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Onos KD, Lin PB, Pandey RS, Persohn SA, Burton CP, Miner EW, Eldridge K, Kanyinda JN, Foley KE, Carter GW, Howell GR, Territo PR. Assessment of neurovascular uncoupling: APOE status is a key driver of early metabolic and vascular dysfunction. Alzheimers Dement 2024. [PMID: 38713704 DOI: 10.1002/alz.13842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 05/09/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia worldwide, with apolipoprotein Eε4 (APOEε4) being the strongest genetic risk factor. Current clinical diagnostic imaging focuses on amyloid and tau; however, new methods are needed for earlier detection. METHODS PET imaging was used to assess metabolism-perfusion in both sexes of aging C57BL/6J, and hAPOE mice, and were verified by transcriptomics, and immunopathology. RESULTS All hAPOE strains showed AD phenotype progression by 8 months, with females exhibiting the regional changes, which correlated with GO-term enrichments for glucose metabolism, perfusion, and immunity. Uncoupling analysis revealed APOEε4/ε4 exhibited significant Type-1 uncoupling (↓ glucose uptake, ↑ perfusion) at 8 and 12 months, while APOEε3/ε4 demonstrated Type-2 uncoupling (↑ glucose uptake, ↓ perfusion), while immunopathology confirmed cell specific contributions. DISCUSSION This work highlights APOEε4 status in AD progression manifests as neurovascular uncoupling driven by immunological activation, and may serve as an early diagnostic biomarker. HIGHLIGHTS We developed a novel analytical method to analyze PET imaging of 18F-FDG and 64Cu-PTSM data in both sexes of aging C57BL/6J, and hAPOEε3/ε3, hAPOEε4/ε4, and hAPOEε3/ε4 mice to assess metabolism-perfusion profiles termed neurovascular uncoupling. This analysis revealed APOEε4/ε4 exhibited significant Type-1 uncoupling (decreased glucose uptake, increased perfusion) at 8 and 12 months, while APOEε3/ε4 demonstrated significant Type-2 uncoupling (increased glucose uptake, decreased perfusion) by 8 months which aligns with immunopathology and transcriptomic signatures. This work highlights that there may be different mechanisms underlying age related changes in APOEε4/ε4 compared with APOEε3/ε4. We predict that these changes may be driven by immunological activation and response, and may serve as an early diagnostic biomarker.
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Affiliation(s)
| | - Peter B Lin
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ravi S Pandey
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Scott A Persohn
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Charles P Burton
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ethan W Miner
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kierra Eldridge
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Kate E Foley
- The Jackson Laboratory, Bar Harbor, Maine, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Gregory W Carter
- The Jackson Laboratory, Bar Harbor, Maine, USA
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | | | - Paul R Territo
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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Onos K, Lin PB, Pandey RS, Persohn SA, Burton CP, Miner EW, Eldridge K, Kanyinda JN, Foley KE, Carter GW, Howell GR, Territo PR. Assessment of Neurovascular Uncoupling: APOE Status is a Key Driver of Early Metabolic and Vascular Dysfunction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.13.571584. [PMID: 38168292 PMCID: PMC10760108 DOI: 10.1101/2023.12.13.571584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia worldwide, with apolipoprotein ε4 (APOEε4) being the strongest genetic risk factor. Current clinical diagnostic imaging focuses on amyloid and tau; however, new methods are needed for earlier detection. METHODS PET imaging was used to assess metabolism-perfusion in both sexes of aging C57BL/6J, and hAPOE mice, and were verified by transcriptomics, and immunopathology. RESULTS All hAPOE strains showed AD phenotype progression by 8 mo, with females exhibiting the regional changes, which correlated with GO-term enrichments for glucose metabolism, perfusion, and immunity. Uncoupling analysis revealed APOEε4/ε4 exhibited significant Type-1 uncoupling (↓ glucose uptake, ↑ perfusion) at 8 and 12 mo, while APOEε3/ε4 demonstrated Type-2 uncoupling (↑ glucose uptake, ↓ perfusion), while immunopathology confirmed cell specific contributions. DISCUSSION This work highlights APOEε4 status in AD progression manifest as neurovascular uncoupling driven by immunological activation, and may serve as an early diagnostic biomarker.
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Affiliation(s)
- Kristen Onos
- The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Peter B. Lin
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ravi S. Pandey
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Scott A. Persohn
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Charles P. Burton
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Ethan W. Miner
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Kierra Eldridge
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | | | - Kate E. Foley
- The Jackson Laboratory, Bar Harbor, ME 04609 USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Gregory W. Carter
- The Jackson Laboratory, Bar Harbor, ME 04609 USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | | | - Paul R. Territo
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis IN 46202 USA
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Xiao Y, Hu Y, Huang K. Atrophy of hippocampal subfields relates to memory decline during the pathological progression of Alzheimer's disease. Front Aging Neurosci 2023; 15:1287122. [PMID: 38149170 PMCID: PMC10749921 DOI: 10.3389/fnagi.2023.1287122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/22/2023] [Indexed: 12/28/2023] Open
Abstract
Background It has been well documented that atrophy of hippocampus and hippocampal subfields is closely linked to cognitive decline in normal aging and patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, evidence is still sparce regarding the atrophy of hippocampus and hippocampal subfields in normal aging adults who later developed MCI or AD. Objective To examine whether atrophy of hippocampus and hippocampal subfields has occurred in normal aging before a diagnosis of MCI or AD. Methods We analyzed structural magnetic resonance imaging (MRI) data of cognitively normal (CN, n = 144), MCI (n = 90), and AD (n = 145) participants obtained from the Alzheimer's Disease Neuroimaging Initiative. The CN participants were categorized into early dementia converters (CN-C) and non-converters (CN-NC) based on their scores of clinical dementia rating after an average of 36.2 months (range: 6-105 months). We extracted the whole hippocampus and hippocampal subfields for each participant using FreeSurfer, and analyzed the differences in volumes of hippocampus and hippocampal subfields between groups. We then examined the associations between volume of hippocampal subfields and delayed recall scores in each group separately. Results Hippocampus and most of the hippocampal subfields demonstrated significant atrophy during the progression of AD. The CN-C and CN-NC groups differed in the left hippocampus-amygdala transition area (HATA). Furthermore, the volume of presubiculum was significantly correlated with delayed recall scores in the CN-NC and AD groups, but not in the CN-C and MCI groups. Conclusion Hippocampal subfield atrophy (i.e., left HATA) had occurred in cognitively normal elderly individuals before clinical symptoms were recognized. Significant associations of presubiculum with delayed recall scores in the CN-NC and AD groups highlight the essential role of the hippocampal subfields in both early dementia detection and AD progression.
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Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
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Heng NYW, Rittman T. Understanding ethnic diversity in open dementia neuroimaging data sets. Brain Commun 2023; 5:fcad308. [PMID: 38025280 PMCID: PMC10667030 DOI: 10.1093/braincomms/fcad308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/22/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023] Open
Abstract
Ethnic differences in dementia are increasingly recognized in epidemiological measures and diagnostic biomarkers. Nonetheless, ethnic diversity remains limited in many study populations. Here, we provide insights into ethnic diversity in open-access neuroimaging dementia data sets. Data sets comprising dementia populations with available data on ethnicity were included. Statistical analyses of sample and effect sizes were based on the Cochrane Handbook. Nineteen databases were included, with 17 studies of healthy groups or a combination of diagnostic groups if breakdown was unavailable and 12 of mild cognitive impairment and dementia groups. Combining all studies on dementia patients, the largest ethnic group was Caucasian (20 547 participants), with the next most common being Afro-Caribbean (1958), followed by Asian (1211). The smallest effect size detectable within the Caucasian group was 0.03, compared to Afro-Caribbean (0.1) and Asian (0.13). Our findings quantify the lack of ethnic diversity in openly available dementia data sets. More representative data would facilitate the development and validation of biomarkers relevant across ethnicities.
