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Jia X, Li Y, Ying Y, Jia X, Tang W, Bian Y, Zhang J, Wang DJJ, Cheng X, Yang Q. Effect of corticosubcortical iron deposition on dysfunction in CADASIL is mediated by white matter microstructural damage. Neuroimage Clin 2023; 39:103485. [PMID: 37542975 PMCID: PMC10407949 DOI: 10.1016/j.nicl.2023.103485] [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: 03/30/2023] [Revised: 07/26/2023] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
Iron dysregulation may attenuate cognitive performance in patients with CADASIL. However, the underlying pathophysiological mechanisms remain incompletely understood. Whether white matter microstructural changes mediate these processes is largely unclear. In the present study, 30 cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients were confirmed via genetic analysis and 30 sex- and age-matched healthy controls underwent multimodal MRI examinations and neuropsychological assessments. Quantitative susceptibility mapping and peak width of skeletonized mean diffusivity (PSMD) were analyzed. Mediation effect analysis was performed to explore the interrelationship between iron deposition, white matter microstructural changes and cognitive deficits in CADASIL. Cognitive deterioration was most affected in memory and executive function, followed by attention and working memory in CADASIL. Excessive iron in the temporal-precuneus pathway and deep gray matter specific to CADASIL were identified. Mediation analysis further revealed that PSMD mediated the relationship between iron concentration and cognitive profile in CADASIL. The present findings provide a new perspective on iron deposition in the corticosubcortical circuit and its contribution to disease-related selective cognitive decline, in which iron concentration may affect cognition by white matter microstructural changes in CADASIL.
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Affiliation(s)
- Xiuqin Jia
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China; Key Lab of Medical Engineering for Cardiovascular Disease, Ministry of Education, Beijing 100020, China
| | - Yingying Li
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Yunqing Ying
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xuejia Jia
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Weijun Tang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yueyan Bian
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Jiajia Zhang
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), USC Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, United States
| | - Xin Cheng
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China.
| | - Qi Yang
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China; Key Lab of Medical Engineering for Cardiovascular Disease, Ministry of Education, Beijing 100020, China.
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2
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Dang C, Wang Y, Li Q, Lu Y. Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers. PSYCHORADIOLOGY 2023; 3:kkad009. [PMID: 38666112 PMCID: PMC11003434 DOI: 10.1093/psyrad/kkad009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/04/2023] [Accepted: 06/20/2023] [Indexed: 04/28/2024]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.
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Affiliation(s)
- Chun Dang
- Department of Periodical Press, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Yanchao Wang
- Department of Neurology, Chifeng University of Affiliated Hospital, Chifeng 024000, China
| | - Qian Li
- Department of Neurology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China
| | - Yaoheng Lu
- Department of General Surgery, Chengdu Integrated Traditional Chinese Medicine and Western Medicine Hospital, Chengdu 610000, China
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3
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Farràs-Permanyer L, Mancho-Fora N, Montalà-Flaquer M, Gudayol-Ferré E, Gallardo-Moreno GB, Zarabozo-Hurtado D, Villuendas-González E, Peró-Cebollero M, Guàrdia-Olmos J. Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment. Brain Sci 2019; 9:E350. [PMID: 31801260 PMCID: PMC6955819 DOI: 10.3390/brainsci9120350] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 11/28/2019] [Indexed: 12/11/2022] Open
Abstract
Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer's disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis.
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Affiliation(s)
- Laia Farràs-Permanyer
- Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain; (N.M.-F.); (M.M.-F.); (M.P.-C.)
- UBICS Institute of Complex Systems & UB Institute of Neurosciences, 08035 Barcelona, Spain
| | - Núria Mancho-Fora
- Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain; (N.M.-F.); (M.M.-F.); (M.P.-C.)
| | - Marc Montalà-Flaquer
- Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain; (N.M.-F.); (M.M.-F.); (M.P.-C.)
| | - Esteve Gudayol-Ferré
- Facultad de Psicología, Universidad Michoacana de San Nicolás Hidalgo, Morelia 58000, Mexico; (E.G.-F.); (E.V.-G.)
| | | | | | - Erwin Villuendas-González
- Facultad de Psicología, Universidad Michoacana de San Nicolás Hidalgo, Morelia 58000, Mexico; (E.G.-F.); (E.V.-G.)
| | - Maribel Peró-Cebollero
- Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain; (N.M.-F.); (M.M.-F.); (M.P.-C.)
