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Kasper J, Caspers S, Lotter LD, Hoffstaedter F, Eickhoff SB, Dukart J. Resting-State Changes in Aging and Parkinson's Disease Are Shaped by Underlying Neurotransmission: A Normative Modeling Study. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:986-997. [PMID: 38679325 DOI: 10.1016/j.bpsc.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/15/2024] [Accepted: 04/16/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Human healthy and pathological aging is linked to a steady decline in brain resting-state activity and connectivity measures. The neurophysiological mechanisms that underlie these changes remain poorly understood. METHODS Making use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we tested in the UK Biobank cohort (n = 25,917) whether and how age- and Parkinson's disease-related resting-state changes in commonly applied local and global activity and connectivity measures colocalize with underlying neurotransmitter systems. RESULTS We found that the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic, and glutamatergic neurotransmission correlated with age-related changes across functional activity and connectivity measures. Colocalization patterns in Parkinson's disease deviated from normative aging trajectories for these, as well as for cholinergic and GABAergic (gamma-aminobutyric acidergic) neurotransmission. The deviation from normal colocalization of brain function and GABAA correlated with disease duration. CONCLUSIONS These findings provide new insights into molecular mechanisms underlying age- and Parkinson's-related brain functional changes by extending the existing evidence elucidating the vulnerability of specific neurochemical attributes to normal aging and Parkinson's disease. The results particularly indicate that alongside dopamine and serotonin, increased vulnerability of glutamatergic, cholinergic, and GABAergic systems may also contribute to Parkinson's disease-related functional alterations. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms that underlie specific clinical conditions.
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Affiliation(s)
- Jan Kasper
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Leon D Lotter
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Max Planck School of Cognition, Leipzig, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Juergen Dukart
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany.
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Li Z, Huang C, Zhao X, Gao Y, Tian S. Abnormal postcentral gyrus voxel-mirrored homotopic connectivity as a biomarker of mild cognitive impairment: A resting-state fMRI and support vector machine analysis. Exp Gerontol 2024; 195:112547. [PMID: 39168359 DOI: 10.1016/j.exger.2024.112547] [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: 05/15/2024] [Revised: 08/11/2024] [Accepted: 08/14/2024] [Indexed: 08/23/2024]
Abstract
BACKGROUND While patients affected by mild cognitive impairment (MCI) exhibit characteristic voxel-mirrored homotopic connectivity (VMHC) alterations, the ability of such VMHC abnormalities to predict the diagnosis of MCI in these patients remains uncertain. As such, this study was performed to evaluate the potential role of VMHC abnormalities in the diagnosis of MCI. METHODS MCI patients and healthy controls (HCs) were enrolled and subjected to resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing. VMHC and support vector machine (SVM) techniques were then used to examine the collected imaging data. RESULTS Totally, 53 MCI patients and 68 healthy controls were recruited. Compared to HCs, MCI patients presented with an increase in postcentral gyrus VMHC. SVM classification demonstrated the ability of postcentral gyrus VMHC values to classify HCs and MCI patients with accuracy, sensitivity, and specificity values of 63.64 %, 71.69 %, and 89.71 %, respectively. CONCLUSION VMHC abnormalities in the postcentral gyrus may be mechanistically involved in the pathophysiological progression of MCI patients, and these abnormal VMHC patterns may also offer utility as a neuroimaging biomarker for MCI patient diagnosis.
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Affiliation(s)
- Ziruo Li
- Department of General Practice, Tianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan 430064, Hubei, China
| | - Chunyan Huang
- Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan 430060, Hubei, China
| | - Xingfu Zhao
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi 214151, Jiangsu, China
| | - Yujun Gao
- Department of Psychiatry, Wuhan Wuchang Hospital, Wuhan University of Science and Technology, Wuhan 430063, Hubei, China.
| | - Shenglan Tian
- Department of General Practice, Tianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan 430064, Hubei, China.
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Morand A, Laniepce A, Cabé N, Boudehent C, Segobin S, Pitel AL. Compensation patterns and altered functional connectivity in alcohol use disorder with and without Korsakoff's syndrome. Brain Commun 2024; 6:fcae294. [PMID: 39309684 PMCID: PMC11414044 DOI: 10.1093/braincomms/fcae294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/12/2024] [Accepted: 09/17/2024] [Indexed: 09/25/2024] Open
Abstract
Alcohol use disorder is a chronic disease characterized by an inappropriate pattern of drinking, resulting in negative consequences for the individual's physical, mental and social health. Korsakoff's syndrome is a complication of alcohol use disorder and is characterized by severe memory and executive deficits. The fronto-cerebellar and Papez circuits are structurally affected in patients with alcohol use disorder with and without Korsakoff's syndrome. The first objective of the present study was to measure the effect of chronic and excessive alcohol consumption on resting-state functional connectivity of these two functional brain networks. The second objective was to identify, for the first time, resting-state functional connectivity abnormalities specific to amnesic patients with Korsakoff's syndrome. In the present study, a neuropsychological assessment and a resting-state functional magnetic resonance imaging examination were conducted in 31 healthy controls (43.6 ± 6.1 years) and 46 patients (46.6 ± 9.1 years) with alcohol use disorder including 14 patients with Korsakoff's syndrome (55.5 ± 5.3 years) to examine the effect of chronic and heavy alcohol consumption on functional connectivity of the fronto-cerebellar and the Papez circuits at rest and the specificity of functional connectivity changes in Korsakoff's syndrome compared to alcohol use disorder without Korsakoff's syndrome. The resting-state functional connectivity analyses focused on the nodes of the fronto-cerebellar and Papez circuits and combined region of interest and graph theory approaches, and whether these alterations are associated with the neuropsychological profile. In patients pooled together compared to controls, lower global efficiency was observed in the fronto-cerebellar circuit. In addition, certain regions of the fronto-cerebellar and Papez circuits were functionally hyperconnected at rest, which positively correlated with executive functions. Patients with Korsakoff's syndrome showed lower resting-state functional connectivity, lower local and global efficiency within the Papez circuit compared to those without Korsakoff's syndrome. Resting-state functional connectivity positively correlated with several cognitive scores in patients with Korsakoff's syndrome. The fronto-cerebellar and Papez circuits, two normally well-segregated networks, are functionally altered by alcohol use disorder. The Papez circuit attempts to compensate for deficits in the fronto-cerebellar circuit, albeit insufficiently as evidenced by patients' overall lower cognitive performance. Korsakoff's syndrome is characterized by altered functional connectivity in the Papez circuit known to be centrally involved in memory.
