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Li J, Li X, Chen F, Li W, Chen J, Zhang B. Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings. Rev Neurosci 2024; 35:373-386. [PMID: 38157429 DOI: 10.1515/revneuro-2023-0098] [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: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.
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Affiliation(s)
- Jing Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Weiping Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Brain Science, Nanjing University, Nanjing, Jiangsu, 210008, China
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Ronat L, Hanganu A, Chylinski D, Van Egroo M, Narbutas J, Besson G, Muto V, Schmidt C, Bahri MA, Phillips C, Salmon E, Maquet P, Vandewalle G, Collette F, Bastin C. Prediction of cognitive decline in healthy aging based on neuropsychiatric symptoms and PET-biomarkers of Alzheimer's disease. J Neurol 2024; 271:2067-2077. [PMID: 38114820 DOI: 10.1007/s00415-023-12131-0] [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: 10/17/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023]
Abstract
Neuropsychiatric symptoms (NPS) have been associated with a risk of accelerated cognitive decline or conversion to dementia of the Alzheimer's Disease (AD) type. Moreover, the NPS were also associated with higher AD biomarkers (brain tau and amyloid burden) even in non-demented patients. But the effect of the relationship between NPS and biomarkers on cognitive decline has not yet been studied. This work aims to assess the relationship between longitudinal cognitive changes and NPS, specifically depression and anxiety, in association with AD biomarkers in healthy middle-aged to older participants. The cohort consisted of 101 healthy participants aged 50-70 years, 66 of whom had neuropsychological assessments of memory, executive functions, and global cognition at a 2-year follow-up. At baseline, NPS were assessed using the Beck Depression and Anxiety Inventories while brain tau and amyloid loads were measured using positron emission topography. For tau burden, THK5351 uptake is used as a proxy of tau and neuroinflammation. Participants, declining or remaining stable at follow-up, were categorized into groups for each cognitive domain. Group classification was investigated using binary logistic regressions based on combined AD biomarkers and the two NPS. The results showed that an association between anxiety and prefrontal amyloid burden significantly classified episodic memory decline, while the classification of global cognitive decline involved temporal and occipital amyloid burden but not NPS. Moreover, depression together with prefrontal and hippocampal tau burden were associated with a decline in memory. The classification of participants based on executive decline was related to depression and mainly prefrontal tau burden. These findings suggest that the combination of NPS and brain biomarkers of AD predicts the occurrence of cognitive decline in aging.
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Affiliation(s)
- Lucas Ronat
- Faculty of Medicine, Department of Medicine, University of Montreal, Montreal, QC, Canada
- Research Centre, University Institute of Geriatrics of Montreal, CIUSSS du Centre-Sud-de-l'Ile-de-Montréal, Montreal, QC, Canada
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Alexandru Hanganu
- Research Centre, University Institute of Geriatrics of Montreal, CIUSSS du Centre-Sud-de-l'Ile-de-Montréal, Montreal, QC, Canada
- Faculty of Arts and Sciences, Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Daphné Chylinski
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Maxime Van Egroo
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Justinas Narbutas
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liege, 4000, Liege, Belgium
| | - Gabriel Besson
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Vincenzo Muto
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Christina Schmidt
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liege, 4000, Liege, Belgium
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Christophe Phillips
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liege, 4000, Liege, Belgium
- Department of Neurology, CHU Liege, 4000, Liege, Belgium
| | - Pierre Maquet
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
- Department of Neurology, CHU Liege, 4000, Liege, Belgium
| | - Gilles Vandewalle
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
- F.R.S.-Fonds National de la Recherche Scientifique, Brussels, Belgium
| | - Fabienne Collette
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liege, 4000, Liege, Belgium
- F.R.S.-Fonds National de la Recherche Scientifique, Brussels, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, 4000, Liège, Belgium.
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liege, 4000, Liege, Belgium.
- F.R.S.-Fonds National de la Recherche Scientifique, Brussels, Belgium.
- Bât. B30 GIGA CRC In Vivo Imaging - Aging and Memory, Quartier Agora, Allée du 6 Août 8, 4000, Liege, Belgium.
