1
|
Sun YW, Lyu XY, Lei XY, Huang MM, Wang ZM, Gao B. Association study of brain structure-function coupling and glymphatic system function in patients with mild cognitive impairment due to Alzheimer's disease. Front Neurosci 2024; 18:1417986. [PMID: 39139498 PMCID: PMC11320604 DOI: 10.3389/fnins.2024.1417986] [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: 04/15/2024] [Accepted: 07/17/2024] [Indexed: 08/15/2024] Open
Abstract
Background Mild cognitive impairment (MCI) is a critical transitional phase from healthy cognitive aging to dementia, offering a unique opportunity for early intervention. However, few studies focus on the correlation of brain structure and functional activity in patients with MCI due to Alzheimer's disease (AD). Elucidating the complex interactions between structural-functional (SC-FC) brain connectivity and glymphatic system function is crucial for understanding this condition. Method The aims of this study were to explore the relationship among SC-FC coupling values, glymphatic system function and cognitive function. 23 MCI patients and 18 healthy controls (HC) underwent diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). DTI analysis along the perivascular space (DTI-ALPS) index and SC-FC coupling values were calculated using DTI and fMRI. Correlation analysis was conducted to assess the relationship between Mini-Mental State Examination (MMSE) scores, DTI-ALPS index, and coupling values. Receiver operating characteristic (ROC) curves was conducted on the SC-FC coupling between the whole brain and subnetworks. The correlation of coupling values with MMSE scores was also analyzed. Result MCI patients (67.74 ± 6.99 years of age) exhibited significantly lower coupling in the whole-brain network and subnetworks, such as the somatomotor network (SMN) and ventral attention network (VAN), than HCs (63.44 ± 6.92 years of age). Whole-brain network coupling was positively correlated with dorsal attention network (DAN), SMN, and visual network (VN) coupling. MMSE scores were significantly positively correlated with whole-brain coupling and SMN coupling. In MCI, whole-brain network demonstrated the highest performance, followed by the SMN and VAN, with the VN, DAN, limbic network (LN), frontoparietal network (FPN), and default mode network (DMN). Compared to HCs, lower DTI-ALPS index was observed in individuals with MCI. Additionally, the left DTI-ALPS index showed a significant positive correlation with MMSE scores and coupling values in the whole-brain network and SMN. Conclusion These findings reveal the critical role of SC-FC coupling values and the ALPS index in cognitive function of MCI. The positive correlations observed in the left DTI-ALPS and whole-brain and SMN coupling values provide a new insight for investigating the asymmetrical nature of cognitive impairments.
Collapse
Affiliation(s)
- Yong-Wen Sun
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xin-Yue Lyu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xiao-Yang Lei
- Department of Neurology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Ming-Ming Huang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Zhen-Min Wang
- Key Laboratory of Brain Imaging, Guizhou Medical University, Guiyang, China
| | - Bo Gao
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Key Laboratory of Brain Imaging, Guizhou Medical University, Guiyang, China
| |
Collapse
|
2
|
Novakova L, Gajdos M, Barton M, Brabenec L, Zeleznikova Z, Moravkova I, Rektorova I. Striato-cortical functional connectivity changes in mild cognitive impairment with Lewy bodies. Parkinsonism Relat Disord 2024; 121:106031. [PMID: 38364623 DOI: 10.1016/j.parkreldis.2024.106031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Functional connectivity changes in clinically overt neurodegenerative diseases such as dementia with Lewy bodies have been described, but studies on connectivity changes in the pre-dementia phase are scarce. OBJECTIVES We concentrated on evaluating striato-cortical functional connectivity differences between patients with Mild Cognitive Impairment with Lewy bodies and healthy controls and on assessing the relation to cognition. METHODS Altogether, we enrolled 77 participants (47 patients, of which 35 met all the inclusion criteria for the final analysis, and 30 age- and gender-matched healthy controls, of which 28 met all the inclusion criteria for the final analysis) to study the seed-based connectivity of the dorsal, middle, and ventral striatum. We assessed correlations between functional connectivity in the regions of between-group differences and neuropsychological scores of interest (visuospatial and executive domains z-scores). RESULTS Subjects with Mild Cognitive Impairment with Lewy Bodies, as compared to healthy controls, showed increased connectivity from the dorsal part of the striatum particularly to the bilateral anterior part of the temporal cortex with an association with executive functions. CONCLUSIONS We were able to capture early abnormal connectivity within cholinergic and noradrenergic pathways that correlated with cognitive functions known to be linked to cholinergic/noradrenergic deficits. The knowledge of specific alterations may improve our understanding of early neural changes in pre-dementia stages and enhance research of disease modifying therapy.
Collapse
Affiliation(s)
- Lubomira Novakova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Martin Gajdos
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Marek Barton
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Lubos Brabenec
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Zaneta Zeleznikova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic; First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ivona Moravkova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic; First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Irena Rektorova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic; First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
| |
Collapse
|
3
|
Han S, Sun Z, Zhao K, Duan F, Caiafa CF, Zhang Y, Solé-Casals J. Early prediction of dementia using fMRI data with a graph convolutional network approach. J Neural Eng 2024; 21:016013. [PMID: 38215493 DOI: 10.1088/1741-2552/ad1e22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/12/2024] [Indexed: 01/14/2024]
Abstract
Objective. Alzheimer's disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs).Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI.Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification.Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at:https://github.com/Shuning-Han/FC-based-GCN.
Collapse
Affiliation(s)
- Shuning Han
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic 08500, Catalonia, Spain
- Image Processing Research Group, RIKEN Center for Advanced Photonics, RIKEN, Wako-Shi, Saitama, Japan
| | - Zhe Sun
- Faculty of Health Data Science, Juntendo University, Urayasu, Chiba, Japan
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, United States of America
| | - Feng Duan
- Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, Nankai University, Tianjin, People's Republic of China
| | - Cesar F Caiafa
- Instituto Argentino de Radioastronomía-CCT La Plata, CONICET / CIC-PBA / UNLP, V. Elisa 1894, Argentina
- Tensor Learning Team, Riken AIP, Tokyo, Tokyo 103-0027, Japan
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, United States of America
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, United States of America
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic 08500, Catalonia, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, United Kingdom
| |
Collapse
|
4
|
Zhang T, Zeng Q, Li K, Liu X, Fu Y, Qiu T, Huang P, Luo X, Liu Z, Peng G. Distinct resting-state functional connectivity patterns of Anterior Insula affected by smoking in mild cognitive impairment. Brain Imaging Behav 2023; 17:386-394. [PMID: 37243752 PMCID: PMC10435406 DOI: 10.1007/s11682-023-00766-6] [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] [Accepted: 03/20/2023] [Indexed: 05/29/2023]
Abstract
Smoking is a modifiable risk factor for Alzheimer's disease (AD). The insula plays a vital role in both smoking and cognition. However, the smoking effects on insula-related networks in cognitively normal controls (CN) and mild cognitive impairment (MCI) patients remain unknown. We identified 129 CN (85 non-smokers and 44 smokers) and 83 MCI (54 non-smokers and 29 smokers). Each underwent neuropsychological assessment and MRI (structural and resting-state functional). Seed-based functional analyses in the anterior and posterior insula were performed to calculate the functional connectivity (FC) with voxels in the whole brain. Mixed-effect analyses were performed to explore the interactive effects on smoking and cognitive status. Associations between FC and neuropsychological scales were assessed. Mixed-effect analyses revealed the FC differences between the right anterior insula (RAI) with the left middle temporal gyrus (LMTG) and that with the right inferior parietal lobule (RIPL) (p < 0.01, cluster level < 0.05, two-tailed, gaussian random field correction). The FC of RAI in both LMTG and RIPL sees a significant decrease in MCI smokers (p < 0.01). Smoking affects insula FC differently between MCI and CN, and could decrease the insula FC in MCI patients. Our study provides evidence of neural mechanisms between smoking and AD.
