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Chiang HS, Lydon EA, Kraut MA, Hart J, Mudar RA. Differences in electroencephalography oscillations between normal aging and mild cognitive impairment during semantic memory retrieval. Eur J Neurosci 2023; 58:2278-2296. [PMID: 37122187 PMCID: PMC10531984 DOI: 10.1111/ejn.16001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 04/16/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023]
Abstract
Semantic memory remains relatively stable with normal cognitive aging and declines in early stages of neurodegenerative disease. We measured electroencephalography (EEG) oscillatory correlates of semantic memory retrieval to examine the effects of normal and pathological aging. Twenty-nine cognitively healthy young adults (YA), 22 cognitively healthy aging adults (HA) and 20 patients with mild cognitive impairment (MCI) completed a semantic memory retrieval task with concurrent EEG recording in which they judged whether two words (features of objects) led to retrieval of an object (retrieval) or not (non-retrieval). Event-related power changes contrasting the two conditions (retrieval vs. non-retrieval) within theta, alpha, low-beta and high-beta EEG frequency bands were examined for normal aging (YA vs. HA) and pathological aging effects (HA vs. MCI). With no behavioural differences between the two normal age groups, we found later theta and alpha event-related power differences between conditions only in YA and a high-beta event-related power difference only in HA. For pathological aging effects, with reduced accuracy in MCI, we found different EEG patterns of early event-related beta power differences between conditions in MCI compared with HA and an event-related low-beta power difference only in HA. Beta oscillations were correlated with behavioural performance only in HA. We conclude that the aging brain relies on faster (beta) oscillations during the semantic memory task. With pathological aging, retrieval accuracy declines and pattern of beta oscillation changes. The findings provide insights about age-related neural mechanisms underlying semantic memory and have implications for early detection of pathological aging.
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Affiliation(s)
- Hsueh-Sheng Chiang
- School of Behavioral and Brain Sciences, The University of Texas at Dallas. 800 W Campbell Rd, Richardson, TX 75080, USA
- Department of Neurology, University of Texas Southwestern Medical Center. 5303 Harry Hines Blvd 8th floor, Dallas, TX 75390, USA
| | - Elizabeth A. Lydon
- Department of Speech and Hearing Science, University of Illinois Urbana-Champaign. 901 S 6th St, Champaign, IL 61820, USA
| | - Michael A. Kraut
- Department of Radiology and Radiological Science, Johns Hopkins University. 1800 Orleans St. Baltimore, MD 21287, USA
| | - John Hart
- School of Behavioral and Brain Sciences, The University of Texas at Dallas. 800 W Campbell Rd, Richardson, TX 75080, USA
- Department of Neurology, University of Texas Southwestern Medical Center. 5303 Harry Hines Blvd 8th floor, Dallas, TX 75390, USA
| | - Raksha A. Mudar
- Department of Speech and Hearing Science, University of Illinois Urbana-Champaign. 901 S 6th St, Champaign, IL 61820, USA
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2
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Kim J, Lee M, Lee MK, Wang SM, Kim NY, Kang DW, Um YH, Na HR, Woo YS, Lee CU, Bahk WM, Kim D, Lim HK. Development of Random Forest Algorithm Based Prediction Model of Alzheimer's Disease Using Neurodegeneration Pattern. Psychiatry Investig 2021; 18:69-79. [PMID: 33561931 PMCID: PMC7897872 DOI: 10.30773/pi.2020.0304] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 10/25/2020] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE Alzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI). METHODS We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer's which is a commonly used segmentation software. RESULTS Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer's (6-8 hours). CONCLUSION Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
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Affiliation(s)
- JeeYoung Kim
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Minho Lee
- Research Institute, NEUROPHET Inc., Seoul, Korea
| | - Min Kyoung Lee
- Department of Radiology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Nak-Young Kim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent's Hospital Seoul, College of Medicine, The Catholic University of Korea, Suwon, Korea
