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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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2
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Chinichian N, Lindner M, Yanchuk S, Schwalger T, Schöll E, Berner R. Modeling brain network flexibility in networks of coupled oscillators: a feasibility study. Sci Rep 2024; 14:5713. [PMID: 38459077 PMCID: PMC10923875 DOI: 10.1038/s41598-024-55753-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.
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Affiliation(s)
- Narges Chinichian
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.
- Psychiatry Department, Charité-Universitätsmedizin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Michael Lindner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Mathematics, Humboldt Universität zu Berlin, Berlin, Germany
- School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Tilo Schwalger
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
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Wei Q, Wang XY, Zhang LJ, Yu CY, Shu HY, Liao XL, Xu SH, Su T, Kang M, Shao Y. A Functional Magnetic Resonance Imaging Study Using Dynamic Amplitude of Low-Frequency Fluctuation to Assess Brain Activity in Patients with Moyamoya Disease. Brain Connect 2023; 13:621-630. [PMID: 37930733 DOI: 10.1089/brain.2023.0017] [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] [Indexed: 11/07/2023] Open
Abstract
Introduction: The purpose of this study was to monitor and record the dynamic brain activity of patients with moyamoya disease (MMD), as well as to study the relationship between brain abnormalities and presenting clinical features. Methods: A total of 16 patients with MMD (2 males and 14 females) were invited to participate in the study, as were healthy controls (HCs) with the same number and sex ratio. In this study, the dynamic amplitude of low-frequency fluctuation (dALFF) was utilized to assess changes in spontaneous brain activity. Moreover, we also used correlation analysis to study the relationship among the measured mean of dALFF, behavioral performances, and the retinal nerve fiber layer and the Hospital Anxiety and Depression Scale (HADS) score to explore the potential relationship between MMD and anxiety and depression. Results: Our study reveals that in MMD, dALFF levels decreased in the left lingual gyrus, right insula, and occipital lobe. Discussion: In this study, we found and discussed the potential relationship between the abnormal activities in multiple brain regions and related functional network disorders in patients with MMD, as well as the damage to brain regions that process emotion and vision, in the hopes of providing more ideas for the clinical diagnosis and treatment of MMD.
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Affiliation(s)
- Qian Wei
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
- Queen Mary School, Nanchang University, Nanchang, China
| | - Xiao-Yu Wang
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Li-Juan Zhang
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Chen-Yu Yu
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Hui-Ye Shu
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Xu-Lin Liao
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - San-Hua Xu
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Ting Su
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Min Kang
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Yi Shao
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
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Yang L, Lu J, Li D, Xiang J, Yan T, Sun J, Wang B. Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models. Brain Sci 2023; 13:1133. [PMID: 37626490 PMCID: PMC10452161 DOI: 10.3390/brainsci13081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.
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Affiliation(s)
- Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Ting Yan
- Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, China;
| | - Jie Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
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Gasparinatou MM, Matzakos N, Vlamos P. Spiking Neural Networks and Mathematical Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:69-79. [PMID: 37486481 DOI: 10.1007/978-3-031-31982-2_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Neural networks are applied in various scientific fields such as medicine, engineering, pharmacology, etc. Investigating operations of neural networks refers to estimating the relationship among single neurons and their contributions to the network as well. Hence, studying a single neuron is an essential process to solve complex brain problems. Mathematical models that simulate neurons and the way they transmit information are proven to be an indispensable tool for neuroscientists. Constructing appropriate mathematical models to simulate information transmission of a biological neural network is a challenge for researchers, as in the real world, identical neurons in terms of their electrophysiological characteristics in different brain regions do not contribute in the same way to information transmission within a neural network due to the intrinsic characteristics. This review highlights four mathematical, single-compartment models: Hodgkin-Huxley, Izhikevich, Leaky Integrate, and Fire and Morris-Lecar, and discusses comparison among them in terms of their biological plausibility, computational complexity, and applications, according to modern literature.
