1
|
Odusami M, Maskeliūnas R, Damaševičius R, Misra S. Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis. Cogn Neurodyn 2024; 18:775-794. [PMID: 38826669 PMCID: PMC11143094 DOI: 10.1007/s11571-023-09993-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/23/2023] [Accepted: 07/17/2023] [Indexed: 06/04/2024] Open
Abstract
In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87-87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.
Collapse
Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | | | - Sanjay Misra
- Department of Applied Data Science, Institute for Energy Technology, Halden, Norway
| |
Collapse
|
2
|
Chen Z, Chen K, Li Y, Geng D, Li X, Liang X, Lu H, Ding S, Xiao Z, Ma X, Zheng L, Ding D, Zhao Q, Yang L. Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI. Hum Brain Mapp 2024; 45:e26529. [PMID: 37991144 PMCID: PMC10789213 DOI: 10.1002/hbm.26529] [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: 09/30/2022] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023] Open
Abstract
Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.
Collapse
Affiliation(s)
- Zhihan Chen
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
| | - Keliang Chen
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuxin Li
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Daoying Geng
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Xiantao Li
- Department of Critical Care MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Xiaoniu Liang
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Huimeng Lu
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Saineng Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Zhenxu Xiao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaoxi Ma
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Ding Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qianhua Zhao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Liqin Yang
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | | |
Collapse
|
3
|
Biswas R, Sripada S. Causal functional connectivity in Alzheimer's disease computed from time series fMRI data. Front Comput Neurosci 2023; 17:1251301. [PMID: 38169714 PMCID: PMC10758424 DOI: 10.3389/fncom.2023.1251301] [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: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.
Collapse
Affiliation(s)
- Rahul Biswas
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | | |
Collapse
|
4
|
Xiao M, Luo Y, Ding C, Chen X, Liu Y, Tang Y, Chen H. Social support and overeating in young women: The role of altering functional network connectivity patterns and negative emotions. Appetite 2023; 191:107069. [PMID: 37837769 DOI: 10.1016/j.appet.2023.107069] [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: 03/16/2023] [Revised: 09/20/2023] [Accepted: 09/29/2023] [Indexed: 10/16/2023]
Abstract
Research suggests that social support has a protective effect on emotional health and emotionally induced overeating. Women are especially more sensitive to benefits from social support when facing eating problems. Although it has been demonstrated that social support can affect the neural processes of emotion regulation and reward perception, it is unclear how social support alters synergistic patterns in large-scale brain networks associated with negative emotions and overeating. We used a large sample of young women aged 17-22 years (N = 360) to examine how social support influences the synchrony of five intrinsic networks (executive control network [ECN], default mode network, salience network [SN], basal ganglia network, and precuneus network [PN]) and how these networks influence negative affect and overeating. Additionally, we explored these analyses in another sample of males (N = 136). After statistically controlling for differences in age and head movement, we observed significant associations of higher levels of social support with increased intra- and inter-network functional synchrony, particularly for ECN-centered network connectivity. Subsequent chain-mediated analyses showed that social support predicted overeating through the ECN-SN and ECN-PN network connectivity and negative emotions. However, these results were not found in men. These findings suggest that social support influences the synergistic patterns within and between intrinsic networks related to inhibitory control, emotion salience, self-referential thinking, and reward sensitivity. Furthermore, they reveal that social support and its neural markers may play a key role in young women's emotional health and eating behavior.
Collapse
Affiliation(s)
- Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yijun Luo
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Cody Ding
- Department of Educational Psychology, Research, and Evaluation, University of Missouri, St. Louis, USA
| | - Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yutian Tang
- Faculty of Arts, University of British Columbia, Canada
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China; Research Center of Psychology and Social Development, Southwest University, Chongqing, China.
| |
Collapse
|
5
|
Belden A, Quinci MA, Geddes M, Donovan NJ, Hanser SB, Loui P. Functional Organization of Auditory and Reward Systems in Aging. J Cogn Neurosci 2023; 35:1570-1592. [PMID: 37432735 PMCID: PMC10513766 DOI: 10.1162/jocn_a_02028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
The intrinsic organization of functional brain networks is known to change with age, and is affected by perceptual input and task conditions. Here, we compare functional activity and connectivity during music listening and rest between younger (n = 24) and older (n = 24) adults, using whole-brain regression, seed-based connectivity, and ROI-ROI connectivity analyses. As expected, activity and connectivity of auditory and reward networks scaled with liking during music listening in both groups. Younger adults show higher within-network connectivity of auditory and reward regions as compared with older adults, both at rest and during music listening, but this age-related difference at rest was reduced during music listening, especially in individuals who self-report high musical reward. Furthermore, younger adults showed higher functional connectivity between auditory network and medial prefrontal cortex that was specific to music listening, whereas older adults showed a more globally diffuse pattern of connectivity, including higher connectivity between auditory regions and bilateral lingual and inferior frontal gyri. Finally, connectivity between auditory and reward regions was higher when listening to music selected by the participant. These results highlight the roles of aging and reward sensitivity on auditory and reward networks. Results may inform the design of music-based interventions for older adults and improve our understanding of functional network dynamics of the brain at rest and during a cognitively engaging task.
Collapse
Affiliation(s)
| | | | | | - Nancy J Donovan
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | | | | |
Collapse
|
6
|
Waschkies KF, Soch J, Darna M, Richter A, Altenstein S, Beyle A, Brosseron F, Buchholz F, Butryn M, Dobisch L, Ewers M, Fliessbach K, Gabelin T, Glanz W, Goerss D, Gref D, Janowitz D, Kilimann I, Lohse A, Munk MH, Rauchmann BS, Rostamzadeh A, Roy N, Spruth EJ, Dechent P, Heneka MT, Hetzer S, Ramirez A, Scheffler K, Buerger K, Laske C, Perneczky R, Peters O, Priller J, Schneider A, Spottke A, Teipel S, Düzel E, Jessen F, Wiltfang J, Schott BH, Kizilirmak JM. Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression. Int J Geriatr Psychiatry 2023; 38:e6007. [PMID: 37800601 DOI: 10.1002/gps.6007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 09/07/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. METHODS In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). RESULTS Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. CONCLUSION Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.
Collapse
Affiliation(s)
- Konrad F Waschkies
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Joram Soch
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Margarita Darna
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anni Richter
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- German Center for Mental Health (DZPG), Munich, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Aline Beyle
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | | | - Friederike Buchholz
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Bonn, Germany
| | - Tatjana Gabelin
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Doreen Goerss
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Daria Gref
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Andrea Lohse
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Department of Neuroradiology, University Hospital LMU, Munich, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Göttingen, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, Texas, USA
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
- School of Medicine, Technical University of Munich, Department of Psychiatry and Psychotherapy, Munich, Germany
- University of Edinburgh and UK DRI, Edinburgh, UK
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Jasmin M Kizilirmak
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Neurodidactics and NeuroLab, Institute for Psychology, University of Hildesheim, Hildesheim, Germany
| |
Collapse
|
7
|
Belden A, Quinci MA, Geddes M, Donovan NJ, Hanser SB, Loui P. Functional Organization of Auditory and Reward Systems in Aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.01.522417. [PMID: 36711696 PMCID: PMC9881869 DOI: 10.1101/2023.01.01.522417] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The intrinsic organization of functional brain networks is known to change with age, and is affected by perceptual input and task conditions. Here, we compare functional activity and connectivity during music listening and rest between younger (N=24) and older (N=24) adults, using whole brain regression, seed-based connectivity, and ROI-ROI connectivity analyses. As expected, activity and connectivity of auditory and reward networks scaled with liking during music listening in both groups. Younger adults show higher within-network connectivity of auditory and reward regions as compared to older adults, both at rest and during music listening, but this age-related difference at rest was reduced during music listening, especially in individuals who self-report high musical reward. Furthermore, younger adults showed higher functional connectivity between auditory network and medial prefrontal cortex (mPFC) that was specific to music listening, whereas older adults showed a more globally diffuse pattern of connectivity, including higher connectivity between auditory regions and bilateral lingual and inferior frontal gyri. Finally, connectivity between auditory and reward regions was higher when listening to music selected by the participant. These results highlight the roles of aging and reward sensitivity on auditory and reward networks. Results may inform the design of music- based interventions for older adults, and improve our understanding of functional network dynamics of the brain at rest and during a cognitively engaging task.