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Affiliation(s)
- Nicholas Yew Wei Heng
- Department of Neurosciences, University of Cambridge, Herchel Smith building, Cambridge Biomedical Campus, Robinson Way, Cambridge CB2 0SZ, UK
| | - Timothy Rittman
- Department of Neurosciences, University of Cambridge, Herchel Smith building, Cambridge Biomedical Campus, Robinson Way, Cambridge CB2 0SZ, UK
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Groechel RC, Tripodis Y, Alosco ML, Mez J, Qiao Qiu W, Goldstein L, Budson AE, Kowall NW, Shaw LM, Weiner M, Jack CR, Killiany RJ. Biomarkers of Alzheimer's disease in Black and/or African American Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Neurobiol Aging 2023; 131:144-152. [PMID: 37639768 PMCID: PMC10528881 DOI: 10.1016/j.neurobiolaging.2023.07.021] [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: 03/17/2023] [Revised: 07/03/2023] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
Majority of dementia research is conducted in non-Hispanic White participants despite a greater prevalence of dementia in other racial groups. To obtain a better understanding of biomarker presentation of Alzheimer's disease (AD) in the non-Hispanic White population, this study exclusively examined AD biomarker abnormalities in 85 Black and/or African American participants within the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants were classified by the ADNI into 3 clinical groups: cognitively normal, mild cognitive impairment, or dementia. Data examined included demographics, apolipoprotein E (APOE) ε4, cerebrospinal fluid (CSF) Aβ1-42, CSF total tau (t-tau), CSF phosphorylated tau (p-tau), 3T magnetic resonance imaging (MRI), and measures of cognition and function. Analyses of variance and covariance showed lower cortical thickness in 5 of 7 selected MRI regions, lower hippocampal volume, greater volume of white matter hyperintensities, lower measures of cognition and function, lower measures of CSF Aβ1-42, and greater measures of CSF t-tau and p-tau between clinical groups. Our findings confirmed greater AD biomarker abnormalities between clinical groups in this sample.
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Affiliation(s)
- Renée C Groechel
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
| | - Yorghos Tripodis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Boston University Alzheimer's Disease Research Center, Boston, MA, USA
| | - Michael L Alosco
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA; Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jesse Mez
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA; Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Wei Qiao Qiu
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA; Department of Psychiatry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Lee Goldstein
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA; Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Andrew E Budson
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA; Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Neil W Kowall
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA; Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Weiner
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | | | - Ronald J Killiany
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA; Boston University Alzheimer's Disease Research Center, Boston, MA, USA; Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
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Mieling M, Göttlich M, Yousuf M, Bunzeck N. Basal forebrain activity predicts functional degeneration in the entorhinal cortex in Alzheimer's disease. Brain Commun 2023; 5:fcad262. [PMID: 37901036 PMCID: PMC10608112 DOI: 10.1093/braincomms/fcad262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/23/2023] [Accepted: 10/07/2023] [Indexed: 10/31/2023] Open
Abstract
Recent models of Alzheimer's disease suggest the nucleus basalis of Meynert (NbM) as an early origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear. We analysed resting-state functional fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (n = 71) at baseline and 2 years later. At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations, differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio. Our findings give novel insights into the pathogenesis of Alzheimer's disease by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.
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Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
| | - Martin Göttlich
- Department of Neurology, University of Lübeck, Lübeck 23562, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck 23562, Germany
| | - Mushfa Yousuf
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck 23562, Germany
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O'Donoghue MC, Blane J, Gillis G, Mitchell R, Lindsay K, Semple J, Pretorius PM, Griffanti L, Fossey J, Raymont V, Martos L, Mackay CE. Oxford brain health clinic: protocol and research database. BMJ Open 2023; 13:e067808. [PMID: 37541753 PMCID: PMC10407419 DOI: 10.1136/bmjopen-2022-067808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 06/22/2023] [Indexed: 08/06/2023] Open
Abstract
INTRODUCTION Despite major advances in the field of neuroscience over the last three decades, the quality of assessments available to patients with memory problems in later life has barely changed. At the same time, a large proportion of dementia biomarker research is conducted in selected research samples that often poorly reflect the demographics of the population of patients who present to memory clinics. The Oxford Brain Health Clinic (BHC) is a newly developed clinical assessment service with embedded research in which all patients are offered high-quality clinical and research assessments, including MRI, as standard. METHODS AND ANALYSIS Here we describe the BHC protocol, including aligning our MRI scans with those collected in the UK Biobank. We evaluate rates of research consent for the first 108 patients (data collection ongoing) and the ability of typical psychiatry-led NHS memory-clinic patients to tolerate both clinical and research assessments. ETHICS AND DISSEMINATION Our ethics and consenting process enables patients to choose the level of research participation that suits them. This generates high rates of consent, enabling us to populate a research database with high-quality data that will be disseminated through a national platform (the Dementias Platform UK data portal).
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Affiliation(s)
- Melissa Clare O'Donoghue
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Jasmine Blane
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Grace Gillis
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | | | - Karen Lindsay
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Juliet Semple
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | | | - Ludovica Griffanti
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Jane Fossey
- Oxford Health NHS Foundation Trust, Oxford, UK
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Vanessa Raymont
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Lola Martos
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Clare E Mackay
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
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Park SW, Yeo NY, Kim Y, Byeon G, Jang JW. Deep learning application for the classification of Alzheimer's disease using 18F-flortaucipir (AV-1451) tau positron emission tomography. Sci Rep 2023; 13:8096. [PMID: 37208383 PMCID: PMC10198973 DOI: 10.1038/s41598-023-35389-w] [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: 12/13/2022] [Accepted: 05/17/2023] [Indexed: 05/21/2023] Open
Abstract
The positron emission tomography (PET) with 18F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively unimpaired (CU) individuals. This study aimed to evaluate the utility of 18F-flortaucipir-PET images and multimodal data integration in the differentiation of CU from MCI or AD through DL. We used cross-sectional data (18F-flortaucipir-PET images, demographic and neuropsychological score) from the ADNI. All data for subjects (138 CU, 75 MCI, 63 AD) were acquired at baseline. The 2D convolutional neural network (CNN)-long short-term memory (LSTM) and 3D CNN were conducted. Multimodal learning was conducted by adding the clinical data with imaging data. Transfer learning was performed for classification between CU and MCI. The AUC for AD classification from CU was 0.964 and 0.947 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.947, and 0.976 in multimodal learning. The AUC for MCI classification from CU had 0.840 and 0.923 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.845, and 0.850 in multimodal learning. The 18F-flortaucipir PET is effective for the classification of AD stage. Furthermore, the effect of combination images with clinical data increased the performance of AD classification.
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Affiliation(s)
- Sang Won Park
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- Department of Medical Informatics, Kangwon National University, Chuncheon, Republic of Korea
| | - Na Young Yeo
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- Department of Big Data Medical Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Gihwan Byeon
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
- Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Republic of Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea.
- Department of Medical Informatics, Kangwon National University, Chuncheon, Republic of Korea.
- Department of Big Data Medical Convergence, Kangwon National University, Chuncheon, Republic of Korea.
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.
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Xiao Y, Liao L, Huang K, Yao S, Gao L. Coupling Between Hippocampal Parenchymal Fraction and Cortical Grey Matter Atrophy at Different Stages of Cognitive Decline. J Alzheimers Dis 2023; 93:791-801. [PMID: 37092228 DOI: 10.3233/jad-230124] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
BACKGROUND Hippocampal atrophy is a significant brain marker of pathology in Alzheimer's disease (AD). The hippocampal parenchymal fraction (HPF) was recently developed to better assess the hippocampal volumetric integrity, and it has been shown to be a sensitive measure of hippocampal atrophy in AD. OBJECTIVE To investigate the clinical relevance of hippocampal volumetric integrity as measured by the HPF and the coupling between the HPF and brain atrophy during AD progression. METHODS We included data from 143 cognitively normal (CN), 101 mild cognitive impairment (MCI), and 125 AD participants. We examined group differences in the HPF, associations between HPF and cognitive ability, and coupling between the HPF and cortical grey matter volume in the CN, MCI, and AD groups. RESULTS We observed progressive decreases in HPF from CN to MCI and from MCI to AD, and increases in the asymmetry of HPF, with the lowest asymmetry index (AI) in the CN group and the highest AI in the AD group. There was a significant association between HPF and cognitive ability across participants. The coupling between HPF and cortical regions was observed in bilateral hippocampus, parahippocampal gyrus, temporal, frontal, and occipital regions, thalamus, and amygdala in CN, MCI, and AD groups, with a greater involvement of temporal, occipital, frontal, and subcortical regions in MCI and AD patients, especially in AD patients. CONCLUSION This study provides novel evidence for the neuroanatomical basis of cognitive decline and brain atrophy during AD progression, which may have important clinical implications for the prognosis of AD.