Collapse
Affiliation(s)
- Tianyi Zhang
- Department of Neurology, The 1st Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qing-Chun Road, Shang- Cheng District, Hangzhou, 310002 China
| | - Qingze Zeng
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanv Fu
- Department of Neurology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Tiantian Qiu
- Department of Radiology, Linyi People’s Hospital, Linyi, China
| | - Peiyu Huang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhirong Liu
- Department of Neurology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Guoping Peng
- Department of Neurology, The 1st Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qing-Chun Road, Shang- Cheng District, Hangzhou, 310002 China
| | - for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
- Department of Neurology, The 1st Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qing-Chun Road, Shang- Cheng District, Hangzhou, 310002 China
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Department of Neurology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Linyi People’s Hospital, Linyi, China
| |
Collapse
|
5
|
Green ZD, Vidoni ED, Swerdlow RH, Burns JM, Morris JK, Honea RA. Increased Functional Connectivity of the Precuneus in Individuals with a Family History of Alzheimer's Disease. J Alzheimers Dis 2023; 91:559-571. [PMID: 36463439 PMCID: PMC9912732 DOI: 10.3233/jad-210326] [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] [Indexed: 11/30/2022]
Abstract
BACKGROUND First-degree relatives of individuals with late-onset Alzheimer's disease (AD) have increased risk for AD, with children of affected parents at an especially high risk. OBJECTIVE We aimed to investigate default mode network connectivity, medial temporal cortex volume, and cognition in cognitively healthy (CH) individuals with (FH+) and without (FH-) a family history of AD, alongside amnestic mild cognitive impairment (aMCI) and AD individuals, to determine the context and directionality of dysfunction in at-risk individuals. Our primary hypothesis was that there would be a linear decline (CH FH- > CH FH+ > aMCI > AD) within the risk groups on all measures of AD risk. METHODS We used MRI and fMRI to study cognitively healthy individuals (n = 28) with and without AD family history (FH+ and FH-, respectively), those with aMCI (n = 31) and early-stage AD (n = 25). We tested connectivity within the default mode network, as well as measures of volume and thickness within the medial temporal cortex and selected seed regions. RESULTS As expected, we identified decreased medial temporal cortex volumes in the aMCI and AD groups compared to cognitively healthy groups. We also observed patterns of connectivity across risk groups that suggest a nonlinear relationship of change, such that the FH+ group showed increased connectivity compared to the FH- and AD groups (CH FH+ > CH FH- > aMCI > AD). This pattern emerged primarily in connectivity between the precuneus and frontal regions. CONCLUSION These results add to a growing literature that suggests compensatory brain function in otherwise cognitively healthy individuals with a family history of AD.
Collapse
Affiliation(s)
- Zachary D. Green
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Eric D. Vidoni
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Russell H. Swerdlow
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jeffrey M. Burns
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jill K. Morris
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Robyn A. Honea
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA,Correspondence to: Robyn A. Honea, University of Kansas School of Medicine, Department of Neurology, University of Kansas Alzheimer’s Disease Research Center, 4350 Shawnee Mission Parkway, Fairway, KS, 66205, USA. Tel.: +1 913 588 5514; E-mail:
| |
Collapse
|
6
|
Silberstein RB, Pipingas A, Scholey AB. Homocysteine Modulates Brain Functional Connectivity in a Memory Retrieval Task. J Alzheimers Dis 2022; 90:199-209. [DOI: 10.3233/jad-220612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Homocysteine, a methionine metabolite, is a recognized risk factor for accelerated age-related cognitive decline and dementia. Objective: In the light of studies indicating increases in brain activity and brain functional connectivity in the early stages of age-related cognitive decline, we undertook a study to examine the relationship between plasma homocysteine levels and brain functional connectivity in a group of late middle-aged males at risk of cognitive decline due to high body mass index and a sedentary lifestyle. Methods: Brain functional connectivity was measured using the steady state visual evoked potential event related partial coherence while 38 participants performed a memory task where each trial comprised an object recognition task followed by a location memory task. Results: We observed a significant transient peak in the correlation between plasma homocysteine levels and fronto-parietal brain functional connectivity immediately before the presentation of the memory location component of the task. Significantly, this correlation was only apparent if the participant pool included individuals with homocysteine concentrations above 11μmole/L. Conclusion: Our findings suggest that the increased brain functional connectivity observed in the earlier stages of age-related cognitive decline reflects pathognomonic changes in brain function and not compensatory changes engaged to enhance task performance. Our findings also suggest that homocysteine interferes with the inhibition of cortical networks where this inhibition is necessary for optimum task performance. Finally, we observed that the effect of homocysteine on brain functional connectivity is only apparent at concentrations above 11μmol/L.
Collapse
Affiliation(s)
- Richard B. Silberstein
- Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia
- Neuro-Insight Pty Ltd, Hawthorn, VIC, Australia
| | - Andrew Pipingas
- Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Andrew B. Scholey
- Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia
| |
Collapse
|
7
|
Liao Z, Sun W, Liu X, Guo Z, Mao D, Yu E, Chen Y. Altered dynamic intrinsic brain activity of the default mode network in Alzheimer’s disease: A resting-state fMRI study. Front Hum Neurosci 2022; 16:951114. [PMID: 36061502 PMCID: PMC9428286 DOI: 10.3389/fnhum.2022.951114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/25/2022] [Indexed: 12/02/2022] Open
Abstract
Objective Static regional homogeneity (ReHo) based on the resting-state functional magnetic resonance imaging (rs-fMRI) has been used to study intrinsic brain activity (IBA) in Alzheimer’s disease (AD). However, few studies have examined dynamic ReHo (dReHo) in AD. In this study, we used rs-fMRI and dReHo to investigate the alterations in dynamic IBA in patients with AD to uncover dynamic imaging markers of AD. Method In total, 111 patients with AD, 29 patients with mild cognitive impairment (MCI), and 73 healthy controls (HCs) were recruited for this study ultimately. After the rs-fMRI scan, we calculated the dReHo values using the sliding window method. ANOVA and post hoc two-sample t-tests were used to detect the differences among the three groups. We used the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) to evaluate the cognitive function of the subjects. The associations between the MMSE score, MoCA score, and dReHo were assessed by the Pearson correlation analysis. Results Significant dReHo variability in the right middle frontal gyrus (MFG) and right posterior cingulate gyrus (PCG) was detected in the three groups through ANOVA. In post hoc analysis, the AD group exhibited significantly greater dReHo variability in the right MFG than the MCI group. Compared with the HC group, the AD group exhibited significantly increased dReHo variability in the right PCG. Furthermore, dReHo variability in the right PCG was significantly negatively correlated with the MMSE and MoCA scores of patients with AD. Conclusion Disrupted dynamic IBA in the DMN might be an important characteristic of AD and could be a potential biomarker for the diagnosis or prognosis of AD.
Collapse
Affiliation(s)
- Zhengluan Liao
- Department of Clinical Medicine, Medical College of Soochow University, Suzhou, China
- Department of Geriatric VIP No. 3 (Department of Clinical Psychology), Rehabilitation Medicine Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
| | - Wangdi Sun
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaozheng Liu
- The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Guo
- Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Dewang Mao
- Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou, China
| | - Enyan Yu
- Department of Psychiatry, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Enyan Yu,
| | - Yan Chen
- Department of Geriatric VIP No. 3 (Department of Clinical Psychology), Rehabilitation Medicine Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
- *Correspondence: Yan Chen,
| |
Collapse
|
8
|
Changes in Brain Volume Resulting from Cognitive Intervention by Means of the Feuerstein Instrumental Enrichment Program in Older Adults with Mild Cognitive Impairment (MCI): A Pilot Study. Brain Sci 2021; 11:brainsci11121637. [PMID: 34942939 PMCID: PMC8699159 DOI: 10.3390/brainsci11121637] [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: 10/26/2021] [Revised: 11/24/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
There is increasing interest in identifying biological and imaging markers for the early detection of neurocognitive decline. In addition, non-pharmacological strategies, including physical exercise and cognitive interventions, may be beneficial for those developing cognitive impairment. The Feuerstein Instrumental Enrichment (FIE) Program is a cognitive intervention based on structural cognitive modifiability and the mediated learning experience (MLE) and aims to promote problem-solving strategies and metacognitive abilities. The FIE program uses a variety of instruments to enhance the cognitive capacity of the individual as a result of mediation. A specific version of the FIE program was developed for the cognitive enhancement of older adults, focusing on strengthening orientation skills, categorization skills, deductive reasoning, and memory. We performed a prospective interventional pilot observational study on older subjects with MCI who participated in 30 mediated FIE sessions (two sessions weekly for 15 weeks). Of the 23 subjects who completed the study, there was a significant improvement in memory on the NeuroTrax cognitive assessment battery. Complete sets of anatomical MRI data for voxel-based morphometry, taken at the beginning and the end of the study, were obtained from 16 participants (mean age 83.5 years). Voxel-based morphometry showed an interesting and unexpected increase in grey matter (GM) in the anterolateral occipital border and the middle cingulate cortex. These initial findings of our pilot study support the design of randomized trials to evaluate the effect of cognitive training using the FIE program on brain volumes and cognitive function.