Collapse
|
8
|
Vedaei F, Mashhadi N, Zabrecky G, Monti D, Navarreto E, Hriso C, Wintering N, Newberg AB, Mohamed FB. Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques. Front Neurosci 2023; 16:1099560. [PMID: 36699521 PMCID: PMC9869678 DOI: 10.3389/fnins.2022.1099560] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration [clinicaltrials.gov], identifier [NCT03241732].
Collapse
Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Najmeh Mashhadi
- Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B. Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| |
Collapse
|
9
|
Chai J, Wu R, Li A, Xue C, Qiang Y, Zhao J, Zhao Q, Yang Q. Classification of mild cognitive impairment based on handwriting dynamics and qEEG. Comput Biol Med 2023; 152:106418. [PMID: 36566627 DOI: 10.1016/j.compbiomed.2022.106418] [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: 07/25/2022] [Revised: 11/01/2022] [Accepted: 12/10/2022] [Indexed: 12/14/2022]
Abstract
Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in patients with mild cognitive impairment (MCI) are important in screening for early dementia in primary care populations. In this study, an automated, non-invasive and rapid detection protocol for mild cognitive impairment based on handwriting kinetics and quantitative EEG analysis was proposed, and a classification model based on a dual fusion of feature and decision layers was designed for clinical decision-marking. Seventy-nine volunteers (39 healthy elderly controls and 40 patients with mild cognitive impairment) were recruited for this study, and the handwritten data and the EEG signals were performed using a tablet and MUSE under four designed handwriting tasks. Sixty-eight features were extracted from the EEG and handwriting parameters of each test. Features selected from both models were fused using a late feature fusion strategy with a weighted voting strategy for decision making, and classification accuracy was compared using three different classifiers under handwritten features, EEG features and fused features respectively. The results show that the dual fusion model can further improve the classification accuracy, with the highest classification accuracy for the combined features and the best classification result of 96.3% using SVM with RBF kernel as the base classifier. In addition, this not only supports the greater significance of multimodal data for differentiating MCI, but also tests the feasibility of using the portable EEG headband as a measure of EEG in patients with cognitive impairment.
Collapse
Affiliation(s)
- Jiali Chai
- College of Information and Computer, Taiyuan University of Technology, 030000, Taiyuan, Shanxi, China.
| | - Ruixuan Wu
- College of Information and Computer, Taiyuan University of Technology, 030000, Taiyuan, Shanxi, China
| | - Aoyu Li
- College of Information and Computer, Taiyuan University of Technology, 030000, Taiyuan, Shanxi, China
| | - Chen Xue
- College of Information and Computer, Taiyuan University of Technology, 030000, Taiyuan, Shanxi, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, 030000, Taiyuan, Shanxi, China.
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, 030000, Taiyuan, Shanxi, China; Jinzhong College of Information, 030600, Taiyuan, Shanxi, China
| | - Qinghua Zhao
- College of Information and Computer, Taiyuan University of Technology, 030000, Taiyuan, Shanxi, China
| | - Qianqian Yang
- Jinzhong College of Information, 030600, Taiyuan, Shanxi, China
| |
Collapse
|
10
|
Liu B, Mao Z, Cui Z, Ling Z, Xu X, He K, Cui M, Feng Z, Yu X, Zhang Y. Cerebellar gray matter alterations predict deep brain stimulation outcomes in Meige syndrome. Neuroimage Clin 2023; 37:103316. [PMID: 36610311 PMCID: PMC9827385 DOI: 10.1016/j.nicl.2023.103316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/21/2022] [Accepted: 01/02/2023] [Indexed: 01/05/2023]
Abstract
BACKGROUND The physiopathologic mechanism of Meige syndrome (MS) has not been clarified, and neuroimaging studies centering on cerebellar changes in MS are scarce. Moreover, even though deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been recognized as an effective surgical treatment for MS, there has been no reliable biomarker to predict its efficacy. OBJECTIVE To characterize the volumetric alterations of gray matter (GM) in the cerebellum in MS and to identify GM measurements related to a good STN-DBS outcome. METHODS We used voxel-based morphometry and lobule-based morphometry to compare the regional and lobular GM differences in the cerebellum between 47 MS patients and 52 normal human controls (HCs), as well as between 31 DBS responders and 10 DBS non-responders. Both volumetric analyses were achieved using the Spatially Unbiased Infratentorial Toolbox (SUIT). Further, we performed partial correlation analyses to probe the relationship between the cerebellar GM changes and clinical scores. Finally, we plotted the receiver operating characteristic (ROC) curve to select biomarkers for MS diagnosis and DBS outcomes prediction. RESULTS Compared to HCs, MS patients had GM atrophy in lobule Crus I, lobule VI, lobule VIIb, lobule VIIIa, and lobule VIIIb. Compared to DBS responders, DBS non-responders had lower GM volume in the left lobule VIIIb. Moreover, partial correlation analyses revealed a positive relationship between the GM volume of the significant regions/lobules and the symptom improvement rate after DBS surgery. ROC analyses demonstrated that the GM volume of the significant cluster in the left lobule VIIIb could not only distinguish MS patients from HCs but also predict the outcomes of STN-DBS surgery with high accuracy. CONCLUSION MS patients display bilateral GM shrinkage in the cerebellum relative to HCs. Regional GM volume of the left lobule VIIIb can be a reliable biomarker for MS diagnosis and DBS outcomes prediction.
Collapse
Affiliation(s)
- Bin Liu
- Medical School of Chinese PLA, Beijing, PR China; Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Zhiqi Mao
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Zhiqiang Cui
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Zhipei Ling
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Kunyu He
- Medical School of Chinese PLA, Beijing, PR China; Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Mengchu Cui
- Medical School of Chinese PLA, Beijing, PR China; Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Zhebin Feng
- Medical School of Chinese PLA, Beijing, PR China; Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China
| | - Xinguang Yu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China; Neurosurgery Institute, Chinese PLA General Hospital, Beijing, PR China.
| | - Yanyang Zhang
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, PR China; Neurosurgery Institute, Chinese PLA General Hospital, Beijing, PR China.
| |
Collapse
|
11
|
Tran DK, Poliakov AV, Friedman SD, Goldstein HE, Shurtleff HA, Bowen K, Patrick KE, Warner M, Novotny EJ, Ojemann JG, Hauptman JS. Concordance of functional MRI memory task and resting-state functional MRI connectivity used in surgical planning for pediatric temporal lobe epilepsy. J Neurosurg Pediatr 2022; 30:394-399. [PMID: 35907201 DOI: 10.3171/2022.6.peds221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/15/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Assessing memory is often critical in surgical evaluation, although difficult to assess in young children and in patients with variable task abilities. While obtaining interpretable data from task-based functional MRI (fMRI) measures is common in compliant and awake patients, it is not known whether functional connectivity MRI (fcMRI) data show equivalent results. If this were the case, it would have substantial clinical and research generalizability. To evaluate this possibility, the authors evaluated the concordance between fMRI and fcMRI data collected in a presurgical epilepsy cohort. METHODS Task-based fMRI data for autobiographical memory tasks and resting-state fcMRI data were collected in patients with epilepsy evaluated at Seattle Children's Hospital between 2010 and 2017. To assess memory-related activation and laterality, signal change in task-based measures was computed as a percentage of the average blood oxygen level-dependent signal over the defined regions of interest. An fcMRI data analysis was performed using 1000 Functional Connectomes Project scripts based on Analysis of Functional NeuroImages and FSL (Functional Magnetic Resonance Imaging of the Brain Software Library) software packages. Lateralization indices (LIs) were estimated for activation and connectivity measures. The concordance between these two measures was evaluated using correlation and regression analysis. RESULTS In this epilepsy cohort studied, the authors observed concordance between fMRI activation and fcMRI connectivity, with an LI regression coefficient of 0.470 (R2 = 0.221, p = 0.00076). CONCLUSIONS Previously published studies have demonstrated fMRI and fcMRI overlap between measures of vision, attention, and language. In the authors' clinical sample, task-based measures of memory and analogous resting-state mapping were similarly linked in pattern and strength. These results support the use of fcMRI methods as a proxy for task-based memory performance in presurgical patients, perhaps including those who are more limited in their behavioral compliance. Future investigations to extend these results will be helpful to explore how the magnitudes of effect are associated with neuropsychological performance and postsurgical behavioral changes.