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Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Liangjun Liao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Kaiyu Huang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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10
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Baldaranov D, Garcia V, Miller G, Donohue MC, Shaw LM, Weiner M, Petersen RC, Aisen P, Raman R, Rafii MS. Safety and tolerability of lumbar puncture for the evaluation of Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12431. [PMID: 37091309 PMCID: PMC10113881 DOI: 10.1002/dad2.12431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 04/25/2023]
Abstract
Introduction Lumbar puncture (LP) to collect and examine cerebrospinal fluid (CSF) is an important option for the evaluation of Alzheimer's disease (AD) biomarkers but it is not routinely performed due to its invasiveness and link to adverse effects (AE). Methods We include all participants who received at least one LP in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Study. For comparison between groups, two-sample t-tests for continuous, and Pearson's chi-square test for categorical variables were performed. Results Two hundred twenty-seven LP-related AEs were reported by 172 participants after 1702 LPs (13.3%). The mean age of participants who reported at least one AE was 69.79 (standard deviation (SD) 6.3) versus none 72.44 (7.17) years (p < 0.001) with female predominance (115/172 = 67.4% vs 435/913 = 48%), and had greater entorhinal cortical thickness and hippocampal volume (3.903 (0.782) vs 3.684 (0.775) mm, p = 0.002; 7.38 (1.06) vs 7.05 (1.15) mm3, p < 0.001), respectively. Discussion We found that younger age, female sex, and greater thickness of the entorhinal cortex were associated with a higher rate of LP-related AE reports.
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Affiliation(s)
- Dobri Baldaranov
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Victoria Garcia
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Garrett Miller
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Michael C. Donohue
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Mike Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ronald C. Petersen
- Department of NeurologyMayo Clinic Alzheimer's Disease Research CenterMayo Clinic Study of AgingMayo Clinic Neurology and NeurosurgeryRochesterMinnesotaUSA
| | - Paul Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Rema Raman
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Michael S. Rafii
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
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Mieling M, Göttlich M, Yousuf M, Bunzeck N. Basal forebrain activity predicts functional degeneration in the entorhinal cortex and decreases with Alzheimer's Disease progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534523. [PMID: 37034733 PMCID: PMC10081194 DOI: 10.1101/2023.03.28.534523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Recent models of Alzheimer's Disease (AD) suggest the nucleus basalis of Meynert (NbM) as the origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear. METHODS We analyzed resting-state (rs)fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n=71) at baseline and two years later. RESULTS At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations (fALFF), differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio. DISCUSSION Our findings give novel insights into the pathogenesis of AD by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.
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Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Martin Göttlich
- Department of Neurology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Mushfa Yousuf
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
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12
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Kapadia A, Billimoria K, Desai P, Grist JT, Heyn C, Maralani P, Symons S, Zaccagna F. Hypoperfusion Precedes Tau Deposition in the Entorhinal Cortex: A Retrospective Evaluation of ADNI-2 Data. J Clin Neurol 2023; 19:131-137. [PMID: 36647226 PMCID: PMC9982189 DOI: 10.3988/jcn.2022.0088] [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: 02/24/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND AND PURPOSE Tau deposition in the entorhinal cortex is the earliest pathological feature of Alzheimer's disease (AD). However, this feature has also been observed in cognitively normal (CN) individuals and those with mild cognitive impairment (MCI). The precise pathophysiology for the development of tau deposition remains unclear. We hypothesized that reduced cerebral perfusion is associated with the development of tau deposition. METHODS A subset of the Alzheimer's Disease Neuroimaging Initiative data set was utilized. Included patients had undergone arterial spin labeling perfusion MRI along with [18F]flortaucipir tau PET at baseline, within 1 year of the MRI, and a follow-up at 6 years. The association between baseline cerebral blood flow (CBF) and the baseline and 6-year tau PET was assessed. Univariate and multivariate linear modeling was performed, with p<0.05 indicating significance. RESULTS Significant differences were found in the CBF between patients with AD and MCI, and CN individuals in the left entorhinal cortex (p=0.013), but not in the right entorhinal cortex (p=0.076). The difference in maximum standardized uptake value ratio between 6 years and baseline was significantly and inversely associated with the baseline mean CBF (p=0.042, R²=0.54) in the left entorhinal cortex but not the right entorhinal cortex. Linear modeling demonstrated that CBF predicted 6-year tau deposition (p=0.015, R²=0.11). CONCLUSIONS The results of this study suggest that a reduction in CBF at the entorhinal cortex precedes tau deposition. Further work is needed to understand the mechanism underlying tau deposition in aging and disease.
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Affiliation(s)
- Anish Kapadia
- Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Division of Neuroradiology, Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
| | - Krish Billimoria
- MD Program, Temetry Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Prarthna Desai
- Department of Medicine, Maharaja Sayajirao University of Baroda, Vadodara, India
| | - James T. Grist
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK.,Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK.,Department of Radiology, Oxford University Hospitals Trust, Oxford, UK.,Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Chris Heyn
- Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Division of Neuroradiology, Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Pejman Maralani
- Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Division of Neuroradiology, Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Sean Symons
- Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Division of Neuroradiology, Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Fulvio Zaccagna
- Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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13
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Groechel RC, Tripodis Y, Alosco ML, Mez J, Qiu WQ, Mercier G, Goldstein L, Budson AE, Kowall N, Killiany RJ. Annualized changes in rate of amyloid deposition and neurodegeneration are greater in participants who become amyloid positive than those who remain amyloid negative. Neurobiol Aging 2023; 127:33-42. [PMID: 37043881 DOI: 10.1016/j.neurobiolaging.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 03/01/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
This study longitudinally examined participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent a conversion in amyloid-beta (Aβ) status in comparison to a group of ADNI participants who did not show a change in amyloid status over the same follow-up period. Participants included 136 ADNI dementia-free participants with 2 florbetapir positron emission tomography (PET) scans. Of these participants, 68 showed amyloid conversion as measured on florbetapir PET, and the other 68 did not. Amyloid converters and non-converters were chosen to have representative demographic data (age, education, sex, diagnostic status, and race). The amyloid converter group showed increased prevalence of APOE ε4 (p < 0.001), greater annualized percent volume loss in selected magnetic resonance imaging (MRI) regions (p < 0.05), lower cerebrospinal fluid Aβ1-42 (p < 0.001), and greater amyloid retention (as measured by standard uptake value ratios) on florbetapir PET scans (p < 0.001) in comparison to the non-converter group. These results provide compelling evidence that important neuropathological changes are occurring alongside amyloid conversion.
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14
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Carcagnì P, Leo M, Del Coco M, Distante C, De Salve A. Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI. SENSORS (BASEL, SWITZERLAND) 2023; 23:1694. [PMID: 36772733 PMCID: PMC9919436 DOI: 10.3390/s23031694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/28/2022] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive impairment. The aim of this work is to improve the automatic detection of dementia in MRI brain data. For this purpose, we used an established pipeline that includes the registration, slicing, and classification steps. The contribution of this research was to investigate for the first time, to our knowledge, three current and promising deep convolutional models (ResNet, DenseNet, and EfficientNet) and two transformer-based architectures (MAE and DeiT) for mapping input images to clinical diagnosis. To allow a fair comparison, the experiments were performed on two publicly available datasets (ADNI and OASIS) using multiple benchmarks obtained by changing the number of slices per subject extracted from the available 3D voxels. The experiments showed that very deep ResNet and DenseNet models performed better than the shallow ResNet and VGG versions tested in the literature. It was also found that transformer architectures, and DeiT in particular, produced the best classification results and were more robust to the noise added by increasing the number of slices. A significant improvement in accuracy (up to 7%) was achieved compared to the leading state-of-the-art approaches, paving the way for the use of CAD approaches in real-world applications.