Collapse
|
9
|
Xing Y, Fu S, Li M, Ma X, Liu M, Liu X, Huang Y, Xu G, Jiao Y, Wu H, Jiang G, Tian J. Regional Neural Activity Changes in Parkinson's Disease-Associated Mild Cognitive Impairment and Cognitively Normal Patients. Neuropsychiatr Dis Treat 2021; 17:2697-2706. [PMID: 34429605 PMCID: PMC8380131 DOI: 10.2147/ndt.s323127] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 07/27/2021] [Indexed: 01/21/2023] Open
Abstract
PURPOSE The aim of this study was to compare regional homogeneity (ReHo) changes in Parkinson's disease mild cognitive impairment (PD-MCI) patients with respect to normal controls (NC) and those with cognitively normal PD (PD-CN). Further, the study investigated the relationship between ReHo changes in PD patients and neuropsychological variation. PATIENTS AND METHODS Thirty PD-MCI, 19 PD-CN, and 21 NC subjects were enrolled. Resting state functional magnetic resonance imaging data of all subjects were collected, and regional brain activity was measured for ReHo. Analysis of covariance for ReHo was determined between the PD-MCI, PD-CN, and NC groups. Spearman rank correlations were assessed using the ReHo maps and data from the neuropsychological tests. RESULTS In comparison with NC, PD-CN patients showed significantly higher ReHo values in the right middle frontal gyrus (MFG) and lower ReHo values in the left supramarginal gyrus, bilateral inferior parietal lobule (IPL), and the right postcentral gyrus (PCG). In comparison with PD-CN patients, PD-MCI patients displayed significantly higher ReHo values in the right PCG, left middle occipital gyrus (MOG) and IPL. No significant correlation between ReHo indices and the neuropsychological scales was observed. CONCLUSION Our finding revealed that decreases in ReHo in the default mode network (DMN) may appear before PD-related cognitive impairment. In order to preserve executive attention capacity, ReHo in the right MFG in PD patients lacking cognition impairment increased for compensation. PD-MCI showed increased ReHo in the left MOG, which might have been caused by visual and visual-spatial dysfunction, and increased ReHo in the left IPL, which might reflect network disturbance and induce cognition deficits.
Collapse
Affiliation(s)
- Yilan Xing
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People's Republic of China.,Department of Neurology of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Shishun Fu
- Department of Medical Imaging of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Meng Li
- Department of Medical Imaging of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Xiaofen Ma
- Department of Medical Imaging of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Mengchen Liu
- Department of Medical Imaging of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Xintong Liu
- Department of Neurology of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Yan Huang
- Department of Neurology of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Guang Xu
- Department of Neurology of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Yonggang Jiao
- Department of Neurology of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Hong Wu
- Department of Neurology of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Guihua Jiang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People's Republic of China.,Department of Medical Imaging of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Junzhang Tian
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People's Republic of China.,Department of Medical Imaging of Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| |
Collapse
|
10
|
Mancho-Fora N, Montalà-Flaquer M, Farràs-Permanyer L, Zarabozo-Hurtado D, Gallardo-Moreno GB, Gudayol-Farré E, Peró-Cebollero M, Guàrdia-Olmos J. Network change point detection in resting-state functional connectivity dynamics of mild cognitive impairment patients. Int J Clin Health Psychol 2020; 20:200-212. [PMID: 32994793 PMCID: PMC7501449 DOI: 10.1016/j.ijchp.2020.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 07/16/2020] [Indexed: 10/27/2022] Open
Abstract
Background/Objective: This study aims to characterize the differences on the short-term temporal network dynamics of the undirected and weighted whole-brain functional connectivity between healthy aging individuals and people with mild cognitive impairment (MCI). The Network Change Point Detection algorithm was applied to identify the significant change points in the resting-state fMRI register, and we analyzed the fluctuations in the topological properties of the sub-networks between significant change points. Method: Ten MCI patients matched by gender and age in 1:1 ratio to healthy controls screened during patient recruitment. A neuropsychological evaluation was done to both groups as well as functional magnetic images were obtained with a Philips 3.0T. All the images were preprocessed and statistically analyzed through dynamic point estimation tools. Results: No statistically significant differences were found between groups in the number of significant change points in the functional connectivity networks. However, an interaction effect of age and state was detected on the intra-participant variability of the network strength. Conclusions: The progression of states was associated to higher variability in the patient's group. Additionally, higher performance in the prospective and retrospective memory scale was associated with higher median network strength.
Collapse
Affiliation(s)
| | - Marc Montalà-Flaquer
- Facultat de Psicologia, Universitat de Barcelona, Spain.,UB Institute of Complex Systems, Universitat de Barcelona, Spain
| | | | | | | | - Esteban Gudayol-Farré
- Facultad de Psicología, Universidad Miochoacana San Nicolás de Hidalgo, Morelia, Mexico
| | - Maribel Peró-Cebollero
- Facultat de Psicologia, Universitat de Barcelona, Spain.,UB Institute of Complex Systems, Universitat de Barcelona, Spain.,Institute of Neuroscience, Universitat de Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Facultat de Psicologia, Universitat de Barcelona, Spain.,UB Institute of Complex Systems, Universitat de Barcelona, Spain.,Institute of Neuroscience, Universitat de Barcelona, Spain
| |
Collapse
|
11
|
Intrinsic functional connectivity, CSF biomarker profiles and their relation to cognitive function in mild cognitive impairment. Acta Neuropsychiatr 2020; 32:206-213. [PMID: 31801648 DOI: 10.1017/neu.2019.49] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Mild cognitive impairment (MCI) often precedes Alzheimer's Dementia (AD), and in a high proportion of individuals affected by MCI, there are already neuropathological processes ongoing that become more evident when patients progress to AD. Accordingly, there is a need for reliable biomarkers to distinguish between normal aging and incipient AD. Recent research suggests that, in addition to established biomarkers such as CSF Aß42, total tau and hyperphosphorylated tau, resting state connectivity established by functional magnetic resonance imaging might also be a feasible biomarker for prodromal stages of AD. In order to explore this possibility, we investigated resting state functional connectivity as well as cerebrospinal fluid (CSF) biomarker profiles in patients with MCI (n = 30; age 66.43 ± 7.06 years) and cognitively healthy controls (n = 38; age 66.89 ± 7.12 years). CSF Aß42, total tau and hyperphosphorylated tau concentrations were correlated with measures of cognitive performance (immediate and delayed recall, global cognition, processing speed). Moreover, MCI-related alterations in intrinsic functional connectivity within the default mode network were investigated using functional resting state MRI. As expected, MCI patients showed decreased CSF Aß42 and increased total tau concentrations. These alterations were associated with cognitive performance. However, there were no differences between MCI patients and cognitively healthy controls regarding intrinsic functional connectivity. In conclusion, our results indicate that CSF protein profiles seem to be more closely related to cognitive decline than alterations in resting state activity. Thus, resting state connectivity might not be a reliable biomarker for early stages of AD.