- UBICS Institute of Complex Systems & UB Institute of Neurosciences, 08035 Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain; (N.M.-F.); (M.M.-F.); (M.P.-C.)
- UBICS Institute of Complex Systems & UB Institute of Neurosciences, 08035 Barcelona, Spain
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4
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Goodman MS, Kumar S, Zomorrodi R, Ghazala Z, Cheam ASM, Barr MS, Daskalakis ZJ, Blumberger DM, Fischer C, Flint A, Mah L, Herrmann N, Bowie CR, Mulsant BH, Rajji TK. Theta-Gamma Coupling and Working Memory in Alzheimer's Dementia and Mild Cognitive Impairment. Front Aging Neurosci 2018; 10:101. [PMID: 29713274 PMCID: PMC5911490 DOI: 10.3389/fnagi.2018.00101] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 03/26/2018] [Indexed: 01/09/2023] Open
Abstract
Working memory deficits are common among individuals with Alzheimer's dementia (AD) or mild cognitive impairment (MCI). Yet, little is known about the mechanisms underlying these deficits. Theta-gamma coupling-the modulation of high-frequency gamma oscillations by low-frequency theta oscillations-is a neurophysiologic process underlying working memory. We assessed the relationship between theta-gamma coupling and working memory deficits in AD and MCI. We hypothesized that: (1) individuals with AD would display the most significant working memory impairments followed by MCI and finally healthy control (HC) participants; and (2) there would be a significant association between working memory performance and theta-gamma coupling across all participants. Ninety-eight participants completed the N-back working memory task during an electroencephalography (EEG) recording: 33 with AD (mean ± SD age: 76.5 ± 6.2), 34 with MCI (mean ± SD age: 74.8 ± 5.9) and 31 HCs (mean ± SD age: 73.5 ± 5.2). AD participants performed significantly worse than control and MCI participants on the 1- and 2-back conditions. Regarding theta-gamma coupling, AD participants demonstrated the lowest level of coupling followed by the MCI and finally control participants on the 2-back condition. Finally, a linear regression analysis demonstrated that theta-gamma coupling (β = 0.69, p < 0.001) was the most significant predictor of 2-back performance. Our results provide evidence for a relationship between altered theta-gamma coupling and working memory deficits in individuals with AD and MCI. They also provide insight into a potential mechanism underlying working memory impairments in these individuals.
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Affiliation(s)
- Michelle S Goodman
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sanjeev Kumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Reza Zomorrodi
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Zaid Ghazala
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Amay S M Cheam
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mera S Barr
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Daniel M Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Corinne Fischer
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Alastair Flint
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Linda Mah
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada
| | - Nathan Herrmann
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Tarek K Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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5
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Scarapicchia V, Mazerolle EL, Fisk JD, Ritchie LJ, Gawryluk JR. Resting State BOLD Variability in Alzheimer's Disease: A Marker of Cognitive Decline or Cerebrovascular Status? Front Aging Neurosci 2018; 10:39. [PMID: 29515434 PMCID: PMC5826397 DOI: 10.3389/fnagi.2018.00039] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Accepted: 02/02/2018] [Indexed: 01/18/2023] Open
Abstract
Background: Alzheimer's disease (AD) is a neurodegenerative disorder that may benefit from early diagnosis and intervention. Therefore, there is a need to identify early biomarkers of AD using non-invasive techniques such as functional magnetic resonance imaging (fMRI). Recently, novel approaches to the analysis of resting-state fMRI data have been developed that focus on the moment-to-moment variability in the blood oxygen level dependent (BOLD) signal. The objective of the current study was to investigate BOLD variability as a novel early biomarker of AD and its associated psychophysiological correlates. Method: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 database from 19 participants with AD and 19 similarly aged controls. For each participant, a map of BOLD signal variability (SDBOLD) was computed as the standard deviation of the BOLD timeseries at each voxel. Group comparisons were performed to examine global differences in resting state SDBOLD in AD versus healthy controls. Correlations were then examined between participant SDBOLD maps and (1) ADNI-derived composite scores of memory and executive function and (2) neuroimaging markers of cerebrovascular status. Results: Between-group comparisons revealed significant (p < 0.05) increases in SDBOLD in patients with AD relative to healthy controls in right-lateralized frontal regions. Lower memory scores and higher WMH burden were associated with greater SDBOLD in the healthy control group (p < 0.1), but not individuals with AD. Conclusion: The current study provides proof of concept of a novel resting state fMRI analysis technique that is non-invasive, easily accessible, and clinically compatible. To further explore the potential of SDBOLD as a biomarker of AD, additional studies in larger, longitudinal samples are needed to better understand the changes in SDBOLD that characterize earlier stages of disease progression and their underlying psychophysiological correlates.