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15
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Dole L, Schilling KG, Kang H, Gore JC, Landman BA. Harmonization of repetition time and scanner effects on estimates of brain hemodynamic response function. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12464:124640X. [PMID: 37465094 PMCID: PMC10353821 DOI: 10.1117/12.2653903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Multisite contributions are essential to improve the reliability and statistical power of imaging studies but introduce a complexity because of different acquisition protocols and scanners. The hemodynamic response function (HRF) is the transform that relates neural activity to the measured blood oxygenation level-dependent (BOLD) signal in MRI and contains information about the latency, amplitude, and duration of neuronal activations. Acquisition variabilities, without adding harmonization techniques, can severely limit our ability to characterize spatial effects. To address this problem, we propose to study and remove variabilities of the sampling rate and scanners on estimates of the HRF. We computed the HRF using a blind deconvolution method in 547 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) across 62 sites and 18 scanners. The approach consists of studying the changes of the response according to repetition times (TR) and scanner models. We applied ComBAT, a statistical multi-site harmonization technique, to evaluate and reduce the scanner and repetition time effects and used the Wilcoxon rank sum test to assess the performance of the harmonization. Results show high scanner and repetition time variabilities (|d| ≥ 0.38, p = 4.5 × 10-5) across features, indicating that using harmonization is crucial in multi-site studies. ComBAT successfully removes the sampling effects and reduces the variance between scanners for 7 out of 10 of the HRF features (|d| ≤ 0.05, p = 0.0052). Scanners effects have been characterized on multi-site datasets, but the repetition time impact has been less studied. We showed that the use of different values of repetition time leads to changes in HRF behavior. Regression modeling changes in the HRF on the harmonized data are not significant (p = 0.0401) which does not allow to conclude how HRF changes with aging.
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Affiliation(s)
- Lucie Dole
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
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Knechtl P, Lehrner J. Visuoconstructional Abilities of Patients With Subjective Cognitive Decline, Mild Cognitive Impairment and Alzheimer's Disease. J Geriatr Psychiatry Neurol 2023:8919887221135549. [PMID: 36630660 DOI: 10.1177/08919887221135549] [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] [Indexed: 01/13/2023]
Abstract
BACKGROUND Via the Vienna Visuoconstructional Test 3.0 (VVT 3.0) delayed recall we combined the assessment of visuoconstructive abilities and memory and investigated the test's potential to support diagnostic processes, including staging and the elaboration of a cognitive profile. METHODS We retrospectively analysed the data of 368 patients of the Department of Neurology, Medical University of Vienna, between 04/2014 and 10/2020 that had performed the VVT 3.0. Our sample involved 70 healthy controls (HC), 29 patients with subjective cognitive decline (SCD), 154 patients with mild cognitive impairment (MCI) and 115 patients with Alzheimer's disease (AD). We investigated the differences in the VVT 3.0 scores, as well as the VVT's ability to differentiate between AD and nonAD by calculating receiver-operating-characteristic (ROC) curves, ideal cut-offs and a logistic regression model. RESULTS Results stated that the VVT 3.0 delayed recall scores were able to differentiate between all diagnostic groups, respectively, except HC-SCD and SCD-MCI. The ROC analyses determined an AUC of 0.890, 95% CI [0.855; 0.925], P < .001, and the ideal cut-off at 29.5 points that maximised sensitivity at 0.896 and specificity at 0.81. The logistic regression model classified 83.4% of AD patients correctly and delivered a significant Cohen's Kappa of 0.619 (P < .001). CONCLUSION As the VVT 3.0 revealed satisfactory values of diagnostic accuracy in our sample, it could enrich clinical diagnosing. However, for more clarity about its informative value in other populations, there remains a need for future studies with other samples.
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Affiliation(s)
- Paula Knechtl
- Department of Neurology, 27271Medical University of Vienna, Wien, Austria
| | - Johann Lehrner
- Department of Neurology, 27271Medical University of Vienna, Wien, Austria
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17
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Xiao Y, Wang J, Huang K, Gao L, Yao S. Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease. Front Aging Neurosci 2023; 14:1064667. [PMID: 36688148 PMCID: PMC9853893 DOI: 10.3389/fnagi.2022.1064667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
Background Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients. Objective This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression. Materials We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD. Results The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.
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Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Kaiyu Huang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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18
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Llano DA, Devanarayan P, Devanarayan V. CSF peptides from VGF and other markers enhance prediction of MCI to AD progression using the ATN framework. Neurobiol Aging 2023; 121:15-27. [PMID: 36368195 DOI: 10.1016/j.neurobiolaging.2022.07.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/15/2022] [Accepted: 07/23/2022] [Indexed: 12/14/2022]
Abstract
The amyloid beta, tau, neurodegenerative markers framework has been proposed to serve as a system to classify and combine biomarkers for Alzheimer's Disease (AD). Although cerebrospinal (CSF) fluid AT (amyloid beta and tau)-based biomarkers have a well-established track record to distinguish AD from control subjects and to predict conversion from mild cognitive impairment (MCI) to AD, there is not an established non-tau based neurodegenerative ("N") marker from CSF. Here, we examine the ability of several candidate peptides in the CSF to serve as "N" markers to both classify disease state and predict MCI to AD conversion. We observed that although many putative N markers involved in synaptic processing and neuroinflammation were able to, when examined in isolation, distinguish MCI converters from non-converters, a derivative from VGF, when combined with AT markers, most strongly enhanced prediction of MCI to AD conversion. Low CSF VGF levels were also predictive of MCI to dementia conversion in the setting of normal AT markers, suggesting that it may serve as a very early predictor of dementia conversion. Other markers derived from neuronal pentraxin 2, GAP-43 and a 14-3-3 protein were also able to enhance MCI to AD prediction when used as a marker of neurodegeneration, but VGF had the highest predictive capacity. Thus, we propose that low levels of VGF in CSF may serve as "N" in the amyloid beta, tau, neurodegenerative markers framework to enhance the prediction of MCI to AD conversion.
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Affiliation(s)
- Daniel A Llano
- Department of Biomedical and Translational Sciences, Carle Illinois College of Medicine, Urbana, IL, USA; Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA; Beckman Institute for Advanced Science and Technology, Urbana, IL, USA; Carle Neuroscience Institute, Urbana, IL, USA.
| | - Priya Devanarayan
- Department of Biology and Schreyer Honors College, Pennsylvania State University, University Park, PA, USA
| | - Viswanath Devanarayan
- Eisai, Inc., Nutley, NJ, USA; Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL, USA
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Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci 2022. [PMID: 36310103 DOI: 10.1111/ejn.15854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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Qin Y, Cui J, Ge X, Tian Y, Han H, Fan Z, Liu L, Luo Y, Yu H. Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features. Front Aging Neurosci 2022; 14:935055. [PMID: 36034132 PMCID: PMC9399682 DOI: 10.3389/fnagi.2022.935055] [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: 05/03/2022] [Accepted: 07/22/2022] [Indexed: 11/13/2022] Open
Abstract
Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.
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Affiliation(s)
- Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yuling Tian
- Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Hongjuan Han
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Zhao Fan
- Center of Translational Medicine, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yanhong Luo
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
- *Correspondence: Hongmei Yu,
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Pyun J, Park YH, Hodges A, Jang J, Bice PJ, Kim S, Saykin AJ, Nho K. Immunity gene IFITM3 variant: Relation to cognition and Alzheimer's disease pathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12317. [PMID: 35769874 PMCID: PMC9212215 DOI: 10.1002/dad2.12317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 01/29/2023]
Abstract
Introduction We investigated single-nucleotide polymorphisms (SNPs) in IFITM3, an innate immunity gene and modulator of amyloid beta in Alzheimer's disease (AD), for association with cognition and AD biomarkers. Methods We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1565) and AddNeuroMed (N = 633) as discovery and replication samples, respectively. We performed gene-based association analysis of SNPs in IFITM3 with cognitive performance and SNP-based association analysis with cognitive decline and amyloid, tau, and neurodegeneration biomarkers for AD. Results Gene-based association analysis showed that IFITM3 was significantly associated with cognitive performance. Particularly, rs10751647 in IFITM3 was associated with less cognitive decline, less amyloid and tau burden, and less brain atrophy in ADNI. The association of rs10751647 with cognitive decline and brain atrophy was replicated in AddNeuroMed. Discussion This suggests that rs10751647 in IFITM3 is associated with less vulnerability for cognitive decline and AD biomarkers, providing mechanistic insight regarding involvement of immunity and infection in AD. Highlights IFITM3 is significantly associated with cognitive performance.rs10751647 in IFITM3 is associated with cognitive decline rates with replication.rs10751647 is associated with amyloid beta load, cerebrospinal fluid phosphorylated tau levels, and brain atrophy.rs10751647 is associated with IFITM3 expression levels in blood and brain.rs10751647 in IFITM3 is related to less vulnerability to Alzheimer's disease pathogenesis.