Collapse
|
12
|
Cera N, Esposito R, Cieri F, Tartaro A. Altered Cingulate Cortex Functional Connectivity in Normal Aging and Mild Cognitive Impairment. Front Neurosci 2019; 13:857. [PMID: 31572106 PMCID: PMC6753224 DOI: 10.3389/fnins.2019.00857] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 07/30/2019] [Indexed: 11/13/2022] Open
Abstract
Purpose Resting-state functional Magnetic Resonance Imaging studies revealed that the brain is organized into specialized networks constituted by regions that show a coherent fluctuation of spontaneous activity. Among these networks, the cingulate cortex appears to play a crucial role, particularly in the default mode network, the dorsal attention network and the salience network. In the present study, we mapped the functional connectivity (FC) pattern of different regions of the cingulate cortex: the anterior cingulate cortex, midcingulate cortex and posterior cingulate cortex/retro splenial cortex, which have been in turn divided into a total of 9 subregions. We compared FC patterns of the cingulate subregions in a sample of mild cognitive impairment patients and healthy elderly subjects. Methods We enrolled 19 healthy elders (age range: 61-72 y.o.) and 16 Mild cognitive impairment patients (age range 64-87 y.o.). All participants had comparable levels of education (8-10 years) and were neurologically examined to exclude visual and motor impairments, major medical conditions, psychiatric or neurological disorders and consumption of psychotropic drugs. The diagnosis of mild cognitive impairment was performed according to Petersen criteria. Subjects were evaluated with Mini-Mental State Examination, Frontal Assessment Battery, and prose memory (Babcock story) tests. In addition, with functional Magnetic Resonance Imaging, we investigated resting-state network activities. Results Healthy elderly, compared to mild cognitive impairment, showed significant increased level of FC for the ventral part of the anterior cingulate cortex in correspondence to the bilateral caudate and ventromedial prefrontal cortex. Moreover, for the midcingulate cortex the healthy elderly group showed increased levels of FC in the somatomotor region, prefrontal cortex, and superior parietal lobule. Meanwhile, the mild cognitive impairment group showed an increased level of FC for the superior frontal gyrus, frontal eye field and orbitofrontal cortex compared to the healthy elderly group. Conclusion Our findings indicate that cognitive decline observed in mild cognitive impairment patients damages the global FC of the cingulate cortex, supporting the idea that abnormalities in resting-state activities of the cingulate cortex could be a useful additional tool in order to better understand the brain mechanisms of MCI.
Collapse
Affiliation(s)
- Nicoletta Cera
- Faculty of Psychology and Educational Science, University of Porto, Porto, Portugal
| | - Roberto Esposito
- Radiology Unit, Azienda Ospedaliera Ospedali Riuniti Marche Nord, Pesaro, Italy
| | - Filippo Cieri
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Armando Tartaro
- Department of Neuroscience, Imaging and Clinical Sciences, Institute of Advanced Biomedical Technologies, D'Annunzio University of Chieti-Pescara, Chieti, Italy
| |
Collapse
|
13
|
Quevenco FC, Schreiner SJ, Preti MG, van Bergen JMG, Kirchner T, Wyss M, Steininger SC, Gietl A, Leh SE, Buck A, Pruessmann KP, Hock C, Nitsch RM, Henning A, Van De Ville D, Unschuld PG. GABA and glutamate moderate beta-amyloid related functional connectivity in cognitively unimpaired old-aged adults. NEUROIMAGE-CLINICAL 2019; 22:101776. [PMID: 30927605 PMCID: PMC6439267 DOI: 10.1016/j.nicl.2019.101776] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 02/03/2019] [Accepted: 03/10/2019] [Indexed: 02/08/2023]
Abstract
Background Effects of beta-amyloid accumulation on neuronal function precede the clinical manifestation of Alzheimer's disease (AD) by years and affect distinct cognitive brain networks. As previous studies suggest a link between beta-amyloid and dysregulation of excitatory and inhibitory neurotransmitters, we aimed to investigate the impact of GABA and glutamate on beta-amyloid related functional connectivity. Methods 29 cognitively unimpaired old-aged adults (age = 70.03 ± 5.77 years) were administered 11C-Pittsburgh Compound B (PiB) positron-emission tomography (PET), and MRI at 7 Tesla (7T) including blood oxygen level dependent (BOLD) functional MRI (fMRI) at rest for measuring static and dynamic functional connectivity. An advanced 7T MR spectroscopic imaging (MRSI) sequence based on the free induction decay acquisition localized by outer volume suppression’ (FIDLOVS) technology was used for gray matter specific measures of GABA and glutamate in the posterior cingulate and precuneus (PCP) region. Results GABA and glutamate MR-spectra indicated significantly higher levels in gray matter than in white matter. A global effect of beta-amyloid on functional connectivity in the frontal, occipital and inferior temporal lobes was observable. Interactive effects of beta-amyloid with gray matter GABA displayed positive PCP connectivity to the frontomedial regions, and the interaction of beta-amyloid with gray matter glutamate indicated positive PCP connectivity to frontal and cerebellar regions. Furthermore, decreased whole-brain but increased fronto-occipital and temporo-parietal dynamic connectivity was found, when GABA interacted with regional beta-amyloid deposits in the amygdala, frontal lobe, hippocampus, insula and striatum. Conclusions GABA, and less so glutamate, may moderate beta-amyloid related functional connectivity. Additional research is needed to better characterize their interaction and potential impact on AD. Combined ultra-high fieldstrength FIDLOVS MRSI at 7 Tesla with 11C-PIB PET. Assessment of gray matter specific levels of GABA and glutamate. Identification of interactive effects of GABA, glutamate and beta-Amyloid. GABA may moderate dysfunctional beta-Amyloid effects on pre-clinical brain pathology.
Collapse
Affiliation(s)
- F C Quevenco
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - S J Schreiner
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Hospital for Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - M G Preti
- Department of Radiology and Medical Informatics, Université de Genève, Switzerland; Institute of Bioengineering, École polytechnique fédérale de Lausanne, Switzerland
| | - J M G van Bergen
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - T Kirchner
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - M Wyss
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - S C Steininger
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Hospital for Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - A Gietl
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - S E Leh
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Hospital for Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - A Buck
- Division of Nuclear Medicine, University Hospital Zurich (USZ), Zurich, Switzerland
| | - K P Pruessmann
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - C Hock
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Hospital for Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - R M Nitsch
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Hospital for Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - A Henning
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; Max Planck Institute for Biological Cybernetics, Tubingen, Germany
| | - D Van De Ville
- Department of Radiology and Medical Informatics, Université de Genève, Switzerland; Institute of Bioengineering, École polytechnique fédérale de Lausanne, Switzerland
| | - P G Unschuld
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Hospital for Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.
| |
Collapse
|
14
|
Eyler LT, Elman JA, Hatton SN, Gough S, Mischel AK, Hagler DJ, Franz CE, Docherty A, Fennema-Notestine C, Gillespie N, Gustavson D, Lyons MJ, Neale MC, Panizzon MS, Dale AM, Kremen WS. Resting State Abnormalities of the Default Mode Network in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis. J Alzheimers Dis 2019; 70:107-120. [PMID: 31177210 PMCID: PMC6697380 DOI: 10.3233/jad-180847] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Large-scale brain networks such as the default mode network (DMN) are often disrupted in Alzheimer's disease (AD). Numerous studies have examined DMN functional connectivity in those with mild cognitive impairment (MCI), a presumed AD precursor, to discover a biomarker of AD risk. Prior reviews were qualitative or limited in scope or approach. OBJECTIVE We aimed to systematically and quantitatively review DMN resting state fMRI studies comparing MCI and healthy comparison (HC) groups. METHODS PubMed was searched for relevant articles. Study characteristics were abstracted and the number of studies showing no group difference or hyper- versus hypo-connnectivity in MCI was tallied. A voxel-wise (ES-SDM) meta-analysis was conducted to identify regional group differences. RESULTS Qualitatively, our review of 57 MCI versus HC comparisons suggests substantial inconsistency; 9 showed no group difference, 8 showed MCI > HC and 22 showed HC > MCI across the brain, and 18 showed regionally-mixed directions of effect. The meta-analysis of 31 studies revealed areas of significant hypo- and hyper-connectivity in MCI, including hypoconnectivity in the posterior cingulate cortex/precuneus (z = -3.1, p < 0.0001). Very few individual studies, however, showed patterns resembling the meta-analytic results. Methodological differences did not appear to explain inconsistencies. CONCLUSIONS The pattern of altered resting DMN function or connectivity in MCI is complex and variable across studies. To date, no index of DMN connectivity qualifies as a useful biomarker of MCI or risk for AD. Refinements to MCI diagnosis, including other biological markers, or longitudinal studies of progression to AD, might identify DMN alterations predictive of AD risk.