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Affiliation(s)
- Alexandrine Morand
- Normandie Université, UNICAEN, INSERM, U1237, PhIND ‘Physiopathology and Imaging of Neurological Disorders’, Team NeuroPresage, Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
- Laboratoire DysCo, Université Paris 8 Vincennes-Saint-Denis, 93526 Saint-Denis, France
| | - Alice Laniepce
- Normandie Université, UNICAEN, INSERM, U1237, PhIND ‘Physiopathology and Imaging of Neurological Disorders’, Team NeuroPresage, Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
- Normandie Université, UNIROUEN, CRFDP (EA 7475), 76000 Rouen, France
| | - Nicolas Cabé
- Normandie Université, UNICAEN, INSERM, U1237, PhIND ‘Physiopathology and Imaging of Neurological Disorders’, Team NeuroPresage, Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
- Service d’addictologie, Centre Hospitalier Universitaire de Caen, 14000 Caen, France
| | - Céline Boudehent
- Normandie Université, UNICAEN, INSERM, U1237, PhIND ‘Physiopathology and Imaging of Neurological Disorders’, Team NeuroPresage, Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
- Service d’addictologie, Centre Hospitalier Universitaire de Caen, 14000 Caen, France
| | - Shailendra Segobin
- Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, GIP Cyceron, 14000 Caen, France
| | - Anne-Lise Pitel
- Normandie Université, UNICAEN, INSERM, U1237, PhIND ‘Physiopathology and Imaging of Neurological Disorders’, Team NeuroPresage, Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
- Institut Universitaire de France (IUF), 75231 Paris, France
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Xiong Z, Bai M, Wang Z, Wang R, Tian C, Wang L, Nie L, Zeng X. Resting-state fMRI network efficiency as a mediator in the relationship between the glymphatic system and cognitive function in obstructive sleep apnea hypopnea syndrome: Insights from a DTI-ALPS investigation. Sleep Med 2024; 119:250-257. [PMID: 38704873 DOI: 10.1016/j.sleep.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/15/2024] [Accepted: 05/02/2024] [Indexed: 05/07/2024]
Abstract
INTRODUCTION Obstructive sleep apnea hypopnea syndrome (OSAHS) is associated with cognitive impairment and physiological complications, necessitating further understanding of its mechanisms. This study investigates the relationship between glymphatic system function, brain network efficiency, and cognitive impairment in OSAHS patients using diffusion tensor image analysis along the perivascular space (DTI-ALPS) and resting-state fMRI. MATERIALS AND METHODS This study included 31 OSAHS patients and 34 age- and gender-matched healthy controls (HC). All participants underwent GE 3.0T magnetic resonance imaging (MRI) with diffusion tensor image (DTI) and resting-state fMRI scans. The DTI-ALPS index and brain functional networks were assessed. Differences between groups and correlations with clinical characteristics were analyzed. Additionally, the mediating role of brain network efficiency was explored. Finally, receiver operating characteristics (ROC) analysis assessed diagnostic performance. RESULTS OSAHS patients had significantly lower ALPS-index (1.268 vs. 1.431, p < 0.0001) and moderate negative correlation with Apnea Hypopnea Index (AHI) (r = -0.389, p = 0.031), as well as moderate positive correlation with Montreal Cognitive Assessment (MoCA) (r = 0.525, p = 0.002). Moreover, global efficiency (Eg) of the brain network was positively correlated with the ALPS-index and MoCA scores in OSAHS patients (r = 0.405, p = 0.024; r = 0.56, p = 0.001, respectively). Furthermore, mediation analysis showed that global efficiency partially mediated the impact of glymphatic system dysfunction on cognitive impairment in OSAHS patients (indirect effect = 4.58, mediation effect = 26.9 %). The AUROC for identifying OSAHS and HC was 0.80 (95 % CI 0.69 to 0.91) using an ALPS-index cut-off of 1.35. CONCLUSIONS OSAHS patients exhibit decreased ALPS-index, indicating impaired glymphatic system function. Dysfunction of the glymphatic system can affect cognitive function in OSAHS by disrupting brain functional network, suggesting a potential underlying pathological mechanism. Additionally, preliminary findings suggest that the ALPS-index may offer promise as a potential indicator for OSAHS.
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Affiliation(s)
- Zhenliang Xiong
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China; Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Mingxian Bai
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Zhongxin Wang
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, China
| | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Chong Tian
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Lihui Wang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Lisha Nie
- GE Healthcare, MR Research China, Beijing, China.
| | - Xianchun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China.
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Zamani J, Jafadideh AT. Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features. RESEARCH SQUARE 2024:rs.3.rs-4549428. [PMID: 38947050 PMCID: PMC11213162 DOI: 10.21203/rs.3.rs-4549428/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.
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Affiliation(s)
- Jafar Zamani
- Department of Psychiatry and Behavioral Sciences, Stanford University, California, USA
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Amiri S, van den Berg M, Nazem-Zadeh MR, Verhoye M, Amiri M, Keliris GA. Nodal degree centrality in the default mode-like network of the TgF344-AD Alzheimer's disease rat model as a measure of early network alterations. NPJ AGING 2024; 10:29. [PMID: 38902224 PMCID: PMC11190202 DOI: 10.1038/s41514-024-00151-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/19/2024] [Indexed: 06/22/2024]
Abstract
This study investigates brain network alterations in the default mode-like network (DMLN) at early stages of disease progression in a rat model of Alzheimer's disease (AD) with application in the development of early diagnostic biomarkers of AD in translational studies. Thirteen male TgF344-AD (TG) rats, and eleven male wild-types (WT) littermates underwent longitudinal resting-state fMRI at the age of 4 and 6 months (pre and early-plaque stages of AD). Alterations in connectivity within DMLN were characterized by calculating the nodal degree (ND), a graph theoretical measure of centrality. The ND values of the left CA2 subregion of the hippocampus was found to be significantly lower in the 4-month-old TG cohort compared to the age-matched WT littermates. Moreover, a lower ND value (hypo-connectivity) was observed in the right prelimbic cortex (prL) and basal forebrain in the 6-month-old TG cohort, compared to the same age WT cohort. Indeed, the ND pattern in the DMLN in both TG and WT cohorts showed significant differences across the two time points that represent pre-plaque and early plaque stages of disease progression. Our findings indicate that lower nodal degree (hypo-connectivity) in the left CA2 in the pre-plaque stage of AD and hypo-connectivity between the basal forebrain and the DMLN regions in the early-plaque stage demonstrated differences in comparison to healthy controls. These results suggest that a graph-theoretical measure such as the nodal degree, can characterize brain networks and improve our insights into the mechanisms underlying Alzheimer's disease.