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Affiliation(s)
- Jung‐Min Pyun
- Department of NeurologySeoul National University Bundang Hospital and Seoul National University College of MedicineSeongnamRepublic of Korea
- Department of NeurologySoonchunhyang University Seoul HospitalSoonchunhyang University College of MedicineSeoulRepublic of Korea
| | - Young Ho Park
- Department of NeurologySeoul National University Bundang Hospital and Seoul National University College of MedicineSeongnamRepublic of Korea
| | - Angela Hodges
- Institute of PsychiatryPsychology & NeuroscienceKing's College LondonLondonUK
| | - Jae‐Won Jang
- Department of NeurologyKangwon National University HospitalChuncheonRepublic of Korea
| | - Paula J. Bice
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - SangYun Kim
- Department of NeurologySeoul National University Bundang Hospital and Seoul National University College of MedicineSeongnamRepublic of Korea
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
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22
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Zamani J, Sadr A, Javadi AH. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative. PLoS One 2022; 17:e0267608. [PMID: 35727837 PMCID: PMC9212187 DOI: 10.1371/journal.pone.0267608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, United Kingdom
- School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
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Chen D, Yi F, Qin Y, Zhang J, Ge X, Han H, Cui J, Bai W, Wu Y, Yu H. A Stacking Framework for Multi-Classification of Alzheimer’s Disease Using Neuroimaging and Clinical Features. J Alzheimers Dis 2022; 87:1627-1636. [DOI: 10.3233/jad-215654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease (AD) is a severe health problem. Challenges still remain in early diagnosis. Objective: The objective of this study was to build a Stacking framework for multi-classification of AD by a combination of neuroimaging and clinical features to improve the performance. Methods: The data we used were from the Alzheimer’s Disease Neuroimaging Initiative database with a total of 493 subjects, including 125 normal control (NC), 121 early mild cognitive impairment, 109 late mild cognitive impairment (LMCI), and 138 AD. We selected structural magnetic resonance imaging (sMRI) features by voting strategy. The imaging features, demographic information, Mini-Mental State Examination, and Alzheimer’s Disease Assessment Scale-Cognitive Subscale were combined together as classification features. We proposed a two-layer Stacking ensemble framework to classify four types of people. The first layer represented support vector machine, random forests, adaptive boosting, and gradient boosting decision tree; the second layer was a logistic regression classifier. Additionally, we analyzed performance of only sMRI feature and combined features and compared the proposed model with four base classifiers. Results: The Stacking model combined with sMRI and non-imaging features outshined four base classifiers with an average accuracy of 86.96% . Compared with using sMRI data alone, sMRI combined with non-imaging features significantly improved diagnostic accuracy, especially in NC versus LMCI and LMCI versus AD by 14.08% . Conclusion: The Stacking framework we used can improve performance in diagnosis of AD using combined features.
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Affiliation(s)
- Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Fuliang Yi
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jiajia Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongjuan Han
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Wenlin Bai
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yan Wu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
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25
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Social isolation reinforces aging-related behavioral inflexibility by promoting neuronal necroptosis in basolateral amygdala. Mol Psychiatry 2022; 27:4050-4063. [PMID: 35840795 PMCID: PMC9284973 DOI: 10.1038/s41380-022-01694-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 02/07/2023]
Abstract
Aging is characterized with a progressive decline in many cognitive functions, including behavioral flexibility, an important ability to respond appropriately to changing environmental contingencies. However, the underlying mechanisms of impaired behavioral flexibility in aging are not clear. In this study, we reported that necroptosis-induced reduction of neuronal activity in the basolateral amygdala (BLA) plays an important role in behavioral inflexibility in 5-month-old mice of the senescence-accelerated mice prone-8 (SAMP8) line, a well-established model with age-related phenotypes. Application of Nec-1s, a specific inhibitor of necroptosis, reversed the impairment of behavioral flexibility in SAMP8 mice. We further observed that the loss of glycogen synthase kinase 3α (GSK-3α) was strongly correlated with necroptosis in the BLA of aged mice and the amygdala of aged cynomolgus monkeys (Macaca fascicularis). Moreover, genetic deletion or knockdown of GSK-3α led to the activation of necroptosis and impaired behavioral flexibility in wild-type mice, while the restoration of GSK-3α expression in the BLA arrested necroptosis and behavioral inflexibility in aged mice. We further observed that GSK-3α loss resulted in the activation of mTORC1 signaling to promote RIPK3-dependent necroptosis. Importantly, we discovered that social isolation, a prevalent phenomenon in aged people, facilitated necroptosis and behavioral inflexibility in 4-month-old SAMP8 mice. Overall, our study not only revealed the molecular mechanisms of the dysfunction of behavioral flexibility in aged people but also identified a critical lifestyle risk factor and a possible intervention strategy.
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Scharre DW, Chang SI, Nagaraja HN, Wheeler NC, Kataki M. Self-Administered Gerocognitive Examination: longitudinal cohort testing for the early detection of dementia conversion. Alzheimers Res Ther 2021; 13:192. [PMID: 34872596 PMCID: PMC8650250 DOI: 10.1186/s13195-021-00930-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/02/2021] [Indexed: 12/21/2022]
Abstract
Background Significant cognitive changes as individuals’ age are not being identified in a timely manner, delaying diagnosis and treatments. Use of brief, multi-domain, self-administered, objective cognitive assessment tools may remove some barriers in assessing and identifying cognitive changes. We compared longitudinal Self-Administered Gerocognitive Examination (SAGE) test scores to non-self-administered Mini-Mental State Examination (MMSE) scores in 5 different diagnostic subgroups. Methods A cohort study evaluating annual rates of change was performed on 665 consecutive patients from Ohio State University Memory Disorders Clinic. Patients with at least two visits 6 months apart evaluated with SAGE and MMSE and classified according to standard clinical criteria as subjective cognitive decline (SCD), mild cognitive impairment (MCI), or Alzheimer’s disease (AD) dementia were included. The pattern of change in SAGE scores was compared to MMSE. One way and repeated measures ANOVA and linear regression models were used. Results Four hundred twenty-four individuals (40 SCD, 94 MCI non-converters to dementia, 70 MCI converters to dementia (49 to AD dementia and 21 to non-AD dementia), 220 AD dementia) met inclusion criteria. SAGE and MMSE scores declined respectively at annual rates of 1.91 points/year (p < 0.0001) and 1.68 points/year (p < 0.0001) for MCI converters to AD dementia, and 1.82 points/year (p < 0.0001) and 2.38 points/year (p < 0.0001) for AD dementia subjects. SAGE and MMSE scores remained stable for SCD and MCI non-converters. Statistically significant decline from baseline scores in SAGE occurred at least 6 months earlier than MMSE for MCI converters to AD dementia (14.4 vs. 20.4 months), MCI converters to non-AD dementia (14.4 vs. 32.9 months), and AD dementia individuals (8.3 vs. 14.4 months). Conclusions SAGE detects MCI conversion to dementia at least 6 months sooner than MMSE. Being self-administered, SAGE also addresses a critical need of removing some barriers in performing cognitive assessments. Limitations of our single-site cohort study include potential referral and sampling biases. Repetitively administering SAGE and identifying stability or decline may provide clinicians with an objective cognitive biomarker impacting evaluation and management choices.
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Affiliation(s)
- Douglas W Scharre
- Division of Cognitive Neurology, Department of Neurology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., 7th Floor, Columbus, OH, 43210, USA.
| | - Shu Ing Chang
- Division of Cognitive Neurology, Department of Neurology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., 7th Floor, Columbus, OH, 43210, USA
| | - Haikady N Nagaraja
- Division of Biostatistics, College of Public Health, The Ohio State University, Cunz Hall, Columbus, OH, 43210, USA
| | - Natalie C Wheeler
- Division of Cognitive Neurology, Department of Neurology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., 7th Floor, Columbus, OH, 43210, USA.,Present Address: Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Maria Kataki
- Division of Cognitive Neurology, Department of Neurology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., 7th Floor, Columbus, OH, 43210, USA
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27
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Park YH, Pyun JM, Hodges A, Jang JW, Bice PJ, Kim S, Saykin AJ, Nho K. Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-β deposition in Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2021; 13:183. [PMID: 34732252 PMCID: PMC8567578 DOI: 10.1186/s13195-021-00919-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 10/18/2021] [Indexed: 11/10/2022]
Abstract
Background The interaction between the brain and periphery might play a crucial role in the development of Alzheimer’s disease (AD). Methods Using blood transcriptomic profile data from two independent AD cohorts, we performed expression quantitative trait locus (cis-eQTL) analysis of 29 significant genetic loci from a recent large-scale genome-wide association study to investigate the effects of the AD genetic variants on gene expression levels and identify their potential target genes. We then performed differential gene expression analysis of identified AD target genes and linear regression analysis to evaluate the association of differentially expressed genes with neuroimaging biomarkers. Results A cis-eQTL analysis identified and replicated significant associations in seven genes (APH1B, BIN1, FCER1G, GATS, MS4A6A, RABEP1, TRIM4). APH1B expression levels in the blood increased in AD and were associated with entorhinal cortical thickness and global cortical amyloid-β deposition. Conclusion An integrative analysis of genetics, blood-based transcriptomic profiles, and imaging biomarkers suggests that APH1B expression levels in the blood might play a role in the pathogenesis of AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00919-z.