Collapse
Affiliation(s)
- Diem Kieu Tran
- 1Department of Neurological Surgery, University of Washington, Seattle
- 2Division of Neurosurgery, Seattle Children's Hospital, Seattle
| | - Andrew V Poliakov
- 2Division of Neurosurgery, Seattle Children's Hospital, Seattle
- 3Department of Radiology, Seattle Children's Hospital, Seattle
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
| | - Seth D Friedman
- 3Department of Radiology, Seattle Children's Hospital, Seattle
| | - Hannah E Goldstein
- 1Department of Neurological Surgery, University of Washington, Seattle
- 2Division of Neurosurgery, Seattle Children's Hospital, Seattle
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
| | - Hillary A Shurtleff
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
- 5Center for Integrated Brain Research, Seattle Children's Hospital, Seattle
- 6Division of Pediatric Neurology, Seattle Children's Hospital, Seattle; and
| | - Katherine Bowen
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
- 6Division of Pediatric Neurology, Seattle Children's Hospital, Seattle; and
| | - Kristina E Patrick
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
- 6Division of Pediatric Neurology, Seattle Children's Hospital, Seattle; and
- 7Department of Neurology, University of Washington, Seattle, Washington
| | - Molly Warner
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
| | - Edward J Novotny
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
- 6Division of Pediatric Neurology, Seattle Children's Hospital, Seattle; and
- 7Department of Neurology, University of Washington, Seattle, Washington
| | - Jeffrey G Ojemann
- 1Department of Neurological Surgery, University of Washington, Seattle
- 2Division of Neurosurgery, Seattle Children's Hospital, Seattle
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
| | - Jason S Hauptman
- 1Department of Neurological Surgery, University of Washington, Seattle
- 2Division of Neurosurgery, Seattle Children's Hospital, Seattle
- 4Neurosciences Center, Seattle Children's Hospital, Seattle
| |
Collapse
|
12
|
Khobo IL, Jankiewicz M, Holmes MJ, Little F, Cotton MF, Laughton B, van der Kouwe AJW, Moreau A, Nwosu E, Meintjes EM, Robertson FC. Multimodal magnetic resonance neuroimaging measures characteristic of early cART-treated pediatric HIV: A feature selection approach. Hum Brain Mapp 2022; 43:4128-4144. [PMID: 35575438 PMCID: PMC9374890 DOI: 10.1002/hbm.25907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 04/03/2022] [Accepted: 04/26/2022] [Indexed: 11/09/2022] Open
Abstract
Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV-related brain alterations can be investigated separately using proton magnetic resonance spectroscopy (1 H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic-net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1 H-MRS in basal measures to sMRI volumes produced the highest classification performancevalidation accuracy = 85 % AUC = 0.80 . The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART-treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.
Collapse
Affiliation(s)
- Isaac L. Khobo
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Marcin Jankiewicz
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
| | - Martha J. Holmes
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Francesca Little
- Department of Statistical SciencesUniversity of Cape TownCape TownSouth Africa
| | - Mark F. Cotton
- Department of Pediatrics & Child Health, Family Center for Research with Ubuntu, Tygerberg HospitalStellenbosch UniversityCape TownSouth Africa
| | - Barbara Laughton
- Department of Pediatrics & Child Health, Family Center for Research with Ubuntu, Tygerberg HospitalStellenbosch UniversityCape TownSouth Africa
| | - Andre J. W. van der Kouwe
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- A.A. Martinos Centre for Biomedical ImagingMassachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | - Emmanuel Nwosu
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
| | - Ernesta M. Meintjes
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
| | - Frances C. Robertson
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
| |
Collapse
|
13
|
Alm KH, Soldan A, Pettigrew C, Faria AV, Hou X, Lu H, Moghekar A, Mori S, Albert M, Bakker A. Structural and Functional Brain Connectivity Uniquely Contribute to Episodic Memory Performance in Older Adults. Front Aging Neurosci 2022; 14:951076. [PMID: 35903538 PMCID: PMC9315224 DOI: 10.3389/fnagi.2022.951076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/15/2022] [Indexed: 01/26/2023] Open
Abstract
In this study, we examined the independent contributions of structural and functional connectivity markers to individual differences in episodic memory performance in 107 cognitively normal older adults from the BIOCARD study. Structural connectivity, defined by the diffusion tensor imaging (DTI) measure of radial diffusivity (RD), was obtained from two medial temporal lobe white matter tracts: the fornix and hippocampal cingulum, while functional connectivity markers were derived from network-based resting state functional magnetic resonance imaging (rsfMRI) of five large-scale brain networks: the control, default, limbic, dorsal attention, and salience/ventral attention networks. Hierarchical and stepwise linear regression methods were utilized to directly compare the relative contributions of the connectivity modalities to individual variability in a composite delayed episodic memory score, while also accounting for age, sex, cerebrospinal fluid (CSF) biomarkers of amyloid and tau pathology (i.e., Aβ42/Aβ40 and p-tau181), and gray matter volumes of the entorhinal cortex and hippocampus. Results revealed that fornix RD, hippocampal cingulum RD, and salience network functional connectivity were each significant independent predictors of memory performance, while CSF markers and gray matter volumes were not. Moreover, in the stepwise model, the addition of sex, fornix RD, hippocampal cingulum RD, and salience network functional connectivity each significantly improved the overall predictive value of the model. These findings demonstrate that both DTI and rsfMRI connectivity measures uniquely contributed to the model and that the combination of structural and functional connectivity markers best accounted for individual variability in episodic memory function in cognitively normal older adults.
Collapse
Affiliation(s)
- Kylie H. Alm
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xirui Hou
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States,*Correspondence: Arnold Bakker,
| |
Collapse
|
14
|
Long Z, Li J, Liao H, Deng L, Du Y, Fan J, Li X, Miao J, Qiu S, Long C, Jing B. A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment. Brain Sci 2022; 12:751. [PMID: 35741636 PMCID: PMC9221217 DOI: 10.3390/brainsci12060751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/29/2022] [Accepted: 06/03/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC). METHODS The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard-Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively. Next, these characteristics were selected with a minimal redundancy maximal relevance algorithm and a sequential feature collection method in single or multi-modalities, and only the optimal features were retained after this procedure. Lastly, the retained characteristics were served as the input features for the support vector machine (SVM)-based method to classify MCI patients, and the performance was estimated with a leave-one-out cross-validation (LOOCV). RESULTS Our proposed method obtained the best 92.00% accuracy, 94.92% specificity and 89.39% sensitivity with the sMRI in AAL-90 and the fMRI in HOA-112 atlas, which was much better than using the single-modal or single-atlas features. CONCLUSION The results demonstrated that the multi-modal and multi-atlas integrated method could effectively recognize MCI patients, which could be extended into various neurological and neuropsychiatric diseases.
Collapse
Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Jie Li
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Haitao Liao
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Li Deng
- Department of Data Assessment and Examination, Hunan Children’s Hospital, Changsha 410007, China;
| | - Yukeng Du
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Jianghua Fan
- Department of Pediatric Emergency Center, Emergency Generally Department I, Hunan Children’s Hospital, Changsha 410007, China;
| | - Xiaofeng Li
- Hunan Guangxiu Hospital, Hunan Normal University, Changsha 410006, China;
| | - Jichang Miao
- Department of Medical Devices, Nanfang Hospital, Guangzhou 510515, China;
| | - Shuang Qiu
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Chaojie Long
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| |
Collapse
|
15
|
A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
|
16
|
Khatri U, Kwon GR. Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI. Front Aging Neurosci 2022; 14:818871. [PMID: 35707703 PMCID: PMC9190953 DOI: 10.3389/fnagi.2022.818871] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate diagnosis of the initial phase of Alzheimer's disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer's and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01-.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson's correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study's primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.