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Affiliation(s)
- Saba Amiri
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Monica van den Berg
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Mohammad-Reza Nazem-Zadeh
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
- Department of neuroscience, Monash university, Melbourne, Vic, Australia
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Mahmood Amiri
- Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
| | - Georgios A Keliris
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium.
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
- Institute of Computer Science, Hellas Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece.
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Yu WY, Sun TH, Hsu KC, Wang CC, Chien SY, Tsai CH, Yang YW. Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Comput Biol Med 2024; 176:108621. [PMID: 38763067 DOI: 10.1016/j.compbiomed.2024.108621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.
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Affiliation(s)
- Wei-Yang Yu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan; Neuroscience Laboratory, Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Neuroscience and Brain Disease Center, College of Medicine, China Medical University, 40402, Taichung, Taiwan
| | - Yu-Wan Yang
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan.
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Tang L, Kebaya LMN, Altamimi T, Kowalczyk A, Musabi M, Roychaudhuri S, Vahidi H, Meyerink P, de Ribaupierre S, Bhattacharya S, de Moraes LTAR, St Lawrence K, Duerden EG. Altered resting-state functional connectivity in newborns with hypoxic ischemic encephalopathy assessed using high-density functional near-infrared spectroscopy. Sci Rep 2024; 14:3176. [PMID: 38326455 PMCID: PMC10850364 DOI: 10.1038/s41598-024-53256-0] [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: 11/06/2023] [Accepted: 01/30/2024] [Indexed: 02/09/2024] Open
Abstract
Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify biomarkers of brain health. Functional near-infrared spectroscopy (fNIRS) can assess resting-state functional connectivity (RSFC) at the bedside. We acquired resting-state fNIRS data from 21 neonates with HIE (postmenstrual age [PMA] = 39.96), in 19 neonates the scans were acquired post-therapeutic hypothermia (TH), and from 20 term-born healthy newborns (PMA = 39.93). Twelve HIE neonates also underwent resting-state functional magnetic resonance imaging (fMRI) post-TH. RSFC was calculated as correlation coefficients amongst the time courses for fNIRS and fMRI data, respectively. The fNIRS and fMRI RSFC maps were comparable. RSFC patterns were then measured with graph theory metrics and compared between HIE infants and healthy controls. HIE newborns showed significantly increased clustering coefficients, network efficiency and modularity compared to controls. Using a support vector machine algorithm, RSFC features demonstrated good performance in classifying the HIE and healthy newborns in separate groups. Our results indicate the utility of fNIRS-connectivity patterns as potential biomarkers for HIE and fNIRS as a new bedside tool for newborns with HIE.
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Affiliation(s)
- Lingkai Tang
- Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada
| | - Lilian M N Kebaya
- Neuroscience, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
- Neonatal-Perinatal Medicine, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Paediatrics, Division of Neonatal-Perinatal Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Talal Altamimi
- Neonatal-Perinatal Medicine, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Alexandra Kowalczyk
- Neonatal-Perinatal Medicine, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Melab Musabi
- Neonatal-Perinatal Medicine, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Sriya Roychaudhuri
- Neonatal-Perinatal Medicine, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Homa Vahidi
- Neuroscience, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Paige Meyerink
- Neonatal-Perinatal Medicine, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Sandrine de Ribaupierre
- Neuroscience, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
- Clinical Neurological Sciences, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Soume Bhattacharya
- Neonatal-Perinatal Medicine, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - Keith St Lawrence
- Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada
- Medical Biophysics, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada
| | - Emma G Duerden
- Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada.
- Neuroscience, Schulich Faculty of Medicine and Dentistry, Western University, London, ON, Canada.
- Applied Psychology, Faculty of Education, Western University, 1137 Western Rd, London, ON, N6G 1G7, Canada.
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Zhang L, Qu J, Ma H, Chen T, Liu T, Zhu D. Exploring Alzheimer's disease: a comprehensive brain connectome-based survey. PSYCHORADIOLOGY 2024; 4:kkad033. [PMID: 38333558 PMCID: PMC10848159 DOI: 10.1093/psyrad/kkad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 02/10/2024]
Abstract
Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it is undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Junqi Qu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Haotian Ma
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Tong Chen
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Tianming Liu
- Department of Computer Science, The University of Georgia, Athens, GA 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
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10
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Fedorov A, Geenjaar E, Wu L, Sylvain T, DeRamus TP, Luck M, Misiura M, Mittapalle G, Hjelm RD, Plis SM, Calhoun VD. Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. Neuroimage 2024; 285:120485. [PMID: 38110045 PMCID: PMC10872501 DOI: 10.1016/j.neuroimage.2023.120485] [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: 08/24/2023] [Revised: 11/15/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023] Open
Abstract
In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.
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Affiliation(s)
- Alex Fedorov
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA.