| | - Hae-Ran Na
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Young Sup Woo
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Won-Myong Bahk
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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3
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Lee EC, Kang JM, Seo S, Seo HE, Lee SY, Park KH, Na DL, Noh Y, Seong JK. Association of Subcortical Structural Shapes With Tau, Amyloid, and Cortical Atrophy in Early-Onset and Late-Onset Alzheimer's Disease. Front Aging Neurosci 2020; 12:563559. [PMID: 33192457 PMCID: PMC7650820 DOI: 10.3389/fnagi.2020.563559] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/05/2020] [Indexed: 11/13/2022] Open
Abstract
The objectives of this study were to compare the topographical subcortical shape and to investigate the effects of tau or amyloid burden on atrophic patterns in early onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). One hundred and sixty-one participants (53 EOAD, 44 LOAD, 33 young controls, and 31 older controls) underwent [18F]THK5351 positron emission tomography (PET), [18F]flutemetamol (FLUTE) PET, and 3T MRI scans. We used surface-based analysis to evaluate subcortical structural shape, permutation-based statistics for group comparisons, and Spearman's correlations to determine associations with THK, FLUTE, cortical thickness, and neuropsychological test results. When compared to their age-matched controls, EOAD patients exhibited shape reduction in the bilateral amygdala, hippocampus, caudate, and putamen, while in LOAD patients, the bilateral amygdala and hippocampus showed decreased shapes. In EOAD, widespread subcortical shrinkage, with less association of the hippocampus, correlated with THK retention and cortical thinning, while in LOAD patients, subcortical structures were limited which had significant correlation with THK or mean cortical thickness. Subcortical structural shape showed less correlation with FLUTE global retention in both EOAD and LOAD. Multiple cognitive domains, except memory function, correlated with the bilateral amygdala, caudate, and putamen in EOAD patients, while more restricted regions in the subcortical structures were correlated with neuropsychological test results in LOAD patients. Subcortical structures were associated with AD hallmarks in EOAD. However, the correlation was limited in LOAD. Moreover, relationship between subcortical structural atrophy and cognitive decline were quite different between EOAD and LOAD. These findings suggest that the effects of Alzheimer's pathologies on subcortical structural changes in EOAD and LOAD and they may have different courses of pathomechanism.
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Affiliation(s)
- Eun-Chong Lee
- School of Biomedical Engineering, Korea University, Seoul, South Korea
| | - Jae Myeong Kang
- Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Seongho Seo
- Department of Neuroscience, College of Medicine, Gachon University, Incheon, South Korea
| | - Ha-Eun Seo
- Neuroscience Research Institute, Gachon University, Incheon, South Korea
| | - Sang-Yoon Lee
- Department of Neuroscience, College of Medicine, Gachon University, Incheon, South Korea
| | - Kee Hyung Park
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Young Noh
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea.,Department of Health Science and Technology, GAIHST, Gachon University, Incheon, South Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, South Korea.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
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4
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Farina FR, Emek-Savaş DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, Whelan R. A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment. Neuroimage 2020; 215:116795. [PMID: 32278090 DOI: 10.1016/j.neuroimage.2020.116795] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.
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Affiliation(s)
- F R Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
| | - D D Emek-Savaş
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, 35160, Turkey; Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
| | - L Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - R Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - H Kiiski
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Department of Neurology, Dokuz Eylul University Medical School, Izmir, 35340, Turkey
| | - R Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland.