Collapse
Affiliation(s)
- Lisa T. Eyler
- Department of Psychiatry, University of California San Diego
- Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System
| | - Jeremy A. Elman
- Department of Psychiatry, University of California San Diego
| | - Sean N Hatton
- Department of Psychiatry, University of California San Diego
- Department of Neurosciences, University of California San Diego
| | - Sarah Gough
- Department of Psychiatry, University of California San Diego
| | - Anna K. Mischel
- Department of Psychiatry, University of California San Diego
| | | | - Carol E. Franz
- Department of Psychiatry, University of California San Diego
| | - Anna Docherty
- Departments of Psychiatry & Human Genetics, University of Utah School of Medicine
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego
- Department of Radiology, University of California San Diego
| | - Nathan Gillespie
- Departments of Psychiatry and Human and Molecular Genetics, Virginia Commonwealth University
| | | | | | - Michael C. Neale
- Departments of Psychiatry and Human and Molecular Genetics, Virginia Commonwealth University
| | | | - Anders M. Dale
- Department of Neurosciences, University of California San Diego
- Department of Radiology, University of California San Diego
| | - William S. Kremen
- Department of Psychiatry, University of California San Diego
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System
| |
Collapse
|
15
|
Hohenfeld C, Werner CJ, Reetz K. Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker? Neuroimage Clin 2018; 18:849-870. [PMID: 29876270 PMCID: PMC5988031 DOI: 10.1016/j.nicl.2018.03.013] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/06/2018] [Accepted: 03/14/2018] [Indexed: 12/14/2022]
Abstract
Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity. While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others. For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias. Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.
Collapse
Affiliation(s)
- Christian Hohenfeld
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Cornelius J Werner
- RWTH Aachen University, Department of Neurology, Aachen, Germany; RWTH Aachen University, Section Interdisciplinary Geriatrics, Aachen, Germany
| | - Kathrin Reetz
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany.
| |
Collapse
|
16
|
Brain Network Alterations in Alzheimer's Disease Identified by Early-Phase PIB-PET. CONTRAST MEDIA & MOLECULAR IMAGING 2018. [PMID: 29531506 PMCID: PMC5817202 DOI: 10.1155/2018/6830105] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The aim of this study was to identify the brain networks from early-phase 11C-PIB (perfusion PIB, pPIB) data and to compare the brain networks of patients with differentiating Alzheimer's disease (AD) with cognitively normal subjects (CN) and of mild cognitively impaired patients (MCI) with CN. Forty participants (14 CN, 12 MCI, and 14 AD) underwent 11C-PIB and 18F-FDG PET/CT scans. Parallel independent component analysis (pICA) was used to identify correlated brain networks from the 11C-pPIB and 18F-FDG data, and a two-sample t-test was used to evaluate group differences in the corrected brain networks between AD and CN, and between MCI and CN. Our study identified a brain network of perfusion (early-phase 11C-PIB) that highly correlated with a glucose metabolism (18F-FDG) brain network and colocalized with the default mode network (DMN) in an AD-specific neurodegenerative cohort. Particularly, decreased 18F-FDG uptake correlated with a decreased regional cerebral blood flow in the frontal, parietal, and temporal regions of the DMN. The group comparisons revealed similar spatial patterns of the brain networks derived from the 11C-pPIB and 18F-FDG data. Our findings indicate that 11C-pPIB derived from the early-phase 11C-PIB could provide complementary information for 18F-FDG examination in AD.
Collapse
|
17
|
Wang C, Pan Y, Liu Y, Xu K, Hao L, Huang F, Ke J, Sheng L, Ma H, Guo W. Aberrant default mode network in amnestic mild cognitive impairment: a meta-analysis of independent component analysis studies. Neurol Sci 2018; 39:919-931. [DOI: 10.1007/s10072-018-3306-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 02/23/2018] [Indexed: 12/24/2022]
|
18
|
Neuroaging through the Lens of the Resting State Networks. BIOMED RESEARCH INTERNATIONAL 2018; 2018:5080981. [PMID: 29568755 PMCID: PMC5820564 DOI: 10.1155/2018/5080981] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 11/27/2017] [Accepted: 12/14/2017] [Indexed: 12/11/2022]
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) allows studying spontaneous brain activity in absence of task, recording changes of Blood Oxygenation Level Dependent (BOLD) signal. rs-fMRI enables identification of brain networks also called Resting State Networks (RSNs) including the most studied Default Mode Network (DMN). The simplicity and speed of execution make rs-fMRI applicable in a variety of normal and pathological conditions. Since it does not require any task, rs-fMRI is particularly useful for protocols on patients, children, and elders, increasing participant's compliance and reducing intersubjective variability due to the task performance. rs-fMRI has shown high sensitivity in identification of RSNs modifications in several diseases also in absence of structural modifications. In this narrative review, we provide the state of the art of rs-fMRI studies about physiological and pathological aging processes. First, we introduce the background of resting state; then we review clinical findings provided by rs-fMRI in physiological aging, Mild Cognitive Impairment (MCI), Alzheimer Dementia (AD), and Late Life Depression (LLD). Finally, we suggest future directions in this field of research and its potential clinical applications.
Collapse
|
19
|
Li Q, Wu X, Xu L, Chen K, Yao L. Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning. Front Comput Neurosci 2018; 11:117. [PMID: 29375356 PMCID: PMC5767247 DOI: 10.3389/fncom.2017.00117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/19/2017] [Indexed: 01/03/2023] Open
Abstract
Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.
Collapse
Affiliation(s)
- Qing Li
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Xia Wu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lele Xu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, United States
| | - Li Yao
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | | |
Collapse
|
20
|
Wang Q, Guo L, Thompson PM, Jack CR, Dodge H, Zhan L, Zhou J. The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 2018; 64:149-169. [PMID: 29865049 PMCID: PMC6272125 DOI: 10.3233/jad-171048] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.
Collapse
Affiliation(s)
- Qi Wang
- Computer Science and Engineering, Michigan State University, East Lansing, MI
| | - Lei Guo
- Mathematics, Statistics & Computer Science Department, University of Wisconsin-Stout, Menomonie, WI
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA
| | | | - Hiroko Dodge
- Michigan Alzheimer's Disease Center and Department of Neurology, University of Michigan, Ann Arbor, MI
- Layton Aging and Alzheimer's Disease Center and Department of Neurology, Oregon Health & Science University, Portland, OR
| | - Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Menomonie, WI
| | - Jiayu Zhou
- Computer Science and Engineering, Michigan State University, East Lansing, MI
| | | |
Collapse
|
21
|
Chong JSX, Liu S, Loke YM, Hilal S, Ikram MK, Xu X, Tan BY, Venketasubramanian N, Chen CLH, Zhou J. Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer's disease. Brain 2017; 140:3012-3022. [PMID: 29053778 PMCID: PMC5841199 DOI: 10.1093/brain/awx224] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 07/12/2017] [Indexed: 01/16/2023] Open
Abstract
Network-sensitive neuroimaging methods have been used to characterize large-scale brain network degeneration in Alzheimer’s disease and its prodrome. However, few studies have investigated the combined effect of Alzheimer’s disease and cerebrovascular disease on brain network degeneration. Our study sought to examine the intrinsic functional connectivity and structural covariance network changes in 235 prodromal and clinical Alzheimer’s disease patients with and without cerebrovascular disease. We focused particularly on two higher-order cognitive networks—the default mode network and the executive control network. We found divergent functional connectivity and structural covariance patterns in Alzheimer’s disease patients with and without cerebrovascular disease. Alzheimer’s disease patients without cerebrovascular disease, but not Alzheimer’s disease patients with cerebrovascular disease, showed reductions in posterior default mode network functional connectivity. By comparison, while both groups exhibited parietal reductions in executive control network functional connectivity, only Alzheimer’s disease patients with cerebrovascular disease showed increases in frontal executive control network connectivity. Importantly, these distinct executive control network changes were recapitulated in prodromal Alzheimer’s disease patients with and without cerebrovascular disease. Across Alzheimer’s disease patients with and without cerebrovascular disease, higher default mode network functional connectivity z-scores correlated with greater hippocampal volumes while higher executive control network functional connectivity z-scores correlated with greater white matter changes. In parallel, only Alzheimer’s disease patients without cerebrovascular disease showed increased default mode network structural covariance, while only Alzheimer’s disease patients with cerebrovascular disease showed increased executive control network structural covariance compared to controls. Our findings demonstrate the differential neural network structural and functional changes in Alzheimer’s disease with and without cerebrovascular disease, suggesting that the underlying pathology of Alzheimer’s disease patients with cerebrovascular disease might differ from those without cerebrovascular disease and reflect a combination of more severe cerebrovascular disease and less severe Alzheimer’s disease network degeneration phenotype.