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Affiliation(s)
- Young Ho Park
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jung-Min Pyun
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Republic of Korea
| | - Angela Hodges
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea
| | - Paula J Bice
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease Center, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease Center, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease Center, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA. .,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
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Zang F, Zhu Y, Zhang Q, Tan C, Wang Q, Xie C. APOE genotype moderates the relationship between LRP1 polymorphism and cognition across the Alzheimer's disease spectrum via disturbing default mode network. CNS Neurosci Ther 2021; 27:1385-1395. [PMID: 34387022 PMCID: PMC8504518 DOI: 10.1111/cns.13716] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022] Open
Abstract
AIMS This study aims to investigate the mechanisms by which apolipoprotein E (APOE) genotype modulates the relationship between low-density lipoprotein receptor-related protein 1 (LRP1) rs1799986 variant on the default mode network (DMN) and cognition in Alzheimer's disease (AD) spectrum populations. METHODS Cross-sectional 168 subjects of AD spectrum were obtained from Alzheimer's Disease Neuroimaging Initiative database with resting-state fMRI scans and neuropsychological scores data. Multivariable linear regression analysis was adopted to investigate the main effects and interaction of LRP1 and disease on the DMN. Moderation and interactive analyses were performed to assess the relationships among APOE, LRP1, and cognition. A support vector machine model was used to classify AD spectrum with altered connectivity as an objective diagnostic biomarker. RESULTS The main effects and interaction of LRP1 and disease were mainly focused on the core hubs of frontal-parietal network. Several brain regions with altered connectivity were correlated with cognitive scores in LRP1-T carriers, but not in non-carriers. APOE regulated the effect of LRP1 on cognitive performance. The functional connectivity of numerous brain regions within LRP1-T carriers yielded strong power for classifying AD spectrum. CONCLUSION These findings suggested LRP1 could affect DMN and provided a stage-dependent neuroimaging biomarker for classifying AD spectrum populations.
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Affiliation(s)
- Feifei Zang
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Yao Zhu
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Qianqian Zhang
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Chang Tan
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Qing Wang
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Chunming Xie
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
- Neuropsychiatric InstituteAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
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Pyun JM, Park YH, Lee KJ, Kim S, Saykin AJ, Nho K. Predictability of polygenic risk score for progression to dementia and its interaction with APOE ε4 in mild cognitive impairment. Transl Neurodegener 2021; 10:32. [PMID: 34465370 PMCID: PMC8406896 DOI: 10.1186/s40035-021-00259-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/14/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The combinatorial effect of multiple genetic factors calculated as a polygenic risk score (PRS) has been studied to predict disease progression to Alzheimer's disease (AD) from mild cognitive impairment (MCI). Previous studies have investigated the performance of PRS in the prediction of disease progression to AD by including and excluding single nucleotide polymorphisms within the region surrounding the APOE gene. These studies may have missed the APOE genotype-specific predictability of PRS for disease progression to AD. METHODS We analyzed 732 MCI from the Alzheimer's Disease Neuroimaging Initiative cohort, including those who progressed to AD within 5 years post-baseline (n = 270) and remained stable as MCI (n = 462). The predictability of PRS including and excluding the APOE region (PRS+APOE and PRS-APOE) on the conversion to AD and its interaction with the APOE ε4 carrier status were assessed using Cox regression analyses. RESULTS PRS+APOE (hazard ratio [HR] 1.468, 95% CI 1.335-1.615) and PRS-APOE (HR 1.293, 95% CI 1.157-1.445) were both associated with a significantly increased risk of MCI progression to dementia. The interaction between PRS+APOE and APOE ε4 carrier status was significant with a P-value of 0.0378. The association of PRSs with the progression risk was stronger in APOE ε4 non-carriers (PRS+APOE: HR 1.710, 95% CI 1.244-2.351; PRS-APOE: HR 1.429, 95% CI 1.182-1.728) than in APOE ε4 carriers (PRS+APOE: HR 1.167, 95% CI 1.005-1.355; PRS-APOE: HR 1.172, 95% CI 1.020-1.346). CONCLUSIONS PRS could predict the conversion of MCI to dementia with a stronger association in APOE ε4 non-carriers than APOE ε4 carriers. This indicates PRS as a potential genetic predictor particularly for MCI with no APOE ε4 alleles.
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Affiliation(s)
- Jung-Min Pyun
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Republic of Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea.
| | - Keon-Joo Lee
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
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Risacher SL, West JD, Deardorff R, Gao S, Farlow MR, Brosch JR, Apostolova LG, McAllister TW, Wu Y, Jagust WJ, Landau SM, Weiner MW, Saykin AJ. Head injury is associated with tau deposition on PET in MCI and AD patients. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12230. [PMID: 34466653 PMCID: PMC8383323 DOI: 10.1002/dad2.12230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/04/2021] [Indexed: 01/20/2023]
Abstract
INTRODUCTION Head injuries (HI) are a risk factor for dementia, but the underlying etiology is not fully known. Understanding whether tau might mediate this relationship is important. METHODS Cognition and tau deposition were compared between 752 individuals with (impaired, n = 302) or without cognitive impairment (CN, n = 450) with amyloid and [18F]flortaucipir positron emission tomography, HI history information, and cognitive testing from the Alzheimer's Disease Neuroimaging Initiative and the Indiana Memory and Aging Study. RESULTS Sixty-three (38 CN, 25 impaired) reported a history of HI. Higher neuropsychiatric scores and poorer memory were observed in those with a history of HI. Tau was higher in individuals with a history of HI, especially those who experienced a loss of consciousness (LOC). Results were driven by impaired individuals, especially amyloid beta-positive individuals with history of HI with LOC. DISCUSSION These findings suggest biological changes, such as greater tau, are associated with HI in individuals with cognitive impairment. Small effect sizes were observed; thus, further studies should replicate and extend these results.
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Affiliation(s)
- Shannon L. Risacher
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - John D. West
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachael Deardorff
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of BiostatisticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Martin R. Farlow
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Jared R. Brosch
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Liana G. Apostolova
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Thomas W. McAllister
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | - Yu‐Chien Wu
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - William J. Jagust
- Helen Wills Neuroscience InstituteUniversity of California, BerkeleyBerkeleyCaliforniaUSA
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California, BerkeleyBerkeleyCaliforniaUSA
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Michael W. Weiner
- Departments of RadiologyMedicine and PsychiatryUniversity of California‐San FranciscoSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
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Prakash J, Wang V, Quinn RE, Mitchell CS. Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations. Brain Sci 2021; 11:977. [PMID: 34439596 PMCID: PMC8392842 DOI: 10.3390/brainsci11080977] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/10/2021] [Accepted: 07/20/2021] [Indexed: 11/20/2022] Open
Abstract
Heterogeneity among Alzheimer's disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [-4.6, +3.8] and cluster-3 [+10.8, -4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [-18.4, -8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population "clusters" using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis.