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5
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Subramanian S, Rajamanickam K, Prakash JS, Ramachandran M. Study on structural atrophy changes and functional connectivity measures in Alzheimer's disease. J Med Imaging (Bellingham) 2020; 7:016002. [PMID: 32118092 DOI: 10.1117/1.jmi.7.1.016002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 02/03/2020] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by the progressive accumulation of neurofibrillary tangles associated with amyloid plaques. We used 80 resting-state functional magnetic resonance imaging and 80 T 1 images acquired using MP-RAGE (magnetization-prepared rapid acquisition gradient echo) from Alzheimer's Disease Neuroimaging Initiative data to detect atrophy changes and functional connectivity patterns of the default mode networks (DMNs). The study subjects were classified into four groups (each with n = 20 ) based on their Mini-Mental State Examination (MMSE) score as follows: cognitively normal (CN), early mild cognitive impairment, late mild cognitive impairment, and AD. The resting-state functional connectivity of the DMN was examined between the groups using the CONN functional connectivity toolbox. Loss of gray matter in AD was observed. Atrophy measured by the volume of selected subcortical regions, using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library's Integrated Registration and Segmentation Tool (FIRST), revealed significant volume loss in AD when compared to CN ( p < 0.05 ). DMNs were selected to assess functional connectivity. The negative connectivity of DMN increased in AD group compared to controls. Graph theory parameters, such as global and local efficiency, betweenness centrality, average path length, and cluster coefficient, were computed. Relatively higher correlation between MMSE and functional metrics ( r = 0.364 , p = 0.001 ) was observed as compared to atrophy measures ( r = 0.303 , p = 0.006 ). In addition, the receiver operating characteristic analysis showed large area under the curve ( A Z ) for functional parameters ( A Z > 0.9 ), compared to morphometric changes ( A Z < 0.8 ). In summary, it is observed that the functional connectivity measures may serve a better predictor in comparison to structural atrophy changes. We postulate that functional connectivity measures have the potential to evolve as a marker for the early detection of AD.
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Affiliation(s)
- Saraswathi Subramanian
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Karunanithi Rajamanickam
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Joy Sebastian Prakash
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Murugesan Ramachandran
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
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6
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Thalamic volume loss as an early sign of amnestic mild cognitive impairment. J Clin Neurosci 2019; 68:168-173. [DOI: 10.1016/j.jocn.2019.07.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 06/27/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022]
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7
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McKetton L, Cohn M, Tang-Wai DF, Sobczyk O, Duffin J, Holmes KR, Poublanc J, Sam K, Crawley AP, Venkatraghavan L, Fisher JA, Mikulis DJ. Cerebrovascular Resistance in Healthy Aging and Mild Cognitive Impairment. Front Aging Neurosci 2019; 11:79. [PMID: 31031616 PMCID: PMC6474328 DOI: 10.3389/fnagi.2019.00079] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 03/19/2019] [Indexed: 12/04/2022] Open
Abstract
Measures of cerebrovascular reactivity (CVR) are used to judge the health of the brain vasculature. In this study, we report the use of several different analyses of blood oxygen dependent (BOLD) fMRI responses to CO2 to provide a number of metrics of CVR based on the sigmoidal resistance response to CO2. To assess possible differences in these metrics with age, we compiled atlases reflecting voxel-wise means and standard deviations for four different age ranges and for a group of patients with mild cognitive impairment (MCI) and compared them. Sixty-seven subjects were recruited for this study and scanned at 3T field strength. Of those, 51 healthy control volunteers between the ages of 18–83 were recruited, and 16 (MCI) subjects between the ages of 61–83 were recruited. Testing was carried out using an automated computer-controlled gas blender to induce hypercapnia in a step and ramp paradigm while monitoring end-tidal partial pressures of CO2. Surprisingly, some resistance sigmoid parameters in the oldest control group were increased compared to the youngest control group. Resistance amplitude maps showed increases in clusters within the temporal cortex, thalamus, corpus callosum and brainstem, and resistance reserve maps showed increases in clusters within the cingulate cortex, frontal gyrus, and corpus callosum. These findings suggest that some aspects of vascular reactivity in parts of the brain are initially maintained with age but then may increase in later years. We found significant reductions in all resistance sigmoid parameters (amplitude, reserve, sensitivity, midpoint, and range) when comparing MCI patients to controls. Additionally, in controls and in MCI patients, amplitude, range, reserve, and sensitivity in white matter (WM) was significantly reduced compared to gray matter (GM). WM midpoints were significantly above those of GM. Our general conclusion is that vascular regulation in terms of cerebral blood flow (CBF) responsiveness to CO2 is not significantly affected by age, but is reduced in MCI. These changes in cerebrovascular regulation demonstrate the value of resistance metrics for mapping areas of dysregulated blood flow in individuals with MCI. They may also be of value in the investigation of patients with vascular risk factors at risk for developing vascular dementia.