Collapse
Affiliation(s)
| | - Goo-Rak Kwon
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| |
Collapse
|
17
|
Hua JC, Xu XM, Xu ZG, Xu JJ, Hu JH, Xue Y, Wu Y. Aberrant Functional Network of Small-World in Sudden Sensorineural Hearing Loss With Tinnitus. Front Neurosci 2022; 16:898902. [PMID: 35663555 PMCID: PMC9160300 DOI: 10.3389/fnins.2022.898902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/20/2022] [Indexed: 11/30/2022] Open
Abstract
Few researchers investigated the topological properties and relationships with cognitive deficits in sudden sensorineural hearing loss (SNHL) with tinnitus. To explore the topological characteristics of the brain connectome following SNHL from the global level and nodal level, we recruited 36 bilateral SNHL patients with tinnitus and 37 well-matched healthy controls. Every subject underwent pure tone audiometry tests, neuropsychological assessments, and MRI scanning. AAL atlas was employed to divide a brain into 90 cortical and subcortical regions of interest, then investigated the global and nodal properties of “small world” network in SNHL and control groups using a graph-theory analysis. The global characteristics include small worldness, cluster coefficient, characteristic path length, local efficiency, and global efficiency. Node properties include degree centrality, betweenness centrality, nodal efficiency, and nodal clustering coefficient. Interregional connectivity analysis was also computed among 90 nodes. We found that the SNHL group had significantly higher hearing thresholds and cognitive impairments, as well as disrupted internal connections among 90 nodes. SNHL group displayed lower AUC of cluster coefficient and path length lambda, but increased global efficiency. The opercular and triangular parts of the inferior frontal gyrus, rectus gyrus, parahippocampal gyrus, precuneus, and amygdala showed abnormal local features. Some of these connectome alterations were correlated with cognitive ability and the duration of SNHL. This study may prove potential imaging biomarkers and treatment targets for future studies.
Collapse
Affiliation(s)
- Jin-Chao Hua
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
| | - Xiao-Min Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhen-Gui Xu
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jing-Hua Hu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuan Xue
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
- *Correspondence: Yuan Xue,
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Yuanqing Wu,
| |
Collapse
|
18
|
Vaghari D, Kabir E, Henson RN. Late combination shows that MEG adds to MRI in classifying MCI versus controls. Neuroimage 2022; 252:119054. [PMID: 35247546 PMCID: PMC8987738 DOI: 10.1016/j.neuroimage.2022.119054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/20/2022] [Accepted: 03/01/2022] [Indexed: 12/12/2022] Open
Abstract
Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.
Collapse
Affiliation(s)
- Delshad Vaghari
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsanollah Kabir
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK.
| |
Collapse
|
19
|
van Loon W, de Vos F, Fokkema M, Szabo B, Koini M, Schmidt R, de Rooij M. Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification. Front Neurosci 2022; 16:830630. [PMID: 35546881 PMCID: PMC9082949 DOI: 10.3389/fnins.2022.830630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022] Open
Abstract
Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.
Collapse
Affiliation(s)
- Wouter van Loon
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands
| | - Frank de Vos
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden, Netherlands
| | - Marjolein Fokkema
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands
| | - Botond Szabo
- Department of Decision Sciences, Bocconi University, Milan, Italy.,Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy
| | - Marisa Koini
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Mark de Rooij
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden, Netherlands
| |
Collapse
|
20
|
Li W, Zhao J, Shen C, Zhang J, Hu J, Xiao M, Zhang J, Chen M. Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis. Front Neuroinform 2022; 16:886365. [PMID: 35571869 PMCID: PMC9100702 DOI: 10.3389/fninf.2022.886365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/30/2022] [Indexed: 11/24/2022] Open
Abstract
Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.
Collapse
Affiliation(s)
- Wenchao Li
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Jiaqi Zhao
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Chenyu Shen
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Jingwen Zhang
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States
| | - Ji Hu
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Mang Xiao
- Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jiyong Zhang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
- *Correspondence: Jiyong Zhang
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States
- Minghan Chen
| |
Collapse
|
21
|
Leach JM, Edwards LJ, Kana R, Visscher K, Yi N, Aban I. The spike-and-slab elastic net as a classification tool in Alzheimer's disease. PLoS One 2022; 17:e0262367. [PMID: 35113902 PMCID: PMC8812870 DOI: 10.1371/journal.pone.0262367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 12/21/2021] [Indexed: 11/18/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia and has received considerable research attention, including using neuroimaging biomarkers to classify patients and/or predict disease progression. Generalized linear models, e.g., logistic regression, can be used as classifiers, but since the spatial measurements are correlated and often outnumber subjects, penalized and/or Bayesian models will be identifiable, while classical models often will not. Many useful models, e.g., the elastic net and spike-and-slab lasso, perform automatic variable selection, which removes extraneous predictors and reduces model variance, but neither model exploits spatial information in selecting variables. Spatial information can be incorporated into variable selection by placing intrinsic autoregressive priors on the logit probabilities of inclusion within a spike-and-slab elastic net framework. We demonstrate the ability of this framework to improve classification performance by using cortical thickness and tau-PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to classify subjects as cognitively normal or having dementia, and by using a simulation study to examine model performance using finer resolution images.
Collapse
Affiliation(s)
- Justin M. Leach
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Lloyd J. Edwards
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Rajesh Kana
- Department of Psychology, University of Alabama, Tuscaloosa, Alabama, United States of America
| | - Kristina Visscher
- Department of Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Inmaculada Aban
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | | |
Collapse
|
22
|
Roger C, Lasbleiz A, Guye M, Dutour A, Gaborit B, Ranjeva JP. The Role of the Human Hypothalamus in Food Intake Networks: An MRI Perspective. Front Nutr 2022; 8:760914. [PMID: 35047539 PMCID: PMC8762294 DOI: 10.3389/fnut.2021.760914] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open
Abstract
Hypothalamus (HT), this small structure often perceived through the prism of neuroimaging as morphologically and functionally homogeneous, plays a key role in the primitive act of feeding. The current paper aims at reviewing the contribution of magnetic resonance imaging (MRI) in the study of the role of the HT in food intake regulation. It focuses on the different MRI techniques that have been used to describe structurally and functionally the Human HT. The latest advances in HT parcellation as well as perspectives in this field are presented. The value of MRI in the study of eating disorders such as anorexia nervosa (AN) and obesity are also highlighted.
Collapse
Affiliation(s)
- Coleen Roger
- Centre de Résonance Magnétique Biologique et Médicale (CRMBM), Centre National de la Recherche Scientifique (CNRS), Université Aix-Marseille, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (CEMEREM), Assistance Publique-Hôpitaux de Marseille (AP-HM), Hôpital Universitaire de la Timone, Marseille, France
| | - Adèle Lasbleiz
- Centre d'Exploration Métabolique par Résonance Magnétique (CEMEREM), Assistance Publique-Hôpitaux de Marseille (AP-HM), Hôpital Universitaire de la Timone, Marseille, France.,Département d'Endocrinologie, Assistance Publique-Hôpitaux de Marseille (AP-HM), Hôpital de la Conception, Marseille, France
| | - Maxime Guye
- Centre de Résonance Magnétique Biologique et Médicale (CRMBM), Centre National de la Recherche Scientifique (CNRS), Université Aix-Marseille, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (CEMEREM), Assistance Publique-Hôpitaux de Marseille (AP-HM), Hôpital Universitaire de la Timone, Marseille, France
| | - Anne Dutour
- Département d'Endocrinologie, Assistance Publique-Hôpitaux de Marseille (AP-HM), Hôpital de la Conception, Marseille, France
| | - Bénédicte Gaborit
- Département d'Endocrinologie, Assistance Publique-Hôpitaux de Marseille (AP-HM), Hôpital de la Conception, Marseille, France
| | - Jean-Philippe Ranjeva
- Centre de Résonance Magnétique Biologique et Médicale (CRMBM), Centre National de la Recherche Scientifique (CNRS), Université Aix-Marseille, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (CEMEREM), Assistance Publique-Hôpitaux de Marseille (AP-HM), Hôpital Universitaire de la Timone, Marseille, France
| |
Collapse
|
23
|
Xiao Z, Chen Z, Chen W, Gao W, He L, Wang Q, Lei X, Qiu J, Feng T, Chen H, Turel O, Bechara A, He Q. OUP accepted manuscript. Cereb Cortex 2022; 32:4605-4618. [PMID: 35059700 PMCID: PMC9383225 DOI: 10.1093/cercor/bhab505] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/04/2021] [Accepted: 12/05/2021] [Indexed: 11/14/2022] Open
Abstract
The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Qinghua He
- Address correspondence to Qinghua He, Faculty of Psychology, Southwest University, 2 Tiansheng Road, 400715 Chongqing, China. , Tel: +86-13647691390
| |
Collapse
|
24
|
Xiao M, Chen X, Yi H, Luo Y, Yan Q, Feng T, He Q, Lei X, Qiu J, Chen H. Stronger functional network connectivity and social support buffer against negative affect during the COVID-19 outbreak and after the pandemic peak. Neurobiol Stress 2021; 15:100418. [PMID: 34805450 PMCID: PMC8592855 DOI: 10.1016/j.ynstr.2021.100418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 01/17/2023] Open
Abstract
Health and financial uncertainties, as well as enforced social distancing, during the COVID-19 pandemic have adversely affected the mental health of people. These impacts are expected to continue even after the pandemic, particularly for those who lack support from family and friends. The salience network (SN), default mode network (DMN), and frontoparietal network (FPN) function in an interconnected manner to support information processing and emotional regulation processes in stressful contexts. In this study, we examined whether functional connectivity of the SN, DMN, and FPN, measured using resting-state functional magnetic resonance imaging before the pandemic, is a neurobiological marker of negative affect (NA) during the COVID-19 pandemic and after its peak in a large sample (N = 496, 360 females); the moderating role of social support in the brain-NA association was also investigated. We found that participants reported an increase in NA during the pandemic compared to before the pandemic, and the NA did not decrease, even after the peak period. People with higher connectivity within the SN and between the SN and the other two networks reported less NA during and after the COVID-19 outbreak peak, and the buffer effect was stronger if their social support was greater. These findings suggest that the functional networks that are responsible for affective processing and executive functioning, as well as the social support from family and friends, play an important role in protecting against NA under stressful and uncontrollable situations.