| | - Eloy Geenjaar
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | | | - Thomas P DeRamus
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Margaux Luck
- Mila - Quebec AI Institute, Montréal, QC, Canada
| | - Maria Misiura
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Girish Mittapalle
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - R Devon Hjelm
- Mila - Quebec AI Institute, Montréal, QC, Canada; Apple Machine Learning Research, Seattle, WA, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
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11
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Behfar Q, Richter N, Kural M, Clemens A, Behfar SK, Folkerts AK, Fassbender R, Kalbe E, Fink GR, Onur OA. Improved connectivity and cognition due to cognitive stimulation in Alzheimer's disease. Front Aging Neurosci 2023; 15:1140975. [PMID: 37662551 PMCID: PMC10470843 DOI: 10.3389/fnagi.2023.1140975] [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: 01/09/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Background Due to the increasing prevalence of Alzheimer's disease (AD) and the limited efficacy of pharmacological treatment, the interest in non-pharmacological interventions, e.g., cognitive stimulation therapy (CST), to improve cognitive dysfunction and the quality of life of AD patients are on a steady rise. Objectives Here, we examined the efficacy of a CST program specifically conceptualized for AD dementia patients and the neural mechanisms underlying cognitive or behavioral benefits of CST. Methods Using neuropsychological tests and MRI-based measurements of functional connectivity, we examined the (neuro-) psychological status and network changes at two time points: pre vs. post-stimulation (8 to 12 weeks) in the intervention group (n = 15) who received the CST versus a no-intervention control group (n = 15). Results After CST, we observed significant improvement in the Mini-Mental State Examination (MMSE), the Alzheimer's Disease Assessment Scale, cognitive subsection (ADAS-cog), and the behavioral and psychological symptoms of dementia (BPSD) scores. These cognitive improvements were associated with an up-regulated functional connectivity between the left posterior hippocampus and the trunk of the left postcentral gyrus. Conclusion Our data indicate that CST seems to induce short-term global cognition and behavior improvements in mild to moderate AD dementia and enhances resting-state functional connectivity in learning- and memory-associated brain regions. These convergent results prove that even in mild to moderate dementia AD, neuroplasticity can be harnessed to alleviate cognitive impairment with CST.
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Affiliation(s)
- Qumars Behfar
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Juelich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Juelich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Merve Kural
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anne Clemens
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Stefan Kambiz Behfar
- Department of Information Systems, Geneva School of Business Administration (HES-SO Genéve), Carouge, Switzerland
| | - Ann-Kristin Folkerts
- Medical Psychology Neuropsychology and Gender Studies and Center for Neuropsychological Diagnostics and Intervention (CeNDI), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ronja Fassbender
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Juelich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Elke Kalbe
- Medical Psychology Neuropsychology and Gender Studies and Center for Neuropsychological Diagnostics and Intervention (CeNDI), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Juelich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Oezguer A. Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Juelich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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12
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Wang SM, Kang DW, Um YH, Kim S, Kim REY, Kim D, Lee CU, Lim HK. Cognitive Normal Older Adults with APOE-2 Allele Show a Distinctive Functional Connectivity Pattern in Response to Cerebral Aβ Deposition. Int J Mol Sci 2023; 24:11250. [PMID: 37511008 PMCID: PMC10380008 DOI: 10.3390/ijms241411250] [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: 05/30/2023] [Revised: 06/29/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
The ε2 allele of apolipoprotein E (ε2) has neuroprotective effects against beta-amyloid (Aβ) pathology in Alzheimer's disease (AD). However, its impact on the functional connectivity and hub efficiency in cognitively normal older adults (CN) with ε2 is unclear. We investigated the functional connectivity differences in the default mode network (DMN), salience network, and central executive network (CEN) between A-PET-negative (N = 29) and A-PET-positive (N = 15) CNs with ε2/ε2 or ε2/ε3 genotypes. The A-PET-positive CNs exhibited a lower anterior DMN functional connectivity, higher posterior DMN functional connectivity, and increased CEN functional connectivity compared to the A-PET-negative CNs. Cerebral Aβ retention was negatively correlated with anterior DMN functional connectivity and positively correlated with posterior DMN and anterior CEN functional connectivity. A graph theory analysis showed that the A-PET-positive CNs displayed a higher betweenness centrality in the middle frontal gyrus (left) and medial fronto-parietal regions (left). The betweenness centrality in the middle frontal gyrus (left) was positively correlated with Aβ retention. Our findings reveal a reversed anterior-posterior dissociation in the DMN functional connectivity and heightened CEN functional connectivity in A-PET-positive CNs with ε2. Hub efficiencies, measured by betweenness centrality, were increased in the DMN and CEN of the A-PET-positive CNs with ε2. These results suggest unique functional connectivity responses to Aβ pathology in CN individuals with ε2.
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Affiliation(s)
- Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Regina E Y Kim
- Research Institute, Neurophet Inc., Seoul 08380, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul 08380, Republic of Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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13
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Chen B, Cui W, Wang S, Sun A, Yu H, Liu Y, He J, Fan G. Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages. Hum Brain Mapp 2023; 44:2176-2190. [PMID: 36661217 PMCID: PMC10028675 DOI: 10.1002/hbm.26201] [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: 06/21/2022] [Revised: 12/08/2022] [Accepted: 12/30/2022] [Indexed: 01/21/2023] Open
Abstract
Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.
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Affiliation(s)
- Boyu Chen
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Wenzhuo Cui
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Shanshan Wang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Anlan Sun
- Yizhun Medical AI Co. Ltd, Beijing, People's Republic of China
| | - Hongmei Yu
- Department of Neurology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yu Liu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Jiachuan He
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
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14
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Zhu B, Li Q, Xi Y, Li X, Yang Y, Guo C. Local Brain Network Alterations and Olfactory Impairment in Alzheimer's Disease: An fMRI and Graph-Based Study. Brain Sci 2023; 13:brainsci13040631. [PMID: 37190596 DOI: 10.3390/brainsci13040631] [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: 01/19/2023] [Revised: 03/10/2023] [Accepted: 03/25/2023] [Indexed: 05/17/2023] Open
Abstract
Alzheimer's disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening.
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Affiliation(s)
- Bing Zhu
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
- Jilin Provincial Key Laboratory for Numerical Simulation, Jilin Normal University, Siping 136000, China
| | - Qi Li
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
- Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528437, China
| | - Yang Xi
- Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528437, China
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| | - Xiujun Li
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
| | - Yu Yang
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun 130021, China
| | - Chunjie Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
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15
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Salazar AP, McGregor HR, Hupfeld KE, Beltran NE, Kofman IS, De Dios YE, Riascos RF, Reuter-Lorenz PA, Bloomberg JJ, Mulavara AP, Wood SJ, Seidler R. Changes in working memory brain activity and task-based connectivity after long-duration spaceflight. Cereb Cortex 2023; 33:2641-2654. [PMID: 35704860 PMCID: PMC10016051 DOI: 10.1093/cercor/bhac232] [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: 02/15/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
We studied the longitudinal effects of approximately 6 months of spaceflight on brain activity and task-based connectivity during a spatial working memory (SWM) task. We further investigated whether any brain changes correlated with changes in SWM performance from pre- to post-flight. Brain activity was measured using functional magnetic resonance imaging while astronauts (n = 15) performed a SWM task. Data were collected twice pre-flight and 4 times post-flight. No significant effects on SWM performance or brain activity were found due to spaceflight; however, significant pre- to post-flight changes in brain connectivity were evident. Superior occipital gyrus showed pre- to post-flight reductions in task-based connectivity with the rest of the brain. There was also decreased connectivity between the left middle occipital gyrus and the left parahippocampal gyrus, left cerebellum, and left lateral occipital cortex during SWM performance. These results may reflect increased visual network modularity with spaceflight. Further, increased visual and visuomotor connectivity were correlated with improved SWM performance from pre- to post-flight, while decreased visual and visual-frontal cortical connectivity were associated with poorer performance post-flight. These results suggest that while SWM performance remains consistent from pre- to post-flight, underlying changes in connectivity among supporting networks suggest both disruptive and compensatory alterations due to spaceflight.