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Affiliation(s)
- Larissa McKetton
- Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON, Canada
| | - Melanie Cohn
- Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - David F Tang-Wai
- Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada.,Department of Medicine, Division of Neurology, University of Toronto and the University Health Network Memory Clinic, Toronto, ON, Canada
| | - Olivia Sobczyk
- Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON, Canada
| | - James Duffin
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Kenneth R Holmes
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Julien Poublanc
- Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON, Canada
| | - Kevin Sam
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Adrian P Crawley
- Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON, Canada
| | - Lashmi Venkatraghavan
- Department of Anaesthesia and Pain Management, University Health Network (UHN), Toronto, ON, Canada
| | - Joseph A Fisher
- Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Department of Anaesthesia and Pain Management, University Health Network (UHN), Toronto, ON, Canada
| | - David J Mikulis
- Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON, Canada.,Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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8
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Qian L, Liu R, Qin R, Zhao H, Xu Y. The associated volumes of sub-cortical structures and cognitive domain in patients of Mild Cognitive Impairment. J Clin Neurosci 2018; 56:56-62. [PMID: 30029954 DOI: 10.1016/j.jocn.2018.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 05/21/2018] [Accepted: 07/08/2018] [Indexed: 10/28/2022]
Abstract
This study aimed to explore the relationship between sub-cortical structures alterations and the cognitive domains in Mild Cognitive Impairment (MCI) patients, expected to find identifying sub-cortical structure markers of MCI progression to dementia. A total of 67 MCI patients (8 subjects refused to follow up) were recruited, who were divided into 21 stable MCI (sMCI) and 38 progress MCI (pMCI), according to cognitive assays. FreeSurfer software was used to perform volumetric measurements of the sub-cortical structures from 3.0 T magnetic resonance scans. Data revealed that pMCI subjects had lower scores in memory, language, executive and visual spatial compared with sMCI subjects. Compared with the sMCI group, the volume of the left thalamus, bilateral hippocampus, corpus callosum posterior and corpus callosum central was smaller in pMCI subjects. Partial correlation and general linear regression analysis showed that the left hippocampus was predicted region for memory, left thalamus was predicted region for language, executive and visual spatial. These current results suggest that the volumes of sub-cortical structures in stable MCI and progress MCI patients were heterogeneous. Among these regions, the left hippocampus was predicted region for memory, left thalamus was predicted region for language, executive and visual spatial, suggesting that these structures might be important for detecting the subtle effects of MCI patients' cognitive domain or to assess the effectiveness of therapeutic intervention for MCI.
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Affiliation(s)
- Lai Qian
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, Jiangsu, China; The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, China; Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China; Nanjing Clinic Medicine Center for Neurological and Psychiatric Diseases, Nanjing, China
| | - Renyuan Liu
- The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, China; Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China; Nanjing Clinic Medicine Center for Neurological and Psychiatric Diseases, Nanjing, China; Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, Jiangsu, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, Jiangsu, China; The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, China; Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China; Nanjing Clinic Medicine Center for Neurological and Psychiatric Diseases, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, Jiangsu, China; The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, China; Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China; Nanjing Clinic Medicine Center for Neurological and Psychiatric Diseases, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, Jiangsu, China; The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, China; Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China; Nanjing Clinic Medicine Center for Neurological and Psychiatric Diseases, Nanjing, China.
