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Wang L, Xu H, Wang M, Brendel M, Rominger A, Shi K, Han Y, Jiang J. A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans. Hum Brain Mapp 2023; 44:6020-6030. [PMID: 37740923 PMCID: PMC10619407 DOI: 10.1002/hbm.26493] [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: 05/07/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.
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Affiliation(s)
- Luyao Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Huanyu Xu
- School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Min Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Matthias Brendel
- Department of Nuclear MedicineUniversity Hospital of Munich, Ludwig Maximilian University of MunichMunichGermany
| | - Axel Rominger
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Kuangyu Shi
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Hainan UniversityHaikouChina
| | - Jiehui Jiang
- School of Life SciencesShanghai UniversityShanghaiChina
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Lefort-Besnard J, Naveau M, Delcroix N, Decker LM, Cignetti F. Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI. Neurobiol Aging 2023; 131:196-208. [PMID: 37689017 DOI: 10.1016/j.neurobiolaging.2023.07.006] [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: 02/06/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 09/11/2023]
Abstract
There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aβ142 concentration for the former and CSF Aβ1-42 concentration, tau concentration as well as grey matter atrophy (especially in the temporal and occipital lobes) for the latter. In contrast, participants from the cluster-derived normal subgroup exhibited an identical profile to CN controls in terms of cognitive performance, brain structure, and CSF biomarker levels. Our comprehensive data analytics strategy provides further evidence that multimodal neuropsychological subtyping is both clinically and neurobiologically meaningful.
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Affiliation(s)
| | - Mikael Naveau
- Normandie Univ, UNICAEN, CNRS, CEA, INSERM, GIP Cyceron, Caen, France
| | - Nicolas Delcroix
- Normandie Univ, UNICAEN, CNRS, CEA, INSERM, GIP Cyceron, Caen, France
| | - Leslie Marion Decker
- Normandie Univ, UNICAEN, INSERM, COMETE, Caen, France; Normandie Univ, UNICAEN, CIREVE, Caen, France.
| | - Fabien Cignetti
- Univ. Grenoble Alpes, CNRS, VetAgro Sup, Grenoble INP, TIMC, Grenoble, France.
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA,Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA,Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Ding C, Du W, Zhang Q, Wang L, Han Y, Jiang J. Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals. Hum Brain Mapp 2021; 42:5051-5062. [PMID: 34291850 PMCID: PMC8449101 DOI: 10.1002/hbm.25599] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/10/2021] [Accepted: 07/12/2021] [Indexed: 11/11/2022] Open
Abstract
The discovery of preclinical Alzheimer's disease (preAD) provides a wide time window for the early intervention of AD. The coupling relationships between glucose and oxygen metabolisms from hybrid PET/MRI can provide complementary information on the brain's physiological state for preAD. In this study, we purpose to explore the change of coupling relationship among 27 normal controls (NCs), 20 preADs, and 15 cognitive impairments (CIs). For each subject, we calculated the Spearman partial correlation between the fractional amplitude of low-frequency fluctuations (fALFF) and the regional homogeneity (ReHo) from functional image (fMRI), and the standard uptake value ratio (SUVR) from [18F] fluorodeoxyglucose positron emission tomography (18 F-FDG PET), in the whole-brain and default mode network (DMN) as a novel potential biomarker. The diagnostic performance of this biomarker was evaluated by the receiver operating characteristic analysis. Significant Spearman correlations between the FDG SUVR and the fALFF/ReHo were found in 98% of subjects. For the DMN-based biomarker, there was a significant decreasing trend for the preAD and CI groups compared to the NC group, whereas no significant difference in preAD based on whole-brain. The correlation ρ value for the FDG SUVR/ReHo showed the highest area under curve of the preAD classification (0.787). The results imply the coupling relationship changed during the preAD stage in the DMN area.