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Affiliation(s)
| | - Heather R McGregor
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Kathleen E Hupfeld
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | | | - Igor S Kofman
- KBR, 601 Jefferson Street, Houston, TX 77002, United States
| | - Yiri E De Dios
- KBR, 601 Jefferson Street, Houston, TX 77002, United States
| | - Roy F Riascos
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, 6431 Fannin Street, Houston, TX 77030, United States
| | - Patricia A Reuter-Lorenz
- Department of Psychology, University of Michigan, 530 Church St., Ann Arbor, MI 48109, United States
| | - Jacob J Bloomberg
- NASA Johnson Space Center, 2101 E NASA Parkway, Houston, TX 77058, United States
| | | | - Scott J Wood
- NASA Johnson Space Center, 2101 E NASA Parkway, Houston, TX 77058, United States
| | - RachaelD Seidler
- Corresponding author: Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States.
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16
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Haddad SMH, Pieruccini-Faria F, Montero-Odasso M, Bartha R. Localized White Matter Tract Integrity Measured by Diffusion Tensor Imaging Is Altered in People with Mild Cognitive Impairment and Associated with Dual-Task and Single-Task Gait Speed. J Alzheimers Dis 2023; 92:1367-1384. [PMID: 36911933 DOI: 10.3233/jad-220476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
BACKGROUND Altered white matter (WM) tract integrity may contribute to mild cognitive impairment (MCI) and gait abnormalities. OBJECTIVE The purpose of this study was to determine whether diffusion tensor imaging (DTI) metrics were altered in specific portions of WM tracts in people with MCI and to determine whether gait speed variations were associated with the specific DTI metric changes. METHODS DTI was acquired in 44 people with MCI and 40 cognitively normal elderly controls (CNCs). Fractional anisotropy (FA) and radial diffusivity (RD) were measured along 18 major brain WM tracts using probabilistic tractography. The average FA and RD along the tracts were compared between the groups using MANCOVA and post-hoc tests. The tracts with FA or RD differences between the groups were examined using an along-tract exploratory analysis to identify locations that differed between the groups. Associations between FA and RD in whole tracts and in the segments of the tracts that differed between the groups and usual/dual-task gait velocities and gross cognition were examined. RESULTS Lower FA and higher RD was observed in right cingulum-cingulate gyrus endings (rh.ccg) of the MCI group compared to the CNC group. These changes were localized to the posterior portions of the rh.ccg and correlated with gait velocities. CONCLUSION Lower FA and higher RD in the posterior portion of the rh.ccg adjacent to the posterior cingulate suggests decreased microstructural integrity in the MCI group. The correlation of these metrics with gait velocities suggests an important role for this tract in maintaining normal cognitive-motor function.
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Affiliation(s)
- Seyyed M H Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Frederico Pieruccini-Faria
- Department of Medicine, Division of Geriatric Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada.,Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada.,Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
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17
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Altered structural and functional homotopic connectivity associated with the progression from mild cognitive impairment to Alzheimer's disease. Psychiatry Res 2023; 319:115000. [PMID: 36502711 DOI: 10.1016/j.psychres.2022.115000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/28/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022]
Abstract
The progressive mild cognitive impairment (pMCI) is associated with an increased risk of Alzheimer's disease (AD). Many studies have reported the disrupted brain alteration during the imminent conversion from pMCI to AD. However, the subtle difference of structural and functional of inter-hemispheric between pMCI and stable mild cognitive impairment (sMCI) remains unknown. In the present study, we scanned the multimodal magnetic resonance imaging of 38 sMCI, 26 pMCI, and 50 healthy controls (HC) and assessed the cognitive function. The voxel-mirrored homotopic connectivity (VMHC) and volume of corpus callosum were calculated. A structural equation modeling (SEM) was established to determine the relationships between the corpus callosum, the inter-hemispheric connectivity, and cognitive assessment. Compared to sMCI, pMCI exhibited decreased VMHC in insular and thalamus, and reduced volume of corpus callosum. SEM results showed that decreased inter-hemispheric connectivity was directly associated with cognitive impairment and corpus callosum atrophy, and corpus callosum atrophy indirectly caused cognitive impairment by mediating inter-hemispheric connectivity in pMCI. In conclusion, the destruction of homotopic connectivity is related to cognitive impairment, and the corpus callosum atrophy partially mediates the association between the homotopic connectivity and cognitive impairment in pMCI.
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18
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Zheng J, Jiao Z, Dai J, Liu T, Shi H. Abnormal cerebral micro-structures in end-stage renal disease patients related to mild cognitive impairment. Eur J Radiol 2022; 157:110597. [DOI: 10.1016/j.ejrad.2022.110597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/20/2022] [Accepted: 11/06/2022] [Indexed: 11/13/2022]
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19
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Moffat G, Zhukovsky P, Coughlan G, Voineskos AN. Unravelling the relationship between amyloid accumulation and brain network function in normal aging and very mild cognitive decline: a longitudinal analysis. Brain Commun 2022; 4:fcac282. [PMID: 36415665 PMCID: PMC9678202 DOI: 10.1093/braincomms/fcac282] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 07/29/2022] [Accepted: 10/31/2022] [Indexed: 06/27/2024] Open
Abstract
Pathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer's disease. The deposition of amyloid beta proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical Alzheimer's disease has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on the cross-sectional differences in the brains of patients with mild cognitive impairment or Alzheimer's disease and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes the early preclinical phases of neurodegeneration. We use a longitudinal approach to examine the brain changes that take place during the early stages of cognitive decline in the Open Access Series of Imaging Studies-3 data set. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two cognitive assessments and two amyloid scans). Over 90% of participants were healthy at baseline. Over 8-10 years, some participants progressed to very mild cognitive impairment (n = 48), while others stayed healthy (n = 226). Participants with cognitive decline show faster amyloid accumulation in the lateral temporal, motor and parts of the lateral prefrontal cortex. These changes in amyloid levels were linked to longitudinal increases in the functional connectivity of select networks, including default mode, frontoparietal and motor components. Our findings advance the understanding of amyloid staging and the corresponding changes in functional organization of large-scale brain networks during the progression of early preclinical Alzheimer's disease.