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9
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Kälin AM, Park MTM, Chakravarty MM, Lerch JP, Michels L, Schroeder C, Broicher SD, Kollias S, Nitsch RM, Gietl AF, Unschuld PG, Hock C, Leh SE. Subcortical Shape Changes, Hippocampal Atrophy and Cortical Thinning in Future Alzheimer's Disease Patients. Front Aging Neurosci 2017; 9:38. [PMID: 28326033 PMCID: PMC5339600 DOI: 10.3389/fnagi.2017.00038] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Accepted: 02/13/2017] [Indexed: 11/13/2022] Open
Abstract
Efficacy of future treatments depends on biomarkers identifying patients with mild cognitive impairment at highest risk for transitioning to Alzheimer's disease. Here, we applied recently developed analysis techniques to investigate cross-sectional differences in subcortical shape and volume alterations in patients with stable mild cognitive impairment (MCI) (n = 23, age range 59–82, 47.8% female), future converters at baseline (n = 10, age range 66–84, 90% female) and at time of conversion (age range 68–87) compared to group-wise age and gender matched healthy control subjects (n = 23, age range 61–81, 47.8% female; n = 10, age range 66–82, 80% female; n = 10, age range 68–82, 70% female). Additionally, we studied cortical thinning and global and local measures of hippocampal atrophy as known key imaging markers for Alzheimer's disease. Apart from bilateral striatal volume reductions, no morphometric alterations were found in cognitively stable patients. In contrast, we identified shape alterations in striatal and thalamic regions in future converters at baseline and at time of conversion. These shape alterations were paralleled by Alzheimer's disease like patterns of left hemispheric morphometric changes (cortical thinning in medial temporal regions, hippocampal total and subfield atrophy) in future converters at baseline with progression to similar right hemispheric alterations at time of conversion. Additionally, receiver operating characteristic curve analysis indicated that subcortical shape alterations may outperform hippocampal volume in identifying future converters at baseline. These results further confirm the key role of early cortical thinning and hippocampal atrophy in the early detection of Alzheimer's disease. But first and foremost, and by distinguishing future converters but not patients with stable cognitive abilities from cognitively normal subjects, our results support the value of early subcortical shape alterations and reduced hippocampal subfield volumes as potential markers for the early detection of Alzheimer's disease.
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Affiliation(s)
- Andrea M Kälin
- Institute for Regenerative Medicine, University of Zurich Schlieren, Switzerland
| | - Min T M Park
- Cerebral Imaging Centre, Douglas Mental Health University InstituteMontreal, QC, Canada; Schulich School of Medicine and Dentistry, Western UniversityLondon, ON, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University InstituteMontreal, QC, Canada; Departments of Psychiatry and Biological and Biomedical Engineering, McGill UniversityMontreal, QC, Canada
| | - Jason P Lerch
- The Hospital for Sick ChildrenToronto, ON, Canada; Department of Medical Biophysics, The University of TorontoToronto, ON, Canada
| | - Lars Michels
- Clinic of Neuroradiology, University Hospital Zurich, University of ZurichZurich, Switzerland; Center for MR Research, University Children's Hospital ZurichZurich, Switzerland
| | - Clemens Schroeder
- Institute for Regenerative Medicine, University of Zurich Schlieren, Switzerland
| | - Sarah D Broicher
- Neuropsychology Unit, Department of Neurology, University Hospital Zurich Zurich, Switzerland
| | - Spyros Kollias
- Clinic of Neuroradiology, University Hospital Zurich, University of Zurich Zurich, Switzerland
| | - Roger M Nitsch
- Institute for Regenerative Medicine, University of Zurich Schlieren, Switzerland
| | - Anton F Gietl
- Institute for Regenerative Medicine, University of Zurich Schlieren, Switzerland
| | - Paul G Unschuld
- Institute for Regenerative Medicine, University of Zurich Schlieren, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, University of Zurich Schlieren, Switzerland
| | - Sandra E Leh
- Institute for Regenerative Medicine, University of Zurich Schlieren, Switzerland
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