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Alexandersen CG, de Haan W, Bick C, Goriely A. A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer's disease. J R Soc Interface 2023; 20:20220607. [PMID: 36596460 PMCID: PMC9810432 DOI: 10.1098/rsif.2022.0607] [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] [Indexed: 01/05/2023] Open
Abstract
Alzheimer's disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer's patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.
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Affiliation(s)
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Christian Bick
- Mathematical Institute, University of Oxford, Oxford, UK,Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands,Amsterdam Neuroscience—Systems and Network Neuroscience, Amsterdam, The Netherlands
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, UK
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7
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Cakir Y. Computational neuronal correlation with enhanced synchronized activity in the basal ganglia and the slowing of thalamic theta and alpha rhythms in Parkinson's disease. Eur J Neurosci 2021; 54:5203-5223. [PMID: 34192822 DOI: 10.1111/ejn.15374] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 06/19/2021] [Accepted: 06/19/2021] [Indexed: 11/27/2022]
Abstract
The aim of this work is computationally to correlate the synchronized neuronal activity of basal ganglia and slowing in theta and alpha rhythms in electroencephalogram (EEG) signal in thalamic region in case of dopamine depletion and decrease of synaptic connections. The used network topology is a scale-free network with constant node degree. The dopamine-modulated type Izikhevich neuron model is used for modeling the striatal region, consisting of fast-spiking interneurons, D1 and D2 type dopamine expressing medium spiny neurons. On the other hand, the ordinary Izikhevich neuron model is used in the modeling of extrastriatal basal ganglia (BG) regions where globus pallidus (GP) subregion neurons have also dopamine-dependent parameters. The thalamic region of the network is mass modeled including inhibitory input from basal ganglia. Depending on the decrease of synaptic connections and dopamine level, the synchronization among basal ganglia neuron populations is investigated. The effect of synaptic delay on synchronization is also considered. It is observed that the decrease of dopamine neurotransmitter and decrease in the number of synaptic connections cause an increased synchronous activity in BG. Also, slowing in theta and alpha bands in thalamus EEG signals is observed. This shows the causal relation between synchronization and power shifting to lower frequency components in the case of neurodegenerative diseases such as Parkinson's disease (PD).
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Affiliation(s)
- Yuksel Cakir
- Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey
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8
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Ibañez A, Fittipaldi S, Trujillo C, Jaramillo T, Torres A, Cardona JF, Rivera R, Slachevsky A, García A, Bertoux M, Baez S. Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes. J Alzheimers Dis 2021; 83:227-248. [PMID: 34275897 PMCID: PMC8461708 DOI: 10.3233/jad-210163] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. OBJECTIVE We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. METHODS Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. RESULTS Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition + CS), and bvFTD versus AD (71.7%, social cognition + CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. CONCLUSION Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.
