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Zhang B, Xu M, Wu Q, Ye S, Zhang Y, Li Z. Definition and analysis of gray matter atrophy subtypes in mild cognitive impairment based on data-driven methods. Front Aging Neurosci 2024; 16:1328301. [PMID: 38894849 PMCID: PMC11183285 DOI: 10.3389/fnagi.2024.1328301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Introduction Mild cognitive impairment (MCI) is an important stage in Alzheimer's disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype. Methods In this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed. Results Three MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE ε4 is an important factor that cause the conversion of MCI to AD. Conclusion It was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI.
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Affiliation(s)
- Baiwen Zhang
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing, China
| | - Meng Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Qing Wu
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing, China
| | - Sicheng Ye
- International College, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Zhang
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing, China
| | - Zufei Li
- Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
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Chen P, Zhang S, Zhao K, Kang X, Rittman T, Liu Y. Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review. Brain Res 2024; 1823:148675. [PMID: 37979603 DOI: 10.1016/j.brainres.2023.148675] [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: 08/02/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Shirui Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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3
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Concordance of Alzheimer’s Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study. Brain Sci 2022; 12:brainsci12020187. [PMID: 35203950 PMCID: PMC8869952 DOI: 10.3390/brainsci12020187] [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: 01/04/2022] [Revised: 01/20/2022] [Accepted: 01/25/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer’s disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear. Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups. The subtypes were identified via a data-driven strategy based on the density features and CT features, respectively. Then, the similarity between the two features in AD subtype definition was analyzed. Results: Four distinct subtypes were discovered by both density and CT features: diffuse atrophy AD, minimal atrophy AD (MAD), left temporal dominant atrophy AD (LTAD), and occipital sparing AD. The matched subtypes exhibited relatively high similarity in atrophy patterns and neuropsychological and neuropathological characteristics. They differed only in MAD and LTAD regarding the carrying of apolipoprotein E ε2. Conclusions: The results verified that different representative morphological GM measurement methods could produce similar AD subtypes. Meanwhile, the influences of apolipoprotein E genotype, asymmetric disease progression, and their interactions should be considered and included in the AD subtype definition. This study provides a valuable reference for selecting features in future studies of AD subtypes.
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4
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Interaction between cognitive reserve and age moderates effect of lesion load on stroke outcome. Sci Rep 2021; 11:4478. [PMID: 33627742 PMCID: PMC7904829 DOI: 10.1038/s41598-021-83927-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 02/01/2021] [Indexed: 01/04/2023] Open
Abstract
The concepts of brain reserve and cognitive reserve were recently suggested as valuable predictors of stroke outcome. To test this hypothesis, we used age, years of education and lesion size as clinically feasible coarse proxies of brain reserve, cognitive reserve, and the extent of stroke pathology correspondingly. Linear and logistic regression models were used to predict cognitive outcome (Montreal Cognitive Assessment) and stroke-induced impairment and disability (NIH Stroke Scale; modified Rankin Score) in a sample of 104 chronic stroke patients carefully controlled for potential confounds. Results revealed 46% of explained variance for cognitive outcome (p < 0.001) and yielded a significant three-way interaction: Larger lesions did not lead to cognitive impairment in younger patients with higher education, but did so in younger patients with lower education. Conversely, even small lesions led to poor cognitive outcome in older patients with lower education, but didn’t in older patients with higher education. We observed comparable three-way interactions for clinical scores of stroke-induced impairment and disability both in the acute and chronic stroke phase. In line with the hypothesis, years of education conjointly with age moderated effects of lesion on stroke outcome. This non-additive effect of cognitive reserve suggests its post-stroke protective impact on stroke outcome.