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Affiliation(s)
- Jayant Prakash
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
- Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Velda Wang
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
| | - Robert E. Quinn
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
- Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Cassie S. Mitchell
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Dage JL, Stomrud E, Janelidze S, Mattsson-Carlgren N, Hansson O. Prediction of future Alzheimer's disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med 2021; 27:1034-1042. [PMID: 34031605 DOI: 10.1038/s41591-021-01348-z] [Citation(s) in RCA: 204] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/12/2021] [Indexed: 02/04/2023]
Abstract
A combination of plasma phospho-tau (P-tau) and other accessible biomarkers might provide accurate prediction about the risk of developing Alzheimer's disease (AD) dementia. We examined this in participants with subjective cognitive decline and mild cognitive impairment from the BioFINDER (n = 340) and Alzheimer's Disease Neuroimaging Initiative (ADNI) (n = 543) studies. Plasma P-tau, plasma Aβ42/Aβ40, plasma neurofilament light, APOE genotype, brief cognitive tests and an AD-specific magnetic resonance imaging measure were examined using progression to AD as outcome. Within 4 years, plasma P-tau217 predicted AD accurately (area under the curve (AUC) = 0.83) in BioFINDER. Combining plasma P-tau217, memory, executive function and APOE produced higher accuracy (AUC = 0.91, P < 0.001). In ADNI, this model had similar AUC (0.90) using plasma P-tau181 instead of P-tau217. The model was implemented online for prediction of the individual probability of progressing to AD. Within 2 and 6 years, similar models had AUCs of 0.90-0.91 in both cohorts. Using cerebrospinal fluid P-tau, Aβ42/Aβ40 and neurofilament light instead of plasma biomarkers did not improve the accuracy significantly. The clinical predictions by memory clinic physicians had significantly lower accuracy (4-year AUC = 0.71). In summary, plasma P-tau, in combination with brief cognitive tests and APOE genotyping, might greatly improve the diagnostic prediction of AD and facilitate recruitment for AD trials.
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Affiliation(s)
- Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden. .,Memory Clinic, Skåne University Hospital, Malmö, Sweden.
| | - Pontus Tideman
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicholas Cullen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | | | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden. .,Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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Gibicar A, Moody AR, Khademi A. Automated Midline Estimation for Symmetry Analysis of Cerebral Hemispheres in FLAIR MRI. Front Aging Neurosci 2021; 13:644137. [PMID: 33994994 PMCID: PMC8118126 DOI: 10.3389/fnagi.2021.644137] [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: 01/13/2021] [Accepted: 03/24/2021] [Indexed: 01/09/2023] Open
Abstract
To perform brain asymmetry studies in large neuroimaging archives, reliable and automatic detection of the interhemispheric fissure (IF) is needed to first extract the cerebral hemispheres. The detection of the IF is often referred to as mid-sagittal plane estimation, as this plane separates the two cerebral hemispheres. However, traditional planar estimation techniques fail when the IF presents a curvature caused by existing pathology or a natural phenomenon known as brain torque. As a result, midline estimates can be inaccurate. In this study, a fully unsupervised midline estimation technique is proposed that is comprised of three main stages: head angle correction, control point estimation and midline generation. The control points are estimated using a combination of intensity, texture, gradient, and symmetry-based features. As shown, the proposed method automatically adapts to IF curvature, is applied on a slice-to-slice basis for more accurate results and also provides accurate delineation of the midline in the septum pellucidum, which is a source of failure for traditional approaches. The method is compared to two state-of-the-art methods for midline estimation and is validated using 75 imaging volumes (~3,000 imaging slices) acquired from 38 centers of subjects with dementia and vascular disease. The proposed method yields the lowest average error across all metrics: Hausdorff distance (HD) was 0.32 ± 0.23, mean absolute difference (MAD) was 1.10 ± 0.38 mm and volume difference was 7.52 ± 5.40 and 5.35 ± 3.97 ml, for left and right hemispheres, respectively. Using the proposed method, the midline was extracted for 5,360 volumes (~275K images) from 83 centers worldwide, acquired by GE, Siemens and Philips scanners. An asymmetry index was proposed that automatically detected outlier segmentations (which were <1% of the total dataset). Using the extracted hemispheres, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and significant differences in biomarker means were found across SCI and MCI and SCI and AD.
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Affiliation(s)
- Adam Gibicar
- Electrical, Computer and Biomedical Engineering Department, Ryerson University, Toronto, ON, Canada
| | - Alan R Moody
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - April Khademi
- Electrical, Computer and Biomedical Engineering Department, Ryerson University, Toronto, ON, Canada.,Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, Toronto, ON, Canada.,Institute for Biomedical Engineering, Science and Technology, A Partnership Between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada
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Disease progression modelling from preclinical Alzheimer's disease (AD) to AD dementia. Sci Rep 2021; 11:4168. [PMID: 33603015 PMCID: PMC7893024 DOI: 10.1038/s41598-021-83585-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 01/29/2021] [Indexed: 11/26/2022] Open
Abstract
To characterize the course of Alzheimer’s disease (AD) over a longer time interval, we aimed to construct a disease course model for the entire span of the disease using two separate cohorts ranging from preclinical AD to AD dementia. We modelled the progression course of 436 patients with AD continuum and investigated the effects of apolipoprotein E ε4 (APOE ε4) and sex on disease progression. To develop a model of progression from preclinical AD to AD dementia, we estimated Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS-cog 13) scores. When calculated as the median of ADAS-cog 13 scores for each cohort, the estimated time from preclinical AD to MCI due to AD was 7.8 years and preclinical AD to AD dementia was 15.2 years. ADAS-cog 13 scores deteriorated most rapidly in women APOE ε4 carriers and most slowly in men APOE ε4 non-carriers (p < 0.001). Our results suggest that disease progression modelling from preclinical AD to AD dementia may help clinicians to estimate where patients are in the disease course and provide information on variation in the disease course by sex and APOE ε4 status.
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Langella S, Sadiq MU, Mucha PJ, Giovanello KS, Dayan E. Lower functional hippocampal redundancy in mild cognitive impairment. Transl Psychiatry 2021; 11:61. [PMID: 33462184 PMCID: PMC7813821 DOI: 10.1038/s41398-020-01166-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 12/07/2020] [Accepted: 12/10/2020] [Indexed: 12/24/2022] Open
Abstract
With an increasing prevalence of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in response to an aging population, it is critical to identify and understand neuroprotective mechanisms against cognitive decline. One potential mechanism is redundancy: the existence of duplicate elements within a system that provide alternative functionality in case of failure. As the hippocampus is one of the earliest sites affected by AD pathology, we hypothesized that functional hippocampal redundancy is protective against cognitive decline. We compared hippocampal functional redundancy derived from resting-state functional MRI networks in cognitively normal older adults, with individuals with early and late MCI, as well as the relationship between redundancy and cognition. Posterior hippocampal redundancy was reduced between cognitively normal and MCI groups, plateauing across early and late MCI. Higher hippocampal redundancy was related to better memory performance only for cognitively normal individuals. Critically, functional hippocampal redundancy did not come at the expense of network efficiency. Our results provide support that hippocampal redundancy protects against cognitive decline in aging.
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Affiliation(s)
- Stephanie Langella
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Muhammad Usman Sadiq
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment. NEUROIMAGE: CLINICAL 2021; 32:102799. [PMID: 34469849 PMCID: PMC8405842 DOI: 10.1016/j.nicl.2021.102799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 07/23/2021] [Accepted: 08/17/2021] [Indexed: 11/23/2022] Open
Abstract
There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer's disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive ('Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aβ42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression.
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Alzheimer's disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline. Mol Psychiatry 2021; 26:5888-5898. [PMID: 34593971 PMCID: PMC8758489 DOI: 10.1038/s41380-021-01263-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/23/2021] [Accepted: 08/04/2021] [Indexed: 11/08/2022]
Abstract
For early detection of Alzheimer's disease, it is important to find biomarkers with predictive value for disease progression and clinical manifestations, such as cognitive decline. Individuals can now be profiled based on their biomarker status for Aβ42 (A) or tau (T) deposition and neurodegeneration (N). The aim of this study was to compare the cerebrospinal fluid (CSF) and imaging (PET/MR) biomarkers in each ATN category and to assess their ability to predict longitudinal cognitive decline. A subset of 282 patients, who had had at the same time PET investigations with amyloid-β and tau tracers, CSF sampling, and structural MRI (18% within 13 months), was selected from the ADNI dataset. The participants were grouped by clinical diagnosis at that time: cognitively normal, subjective memory concern, early or late mild cognitive impairment, or AD. Agreement between CSF (amyloid-β-1-42(A), phosphorylated-Tau181(T), total-Tau(N)), and imaging (amyloid-β PET (florbetaben and florbetapir)(A), tau PET (flortaucipir)(T), hippocampal volume (MRI)(N)) positivity in ATN was assessed with Cohen's Kappa. Linear mixed-effects models were used to predict decline in the episodic memory. There was moderate agreement between PET and CSF for A biomarkers (Kappa = 0.39-0.71), while only fair agreement for T biomarkers (Kappa ≤ 0.40, except AD) and discordance for N biomarkers across all groups (Kappa ≤ 0.14) was found. Baseline PET tau predicted longitudinal decline in episodic memory irrespective of CSF p-Tau181 positivity (p ≤ 0.02). Baseline PET tau and amyloid-β predicted decline in episodic memory (p ≤ 0.0001), but isolated PET amyloid-β did not. Isolated PET Tau positivity was only observed in 2 participants (0.71% of the sample). While results for amyloid-β were similar using CSF or imaging, CSF and imaging results for tau and neurodegeneration were not interchangeable. PET tau positivity was superior to CSF p-Tau181 and PET amyloid-β in predicting cognitive decline in the AD continuum within 3 years of follow-up.