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Affiliation(s)
| | - Erin L. Mazerolle
- Department of Radiology and The Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - John D. Fisk
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada
- Department of Psychology, Nova Scotia Health Authority, Queen Elizabeth II Health Sciences Centre, Halifax, NS, Canada
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada
- Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Lesley J. Ritchie
- Department of Clinical Health Psychology, University of Manitoba, Winnipeg, MB, Canada
| | - Jodie R. Gawryluk
- Department of Psychology, University of Victoria, Victoria, BC, Canada
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6
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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]
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7
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APOE moderates compensatory recruitment of neuronal resources during working memory processing in healthy older adults. Neurobiol Aging 2017; 56:127-137. [PMID: 28528773 DOI: 10.1016/j.neurobiolaging.2017.04.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 03/17/2017] [Accepted: 04/13/2017] [Indexed: 11/20/2022]
Abstract
The APOE ε4 allele increases the risk for sporadic Alzheimer's disease and modifies brain activation patterns of numerous cognitive domains. We assessed cognitively intact older adults with a letter n-back task to determine if previously observed increases in ε4 carriers' working-memory-related brain activation are compensatory such that they serve to maintain working memory function. Using multiple regression models, we identified interactions of APOE variant and age in bilateral hippocampus independently from task performance: ε4 carriers only showed a decrease in activation with increasing age, suggesting high sensitivity of fMRI data for detecting changes in Alzheimer's disease-relevant brain areas before cognitive decline. Moreover, we identified ε4 carriers to show higher activations in task-negative medial and task-positive inferior frontal areas along with better performance under high working memory load relative to non-ε4 carriers. The increased frontal recruitment is compatible with models of neuronal compensation, extends on existing evidence, and suggests that ε4 carriers require additional neuronal resources to successfully perform a demanding working memory task.
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8
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Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment. Brain Imaging Behav 2016; 9:663-77. [PMID: 25355371 DOI: 10.1007/s11682-014-9320-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In recent years, functional connectomics signatures have been shown to be a very valuable tool in characterizing and differentiating brain disorders from normal controls. However, if the functional connectivity alterations in a brain disease are localized within sub-networks of a connectome, then accurate identification of such disease-specific sub-networks is critical and this capability entails both fine-granularity definition of connectome nodes and effective clustering of connectome nodes into disease-specific and non-disease-specific sub-networks. In this work, we adopted the recently developed DICCCOL (dense individualized and common connectivity-based cortical landmarks) system as a fine-granularity high-resolution connectome construction method to deal with the first issue, and employed an effective variant of non-negative matrix factorization (NMF) method to pinpoint disease-specific sub-networks, which we called atomic connectomics signatures in this work. We have implemented and applied this novel framework to two mild cognitive impairment (MCI) datasets from two different research centers, and our experimental results demonstrated that the derived atomic connectomics signatures can effectively characterize and differentiate MCI patients from their normal controls. In general, our work contributed a novel computational framework for deriving descriptive and distinctive atomic connectomics signatures in brain disorders.
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9
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Farràs-Permanyer L, Guàrdia-Olmos J, Peró-Cebollero M. Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art. Front Psychol 2015; 6:1095. [PMID: 26300802 PMCID: PMC4523742 DOI: 10.3389/fpsyg.2015.01095] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 07/16/2015] [Indexed: 11/30/2022] Open
Abstract
In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results.
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Affiliation(s)
- Laia Farràs-Permanyer
- Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, Universitat de Barcelona Barcelona, Spain ; Institut de Recerca en Cervell, Cognició i Conducta Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, Universitat de Barcelona Barcelona, Spain ; Institut de Recerca en Cervell, Cognició i Conducta Barcelona, Spain
| | - Maribel Peró-Cebollero
- Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, Universitat de Barcelona Barcelona, Spain ; Institut de Recerca en Cervell, Cognició i Conducta Barcelona, Spain
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10
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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: 138] [Impact Index Per Article: 15.3] [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.