Collapse
Affiliation(s)
- Joanna Su Xian Chong
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore
| | - Siwei Liu
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore
| | - Yng Miin Loke
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore
| | - Saima Hilal
- Department of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.,Memory Ageing and Cognition Centre, National University Health System, Singapore
| | - Mohammad Kamran Ikram
- Memory Ageing and Cognition Centre, National University Health System, Singapore.,Duke-National University of Singapore Medical School, Singapore
| | - Xin Xu
- Department of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.,Memory Ageing and Cognition Centre, National University Health System, Singapore
| | | | | | - Christopher Li-Hsian Chen
- Department of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.,Memory Ageing and Cognition Centre, National University Health System, Singapore
| | - Juan Zhou
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore.,Clinical Imaging Research Centre, The Agency for Science, Technology and Research and National University of Singapore, Singapore
| |
Collapse
|
22
|
Krajcovicova L, Barton M, Elfmarkova-Nemcova N, Mikl M, Marecek R, Rektorova I. Changes in connectivity of the posterior default network node during visual processing in mild cognitive impairment: staged decline between normal aging and Alzheimer’s disease. J Neural Transm (Vienna) 2017; 124:1607-1619. [DOI: 10.1007/s00702-017-1789-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Accepted: 08/31/2017] [Indexed: 02/13/2023]
|
23
|
Li M, Zheng G, Zheng Y, Xiong Z, Xia R, Zhou W, Wang Q, Liang S, Tao J, Chen L. Alterations in resting-state functional connectivity of the default mode network in amnestic mild cognitive impairment: an fMRI study. BMC Med Imaging 2017; 17:48. [PMID: 28814282 PMCID: PMC5559812 DOI: 10.1186/s12880-017-0221-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 07/31/2017] [Indexed: 01/04/2023] Open
Abstract
Background Amnestic mild cognitive impairment (aMCI) is characterized by cognitive functional decline, especially in memory. Resting-state functional magnetic resonance imaging (fMRI) has been widely used in neuroimaging studies that explore alterations between patients and normal individuals to elucidate the pathological mechanisms of different diseases. The current study was performed to investigate alterations in the functional connectivity of the default mode network (DMN) in aMCI patients compared to healthy elderly controls, as well as further define the association between neurological alterations and memory function. Methods Twenty-five aMCI patients and 25 healthy individuals were recruited and underwent both fMRI and neuropsychological examinations. fMRI data was analyzed by independent component analysis. Results Compared to healthy individuals, aMCI patients exhibited a significant increase in functional connectivity between the DMN and right-middle and right-superior frontal gyri, left-middle occipital gyrus, and left-middle temporal gyrus, but reduced functional connectivity between the DMN and left-middle and left-inferior frontal gyri and left insula. These alterations were found to be associated with reduced memory function. Conclusions aMCI patients exhibited abnormal functional connectivity between the DMN and certain brain regions which is associated with changes in memory function associated with aMCI. Electronic supplementary material The online version of this article (doi:10.1186/s12880-017-0221-9) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Moyi Li
- Department of Rehabilitation, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.,College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Guohua Zheng
- College of Health Information Technology and Management, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China.
| | - Yuhui Zheng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Zhenyu Xiong
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Rui Xia
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Wenji Zhou
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Qin Wang
- The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250001, China
| | - Shengxiang Liang
- Physical Science and Technology College, Zhengzhou University, Zhengzhou, 450001, China.,Division of Nuclear Technology and Applications, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China.,Beijing Engineering Research Center of Radiographic Techniques and Equipment, Beijing, 100049, China
| | - Jing Tao
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Lidian Chen
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China. .,Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, 350003, China.
| |
Collapse
|
24
|
Cieri F, Esposito R, Cera N, Pieramico V, Tartaro A, di Giannantonio M. Late-Life Depression: Modifications of Brain Resting State Activity. J Geriatr Psychiatry Neurol 2017; 30:140-150. [PMID: 28355945 DOI: 10.1177/0891988717700509] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Late-life depression (LLD) is a common emotional and mental disability in the elderly population characterized by the presence of depressed mood, the loss of interest or pleasure in daily activities, and other depression symptoms. It has a serious effect on the quality of life of elderly individuals and increases their risk of developing physical and mental diseases. It is an important area of research, given the growing elderly population. Brain functional connectivity modifications represent one of the neurobiological biomarker for LLD even if to date remains poorly understood. In our study, we enrolled 10 elderly patients with depressive symptoms compared to 11 age-matched healthy controls. All participants were evaluated by means of neuropsychological tests and underwent the same functional magnetic resonance imaging (fMRI) protocol to evaluate modifications of brain resting state functional connectivity. Between-group differences were observed for the Geriatric Depression Scale and Hamilton Depression Rating Scale, with higher scores for patients with LLD. Voxel-wise, 1-way analysis of variance revealed between-group differences in left frontoparietal network (lFPN) and sensory motor network (SMN): Increased intrinsic connectivity in the LLD group was observed in the left dorsolateral prefrontal cortex and in the left superior parietal lobule of the lFPN and increased intrinsic connectivity in the LLD group was observed in the bilateral primary somatosensory cortex of the SMN. Our findings support the use of resting state fMRI as a potential biomarker for LLD; even if to confirm the relationship between brain changes and the pathophysiology of LLD, longitudinal neuroimaging studies are required.
Collapse
Affiliation(s)
- Filippo Cieri
- 1 Department of Neuroscience, Imaging and Clinical Science, University G. d'Annunzio, Chieti, Italy
| | - Roberto Esposito
- 1 Department of Neuroscience, Imaging and Clinical Science, University G. d'Annunzio, Chieti, Italy
| | - Nicoletta Cera
- 2 Faculty of Psychology and Educational science, Center for Psychology at the University of Porto (CPUP), University of Porto, Porto, Portugal
| | - Valentina Pieramico
- 1 Department of Neuroscience, Imaging and Clinical Science, University G. d'Annunzio, Chieti, Italy
| | - Armando Tartaro
- 1 Department of Neuroscience, Imaging and Clinical Science, University G. d'Annunzio, Chieti, Italy
| | - Massimo di Giannantonio
- 1 Department of Neuroscience, Imaging and Clinical Science, University G. d'Annunzio, Chieti, Italy
| |
Collapse
|
25
|
|
26
|
Dipasquale O, Cercignani M. Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions. FUNCTIONAL NEUROLOGY 2017; 31:191-203. [PMID: 28072380 DOI: 10.11138/fneur/2016.31.4.191] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Non-invasive mapping of brain functional connectivity (FC) has played a fundamental role in neuroscience, and numerous scientists have been fascinated by its ability to reveal the brain's intricate morphology and functional properties. In recent years, two different techniques have been developed that are able to explore FC in pathophysiological conditions and to provide simple and non-invasive biomarkers for the detection of disease onset, severity and progression. These techniques are independent component analysis, which allows a network-based functional exploration of the brain, and graph theory, which provides a quantitative characterization of the whole-brain FC. In this paper we provide an overview of these two techniques and some examples of their clinical applications in the most common neurodegenerative disorders associated with cognitive decline, including mild cognitive impairment, Alzheimer's disease, Parkinson's disease, dementia with Lewy Bodies and behavioral variant frontotemporal dementia.