Collapse
Affiliation(s)
- Mingyue Xiao
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Ximei Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Haijing Yi
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Yijun Luo
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Qiaoling Yan
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| |
Collapse
|
25
|
Wakasugi N, Hanakawa T. It Is Time to Study Overlapping Molecular and Circuit Pathophysiologies in Alzheimer's and Lewy Body Disease Spectra. Front Syst Neurosci 2021; 15:777706. [PMID: 34867224 PMCID: PMC8637125 DOI: 10.3389/fnsys.2021.777706] [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] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/28/2021] [Indexed: 12/30/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia due to neurodegeneration and is characterized by extracellular senile plaques composed of amyloid β1 - 42 (Aβ) as well as intracellular neurofibrillary tangles consisting of phosphorylated tau (p-tau). Dementia with Lewy bodies constitutes a continuous spectrum with Parkinson's disease, collectively termed Lewy body disease (LBD). LBD is characterized by intracellular Lewy bodies containing α-synuclein (α-syn). The core clinical features of AD and LBD spectra are distinct, but the two spectra share common cognitive and behavioral symptoms. The accumulation of pathological proteins, which acquire pathogenicity through conformational changes, has long been investigated on a protein-by-protein basis. However, recent evidence suggests that interactions among these molecules may be critical to pathogenesis. For example, Aβ/tau promotes α-syn pathology, and α-syn modulates p-tau pathology. Furthermore, clinical evidence suggests that these interactions may explain the overlapping pathology between AD and LBD in molecular imaging and post-mortem studies. Additionally, a recent hypothesis points to a common mechanism of prion-like progression of these pathological proteins, via neural circuits, in both AD and LBD. This suggests a need for understanding connectomics and their alterations in AD and LBD from both pathological and functional perspectives. In AD, reduced connectivity in the default mode network is considered a hallmark of the disease. In LBD, previous studies have emphasized abnormalities in the basal ganglia and sensorimotor networks; however, these account for movement disorders only. Knowledge about network abnormalities common to AD and LBD is scarce because few previous neuroimaging studies investigated AD and LBD as a comprehensive cohort. In this paper, we review research on the distribution and interactions of pathological proteins in the brain in AD and LBD, after briefly summarizing their clinical and neuropsychological manifestations. We also describe the brain functional and connectivity changes following abnormal protein accumulation in AD and LBD. Finally, we argue for the necessity of neuroimaging studies that examine AD and LBD cases as a continuous spectrum especially from the proteinopathy and neurocircuitopathy viewpoints. The findings from such a unified AD and Parkinson's disease (PD) cohort study should provide a new comprehensive perspective and key data for guiding disease modification therapies targeting the pathological proteins in AD and LBD.
Collapse
Affiliation(s)
- Noritaka Wakasugi
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Integrated Neuroanatomy and Neuroimaging, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| |
Collapse
|
26
|
Knudsen LV, Gazerani P, Michel TM, Vafaee MS. The role of multimodal MRI in mild cognitive impairment and Alzheimer's disease. J Neuroimaging 2021; 32:148-157. [PMID: 34752671 DOI: 10.1111/jon.12940] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND PURPOSE Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD), where neurodegeneration is not as considerable, thereby potentially increasing the effect of treatments. Therefore, highly sensitive and specific classification of subjects with MCI is necessary, where various MRI modalities have displayed promise. METHODS Structural, diffusion, and resting-state (RS) functional MRI analyses were performed on the AD (n = 26), MCI (n = 5), and healthy control (HC) (n = 14) group. Structural analysis was performed via voxel-based morphometry (VBM) and volumetric subcortical segmentation analysis. Fractional anisotropy and mean diffusivity were estimated during the diffusion analysis. RS analysis investigated seed-based functional connectivity. Classification via support vector machine was performed to evaluate which MRI modality most accurately differentiated the groups. Multiple linear regression was conducted to evaluate the MRI modalities correlation with clinical assessment scores. RESULTS Classification of MCI and HC displayed highest accuracy based on diffusion MRI, which besides demonstrated high correlation with clinical scores. Classification was equally accurate in AD, when using VBM or diffusion tensor imaging measures. Yet, more variance was explained by VBM measures in the clinical assessment scores of the AD group. CONCLUSIONS This study highlights the potential of diffusion MRI in differentiating MCI from HC and AD. However, the results need to be interpreted with caution as sample size and artifacts in the MRI data probably influenced the results.
Collapse
Affiliation(s)
- Laust Vind Knudsen
- Research Unit for Psychiatry, Odense University Hospital, Odense, Denmark
| | - Parisa Gazerani
- Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark.,Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, 0130, Norway
| | - Tanja Maria Michel
- Research Unit for Psychiatry, Odense University Hospital, Odense, Denmark
| | | |
Collapse
|
27
|
Vilor-Tejedor N, Garrido-Martín D, Rodriguez-Fernandez B, Lamballais S, Guigó R, Gispert JD. Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO! Comput Struct Biotechnol J 2021; 19:5800-5810. [PMID: 34765095 PMCID: PMC8567328 DOI: 10.1016/j.csbj.2021.10.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 12/01/2022] Open
Abstract
Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.
Collapse
Affiliation(s)
- Natalia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | | | - Sander Lamballais
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
| |
Collapse
|
28
|
Zhang L, Wang L, Gao J, Risacher SL, Yan J, Li G, Liu T, Zhu D. Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Med Image Anal 2021; 72:102082. [PMID: 34004495 DOI: 10.1016/j.media.2021.102082] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 03/20/2021] [Accepted: 04/13/2021] [Indexed: 01/22/2023]
Abstract
Multimodal fusion of different types of neural image data provides an irreplaceable opportunity to take advantages of complementary cross-modal information that may only partially be contained in single modality. To jointly analyze multimodal data, deep neural networks can be especially useful because many studies have suggested that deep learning strategy is very efficient to reveal complex and non-linear relations buried in the data. However, most deep models, e.g., convolutional neural network and its numerous extensions, can only operate on regular Euclidean data like voxels in 3D MRI. The interrelated and hidden structures that beyond the grid neighbors, such as brain connectivity, may be overlooked. Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied. In this work, we developed a graph-based deep neural network to simultaneously model brain structure and function in Mild Cognitive Impairment (MCI): the topology of the graph is initialized using structural network (from diffusion MRI) and iteratively updated by incorporating functional information (from functional MRI) to maximize the capability of differentiating MCI patients from elderly normal controls. This resulted in a new connectome by exploring "deep relations" between brain structure and function in MCI patients and we named it as Deep Brain Connectome. Though deep brain connectome is learned individually, it shows consistent patterns of alteration comparing to structural network at group level. With deep brain connectome, our developed deep model can achieve 92.7% classification accuracy on ADNI dataset.
Collapse
Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Li Wang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA; Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Jean Gao
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Jingwen Yan
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Gang Li
- Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7160, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA.
| | | |
Collapse
|
29
|
Perez-Gonzalez J, Jiménez-Ángeles L, Rojas Saavedra K, Barbará Morales E, Medina-Bañuelos V. Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers. Phys Med Biol 2021; 66. [PMID: 34167090 DOI: 10.1088/1361-6560/ac0e77] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 06/24/2021] [Indexed: 11/11/2022]
Abstract
Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.