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5
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Zhang B, Lin L, Wu S, Al-Masqari ZHMA. Multiple Subtypes of Alzheimer's Disease Base on Brain Atrophy Pattern. Brain Sci 2021; 11:brainsci11020278. [PMID: 33672406 PMCID: PMC7926857 DOI: 10.3390/brainsci11020278] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/19/2021] [Accepted: 02/20/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer’s disease (AD) is a disease of a heterogeneous nature, which can be disentangled by exploring the characteristics of each AD subtype in the brain structure, neuropathology, and cognition. In this study, a total of 192 AD and 228 cognitively normal (CN) subjects were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Based on the cortical thickness patterns, the mixture of experts method (MOE) was applied to the implicit model spectrum of transforms lined with each AD subtype, then their neuropsychological and neuropathological characteristics were analyzed. Furthermore, the piecewise linear classifiers composed of each AD subtype and CN were resolved, and each subtype was comprehensively explained. The following four distinct AD subtypes were discovered: bilateral parietal, frontal, and temporal atrophy AD subtype (occipital sparing AD subtype (OSAD), 29.2%), left temporal dominant atrophy AD subtype (LTAD, 22.4%), minimal atrophy AD subtype (MAD, 16.1%), and diffuse atrophy AD subtype (DAD, 32.3%). These four subtypes display their own characteristics in atrophy pattern, cognition, and neuropathology. Compared with the previous studies, our study found that some AD subjects showed obvious asymmetrical atrophy in left lateral temporal-parietal cortex, OSAD presented the worst cerebrospinal fluid levels, and MAD had the highest proportions of APOE ε4 and APOE ε2. The subtype characteristics were further revealed from the aspect of the model, making it easier for clinicians to understand. The results offer an effective support for individual diagnosis and prognosis.
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Affiliation(s)
- Baiwen Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Lan Lin
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
- Correspondence: ; Tel.: +86-10-6739-1610
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Zakarea H. M. A. Al-Masqari
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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6
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Abstract
Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.
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Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- University Hospital of of Old Age Psychiatry and Psychotherapy, University of Bern, 3008 Bern, Switzerland
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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7
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Bolton TAW, Freitas LGA, Jochaut D, Giraud AL, Van De Ville D. Neural responses in autism during movie watching: Inter-individual response variability co-varies with symptomatology. Neuroimage 2020; 216:116571. [PMID: 31987996 DOI: 10.1016/j.neuroimage.2020.116571] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 12/13/2019] [Accepted: 01/17/2020] [Indexed: 12/22/2022] Open
Abstract
Naturalistic movie paradigms are exquisitely dynamic by nature, yet dedicated analytical methods typically remain static. Here, we deployed a dynamic inter-subject functional correlation (ISFC) analysis to study movie-driven functional brain changes in a population of male young adults diagnosed with autism spectrum disorder (ASD). We took inspiration from the resting-state research field in generating a set of whole-brain ISFC states expressed by the analysed ASD and typically developing (TD) subjects along time. Change points of state expression often involved transitions between different scenes of the movie, resulting in the reorganisation of whole-brain ISFC patterns to recruit different functional networks. Both subject populations showed idiosyncratic state expression at dedicated time points, but only TD subjects were also characterised by episodes of homogeneous recruitment. The temporal fluctuations in both quantities, as well as in cross-population dissimilarity, were tied to contextual movie cues. The prominent idiosyncrasy seen in ASD subjects was linked to individual symptomatology by partial least squares analysis, as different temporal sequences of ISFC states were expressed by subjects suffering from social and verbal communication impairments, as opposed to nonverbal communication deficits and stereotypic behaviours. Furthermore, the temporal expression of several of these states was correlated with the movie context, the presence of faces on screen, or overall luminosity. Overall, our results support the use of dynamic analytical frameworks to fully exploit the information obtained by naturalistic stimulation paradigms. They also show that autism should be understood as a multi-faceted disorder, in which the functional brain alterations seen in a given subject will vary as a function of the extent and balance of expressed symptoms.