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Affiliation(s)
- Gemma Moffat
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Peter Zhukovsky
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Gillian Coughlan
- Rotman Research Institute, Baycrest Hospital, Toronto, ON, M6A 2E1, Canada
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
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20
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The impact of aging on human brain network target controllability. Brain Struct Funct 2022; 227:3001-3015. [DOI: 10.1007/s00429-022-02584-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 10/09/2022] [Indexed: 11/27/2022]
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21
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Zheng J, Sun Q, Wu X, Dou W, Pan J, Jiao Z, Liu T, Shi H. Brain Micro-Structural and Functional Alterations for Cognitive Function Prediction in the End-Stage Renal Disease Patients Undergoing Maintenance Hemodialysis. Acad Radiol 2022; 30:1047-1055. [PMID: 35879210 DOI: 10.1016/j.acra.2022.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/19/2022] [Accepted: 06/25/2022] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES The goal of this study was to investigate the relationship between altered brain micro-structure and function, and cognitive function in patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis. Specially, diffusion kurtosis imaging (DKI), the resting-state functional connectivity (FC) algorithm, and the least squares support vector regression machine (LSSVRM) were utilized to conduct our study. MATERIALS AND METHODS A total of 50 patients and 36 matched healthy controls were prospectively enrolled in our study. All subjects completed the Montreal cognitive assessment scale (MoCA) test. DKI and resting-state functional magnetic resonance imaging were measured. Relationship between DKI parameters, FC, and MoCA scores was evaluated. LSSVRM combined with the whale optimization algorithm (WOA) was used to predict cognitive function scores. RESULTS In ESRD patients, altered DKI metrics were identified in 12 brain regions. Furthermore, we observed changes in FC values based on regions of interest (ROIs) in nine brain regions, involved in default mode network (DMN), frontoparietal network (FPN), and the limbic system. Significant correlations among DKI values, FC values, and MoCA scores were found. To some extent, altered FC showed significant correlations with changed DKI parameters. Furthermore, optimized prediction models were applied to more accurately predict the cognitive function associated with ESRD patients. CONCLUSION Micro-structural and functional brain changes were found in ESRD patients, which may account for the onset of cognitive impairment in affected patients. These quantitative parameters combined with our optimized prediction model may be helpful to establish more reliable imaging markers to detect and monitor cognitive impairment associated with ESRD.
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Affiliation(s)
- Jiahui Zheng
- Department of Radiology, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, 29 Xinglong Lane, Changzhou, 213003, Jiangsu, China
| | - Qing Sun
- Department of Nephrology, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Xiangxiang Wu
- Department of Radiology, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, 29 Xinglong Lane, Changzhou, 213003, Jiangsu, China
| | - Weiqiang Dou
- GE Healthcare, MR Research China, Beijing, P.R., Beijing, China
| | - Jiechang Pan
- Department of Radiology, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, 29 Xinglong Lane, Changzhou, 213003, Jiangsu, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, 29 Xinglong Lane, Changzhou, 213003, Jiangsu, China.
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22
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Faldu KG, Shah JS. Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis. Expert Rev Mol Diagn 2022; 22:681-703. [PMID: 35855631 DOI: 10.1080/14737159.2022.2104639] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD. AREAS COVERED This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments. EXPERT OPINION It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.
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Affiliation(s)
- Khushboo Govind Faldu
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
| | - Jigna Samir Shah
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
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23
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Malagurski B, Deschwanden PF, Jäncke L, Mérillat S. Longitudinal functional connectivity patterns of the default mode network in healthy older adults. Neuroimage 2022; 259:119414. [PMID: 35760292 DOI: 10.1016/j.neuroimage.2022.119414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 06/18/2022] [Accepted: 06/23/2022] [Indexed: 11/29/2022] Open
Abstract
Cross-sectional studies have consistently identified age-associated alterations in default mode network (DMN) functional connectivity (FC). Yet, research on longitudinal trajectories of FC changes of the DMN in healthy aging is less conclusive. For the present study, we used a resting state functional MRI dataset drawn from the Longitudinal Healthy Aging Brain Database Project (LHAB) collected in 5 occasions over a course of 7 years (baseline N = 232, age range: 64-87 y, mean age = 70.85 y). FC strength changes within the DMN and its regions were investigated using a network-based statistical method suitable for the analysis of longitudinal data. The average DMN FC strength remained stable, however, various DMN components showed differential age- and time-related effects. Our results revealed a complex pattern of longitudinal change seen as decreases and increases of FC strength encompassing the majority of DMN regions, while age-related effects were negative and present in select brain areas. These findings testify to the growing importance of longitudinal studies using more sophisticated fine-grained tools needed to highlight the complexity of the functional reorganization of DMN with healthy aging.
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Affiliation(s)
- Brigitta Malagurski
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland
| | | | - Lutz Jäncke
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland; Division of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Susan Mérillat
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland.
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24
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Zamani J, Sadr A, Javadi AH. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative. PLoS One 2022; 17:e0267608. [PMID: 35727837 PMCID: PMC9212187 DOI: 10.1371/journal.pone.0267608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, United Kingdom
- School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
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25
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何 勇, 张 利, 房 珊, 曾 雅, 杨 威, 陈 卫, 邵 玉, 程 瑞, 叶 祥, 徐 冬. [The measurements of the similarity of dynamic brain functional network]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2022; 39:237-247. [PMID: 35523544 PMCID: PMC9927339 DOI: 10.7507/1001-5515.202103079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 12/28/2021] [Indexed: 06/14/2023]
Abstract
Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.