Collapse
Affiliation(s)
- Jorge Perez-Gonzalez
- Unidad Académica del Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas en el Estado de Yucatán, UNAM, Yucatán, México
| | - Luis Jiménez-Ángeles
- Department of Biomedical Systems Engineering, Engineering Faculty, UNAM, Mexico City, México
| | - Karla Rojas Saavedra
- Health Sciences Department, Universidad del Valle de México, Mexico City, México
| | | | | |
Collapse
|
30
|
Durusoy G, Yldrm Z, Dal DY, Ulasoglu-Yildiz C, Kurt E, Bayr G, Ozacar E, Ozarslan E, Demirtas-Tatldede A, Bilgic B, Demiralp T, Gurvit H, Kabakcoglu A, Acar B. B-Tensor: Brain Connectome Tensor Factorization for Alzheimer's Disease. IEEE J Biomed Health Inform 2021; 25:1591-1600. [PMID: 32915753 DOI: 10.1109/jbhi.2020.3023610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
AD is the highly severe part of the dementia spectrum and impairs cognitive abilities of individuals, bringing economic, societal and psychological burdens beyond the diseased. A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e., sNETs and fNETs, respectively. We propose to use tensor representation (B-tensor) of uni-modal and multi-modal brain connectomes to define a low-dimensional space via tensor factorization. We show on a cohort of 47 subjects, spanning the spectrum of dementia, that diagnosis with an accuracy of 77% to 100% is achievable in a 5D connectome space using different structural and functional connectome constructions in a uni-modal and multi-modal fashion. We further show that multi-modal tensor factorization improves the results suggesting complementary information in structure and function. A neurological assessment of the connectivity patterns identified largely agrees with prior knowledge, yet also suggests new associations that may play a role in the disease progress.
Collapse
|
31
|
Ibrahim B, Suppiah S, Ibrahim N, Mohamad M, Hassan HA, Nasser NS, Saripan MI. Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review. Hum Brain Mapp 2021; 42:2941-2968. [PMID: 33942449 PMCID: PMC8127155 DOI: 10.1002/hbm.25369] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 12/20/2022] Open
Abstract
Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.
Collapse
Affiliation(s)
- Buhari Ibrahim
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.,Department of Physiology, Faculty of Basic Medical Sciences, Bauchi State University Gadau, Gadau, Nigeria
| | - Subapriya Suppiah
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Normala Ibrahim
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mazlyfarina Mohamad
- Centre for Diagnostic and Applied Health Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Hasyma Abu Hassan
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Nisha Syed Nasser
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - M Iqbal Saripan
- Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| |
Collapse
|
32
|
Ding Y, Zhao K, Che T, Du K, Sun H, Liu S, Zheng Y, Li S, Liu B, Liu Y. Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study. Cereb Cortex 2021; 31:3950-3961. [PMID: 33884402 DOI: 10.1093/cercor/bhab061] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/22/2021] [Indexed: 12/20/2022] Open
Abstract
Growing evidence indicates that amyloid-beta (Aβ) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aβ positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aβ PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aβ, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aβ PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.
Collapse
Affiliation(s)
- Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Kun Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.,Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tongtong Che
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Shu Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China.,Pazhou Lab, Guangzhou 510330, China.,School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | | |
Collapse
|
33
|
Masoudi B, Daneshvar S, Razavi SN. Multi-modal neuroimaging feature fusion via 3D Convolutional Neural Network architecture for schizophrenia diagnosis. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.
Collapse
Affiliation(s)
- Babak Masoudi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sabalan Daneshvar
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
- Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
| | - Seyed Naser Razavi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| |
Collapse
|
34
|
Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease. Neuroinformatics 2021; 19:57-78. [PMID: 32524428 DOI: 10.1007/s12021-020-09469-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 5% up to 40% relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package ( www.clinica.run ) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML .
Collapse
|
35
|
Xiao R, Cui X, Qiao H, Zheng X, Zhang Y, Zhang C, Liu X. Early diagnosis model of Alzheimer’s disease based on sparse logistic regression with the generalized elastic net. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102362] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
36
|
Becerra-Laparra I, Cortez-Conradis D, Garcia-Lazaro HG, Martinez-Lopez M, Roldan-Valadez E. Radial diffusivity is the best global biomarker able to discriminate healthy elders, mild cognitive impairment, and Alzheimer's disease: A diagnostic study of DTI-derived data. Neurol India 2021; 68:427-434. [PMID: 32415019 DOI: 10.4103/0028-3886.284376] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Introduction For the past two decades, diffusion tensor imaging (DTI)-derived metrics allowed the characterization of Alzheimer's disease (AzD). Previous studies reported only a few parameters (most commonly fractional anisotropy, mean diffusivity, and axial and radial diffusivities measured at selected regions). We aimed to assess the diagnostic performance of 11 DTI-derived tensor metrics by using a global approach. Materials and Methods A prospective study performed in 34 subjects: 12 healthy elders, 11 mild cognitive impairment (MCI) patients, and 11 patients with AzD. Postprocessing of DTI magnetic resonance imaging allowed the calculation of 11 tensor metrics. Anisotropies included fractional (FA), and relative (RA). Diffusivities considered simple isotropic diffusion (p), simple anisotropic diffusion (q), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Tensors included the diffusion tensor total magnitude (L); and the linear (Cl), planar (Cp), and spherical tensors (Cs). We performed a multivariate discriminant analysis and diagnostic tests assessment. Results RD was the only variable selected to assemble a predictive model: Wilks' λ = 0.581, χ2 (2) = 14.673, P = 0.001. The model's overall accuracy was 64.5%, with areas under the curve of 0.81, 0.73 and 0.66 to diagnose AzD, MCI, and healthy brains, respectively. Conclusions Global DTI-derived RD alone can discriminate between healthy elders, MCI, and AzD patients. Although this study proves evidence of a potential biomarker, it does not provide clinical guidance yet. Additional studies comparing DTI metrics might determine their usefulness to monitor disease progression, measure outcome in drug trials, and even perform the screening of pre-AzD subjects.
Collapse
Affiliation(s)
- Ivonne Becerra-Laparra
- Deputy Director of Academic Affairs and Education and Geriatrics Unit, Medica Sur Clinic and Foundation, Mexico City, Mexico
| | | | | | | | - Ernesto Roldan-Valadez
- Hospital General de Mexico "Dr. Eduardo Liceaga", Mexico City, Mexico; I.M. Sechenov First Moscow State Medical University (Sechenov University), Department of Radiology, Moscow, Russia
| |
Collapse
|
37
|
Feis RA, van der Grond J, Bouts MJRJ, Panman JL, Poos JM, Schouten TM, de Vos F, Jiskoot LC, Dopper EGP, van Buchem MA, van Swieten JC, Rombouts SARB. Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept. Brain Commun 2021; 2:fcaa079. [PMID: 33543126 PMCID: PMC7846185 DOI: 10.1093/braincomms/fcaa079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 04/29/2020] [Accepted: 05/11/2020] [Indexed: 11/14/2022] Open
Abstract
Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10–20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms (‘convert’) within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia (‘converters’), while 35 had not (‘non-converters’). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials.
Collapse
Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Jackie M Poos
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands.,Dementia Research Centre, University College London, London, WC1N 3AR, UK
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| |
Collapse
|
38
|
Abnormal cortical regions and subsystems in whole brain functional connectivity of mild cognitive impairment and Alzheimer's disease: a preliminary study. Aging Clin Exp Res 2021; 33:367-381. [PMID: 32277436 DOI: 10.1007/s40520-020-01539-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 03/24/2020] [Indexed: 12/12/2022]
Abstract
The disease roots of Alzheimer's disease (AD) are unknown. Functional connection (FC) methodology based on functional MRI data is an effective lever to investigate macroscopic neural activity patterns. However, regional properties of brain architecture have been less investigated by special markers of graph indexes in general mental disorders. In terms of the set of the abnormal edges in the FCs matrix, this paper introduces the strength index (S-scores) of region centrality on the principle of holism. Then, the important process is to investigate the S-scores of regions and subsystems in 36 healthy controls, 38 mild cognitive impairment (MCI) patients and 34 AD patients. At the edge level, abnormal FCs is numerically increasing progressively from MCI to AD brains. At the region level, the CUN.L, PAL.R, THA.L, and TPOsup.R regions are highlighted with abnormal S-scores in MCI patients. By comparison, more regions are abnormal in AD patients, which are PreCG.L, INS.R, DCG.L, AMYG.R, IOG.R, FFG.L, PoCG.L, PCUN.R, TPOsup.L, MTG.L, and TPOmid.L. Importantly, the regions in DMN have abnormal S-scores in AD groups. At the module level, the S-scores of frontal, parietal, occipital lobe, and cerebellum are found in MCI and AD patients. Meanwhile, the abnormal lateralization is inferred because of the S-scores of left and top hemisphere in the AD group. Though this is strictly a contrastive study, the S-score may be a meaningful imaging marker for excavating AD psychopathology.
