29/2020] [Indexed: 12/26/2022] Open
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14
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GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography. J Neurosci 2020; 40:1640-1649. [PMID: 31915255 DOI: 10.1523/jneurosci.1689-19.2019] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 11/25/2019] [Accepted: 12/25/2019] [Indexed: 12/15/2022] Open
Abstract
To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, as generative models of magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults. This paradigm induces robust perturbations that permeate frontotemporal networks, including an evoked 'mismatch negativity' response and transiently induced oscillations. Here, we probe GABAergic influences in the networks using double-blind placebo-controlled randomized-crossover administration of the GABA reuptake inhibitor, tiagabine (oral, 10 mg) in healthy older adults. We demonstrate the facility of conductance-based neural mass mean-field models, incorporating local synaptic connectivity, to investigate laminar-specific and GABAergic mechanisms of the auditory response. The neuronal model accurately recapitulated the observed magnetoencephalographic data. Using parametric empirical Bayes for optimal model inversion across both drug sessions, we identify the effect of tiagabine on GABAergic modulation of deep pyramidal and interneuronal cell populations. We found a transition of the main GABAergic drug effects from auditory cortex in standard trials to prefrontal cortex in deviant trials. The successful integration of pharmaco- magnetoencephalography with dynamic causal models of frontotemporal networks provides a potential platform on which to evaluate the effects of disease and pharmacological interventions.SIGNIFICANCE STATEMENT Understanding human brain function and developing new treatments require good models of brain function. We tested a detailed generative model of cortical microcircuits that accurately reproduced human magnetoencephalography, to quantify network dynamics and connectivity in frontotemporal cortex. This approach identified the effect of a test drug (GABA-reuptake inhibitor, tiagabine) on neuronal function (GABA-ergic dynamics), opening the way for psychopharmacological studies in health and disease with the mechanistic precision afforded by generative models of the brain.
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