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Affiliation(s)
- 勇权 何
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 利 张
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 珊 房
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 雅琴 曾
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 威 杨
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 卫东 陈
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 玉玲 邵
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 瑞动 程
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 祥明 叶
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
| | - 冬溶 徐
- 华东师范大学 物理与电子科学学院 上海市磁共振重点实验室(上海 200062)Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China
- 杭州医学院附属人民医院 浙江省人民医院 康复科(杭州 310014)Department of Rehabilitation Medicine, Zhejiang Province People’s Hospital, Hangzhou Medical College, Hangzhou 310014, P. R. China
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26
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Coelho A, Magalhães R, Moreira PS, Amorim L, Portugal-Nunes C, Castanho T, Santos NC, Sousa N, Fernandes HM. A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort. Hum Brain Mapp 2022; 43:2419-2443. [PMID: 35274787 PMCID: PMC9057102 DOI: 10.1002/hbm.25773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 12/20/2021] [Accepted: 12/27/2021] [Indexed: 11/18/2022] Open
Abstract
Connectivity‐based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes—the foundations of brain network fingerprinting—by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole‐brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort—MGH‐HCP. State‐of‐the‐art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain's structural network involving the decrease of intra‐hemispheric, increase of inter‐hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.
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Affiliation(s)
- Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Pedro S Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Liliana Amorim
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Carlos Portugal-Nunes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Teresa Castanho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Henrique M Fernandes
- Center for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark.,Department of Psychiatry, University of Oxford, Oxford, UK
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27
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Hengenius JB, Bohnen NI, Rosso A, Huppert TJ, Rosano C. Cortico-striatal functional connectivity and cerebral small vessel disease: Contribution to mild Parkinsonian signs. J Neuroimaging 2022; 32:352-362. [PMID: 34957653 PMCID: PMC9119198 DOI: 10.1111/jon.12949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Mild Parkinsonian signs (MPS) are common in older adults. We hypothesized that MPS are associated with lower functional connectivity (FC) in dopamine-dependent cortico-striatal networks, and these associations vary with white matter hyperintensity (WMH), a risk factor for MPS. METHODS We examined resting-state functional MRI in 266 participants (mean age 83; 57% female; 41% African American) with data on MPS (Unified Parkinson's Disease Rating Scale), demographics, cognition, muscle-skeletal, and cardiometabolic health. FC between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with MPS, adjusted for covariates. Interactions of FC by WMH were tested; and analyses were repeated stratified by WMH above/below the median. RESULTS Compared to those without MPS, those with MPS had lower cortico-striatal FC in the left executive network (adjusted odds ratio [95% confidence interval], p-value: 0.188 [0.043, 0.824], .027). The interaction with WMH was p = .064; left executive FC was inversely associated with MPS for high WMH (0.077 [0.010, 0.599], .014) but not low WMH participants (1.245 [0.128, 12.132], .850). CONCLUSIONS MPS appear related to lower executive network FC, robust to adjustment for other risk factors, and stronger for those with higher burden of WMH. Future longitudinal studies should examine the interplay between cerebral small vessel disease and connectivity influencing MPS.
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Affiliation(s)
- James B. Hengenius
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Nicolaas I. Bohnen
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Andrea Rosso
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Theodore J. Huppert
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Caterina Rosano
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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28
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Van der Linden A, Hoehn M. Monitoring Neuronal Network Disturbances of Brain Diseases: A Preclinical MRI Approach in the Rodent Brain. Front Cell Neurosci 2022; 15:815552. [PMID: 35046778 PMCID: PMC8761853 DOI: 10.3389/fncel.2021.815552] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/06/2021] [Indexed: 12/20/2022] Open
Abstract
Functional and structural neuronal networks, as recorded by resting-state functional MRI and diffusion MRI-based tractography, gain increasing attention as data driven whole brain imaging methods not limited to the foci of the primary pathology or the known key affected regions but permitting to characterize the entire network response of the brain after disease or injury. Their connectome contents thus provide information on distal brain areas, directly or indirectly affected by and interacting with the primary pathological event or affected regions. From such information, a better understanding of the dynamics of disease progression is expected. Furthermore, observation of the brain's spontaneous or treatment-induced improvement will contribute to unravel the underlying mechanisms of plasticity and recovery across the whole-brain networks. In the present review, we discuss the values of functional and structural network information derived from systematic and controlled experimentation using clinically relevant animal models. We focus on rodent models of the cerebral diseases with high impact on social burdens, namely, neurodegeneration, and stroke.
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Affiliation(s)
- Annemie Van der Linden
- Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Mathias Hoehn
- Research Center Jülich, Institute 3 for Neuroscience and Medicine, Jülich, Germany
- *Correspondence: Mathias Hoehn
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29
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Zhang D, Chen Y, Wu H, Lin L, Xie Q, Chen C, Jing L, Wu J. Associations of the Disrupted Functional Brain Network and Cognitive Function in End-Stage Renal Disease Patients on Maintenance Hemodialysis: A Graph Theory-Based Study of Resting-State Functional Magnetic Resonance Imaging. Front Hum Neurosci 2021; 15:716719. [PMID: 34966264 PMCID: PMC8710547 DOI: 10.3389/fnhum.2021.716719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Cognitive impairment (CI) is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis (MHD). Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients. Methods: A total of 39 patients on MHD (19 cases with CI and 20 without) and 25 healthy controls (HCs) matched for age, sex, and years of education were enrolled in the study. Resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted high-resolution anatomical data were obtained, and functional brain networks for each subject were constructed. The brain network parameters at the global and regional levels were calculated, and a one-way analysis of covariance was used to compare the differences across the three groups. The associations between the changed graph-theory parameters and cognitive function scores in patients on MHD were evaluated using Spearman correlation analysis. Results: Compared with HCs, the global parameters [sigma, gamma, and local efficiency (Eloc)] in both patient groups decreased significantly (p < 0.05, Bonferroni corrected). The clustering coefficient (Cp) in patients with CI was significantly lower than that in the other two groups (p < 0.05, Bonferroni corrected). The regional parameters were significantly lower in the right superior frontal gyrus, dorsolateral (SFGdor) and gyrus rectus (REC) of patients with CI than those of patients without CI; however the nodal local efficiency in the left amygdala was significantly increased (all p < 0.05, Bonferroni corrected). The global Cp and regional parameters in the three brain regions (right SFGdor, REC, and left amygdala) were significantly correlated with the cognitive function scores (all FDR q < 0.05). Conclusion: This study confirmed that the topology of the functional brain network was disrupted in patients on MHD with and without CI and the disruption of brain network was more severe in patients with CI. The abnormal brain network parameters are closely related to cognitive function in patients on MHD.