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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
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11
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Scheller E, Minkova L, Leitner M, Klöppel S. Attempted and successful compensation in preclinical and early manifest neurodegeneration - a review of task FMRI studies. Front Psychiatry 2014; 5:132. [PMID: 25324786 PMCID: PMC4179340 DOI: 10.3389/fpsyt.2014.00132] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 09/08/2014] [Indexed: 01/20/2023] Open
Abstract
Several models of neural compensation in healthy aging have been suggested to explain brain activity that aids to sustain cognitive function. Applying recently suggested criteria of "attempted" and "successful" compensation, we reviewed existing literature on compensatory mechanisms in preclinical Huntington's disease (HD) and amnestic mild cognitive impairment (aMCI). Both disorders constitute early stages of neurodegeneration ideal for examining compensatory mechanisms and developing targeted interventions. We strived to clarify whether compensation criteria derived from healthy aging populations can be applied to early neurodegeneration. To concentrate on the close coupling of cognitive performance and brain activity, we exclusively addressed task fMRI studies. First, we found evidence for parallels in compensatory mechanisms between healthy aging and neurodegenerative disease. Several studies fulfilled criteria of attempted compensation, while reports of successful compensation were largely absent, which made it difficult to conclude on. Second, comparing working memory studies in preclinical HD and aMCI, we identified similar compensatory patterns across neurodegenerative disorders in lateral and medial prefrontal cortex. Such patterns included an inverted U-shaped relationship of neurodegeneration and compensatory activity spanning from preclinical to manifest disease. Due to the lack of studies systematically targeting all criteria of compensation, we propose an exemplary study design, including the manipulation of compensating brain areas by brain stimulation. Furthermore, we delineate the benefits of targeted interventions by non-invasive brain stimulation, as well as of unspecific interventions such as physical activity or cognitive training. Unambiguously detecting compensation in early neurodegenerative disease will help tailor interventions aiming at sustained overall functioning and delayed clinical disease onset.
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Affiliation(s)
- Elisa Scheller
- Section of Gerontopsychiatry and Neuropsychology, Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Freiburg, Germany
- Freiburg Brain Imaging Center (FBI), University Medical Center Freiburg, Freiburg, Germany
- Laboratory for Biological and Personality Psychology, Department of Psychology, University of Freiburg, Freiburg, Germany
| | - Lora Minkova
- Section of Gerontopsychiatry and Neuropsychology, Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Freiburg, Germany
- Freiburg Brain Imaging Center (FBI), University Medical Center Freiburg, Freiburg, Germany
- Laboratory for Biological and Personality Psychology, Department of Psychology, University of Freiburg, Freiburg, Germany
| | - Mathias Leitner
- Section of Gerontopsychiatry and Neuropsychology, Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Freiburg, Germany
- Freiburg Brain Imaging Center (FBI), University Medical Center Freiburg, Freiburg, Germany
| | - Stefan Klöppel
- Section of Gerontopsychiatry and Neuropsychology, Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Freiburg, Germany
- Freiburg Brain Imaging Center (FBI), University Medical Center Freiburg, Freiburg, Germany
- Department of Neurology, University Medical Center Freiburg, Freiburg, Germany
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Migo E, Mitterschiffthaler M, O’Daly O, Dawson G, Dourish C, Craig K, Simmons A, Wilcock G, McCulloch E, Jackson S, Kopelman M, Williams S, Morris R. Alterations in working memory networks in amnestic mild cognitive impairment. AGING NEUROPSYCHOLOGY AND COGNITION 2014; 22:106-27. [DOI: 10.1080/13825585.2014.894958] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- E.M. Migo
- King’s College London, Department of Neuroimaging, Institute of Psychiatry, London, UK
- King’s College London, Department of Psychological Medicine, Institute of Psychiatry, London, UK
| | - M. Mitterschiffthaler
- King’s College London, Department of Neuroimaging, Institute of Psychiatry, London, UK
- Department for Psychotherapy and Psychosomatics, Campus Innenstadt, Ludwig-Maximilians-University, Munich, Germany
| | - O. O’Daly
- King’s College London, Department of Neuroimaging, Institute of Psychiatry, London, UK
| | | | | | | | - A. Simmons
- King’s College London, Department of Neuroimaging, Institute of Psychiatry, London, UK
| | - G.K. Wilcock
- OPTIMA Project, Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - E. McCulloch
- OPTIMA Project, Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - S.H.D. Jackson
- Clinical Age Research Unit, King’s College Hospital, London, UK
| | - M.D. Kopelman
- King’s College London, Department of Psychological Medicine, Institute of Psychiatry, London, UK
| | - S.C.R. Williams
- King’s College London, Department of Neuroimaging, Institute of Psychiatry, London, UK
| | - R.G. Morris
- King’s College London, Department of Neuroimaging, Institute of Psychiatry, London, UK
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