Collapse
|
39
|
Bai X, Zheng J, Zhang B, Luo Y. Cognitive Dysfunction and Neurophysiologic Mechanism of Breast Cancer Patients Undergoing Chemotherapy Based on Resting State Functional Magnetic Resonance Imaging. World Neurosurg 2020; 149:406-412. [PMID: 33096278 DOI: 10.1016/j.wneu.2020.10.066] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 01/28/2023]
Abstract
We studied chemotherapy-related cognitive impairment via resting state (RS)-functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) in 19 cases of patients with early breast cancer. White matter neuropsychological test treatment were carried out before and after chemotherapy, RS-fMRI, and DTI evaluation. In RS-fMRI with regional homogeneity (ReHo) reflects brain activity. In the DTI with fractional anisotropy (FA) reflect the integrity of the white matter. Determining the region of interest by image analysis, we calculated the neuropsychologic test score using the paired t-test and FA change ReHo values of regions of interest. Finally after the test treatment, in the chemotherapy group for pairing correlation analysis t-test scores change in meaningful inspection and change ReHo and FA. Chemotherapy after chemotherapy than before chemotherapy difference memory test and self-evaluation of cognitive (P < 0.05). ReHo value increases occurred in the right orbitofrontal region and the left dorsolateral prefrontal cortex. Declines in brain regions were the anterior inferior cerebellar lobe, cerebellar lobe, right middle temporal gyrus and the superior temporal gyrus, the lower right of the center area, and the central gyrus. This prospective study on resting state and RS-fMRI functional magnetic resonance DTI study DTI sequence combination chemotherapy for breast cancer-related cognitive disorders supports the "chemo brain" point of view. Chemotherapy can cause memory decline, accompanied by a partial area of the brain and white matter integrity in brain activity changes. Prompt clinical treatment RS-fMRI and DTI have potential applications in assessing chemotherapy-related cognitive impairment.
Collapse
Affiliation(s)
- Xiaoru Bai
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Jian Zheng
- Department of Medical Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Bin Zhang
- Department of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yahong Luo
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China.
| |
Collapse
|
40
|
Jin D, Wang P, Zalesky A, Liu B, Song C, Wang D, Xu K, Yang H, Zhang Z, Yao H, Zhou B, Han T, Zuo N, Han Y, Lu J, Wang Q, Yu C, Zhang X, Zhang X, Jiang T, Zhou Y, Liu Y. Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease. Hum Brain Mapp 2020; 41:3379-3391. [PMID: 32364666 PMCID: PMC7375114 DOI: 10.1002/hbm.25023] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 03/26/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD-associated functional brain alterations using one of the world's largest resting-state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta-analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default-mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid-β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave-one-site-out cross-validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini-Mental State Examination scores: 0.56, p < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.
Collapse
Affiliation(s)
- Dan Jin
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne and Melbourne HealthMelbourneVictoriaAustralia
- Department of Biomedical EngineeringUniversity of MelbourneMelbourneVictoriaAustralia
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Kaibin Xu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Hongwei Yang
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijingChina
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijingChina
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjinChina
| | - Nianming Zuo
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Beijing Institute of GeriatricsBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Qing Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Xinqing Zhang
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijingChina
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| |
Collapse
|
41
|
Gupta Y, Kim JI, Kim BC, Kwon GR. Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype. Front Aging Neurosci 2020; 12:238. [PMID: 32848713 PMCID: PMC7406801 DOI: 10.3389/fnagi.2020.00238] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/08/2020] [Indexed: 12/26/2022] Open
Abstract
Graphical, voxel, and region-based analysis has become a popular approach to studying neurodegenerative disorders such as Alzheimer's disease (AD) and its prodromal stage [mild cognitive impairment (MCI)]. These methods have been used previously for classification or discrimination of AD in subjects in a prodromal stage called stable MCI (MCIs), which does not convert to AD but remains stable over a period of time, and converting MCI (MCIc), which converts to AD, but the results reported across similar studies are often inconsistent. Furthermore, the classification accuracy for MCIs vs. MCIc is limited. In this study, we propose combining different neuroimaging modalities (sMRI, FDG-PET, AV45-PET, DTI, and rs-fMRI) with the apolipoprotein-E genotype to form a multimodal system for the discrimination of AD, and to increase the classification accuracy. Initially, we used two well-known analyses to extract features from each neuroimage for the discrimination of AD: whole-brain parcelation analysis (or region-based analysis), and voxel-wise analysis (or voxel-based morphometry). We also investigated graphical analysis (nodal and group) for all six binary classification groups (AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs). Data for a total of 129 subjects (33 AD, 30 MCIs, 31 MCIc, and 35 HCs) for each imaging modality were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage. These data also include two APOE genotype data points for the subjects. Moreover, we used the 2-mm AICHA atlas with the NiftyReg registration toolbox to extract 384 brain regions from each PET (FDG and AV45) and sMRI image. For the rs-fMRI images, we used the DPARSF toolbox in MATLAB for the automatic extraction of data and the results for REHO, ALFF, and fALFF. We also used the pyClusterROI script for the automatic parcelation of each rs-fMRI image into 200 brain regions. For the DTI images, we used the FSL (Version 6.0) toolbox for the extraction of fractional anisotropy (FA) images to calculate a tract-based spatial statistic. Moreover, we used the PANDA toolbox to obtain 50 white-matter-region-parcellated FA images on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.
Collapse
Affiliation(s)
- Yubraj Gupta
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| | - Ji-In Kim
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| | - Byeong Chae Kim
- Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea
| | - Goo-Rak Kwon
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| |
Collapse
|
42
|
de Vos F, Schouten TM, Koini M, Bouts MJRJ, Feis RA, Lechner A, Schmidt R, van Buchem MA, Verhey FRJ, Olde Rikkert MGM, Scheltens P, de Rooij M, van der Grond J, Rombouts SARB. Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients. NEUROIMAGE-CLINICAL 2020; 27:102303. [PMID: 32554321 PMCID: PMC7303669 DOI: 10.1016/j.nicl.2020.102303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 05/29/2020] [Accepted: 05/30/2020] [Indexed: 01/04/2023]
Abstract
Multimodal MRI AD classification models were pre-trained on AD patients and controls. Generalisation of these models was tested on a multi-centre memory clinic data set. AD scores were assigned to AD patients, MCI patients and memory complainers. Anatomical MRI performed better than diffusion MRI and resting state fMRI. Combining imaging modalities did not improve the results over anatomical MRI only.
Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer’s disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics. To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models. For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model. In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice.
Collapse
Affiliation(s)
- Frank de Vos
- Institute of Psychology, Leiden University, the Netherlands; Department of Radiology, Leiden University Medical Center, the Netherlands; Leiden Institute for Brain and Cognition, the Netherlands.