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Affiliation(s)
- Die Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.,Department of Radiology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Yingying Chen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.,Department of Radiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Hua Wu
- Department of Nephrology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Lin Lin
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qing Xie
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Chen Chen
- Department of Nephrology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Li Jing
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
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30
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Zhou Y, Liu Z, Sun Y, Zhang H, Ruan J. Altered EEG Brain Networks in Patients with Acute Peripheral Herpes Zoster. J Pain Res 2021; 14:3429-3436. [PMID: 34754236 PMCID: PMC8570286 DOI: 10.2147/jpr.s329068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/22/2021] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE To investigate whether the brain networks changed in patients with acute peripheral herpes zoster (HZ). METHODS We reviewed the EEG database in Jianyang People's Hospital. Patients with acute HZ (n=71) were enrolled from January 2016 to December 2020. Each included subject underwent a ten-minute and 16-channel EEG examination. Five epochs of 10-second EEG data in resting-state were collected from each HZ patient. Five 10-second resting-state EEG epochs from sex- and age-matched healthy controls (HC, n=71) who reported no history of neurological or psychiatric disorders and visited the hospital for routine physical examinations were collected. Brain network and graph theory analysis based on phase locking value parameter and functional ICA were performed using a self-writing Matlab code and the LORETA KEY tool. RESULTS Compared with the HC group, the HZ patients showed significant altered brain networks. The graph theory analysis revealed that the clustering coefficient and local efficiency of full band in HZ patients were lower than those in HC group (P<0.05). In beta band, the global efficiency and local efficiency of HZ patients group decreased, compared with healthy group (P<0.05). The functional ICA showed that three components showed significant differences between the two groups. In component 2, HZ patients showed excess superior frontal gyrus (BA10) neuro oscillation in delta band and less medial frontal gyrus (BA 11) neuro oscillation in beta and gamma bands than that in HCs. And for component 3, the alpha band of the HZ patients presented increased neuro activities in superior frontal gyrus (BA 11) and decreased neuro activities in occipital lobe (BA 18). In component 4, the inferior frontal gyrus (BA 47) showed excess activity in the left hemisphere and reduced activity in the right hemisphere in delta band, compared with HC group. CONCLUSION Altered brain networks exist in resting-state EEG data of patients with acute HZ. The changes of EEG brain networks in HZ patients are characterized by decreased global efficiency and local efficiency in beta band. Moreover, the spontaneous oscillation of some brain regions involving pain management and the connectivity of default mode network changed in HZ patients. Our study provided novel understanding of HZ from an electrophysiological view, and led to converging evidence for treatment of HZ with neural regulation in future.
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Affiliation(s)
- Yan Zhou
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, People’s Republic of China
- Department of Neurology, Jianyang People’s Hospital, Jianyang, 641400, People’s Republic of China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, People’s Republic of China
| | - Zhenqin Liu
- Department of Dermatology, Jianyang People’s Hospital, Jianyang, 641400, People’s Republic of China
| | - Yuanmei Sun
- Department of Dermatology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400010, People’s Republic of China
| | - Hao Zhang
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, People’s Republic of China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, People’s Republic of China
| | - Jianghai Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, People’s Republic of China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, People’s Republic of China
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Zhu Y, Zang F, Wang Q, Zhang Q, Tan C, Zhang S, Hu T, Qi L, Xu S, Ren Q, Xie C. Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. Behav Brain Res 2021; 411:113387. [PMID: 34048872 DOI: 10.1016/j.bbr.2021.113387] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/19/2021] [Accepted: 05/22/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. METHOD This study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer's disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant's AVLT-DR score. RESULTS The negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe. CONCLUSION Our findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.
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Affiliation(s)
- Yao Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Feifei Zang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Qianqian Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Chang Tan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Shaoke Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Tianjian Hu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Lingyu Qi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Shouyong Xu
- Department of Radiology, Geriatric Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210000, China.
| | - Qingguo Ren
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China.
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China.
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Boroda E, Armstrong M, Gilmore CS, Gentz C, Fenske A, Fiecas M, Hendrickson T, Roediger D, Mueller B, Kardon R, Lim K. Network topology changes in chronic mild traumatic brain injury (mTBI). Neuroimage Clin 2021; 31:102691. [PMID: 34023667 PMCID: PMC8163989 DOI: 10.1016/j.nicl.2021.102691] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/14/2021] [Accepted: 05/01/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND In mild traumatic brain injury (mTBI), diffuse axonal injury results in disruption of functional networks in the brain and is thought to be a major contributor to cognitive dysfunction even years after trauma. OBJECTIVE Few studies have assessed longitudinal changes in network topology in chronic mTBI. We utilized a graph theoretical approach to investigate alterations in global network topology based on resting-state functional connectivity in veterans with chronic mTBI. METHODS 50 veterans with chronic mTBI (mean of 20.7 yrs. from trauma) and 40 age-matched controls underwent two functional magnetic resonance imaging scans 18 months apart. Graph theory analysis was used to quantify network topology measures (density, clustering coefficient, global efficiency, and modularity). Hierarchical linear mixed models were used to examine longitudinal change in network topology. RESULTS With all network measures, we found a significant group × time interaction. At baseline, brain networks of individuals with mTBI were less clustered (p = 0.03) and more modular (p = 0.02) than those of HC. Over time, the mTBI networks became more densely connected (p = 0.002), with increased clustering (p = 0.001) and reduced modularity (p < 0.001). Network topology did not change across time in HC. CONCLUSION These findings demonstrate that brain networks of individuals with mTBI remain plastic decades after injury and undergo significant changes in network topology even at the later phase of the disease.
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Affiliation(s)
- Elias Boroda
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | | | | | - Carrie Gentz
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Alicia Fenske
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Mark Fiecas
- Center for the Prevention and Treatment of Visual Loss, Iowa City VA Healthcare System, Iowa City, IA, USA
| | - Tim Hendrickson
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Donovan Roediger
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Bryon Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Randy Kardon
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA
| | - Kelvin Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA; Minneapolis VA Health Care System, Minneapolis, MN, USA; School of Public Health, Department of Biostatistics, University of Minnesota, Minneapolis, MN, USA
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