Collapse
|
27
|
Modifications in resting state functional anticorrelation between default mode network and dorsal attention network: comparison among young adults, healthy elders and mild cognitive impairment patients. Brain Imaging Behav 2017; 12:127-141. [DOI: 10.1007/s11682-017-9686-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
28
|
Qiu T, Luo X, Shen Z, Huang P, Xu X, Zhou J, Zhang M. Disrupted Brain Network in Progressive Mild Cognitive Impairment Measured by Eigenvector Centrality Mapping is Linked to Cognition and Cerebrospinal Fluid Biomarkers. J Alzheimers Dis 2016; 54:1483-1493. [PMID: 27589525 DOI: 10.3233/jad-160403] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Tiantian Qiu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhujing Shen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiong Zhou
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | | |
Collapse
|
29
|
Wang S, Zhang Y, Liu G, Phillips P, Yuan TF. Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging. J Alzheimers Dis 2016; 50:233-48. [PMID: 26682696 DOI: 10.3233/jad-150848] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer's disease (AD) in its early stages. OBJECTIVE However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method. METHODS In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student's t-test, and Welch's t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis. RESULTS The results showed that "3D-DF+WTT+TSVM" achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications. CONCLUSIONS The 3D-DF is effective in AD subject and related region detection.
Collapse
Affiliation(s)
- Shuihua Wang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Yudong Zhang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Ge Liu
- Translational Imaging Division & MRI Unit, Columbia University & New York State Psychiatric Institute, New York, NY, USA
| | - Preetha Phillips
- School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA
| | - Ti-Fei Yuan
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| |
Collapse
|
30
|
|
31
|
Zhang Y, Wang S. Detection of Alzheimer's disease by displacement field and machine learning. PeerJ 2015; 3:e1251. [PMID: 26401461 PMCID: PMC4579022 DOI: 10.7717/peerj.1251] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 08/29/2015] [Indexed: 12/26/2022] Open
Abstract
Aim. Alzheimer's disease (AD) is a chronic neurodegenerative disease. Recently, computer scientists have developed various methods for early detection based on computer vision and machine learning techniques. Method. In this study, we proposed a novel AD detection method by displacement field (DF) estimation between a normal brain and an AD brain. The DF was treated as the AD-related features, reduced by principal component analysis (PCA), and finally fed into three classifiers: support vector machine (SVM), generalized eigenvalue proximal SVM (GEPSVM), and twin SVM (TSVM). The 10-fold cross validation repeated 50 times. Results. The results showed the "DF + PCA + TSVM" achieved the accuracy of 92.75 ± 1.77, sensitivity of 90.56 ± 1.15, specificity of 93.37 ± 2.05, and precision of 79.61 ± 2.21. This result is better than or comparable with not only the other proposed two methods, but also ten state-of-the-art methods. Besides, our method discovers the AD is related to following brain regions disclosed in recent publications: Angular Gyrus, Anterior Cingulate, Cingulate Gyrus, Culmen, Cuneus, Fusiform Gyrus, Inferior Frontal Gyrus, Inferior Occipital Gyrus, Inferior Parietal Lobule, Inferior Semi-Lunar Lobule, Inferior Temporal Gyrus, Insula, Lateral Ventricle, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterior Cingulate, Precentral Gyrus, Precuneus, Sub-Gyral, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, and Uncus. Conclusion. The displacement filed is effective in detection of AD and related brain-regions.
Collapse
Affiliation(s)
- Yudong Zhang
- School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, China
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Shuihua Wang
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| |
Collapse
|
32
|
Griffanti L, Dipasquale O, Laganà MM, Nemni R, Clerici M, Smith SM, Baselli G, Baglio F. Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease. Front Hum Neurosci 2015; 9:449. [PMID: 26321937 PMCID: PMC4531245 DOI: 10.3389/fnhum.2015.00449] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 07/28/2015] [Indexed: 01/01/2023] Open
Abstract
Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest—crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier—FIX—cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment.
Collapse
Affiliation(s)
- Ludovica Griffanti
- IRCCS, Fondazione Don Carlo GnocchiMilan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di MilanoMilan, Italy
- Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional MRI of the Brain, University of OxfordOxford, UK
- *Correspondence: Ludovica Griffanti, Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, UK
| | - Ottavia Dipasquale
- IRCCS, Fondazione Don Carlo GnocchiMilan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di MilanoMilan, Italy
| | | | - Raffaello Nemni
- IRCCS, Fondazione Don Carlo GnocchiMilan, Italy
- Physiopatholgy Department, Università degli Studi di MilanoMilan, Italy
| | - Mario Clerici
- IRCCS, Fondazione Don Carlo GnocchiMilan, Italy
- Physiopatholgy Department, Università degli Studi di MilanoMilan, Italy
| | - Stephen M. Smith
- Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional MRI of the Brain, University of OxfordOxford, UK
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di MilanoMilan, Italy
| | | |
Collapse
|
33
|
Li H, Hou X, Liu H, Yue C, He Y, Zuo X. Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a meta-analysis of 75 fMRI studies. Hum Brain Mapp 2015; 36:1217-32. [PMID: 25411150 PMCID: PMC6869191 DOI: 10.1002/hbm.22689] [Citation(s) in RCA: 136] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 10/03/2014] [Accepted: 11/03/2014] [Indexed: 11/11/2022] Open
Abstract
Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta-analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task-based fMRI studies in MCI and AD published between January 1990 and January 2014. Activation likelihood estimation meta-analyses were conducted to compare the significant group differences in brain activation, the significant voxels were overlaid onto seven referenced neuronal cortical networks derived from the resting-state fMRI data of 1,000 healthy participants. Thirty-nine task-based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI-related meta-analysis while 36 task-based fMRI studies (421 AD patients and 512 healthy controls) were included in AD-related meta-analysis. The meta-analytic results revealed that MCI and AD showed abnormal regional brain activation as well as large-scale brain networks. MCI patients showed hypoactivation in default, frontoparietal, and visual networks relative to healthy controls, whereas AD-related hypoactivation mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI-related and AD-related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large-scale networks in fulfilling the cognitive tasks. These system-level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD.
Collapse
Affiliation(s)
- Hui‐Jie Li
- Key Laboratory of Behavioral ScienceInstitute of PsychologyChinese Academy of SciencesBeijing100101China
| | - Xiao‐Hui Hou
- Key Laboratory of Behavioral ScienceInstitute of PsychologyChinese Academy of SciencesBeijing100101China
- University of Chinese Academy of SciencesBeijing100049China
| | - Han‐Hui Liu
- Youth Work DepartmentChina Youth University of Political StudiesBeijing100089China
| | - Chun‐Lin Yue
- Institute of Sports MedicineSoochow UniversitySuzhou215006China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing100875China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
| | - Xi‐Nian Zuo
- Key Laboratory of Behavioral ScienceInstitute of PsychologyChinese Academy of SciencesBeijing100101China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
| |
Collapse
|
34
|
Venkatesan UM, Dennis NA, Hillary FG. Chronology and chronicity of altered resting-state functional connectivity after traumatic brain injury. J Neurotrauma 2015; 32:252-64. [PMID: 24955788 PMCID: PMC4347859 DOI: 10.1089/neu.2013.3318] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Whereas traumatic brain injury (TBI) results in widespread disruption of neural networks, changes in regional resting-state functional connectivity patterns after insult remain unclear. Specifically, little is known about the chronology of emergent connectivity alterations and whether they persist after a critical recovery window. We used resting-state functional magnetic resonance imaging and seed-voxel correlational analyses in both cross-sectional and longitudinal designs to probe intrinsic connectivity patterns involving the posterior cingulate cortex (PCC) and hippocampi, regions shown to be important in the default mode network (DMN) and vulnerable to neuropathology. A total of 22 participants in the chronic stage of moderate-to-severe TBI and 18 healthy controls were included for cross-sectional study. Longitudinal analyses included 13 individuals in the TBI group for whom data approximately 3 months after injury (subacute) were available. Overall, results indicated dissociable connectivity trajectories of the PCC and hippocampi during recovery from TBI, with PCC alterations characterized by early hypersynchrony with the anterior DMN that is gradually reduced, and hippocampal changes marked by increasing synchrony with proximal cortex and subcortex. The PCC also showed increasing antiphase synchrony with posterior attentional regions, and the hippocampi showed decreasing antiphase synchrony with frontal attentional regions. Antiphase synchrony of the hippocampus and dorsolateral prefrontal cortex at the subacute stage of TBI was positively associated with attentional performance on neuropsychological tests at both the subacute and chronic stages. Our findings highlight the heterogeneity of regional whole-brain connectivity changes after TBI, and suggest that residual connectivity alterations exist in the clinically stable phase of TBI. Parallels between the chronicity of the observed effects and findings in neurodegenerative disease are discussed in the context of potential long-term outcomes of TBI.