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Affiliation(s)
- Changchang Ding
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Wenying Du
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Qi Zhang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Luyao Wang
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Biomedical Engineering InstituteHainan UniversityHaikouChina
| | - Jiehui Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information EngineeringShanghai UniversityShanghaiChina
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Fuller JT, Choudhury TK, Lowe DA, Balsis S. Hallucinations and Delusions Signal Alzheimer's Associated Cognitive Dysfunction More Strongly Compared to Other Neuropsychiatric Symptoms. J Gerontol B Psychol Sci Soc Sci 2021; 75:1894-1904. [PMID: 30877750 DOI: 10.1093/geronb/gbz032] [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: 11/05/2018] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVES Neuropsychiatric symptoms (NPS) are common among individuals with dementia of the Alzheimer's type (DAT). We sought to characterize which NPS more purely relate to cognitive dysfunction in DAT, relative to other NPS. METHOD Demographic, neurocognitive, neuroimaging, and NPS data were mined from the Alzheimer's Disease Neuroimaging Initiative database (n = 906). Using factor analysis, we analyzed the degree to which individual NPS were associated with DAT-associated cognitive dysfunction. We also employed item response theory to graphically depict the ability of individual NPS to index DAT-associated cognitive dysfunction across a continuum ranging from cognitively normal to mild DAT. RESULTS Psychotic symptoms (hallucinations and delusions) were more strongly related to the continuum of DAT-associated cognitive dysfunction than other NPS, with the strength of the relationship peaking at high levels of disease severity. Psychotic symptoms also negatively correlated with brain volume and did not relate to the presence of vision problems. Aberrant motor behavior and apathy had relatively smaller associations with DAT-associated cognitive dysfunction, while other NPS showed minimal associations. DISCUSSION Psychotic symptoms most strongly indexed DAT-associated cognitive dysfunction, whereas other NPS, such as depression and anxiety, were not as precisely related to the DAT-associated cognitive dysfunction.
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Affiliation(s)
- Joshua T Fuller
- Department of Psychological and Brain Sciences, Boston University, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Tabina K Choudhury
- Department of Psychological and Brain Sciences, Texas A&M University, College Station
| | - Deborah A Lowe
- Department of Psychiatry and Behavioral Sciences, University of Oklahoma Health Sciences Center, Oklahoma City
| | - Steve Balsis
- Department of Psychology, University of Massachusetts Lowell
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Kim T, Kim SY, Agarwal V, Cohen A, Roush R, Chang YF, Cheng Y, Snitz B, Huppert TJ, Bagic A, Kamboh MI, Doman J, Becker JT. Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults. Neuroimage 2021; 233:117956. [PMID: 33716158 PMCID: PMC8145789 DOI: 10.1016/j.neuroimage.2021.117956] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/03/2021] [Accepted: 03/05/2021] [Indexed: 12/11/2022] Open
Abstract
Changes of cardiac-induced regional pulsatility can be associated with specific regions of brain volumetric changes, and these are related with cognitive alterations. Thus, mapping of cardiac pulsatility over the entire brain can be helpful to assess these relationships. A total of 108 subjects (age: 66.5 ± 8.4 years, 68 females, 52 healthy controls, 11 subjective cognitive decline, 17 impaired without complaints, 19 MCI and 9 AD) participated. The pulsatility map was obtained directly from resting-state functional MRI time-series data at 3T. Regional brain volumes were segmented from anatomical MRI. Multidomain neuropsychological battery was performed to test memory, language, attention and visuospatial construction. The Montreal Cognitive Assessment (MoCA) was also administered. The sparse partial least square (SPLS) method, which is desirable for better interpreting high-dimensional variables, was applied for the relationship between the entire brain voxels of pulsatility and 45 segmented brain volumes. A multiple holdout SPLS framework was used to optimize sparsity for assessing the pulsatility-volume relationship model and to test the reliability by fitting the models to 9 different splits of the data. We found statistically significant associations between subsets of pulsatility voxels and subsets of segmented brain volumes by rejecting the omnibus null hypothesis (any of 9 splits has p < 0.0056 (=0.05/9) with the Bonferroni correction). The pulsatility was positively associated with the lateral ventricle, choroid plexus, inferior lateral ventricle, and 3rd ventricle and negatively associated with hippocampus, ventral DC, and thalamus volumes for the first pulsatility-volume relationship. The pulsatility had an additional negative relationship with the amygdala and brain stem volumes for the second pulsatility-volume relationship. The spatial distribution of correlated pulsatility was observed in major feeding arteries to the brain regions, ventricles, and sagittal sinus. The indirect mediating pathways through the volumetric changes were statistically significant between the pulsatility and multiple cognitive measures (p < 0.01). Thus, the cerebral pulsatility, along with volumetric measurements, could be a potential marker for better understanding of pathophysiology and monitoring disease progression in age-related neurodegenerative disorders.
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Affiliation(s)
- Tae Kim
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA.
| | - Sang-Young Kim
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Vikas Agarwal
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Annie Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA
| | - Rebecca Roush
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Yue-Fang Chang
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, USA
| | - Yu Cheng
- Departments of Statistics and Biostatistics, University of Pittsburgh, Pittsburgh, USA
| | - Beth Snitz
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Theodore J Huppert
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA; Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
| | - Anto Bagic
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, USA
| | - Jack Doman
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA
| | - James T Becker
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA
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Affiliation(s)
- Nicole D Anderson
- Rotman Research Institute, Baycrest, Toronto, Canada.,Departments of Psychology and Psychiatry, University of Toronto Canada
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