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Koshmanova E, Berger A, Beckers E, Campbell I, Mortazavi N, Sharifpour R, Paparella I, Balda F, Berthomier C, Degueldre C, Salmon E, Lamalle L, Bastin C, Van Egroo M, Phillips C, Maquet P, Collette F, Muto V, Chylinski D, Jacobs HI, Talwar P, Sherif S, Vandewalle G. Locus coeruleus activity while awake is associated with REM sleep quality in older individuals. JCI Insight 2023; 8:e172008. [PMID: 37698926 PMCID: PMC10619502 DOI: 10.1172/jci.insight.172008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/06/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUNDThe locus coeruleus (LC) is the primary source of norepinephrine in the brain and regulates arousal and sleep. Animal research shows that it plays important roles in the transition between sleep and wakefulness, and between slow wave sleep and rapid eye movement sleep (REMS). It is unclear, however, whether the activity of the LC predicts sleep variability in humans.METHODSWe used 7-Tesla functional MRI, sleep electroencephalography (EEG), and a sleep questionnaire to test whether the LC activity during wakefulness was associated with sleep quality in 33 healthy younger (~22 years old; 28 women, 5 men) and 19 older (~61 years old; 14 women, 5 men) individuals.RESULTSWe found that, in older but not in younger participants, higher LC activity, as probed during an auditory attentional task, was associated with worse subjective sleep quality and with lower power over the EEG theta band during REMS. The results remained robust even when accounting for the age-related changes in the integrity of the LC.CONCLUSIONThese findings suggest that LC activity correlates with the perception of the sleep quality and an essential oscillatory mode of REMS, and we found that the LC may be an important target in the treatment of sleep- and age-related diseases.FUNDINGThis work was supported by Fonds National de la Recherche Scientifique (FRS-FNRS, T.0242.19 & J. 0222.20), Action de Recherche Concertée - Fédération Wallonie-Bruxelles (ARC SLEEPDEM 17/27-09), Fondation Recherche Alzheimer (SAO-FRA 2019/0025), ULiège, and European Regional Development Fund (Radiomed & Biomed-Hub).
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Affiliation(s)
- Ekaterina Koshmanova
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Alexandre Berger
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Institute of Neuroscience (IoNS), Université Catholique de Louvain (UCLouvain), Brussels, Belgium
- Synergia Medical SA, Mont-Saint-Guibert, Belgium
| | - Elise Beckers
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Islay Campbell
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Nasrin Mortazavi
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Roya Sharifpour
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Ilenia Paparella
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Fermin Balda
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | | | - Christian Degueldre
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Eric Salmon
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Neurology Department, Centre Hospitalier Universitaire de Liège, Liège, Belgium
- PsyNCog and
| | - Laurent Lamalle
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Christine Bastin
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- PsyNCog and
| | - Maxime Van Egroo
- Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Christophe Phillips
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- In Silico Medicine Unit, GIGA-Institute, ULiège, Liège, Belgium
| | - Pierre Maquet
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Neurology Department, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Fabienne Collette
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- PsyNCog and
| | - Vincenzo Muto
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Daphne Chylinski
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Heidi I.L. Jacobs
- Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Puneet Talwar
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Siya Sherif
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Gilles Vandewalle
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
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Chylinski D, Narbutas J, Balteau E, Collette F, Bastin C, Berthomier C, Salmon E, Maquet P, Carrier J, Phillips C, Lina JM, Vandewalle G, Van Egroo M. Frontal grey matter microstructure is associated with sleep slow waves characteristics in late midlife. Sleep 2022; 45:zsac178. [PMID: 35869626 PMCID: PMC9644125 DOI: 10.1093/sleep/zsac178] [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: 03/24/2022] [Revised: 07/13/2022] [Indexed: 07/25/2023] Open
Abstract
STUDY OBJECTIVES The ability to generate slow waves (SW) during non-rapid eye movement (NREM) sleep decreases as early as the 5th decade of life, predominantly over frontal regions. This decrease may concern prominently SW characterized by a fast switch from hyperpolarized to depolarized, or down-to-up, state. Yet, the relationship between these fast and slow switcher SW and cerebral microstructure in ageing is not established. METHODS We recorded habitual sleep under EEG in 99 healthy late midlife individuals (mean age = 59.3 ± 5.3 years; 68 women) and extracted SW parameters (density, amplitude, frequency) for all SW as well as according to their switcher type (slow vs. fast). We further used neurite orientation dispersion and density imaging (NODDI) to assess microstructural integrity over a frontal grey matter region of interest (ROI). RESULTS In statistical models adjusted for age, sex, and sleep duration, we found that a lower SW density, particularly for fast switcher SW, was associated with a reduced orientation dispersion of neurites in the frontal ROI (p = 0.018, R2β* = 0.06). In addition, overall SW frequency was positively associated with neurite density (p = 0.03, R2β* = 0.05). By contrast, we found no significant relationships between SW amplitude and NODDI metrics. CONCLUSIONS Our findings suggest that the complexity of neurite organization contributes specifically to the rate of fast switcher SW occurrence in healthy middle-aged individuals, corroborating slow and fast switcher SW as distinct types of SW. They further suggest that the density of frontal neurites plays a key role for neural synchronization during sleep. TRIAL REGISTRATION NUMBER EudraCT 2016-001436-35.