| | - Tijn M Schouten
- Institute of Psychology, Leiden University, the Netherlands; Department of Radiology, Leiden University Medical Center, the Netherlands; Leiden Institute for Brain and Cognition, the Netherlands
| | - Marisa Koini
- Department of Neurology, Medical University of Graz, Austria
| | - Mark J R J Bouts
- Institute of Psychology, Leiden University, the Netherlands; Department of Radiology, Leiden University Medical Center, the Netherlands; Leiden Institute for Brain and Cognition, the Netherlands
| | - Rogier A Feis
- Institute of Psychology, Leiden University, the Netherlands; Department of Radiology, Leiden University Medical Center, the Netherlands; Leiden Institute for Brain and Cognition, the Netherlands
| | - Anita Lechner
- Department of Neurology, Medical University of Graz, Austria
| | | | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Center, the Netherlands
| | - Frans R J Verhey
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNS), Alzheimer Centrum Limburg, Maastricht University, the Netherlands
| | - Marcel G M Olde Rikkert
- Department of Geriatric Medicine, Radboudumc Alzheimer Centre, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Geriatric Medicine, Radboudumc Alzheimer Centre, Donders Institute for Medical Neurosciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Mark de Rooij
- Institute of Psychology, Leiden University, the Netherlands; Leiden Institute for Brain and Cognition, the Netherlands
| | | | - Serge A R B Rombouts
- Institute of Psychology, Leiden University, the Netherlands; Department of Radiology, Leiden University Medical Center, the Netherlands; Leiden Institute for Brain and Cognition, the Netherlands
| |
Collapse
|
43
|
Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS, AND APPLICATIONS 2020; 16:1-35. [DOI: 10.1145/3344998] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 07/01/2019] [Indexed: 08/30/2023]
Abstract
Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
Collapse
Affiliation(s)
- M. Tanveer
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - B. Richhariya
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - R. U. Khan
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - A. H. Rashid
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India
| | - P. Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - M. Prasad
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| | - C. T. Lin
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| |
Collapse
|
44
|
Gill S, Mouches P, Hu S, Rajashekar D, MacMaster FP, Smith EE, Forkert ND, Ismail Z. Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data. J Alzheimers Dis 2020; 75:277-288. [PMID: 32250302 PMCID: PMC7306896 DOI: 10.3233/jad-191169] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used for this purpose. OBJECTIVES To investigate if baseline mild behavioral impairment (MBI) status used for NPS quantification along with brain morphology features are predictive of follow-up diagnosis, median 40 months later in patients with normal cognition (NC) or MCI. METHOD Baseline neuroimaging, neuropsychiatric, and clinical data from 102 individuals with NC and 239 with MCI were extracted from the Alzheimer's Disease Neuroimaging Initiative database. Neuropsychiatric inventory questionnaire items were transformed to MBI domains using a published algorithm. Diagnosis at latest follow-up was used as the outcome variable and ground truth classification. A logistic model tree classifier combined with information gain feature selection was trained to predict follow-up diagnosis. RESULTS In the binary classification (NC versus MCI/AD), the optimal ML model required only two features from over 200, MBI total score and left hippocampal volume. These features correctly classified participants as remaining normal or developing cognitive impairment with 84.4% accuracy (area under the receiver operating characteristics curve [ROC-AUC] = 0.86). Seven features were selected for the three-class model (NC versus MCI versus dementia) achieving an accuracy of 58.8% (ROC-AUC=0.73). CONCLUSION Baseline NPS, categorized for MBI domain and duration, have prognostic utility in addition to brain morphology measures for predicting diagnosis change using ML. MBI total score, followed by impulse dyscontrol and affective dysregulation were most predictive of future diagnosis.
Collapse
Affiliation(s)
- Sascha Gill
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Pauline Mouches
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Sophie Hu
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Community Health Science, University of Calgary, Calgary, Alberta, Canada
| | - Deepthi Rajashekar
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Frank P. MacMaster
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
- Addictions and Mental Health Strategic Clinical Network, Alberta, Canada
| | - Eric E. Smith
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Nils D. Forkert
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Zahinoor Ismail
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Community Health Science, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
- O’Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
| | | |
Collapse
|
45
|
Feis RA, Bouts MJRJ, de Vos F, Schouten TM, Panman JL, Jiskoot LC, Dopper EGP, van der Grond J, van Swieten JC, Rombouts SARB. A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers. J Neurol Neurosurg Psychiatry 2019; 90:1207-1214. [PMID: 31203211 DOI: 10.1136/jnnp-2019-320774] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/26/2019] [Accepted: 05/12/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up ('converters') and non-converting carriers ('non-converters'). METHODS We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time. RESULTS Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001). CONCLUSIONS Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis.
Collapse
Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands .,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Elise G P Dopper
- Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands.,Department of Clinical Genetics, VU University Medical Centre, Amsterdam, The Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| |
Collapse
|
46
|
Gupta Y, Lama RK, Kwon GR. Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Front Comput Neurosci 2019; 13:72. [PMID: 31680923 PMCID: PMC6805777 DOI: 10.3389/fncom.2019.00072] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 10/01/2019] [Indexed: 01/15/2023] Open
Abstract
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.
Collapse
|
47
|
Hojjati SH, Ebrahimzadeh A, Babajani-Feremi A. Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI. Front Neurol 2019; 10:904. [PMID: 31543860 PMCID: PMC6730495 DOI: 10.3389/fneur.2019.00904] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 08/05/2019] [Indexed: 12/29/2022] Open
Abstract
Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.
Collapse
Affiliation(s)
- Seyed Hani Hojjati
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States.,Department of Electrical Engineering, Babol University of Technology, Babol, Iran.,Neuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United States
| | - Ata Ebrahimzadeh
- Department of Electrical Engineering, Babol University of Technology, Babol, Iran
| | - Abbas Babajani-Feremi
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States.,Neuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United States.,Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, United States
| |
Collapse
|
48
|
Donepezil's Effects on Brain Functions of Patients With Alzheimer Disease: A Regional Homogeneity Study Based on Resting-State Functional Magnetic Resonance Imaging. Clin Neuropharmacol 2019; 42:42-48. [PMID: 30875345 PMCID: PMC6426347 DOI: 10.1097/wnf.0000000000000324] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE Donepezil is known to increase cholinergic synaptic transmission in Alzheimer disease (AD), although how it affects cortical brain activity and how it consequently affects brain functions need further clarification. To investigate the therapeutic mechanism of donepezil underlying its effect on brain function, regional homogeneity (ReHo) technology was used in this study. PATIENTS AND METHODS This study included 11 mild-to-moderate AD patients who completed 24 weeks of donepezil treatment and 11 matched healthy controls. All participants finished neuropsychological assessment and resting-state functional magnetic resonance imaging scanning to compare whole-brain ReHo before and after donepezil treatment. RESULTS Significantly decreased Alzheimer's Disease Assessment Scale-Cognitive Subscale scores (P = 0.010) and increased Mini-Mental State Examination scores (P = 0.043) were observed in the AD patients. In addition, in the right gyrus rectus (P = 0.021), right precentral gyrus (P = 0.026), and left superior temporal gyrus (P = 0.043) of the AD patients, decreased ReHo was exhibited. CONCLUSION Donepezil-mediated improvement of cognitive function in AD patients is linked to spontaneous brain activities of the right gyrus rectus, right precentral gyrus, and left superior temporal gyrus, which could be used as potential biomarkers for monitoring the therapeutic effect of donepezil.
Collapse
|
49
|
Bouts MJRJ, van der Grond J, Vernooij MW, Koini M, Schouten TM, de Vos F, Feis RA, Cremers LGM, Lechner A, Schmidt R, de Rooij M, Niessen WJ, Ikram MA, Rombouts SARB. Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. Hum Brain Mapp 2019; 40:2711-2722. [PMID: 30803110 PMCID: PMC6563478 DOI: 10.1002/hbm.24554] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 01/30/2019] [Accepted: 02/09/2019] [Indexed: 01/18/2023] Open
Abstract
Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.
Collapse
Affiliation(s)
- Mark J. R. J. Bouts
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | | | - Meike W. Vernooij
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Marisa Koini
- Department of NeurologyMedical University of GrazAustria
| | - Tijn M. Schouten
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Frank de Vos
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Rogier A. Feis
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Lotte G. M. Cremers
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Anita Lechner
- Department of NeurologyMedical University of GrazAustria
| | | | - Mark de Rooij
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Wiro J. Niessen
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Medical InformaticsErasmus MC University Medical CenterRotterdamthe Netherlands
- Faculty of Applied SciencesDelft University of TechnologyDelftthe Netherlands
| | - M. Arfan Ikram
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of NeurologyErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Serge A. R. B. Rombouts
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| |
Collapse
|
50
|
Bouts MJRJ, Möller C, Hafkemeijer A, van Swieten JC, Dopper E, van der Flier WM, Vrenken H, Wink AM, Pijnenburg YAL, Scheltens P, Barkhof F, Schouten TM, de Vos F, Feis RA, van der Grond J, de Rooij M, Rombouts SARB. Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging. J Alzheimers Dis 2019; 62:1827-1839. [PMID: 29614652 DOI: 10.3233/jad-170893] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND/OBJECTIVE Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
Collapse
Affiliation(s)
- Mark J R J Bouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Christiane Möller
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Anne Hafkemeijer
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - John C van Swieten
- Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elise Dopper
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands.,Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, UK
| | - Tijn M Schouten
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Frank de Vos
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Rogier A Feis
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mark de Rooij
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Serge A R B Rombouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| |
Collapse
|