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Affiliation(s)
- Agustín Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Global Brain Health Institute, University of California, San Francisco, CA, USA
- Global Brain Health Institute, Trinity College Dublin (TCD), Dublin, Ireland
| | - Sol Fittipaldi
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | | | - Tania Jaramillo
- Instituto de Psicología, Universidad del Valle, Cali, Colombia
| | | | - Juan F. Cardona
- Instituto de Psicología, Universidad del Valle, Cali, Colombia
| | - Rodrigo Rivera
- Neuroradiology Department, Instituto de Neurocirugia, Universidad de Chile, Santiago, Chile
| | - Andrea Slachevsky
- Geroscience Center for Brain Health and Metabolism (GERO), Faculty of Medicine, University of Chile, Santiago, Chile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department - ICBM, Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Adolfo García
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Global Brain Health Institute, University of California, San Francisco, CA, USA
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Maxime Bertoux
- Lille Center of Excellence for Neurodegenerative Disorders (LICEND), CHU Lille, U1172 - Lille Neurosciences & Cognition, Université de Lille, Inserm, Lille, France
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Li Q, Cao X, Liu S, Li Z, Wang Y, Cheng L, Yang C, Xu Y. Dynamic Alterations of Amplitude of Low-Frequency Fluctuations in Patients With Drug-Naïve First-Episode Early Onset Schizophrenia. Front Neurosci 2020; 14:901. [PMID: 33122982 PMCID: PMC7573348 DOI: 10.3389/fnins.2020.00901] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 08/03/2020] [Indexed: 12/26/2022] Open
Abstract
Abnormalities in static neural activity have been widely reported in early onset schizophrenia (EOS). However, dynamic brain activity alterations over time in EOS are unclear. Here, we investigated whether temporal dynamic changes in spontaneous neural activity are influenced by EOS. A total of 78 drug-naïve first-episode patients with EOS and 90 healthy controls (HCs) were enrolled in this study. Dynamic amplitude of low-frequency fluctuations (dALFF) was performed to examine the abnormal time-varying local neural activity in EOS. Furthermore, we investigated the relationships between abnormalities in dALFF variability and clinical characteristics in EOS patients. Compared to HCs, EOS patients showed significantly decreased dALFF variability in the bilateral precuneus, right superior marginal gyrus, right post-central gyrus and increased dALFF in the right middle temporal gyrus (MTG). Moreover, increased dALFF variability in MTG was negatively associated with negative symptoms in EOS. Our findings reveal increased dynamic local neural activity in higher order networks of the cortex, suggesting that enhanced spontaneous brain activity may be a predominant neural marker for brain maturation. In addition, decreased dALFF variability in the default mode network (DMN) and limbic system may reflect unusually dynamic neural activity. This dysfunctional brain activity could distinguish between patients and HCs and deepen our understanding of the pathophysiological mechanisms of EOS.
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Affiliation(s)
- Qiang Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Xiaohua Cao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Zexuan Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yanfang Wang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Long Cheng
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Chengxiang Yang
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yong Xu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.,Department of Mental Health, Shanxi Medical University, Taiyuan, China
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10
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Yu C, Lu W, Qiu J, Wang F, Li J, Wang L. Alterations of the Whole Cerebral Blood Flow in Patients With Different Total Cerebral Small Vessel Disease Burden. Front Aging Neurosci 2020; 12:175. [PMID: 32655393 PMCID: PMC7324936 DOI: 10.3389/fnagi.2020.00175] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/19/2020] [Indexed: 12/18/2022] Open
Abstract
Background Cerebral small vessel disease (CSVD) is a common age-related vascular disease of the brain associated with slowly accumulating tissue damage. At present, total CSVD burden score is a commonly used method to evaluate the severity of the disease. Purpose To observe whether global and regional cerebral perfusion is related to total CSVD score and to explore global and regional cerebral blood flow (CBF) changes in patients with different degrees of CSVD. Methods We collected 130 subjects with different total burden score of CSVD (0 point: 33 subjects, 1 point: 39 subjects, 2 points: 24 subjects, 3 points: 24 subjects, 4 points: 10 subjects). Total CSVD burden score was evaluated by clinically routine sequences (T2WI, T2-FLAIR, T1WI, DWI, and SWAN sequence). Global and regional CBF were calculated and correlation analysis was used to investigate the relationship between total CSVD score and CBF of the whole brain and several brain regions. Results The analysis results showed that there was a negative correlation between total CSVD burden score and global CBF (r = −0.33, p = 0.001). Total CSVD burden score also had moderately negative correlations with CBF of almost all the brain regions. Conclusion CSVD is a disease that affects the whole brain. With the increase of total CSVD burden score, the global and regional CBF decreased. The CSVD total burden score could be used to evaluate the overall condition of brain perfusion.
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Affiliation(s)
- Chunyan Yu
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Weizhao Lu
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Jianfeng Qiu
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Feng Wang
- The Second Affiliated Hospital of Shandong First Medical University, Tai'an, China
| | | | - Liru Wang
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
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