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Affiliation(s)
- Daphne Chylinski
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Justinas Narbutas
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Evelyne Balteau
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Fabienne Collette
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | | | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
- Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Pierre Maquet
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Julie Carrier
- CARSM, CIUSSS of Nord-de l’Île-de-Montréal, Montreal, Canada
- Department of Psychology, University of Montreal, Canada
| | - Christophe Phillips
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- GIGA-In Silico Medicine, University of Liège, Liège, Belgium
| | - Jean-Marc Lina
- CARSM, CIUSSS of Nord-de l’Île-de-Montréal, Montreal, Canada
- Department of Psychology, University of Montreal, Canada
| | - Gilles Vandewalle
- Corresponding authors. Gilles Vandewalle, GIGA-Cyclotron Research Centre-In Vivo Imaging, Bâtiment B30, Université de Liège, Allée du Six Août, 8, 4000 Liège, Belgium.
| | - Maxime Van Egroo
- Maxime Van Egroo, Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, Maastricht, The Netherlands.
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Gjini K, Casey C, Tanabe S, Bo A, Parker M, White M, Kunkel D, Lennertz R, Pearce RA, Betthauser T, Christian BT, Johnson SC, Bendlin BB, Sanders RD. Greater tau pathology is associated with altered predictive coding. Brain Commun 2022; 4:fcac209. [PMID: 36226138 PMCID: PMC9547525 DOI: 10.1093/braincomms/fcac209] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/04/2022] [Accepted: 08/12/2022] [Indexed: 01/25/2023] Open
Abstract
Altered predictive coding may underlie the reduced auditory mismatch negativity amplitude observed in patients with dementia. We hypothesized that accumulating dementia-associated pathologies, including amyloid and tau, lead to disturbed predictions of our sensory environment. This would manifest as increased reliance on 'observed' sensory information with an associated increase in feedforward, and decrease in feedback, signalling. To test this hypothesis, we studied a cross-sectional cohort of participants who underwent PET imaging and high-density EEG during an oddball paradigm, and used dynamic casual modelling and Bayesian statistics to make inferences about the neuronal architectures (generators) and mechanisms (effective connectivity) underlying the observed auditory-evoked responses. Amyloid-β imaging with [C-11] Pittsburgh Compound-B PET was qualitatively rated using established criteria. Tau-positive PET scans, with [F-18]MK-6240, were defined by an MK-6240 standardized uptake value ratio positivity threshold at 2 standard deviations above the mean of the Amyloid(-) group in the entorhinal cortex (entorhinal MK-6240 standardized uptake value ratio > 1.27). The cross-sectional cohort included a total of 56 participants [9 and 13 participants in the Tau(+) and Amyloid(+) subgroups, respectively: age interquartile range of (73.50-75.34) and (70.5-75.34) years, 56 and 69% females, respectively; 46 and 43 participants in the Tau(-) and Amyloid(-) subgroups, respectively: age interquartile range of (62.72-72.5) and (62.64-72.48) years, 67 and 65% females, respectively]. Mismatch negativity amplitudes were significantly smaller in Tau+ subgroup than Tau- subgroup (cluster statistics corrected for multiple comparisons: P = 0.028). Dynamic causal modelling showed that tau pathology was associated with increased feedforward connectivity and decreased feedback connectivity, with increased excitability of superior temporal gyrus but not inferior frontal regions. This effect on superior temporal gyrus was consistent with the distribution of tau disease on PET in these participants, indicating that the observed differences in mismatch negativity reflect pathological changes evolving in preclinical dementia. Exclusion of participants with diagnosed mild cognitive impairment or dementia did not affect the results. These observational data provide proof of concept that abnormalities in predictive coding may be detected in the preclinical phase of Alzheimer's disease. This framework also provides a construct to understand how progressive impairments lead to loss of orientation to the sensory world in dementia. Based on our modelling results, plus animal models indicating that Alzheimer's disease pathologies produce hyperexcitability of higher cortical regions through local disinhibition, mismatch negativity might be a useful monitor to deploy as strategies that target interneuron dysfunction are developed.
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Affiliation(s)
- Klevest Gjini
- Department of Neurology, University of Wisconsin, Madison, WI, USA
| | - Cameron Casey
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Sean Tanabe
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Amber Bo
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Margaret Parker
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Marissa White
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - David Kunkel
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Richard Lennertz
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Robert A Pearce
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Tobey Betthauser
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | | | | | | | - Robert D Sanders
- Specialty of Anaesthetics, University of Sydney, Camperdown, Australia
- Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown, Australia
- Central Clinical School & NHMRC Clinical Trials Centre, Institute of Academic Surgery, Camperdown, Australia
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