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Affiliation(s)
- Thomas A W Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
| | - Lorena G A Freitas
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Delphine Jochaut
- Department of Neuroscience, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva (UNIGE), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
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8
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Marquand AF, Kia SM, Zabihi M, Wolfers T, Buitelaar JK, Beckmann CF. Conceptualizing mental disorders as deviations from normative functioning. Mol Psychiatry 2019; 24:1415-1424. [PMID: 31201374 PMCID: PMC6756106 DOI: 10.1038/s41380-019-0441-1] [Citation(s) in RCA: 152] [Impact Index Per Article: 30.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 04/15/2019] [Accepted: 04/29/2019] [Indexed: 01/06/2023]
Abstract
Normative models are a class of emerging statistical techniques useful for understanding the heterogeneous biology underlying psychiatric disorders at the level of the individual participant. Analogous to normative growth charts used in paediatric medicine for plotting child development in terms of height or weight as a function of age, normative models chart variation in clinical cohorts in terms of mappings between quantitative biological measures and clinically relevant variables. An emerging body of literature has demonstrated that such techniques are excellent tools for parsing the heterogeneity in clinical cohorts by providing statistical inferences at the level of the individual participant with respect to the normative range. Here, we provide a unifying review of the theory and application of normative modelling for understanding the biological and clinical heterogeneity underlying mental disorders. We first provide a statistically grounded yet non-technical overview of the conceptual underpinnings of normative modelling and propose a conceptual framework to link the many different methodological approaches that have been proposed for this purpose. We survey the literature employing these techniques, focusing principally on applications of normative modelling to quantitative neuroimaging-based biomarkers in psychiatry and, finally, we provide methodological considerations and recommendations to guide future applications of these techniques. We show that normative modelling provides a means by which the importance of modelling individual differences can be brought from theory to concrete data analysis procedures for understanding heterogeneous mental disorders and ultimately a promising route towards precision medicine in psychiatry.
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Affiliation(s)
- Andre F. Marquand
- 0000000122931605grid.5590.9Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands ,0000 0004 0444 9382grid.10417.33Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands ,0000 0001 2322 6764grid.13097.3cDepartment of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, UK
| | - Seyed Mostafa Kia
- 0000000122931605grid.5590.9Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands ,0000 0004 0444 9382grid.10417.33Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Mariam Zabihi
- 0000000122931605grid.5590.9Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands ,0000 0004 0444 9382grid.10417.33Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Thomas Wolfers
- 0000000122931605grid.5590.9Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands ,0000 0004 0444 9382grid.10417.33Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Jan K. Buitelaar
- 0000000122931605grid.5590.9Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands ,0000 0004 0444 9382grid.10417.33Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands ,Karakter Child and Adolescent Psychiatric University Centre, Nijmegen, the Netherlands
| | - Christian F. Beckmann
- 0000000122931605grid.5590.9Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands ,0000 0004 0444 9382grid.10417.33Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands ,0000 0004 1936 8948grid.4991.5Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
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9
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Eavani H, Habes M, Satterthwaite TD, An Y, Hsieh MK, Honnorat N, Erus G, Doshi J, Ferrucci L, Beason-Held LL, Resnick SM, Davatzikos C. Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods. Neurobiol Aging 2018; 71:41-50. [PMID: 30077821 PMCID: PMC6162110 DOI: 10.1016/j.neurobiolaging.2018.06.013] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 06/04/2018] [Accepted: 06/07/2018] [Indexed: 12/19/2022]
Abstract
Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns.
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Affiliation(s)
- Harini Eavani
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Theodore D Satterthwaite
- Department of Psychiatry, Brain Behavior Laboratory, University of Pennsylvania, Philadelphia, PA, USA
| | - Yang An
- National Institute on Aging, Baltimore, MD, USA
| | - Meng-Kang Hsieh
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicolas Honnorat
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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10
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Adapting the concepts of brain and cognitive reserve to post-stroke cognitive deficits: Implications for understanding neglect. Cortex 2017; 97:327-338. [DOI: 10.1016/j.cortex.2016.12.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Revised: 08/03/2016] [Accepted: 12/04/2016] [Indexed: 01/17/2023]
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11
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Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF. Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:433-447. [PMID: 27642641 PMCID: PMC5013873 DOI: 10.1016/j.bpsc.2016.04.002] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 04/06/2016] [Accepted: 04/06/2016] [Indexed: 01/03/2023]
Abstract
Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders, aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the European Roadmap for Mental Health Research, both of which emphasize finding stratifications that are based on biological systems and that cut across current classifications. We first introduce the basic concepts for stratifying psychiatric disorders and then provide a methodologically oriented and critical review of the existing literature. This shows that the predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders we review, but for most disorders it has not converged on a consistent set of subgroups. We highlight problems with current approaches that are not widely recognized and discuss the importance of validation to ensure that the derived subgroups index clinically relevant variation. Finally, we review emerging techniques-such as those that estimate normative models for mappings between biology and behavior-that provide new ways to parse the heterogeneity underlying psychiatric disorders and evaluate all methods to meeting the objectives of such as the National Institute of Mental Health Research Domain Criteria and Roadmap for Mental Health Research.