Collapse
Affiliation(s)
- Umesh M. Venkatesan
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania
| | - Nancy A. Dennis
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania
| | - Frank G. Hillary
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania
- Department of Neurology, Penn State Hershey Medical Center, Hershey, Pennsylvania
| |
Collapse
|
35
|
Zhang X, Hu B, Ma X, Xu L. Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression. IEEE Trans Nanobioscience 2015; 14:237-47. [PMID: 25700453 DOI: 10.1109/tnb.2015.2403274] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Mild cognitive impairment (MCI) has been considered as a transition phase to Alzheimer's disease (AD), and the diagnosis of MCI may help patients to carry out appropriate treatments to delay or even prevent AD. Recent advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been widely used to get more comprehensive understanding of neurological disorders at a whole-brain connectivity level. However, how to explore effective brain functional connectivity from fMRI data is still a challenge especially when the ultimate goal is to train classifiers for discriminating patients effectively. In our research, we studied the functional connectivity of the whole brain by calculating Pearson's correlation coefficients based on rs-fMRI data, and proposed a set of novel features by applying Two Sample T-Test on the correlation coefficients matrix to identify the most discriminative correlation coefficients. We trained a L2-regularized Logistic Regression classifier based on the five novel features for the first time and evaluated the classification performance via leave-one-out cross validation. We also iterated 10-fold cross validation ten times in order to evaluate the statistical significance of our method. The experiment result demonstrates that classification accuracy and the area under receiver operating characteristic (ROC) curve in our method are 87.5% and 0.929 respectively, and the statistical results prove that our method is statistically significant better than other three algorithms, which means our method could be meaningful to assist physicians efficiently in "real-world" diagnostic situations.
Collapse
|
36
|
Maarouf CL, Kokjohn TA, Walker DG, Whiteside CM, Kalback WM, Whetzel A, Sue LI, Serrano G, Jacobson SA, Sabbagh MN, Reiman EM, Beach TG, Roher AE. Biochemical assessment of precuneus and posterior cingulate gyrus in the context of brain aging and Alzheimer's disease. PLoS One 2014; 9:e105784. [PMID: 25166759 PMCID: PMC4148328 DOI: 10.1371/journal.pone.0105784] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Accepted: 07/24/2014] [Indexed: 12/12/2022] Open
Abstract
Defining the biochemical alterations that occur in the brain during “normal” aging is an important part of understanding the pathophysiology of neurodegenerative diseases and of distinguishing pathological conditions from aging-associated changes. Three groups were selected based on age and on having no evidence of neurological or significant neurodegenerative disease: 1) young adult individuals, average age 26 years (n = 9); 2) middle-aged subjects, average age 59 years (n = 5); 3) oldest-old individuals, average age 93 years (n = 6). Using ELISA and Western blotting methods, we quantified and compared the levels of several key molecules associated with neurodegenerative disease in the precuneus and posterior cingulate gyrus, two brain regions known to exhibit early imaging alterations during the course of Alzheimer’s disease. Our experiments revealed that the bioindicators of emerging brain pathology remained steady or decreased with advancing age. One exception was S100B, which significantly increased with age. Along the process of aging, neurofibrillary tangle deposition increased, even in the absence of amyloid deposition, suggesting the presence of amyloid plaques is not obligatory for their development and that limited tangle density is a part of normal aging. Our study complements a previous assessment of neuropathology in oldest-old subjects, and within the limitations of the small number of individuals involved in the present investigation, it adds valuable information to the molecular and structural heterogeneity observed along the course of aging and dementia. This work underscores the need to examine through direct observation how the processes of amyloid deposition unfold or change prior to the earliest phases of dementia emergence.
Collapse
Affiliation(s)
- Chera L. Maarouf
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Tyler A. Kokjohn
- Department of Microbiology, Midwestern University, Glendale, Arizona, United States of America
| | - Douglas G. Walker
- Laboratory of Neuroinflammation, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Charisse M. Whiteside
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Walter M. Kalback
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alexis Whetzel
- Laboratory of Neuroinflammation, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Lucia I. Sue
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Geidy Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Sandra A. Jacobson
- Cleo Roberts Center for Clinical Research, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Marwan N. Sabbagh
- Cleo Roberts Center for Clinical Research, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, Arizona, United States of America
| | - Thomas G. Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alex E. Roher
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
- * E-mail:
| |
Collapse
|
37
|
Disruption of Resting Functional Connectivity in Alzheimer’s Patients and At-Risk Subjects. Curr Neurol Neurosci Rep 2014; 14:491. [DOI: 10.1007/s11910-014-0491-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
|
38
|
Li C, Tian L. Association between resting-state coactivation in the parieto-frontal network and intelligence during late childhood and adolescence. AJNR Am J Neuroradiol 2014; 35:1150-6. [PMID: 24557703 DOI: 10.3174/ajnr.a3850] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE A number of studies have associated the adult intelligence quotient with the structure and function of the bilateral parieto-frontal networks, whereas the relationship between intelligence quotient and parieto-frontal network function has been found to be relatively weak in early childhood. Because both human intelligence and brain function undergo protracted development into adulthood, the purpose of the present study was to provide a better understanding of the development of the parieto-frontal network-intelligence quotient relationship. MATERIALS AND METHODS We performed independent component analysis of resting-state fMRI data of 84 children and 50 adolescents separately and then correlated full-scale intelligence quotient with the spatial maps of the bilateral parieto-frontal networks of each group. RESULTS In children, significant positive spatial-map versus intelligence quotient correlations were detected in the right angular gyrus and inferior frontal gyrus in the right parieto-frontal network, and no significant correlation was observed in the left parieto-frontal network. In adolescents, significant positive correlation was detected in the left inferior frontal gyrus in the left parieto-frontal network, and the correlations in the frontal pole in the 2 parieto-frontal networks were only marginally significant. CONCLUSIONS The present findings not only support the critical role of the parieto-frontal networks for intelligence but indicate that the relationship between intelligence quotient and the parieto-frontal network in the right hemisphere has been well established in late childhood, and that the relationship in the left hemisphere was also established in adolescence.
Collapse
Affiliation(s)
- C Li
- From the Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - L Tian
- From the Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.
| |
Collapse
|
39
|
Lim JS, Park YH, Jang JW, Park SY, Kim S. Differential white matter connectivity in early mild cognitive impairment according to CSF biomarkers. PLoS One 2014; 9:e91400. [PMID: 24614676 PMCID: PMC3948821 DOI: 10.1371/journal.pone.0091400] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 02/11/2014] [Indexed: 11/19/2022] Open
Abstract
Mild cognitive impairment (MCI) is a heterogeneous group and certain MCI subsets eventually convert to dementia. Cerebrospinal fluid (CSF) biomarkers are known to predict this conversion. We sought evidence for the differences in white matter connectivity between early amnestic MCI (EMCI) subgroups according to a CSF phosphorylated tau181p/amyloid beta1-42 ratio of 0.10. From the Alzheimer's Disease Neuroimaging Initiative database, 16 high-ratio, 25 low-ratio EMCI patients, and 20 normal controls with diffusion tensor images and CSF profiles were included. Compared to the high-ratio group, radial diffusivity significantly increased in both sides of the corpus callosum and the superior and inferior longitudinal fasciculus in the low-ratio group. In widespread white matter skeleton regions, the low-ratio group showed significantly increased mean, axial, and radial diffusivity compared to normal controls. However, the high-ratio group showed no differences when compared to the normal group. In conclusion, our study revealed that there were significant differences in white matter connectivity between EMCI subgroups according to CSF phosphorylated tau181p/amyloid beta1-42 ratios.
Collapse
Affiliation(s)
- Jae-Sung Lim
- Department of Neurology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Young Ho Park
- Clinical Neuroscience Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jae-Won Jang
- Clinical Neuroscience Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - So Yong Park
- Clinical Neuroscience Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - SangYun Kim
- Clinical Neuroscience Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
- * E-mail:
| | | |
Collapse
|