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Affiliation(s)
- Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Department of Neuroimaging (AFM), Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Maarten Mennes
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Jan Buitelaar
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Karakter Child and Adolescent Psychiatric University Centre, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (CFB), University of Oxford, London, United Kingdom
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12
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Salami A, Wåhlin A, Kaboodvand N, Lundquist A, Nyberg L. Longitudinal Evidence for Dissociation of Anterior and Posterior MTL Resting-State Connectivity in Aging: Links to Perfusion and Memory. Cereb Cortex 2016; 26:3953-3963. [PMID: 27522073 PMCID: PMC5028008 DOI: 10.1093/cercor/bhw233] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 07/06/2016] [Indexed: 12/12/2022] Open
Abstract
Neuroimaging studies of spontaneous signal fluctuations as measured by resting-state functional magnetic resonance imaging have revealed age-related alterations in the functional architecture of brain networks. One such network is located in the medial temporal lobe (MTL), showing structural and functional variations along the anterior–posterior axis. Past cross-sectional studies of MTL functional connectivity (FC) have yielded discrepant findings, likely reflecting the fact that specific MTL subregions are differentially affected in aging. Here, using longitudinal resting-state data from 198 participants, we investigated 5-year changes in FC of the anterior and posterior MTL. We found an opposite pattern, such that the degree of FC within the anterior MTL declined after age 60, whereas elevated FC within the posterior MTL was observed along with attenuated posterior MTL-cortical connectivity. A significant negative change–change relation was observed between episodic-memory decline and elevated FC in the posterior MTL. Additional analyses revealed age-related cerebral blood flow (CBF) increases in posterior MTL at the follow-up session, along with a positive relation of elevated FC and CBF, suggesting that elevated FC is a metabolically demanding alteration. Collectively, our findings indicate that elevated FC in posterior MTL along with increased local perfusion is a sign of brain aging that underlie episodic-memory decline.
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Affiliation(s)
- Alireza Salami
- Umeå Center for Functional Brain Imaging, S-90187, Umeå, Sweden.,Department of Integrative Medical Biology, Physiology Section, Umeå University, S-901 87 Umeå, Sweden.,Aging Research Center, Karolinska Institutet and Stockholm University, SE-113 30, Stockholm, Sweden
| | - Anders Wåhlin
- Umeå Center for Functional Brain Imaging, S-90187, Umeå, Sweden.,Department of Radiation Sciences, Radiation Physics, Umeå University, S-901 87 Umeå, Sweden
| | - Neda Kaboodvand
- Umeå Center for Functional Brain Imaging, S-90187, Umeå, Sweden.,Aging Research Center, Karolinska Institutet and Stockholm University, SE-113 30, Stockholm, Sweden
| | - Anders Lundquist
- Umeå Center for Functional Brain Imaging, S-90187, Umeå, Sweden.,Department of Integrative Medical Biology, Physiology Section, Umeå University, S-901 87 Umeå, Sweden
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging, S-90187, Umeå, Sweden.,Department of Integrative Medical Biology, Physiology Section, Umeå University, S-901 87 Umeå, Sweden.,Department of Radiation Sciences, Diagnostic Radiology, Umeå University, S-901 87 Umeå, Sweden
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