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Zhu Y, Gong L, He C, Wang Q, Ren Q, Xie C. Default Mode Network Connectivity Moderates the Relationship Between the APOE Genotype and Cognition and Individualizes Identification Across the Alzheimer's Disease Spectrum. J Alzheimers Dis 2020; 70:843-860. [PMID: 31282419 DOI: 10.3233/jad-190254] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Although previous studies have investigated the effects of the apolipoprotein E (APOE) ɛ4 genotype on the default mode network (DMN) in the Alzheimer's disease (AD) spectrum, it is still unclear how the APOE genotype regulates the DMN and subsequently affects cognitive decline in the AD spectrum. One hundred sixty-nine subjects with resting-state functional magnetic resonance imaging data and neuropsychological test scores were selected from the Alzheimer's Disease Neuroimaging Initiative. The main effects and interaction of the APOE genotype and disease status on the DMN were explored. A moderation analysis was performed to investigate the relationship among the APOE genotype, DMN connectivity, and cognition. Additionally, the pair-wised DMN connectivity was used to classify AD spectrum, and the classification accuracy was validated. Compared to APOEɛ4 non-carriers, APOEɛ4 carriers showed the opposite trajectory of DMN connectivity across the AD spectrum. Specifically, the strengths in the posterior cingulate cortex (PCC) connecting with the right precuneus, insular, and fusiform area (FFA) were positively correlated with Mini-Mental State Examination (MMSE) scores in APOEɛ4 non-carriers but not in APOEɛ4 carriers. Furthermore, PCC-right FFA connectivity could moderate the effects of the APOE genotype on MMSE scores across the disease groups. More importantly, using a receiver operating characteristic analysis, these altered connectivities yielded strong classification powers in a pathological stage-dependent manner in the AD spectrum. These findings first identified the intrinsic DMN connectivity moderating the effect of the APOE genotype on cognition and provided a pathological stage-dependent neuroimaging biomarker for early differentiation of the AD spectrum.
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Affiliation(s)
- Yao Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Liang Gong
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Qingguo Ren
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
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Park YH, Hodges A, Simmons A, Lovestone S, Weiner MW, Kim S, Saykin AJ, Nho K. Association of blood-based transcriptional risk scores with biomarkers for Alzheimer disease. NEUROLOGY-GENETICS 2020; 6:e517. [PMID: 33134515 PMCID: PMC7577551 DOI: 10.1212/nxg.0000000000000517] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 08/24/2020] [Indexed: 12/20/2022]
Abstract
Objective To determine whether transcriptional risk scores (TRSs), a summation of polarized expression levels of functional genes, reflect the risk of Alzheimer disease (AD). Methods Blood transcriptome data were from Caucasian participants, which included AD, mild cognitive impairment, and cognitively normal controls (CN) in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 661) and AddNeuroMed (n = 674) cohorts. To calculate TRSs, we selected functional genes that were expressed under the control of the AD risk loci and were identified as being responsible for AD by using Bayesian colocalization and mendelian randomization methods. Regression was used to investigate the association of the TRS with diagnosis (AD vs CN) and MRI biomarkers (entorhinal thickness and hippocampal volume). Regression was also used to evaluate whether expression of each functional gene was associated with AD diagnosis. Results The TRS was significantly associated with AD diagnosis, hippocampal volume, and entorhinal cortical thickness in the ADNI. The association of the TRS with AD diagnosis and entorhinal cortical thickness was also replicated in AddNeuroMed. Among functional genes identified to calculate the TRS, CD33 and PILRA were significantly upregulated, and TRAPPC6A was significantly downregulated in patients with AD compared with CN, all of which were identified in the ADNI and replicated in AddNeuroMed. Conclusions The blood-based TRS is significantly associated with AD diagnosis and neuroimaging biomarkers. In blood, CD33 and PILRA were known to be associated with uptake of β-amyloid and herpes simplex virus 1 infection, respectively, both of which may play a role in the pathogenesis of AD. Classification of evidence The study is rated Class III because of the case control design and the risk of spectrum bias.
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Affiliation(s)
- Young Ho Park
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
| | - Angela Hodges
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
| | - Andrew Simmons
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
| | - Simon Lovestone
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
| | - Michael W Weiner
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
| | - SangYun Kim
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis
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Predicting Mental Decline Rates in Mild Cognitive Impairment From Baseline MRI Volumetric Data. Alzheimer Dis Assoc Disord 2020; 35:1-7. [PMID: 32925201 DOI: 10.1097/wad.0000000000000406] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/08/2020] [Indexed: 01/18/2023]
Abstract
PURPOSE In mild cognitive impairment (MCI), identifying individuals at high risk for progressive cognitive deterioration can be useful for prognostication and intervention. This study quantitatively characterizes cognitive decline rates in MCI and tests whether volumetric data from baseline magnetic resonance imaging (MRI) can predict accelerated cognitive decline. METHODS The authors retrospectively examined Alzheimer Disease Neuroimaging Initiative data to obtain serial Mini-Mental Status Exam (MMSE) scores, diagnoses, and the following baseline MRI volumes: total intracranial volume, whole-brain and ventricular volumes, and volumes of the hippocampus, entorhinal cortex, fusiform gyrus, and medial temporal lobe. Subjects with <24 months or <4 measurements of MMSE data were excluded. Predictive modeling of fast cognitive decline (defined as >0.6/year) from baseline volumetric data was performed on subjects with MCI using a single hidden layer neural network. RESULTS Among 698 baseline MCI subjects, the median annual decline in the MMSE score was 1.3 for converters to dementia versus 0.11 for stable MCI (P<0.001). A 0.6/year threshold captured dementia conversion with 82% accuracy (sensitivity 79%, specificity 85%, area under the receiver operating characteristic curve 0.88). Regional volumes on baseline MRI predicted fast cognitive decline with a test accuracy of 71%. DISCUSSION An MMSE score decrease of >0.6/year is associated with MCI-to-dementia conversion and can be predicted from baseline MRI.
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Computerized cognitive performance assessments in the Brooklyn Cognitive Impairments in Health Disparities Pilot Study. Alzheimers Dement 2020; 15:1420-1426. [PMID: 31753288 DOI: 10.1016/j.jalz.2019.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 07/01/2019] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Detecting cognitive impairment in diverse, health disparities communities is an urgent health care priority. METHODS The Brooklyn Cognitive Impairments in Health Disparities Pilot Study investigated quantitative aspects and liking of a computerized cognitive performance assessment, Cognigram, among individuals ≥ 40 years in traditional and nontraditional primary care settings. RESULTS Cognigram was piloted in the Emergency Department, Family Medicine, and Geriatric Psychiatry clinics: 58 adults (23 men, 35 women), 67.9 ± 9.8 years (range 43-91), completed the Cognigram and 5-item liking survey. The observed liking range was 2 to maximum score 5 (67% scored 4-5; no sex or age differences). DISCUSSION The Cognigram was well liked in waiting rooms of primary care settings. Assistance from a trained adult and clinic endorsement were keys to success. How the Cognigram performs in a geographically compact, population-dense global setting, such as Brooklyn with high vascular disease risk and a plethora of health disparities, is being tested.
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Brito LM, Ribeiro-dos-Santos Â, Vidal AF, de Araújo GS. Differential Expression and miRNA-Gene Interactions in Early and Late Mild Cognitive Impairment. BIOLOGY 2020; 9:biology9090251. [PMID: 32872134 PMCID: PMC7565463 DOI: 10.3390/biology9090251] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/21/2020] [Accepted: 06/24/2020] [Indexed: 12/19/2022]
Abstract
Mild cognitive impairment (MCI) and Alzheimer's Disease (AD) are complex diseases with their molecular architecture not elucidated. APOE, Amyloid Beta Precursor Protein (APP), and Presenilin-1 (PSEN1) are well-known genes associated with both MCI and AD. Recently, epigenetic alterations and dysregulated regulatory elements, such as microRNAs (miRNAs), have been reported associated with neurodegeneration. In this study, differential expression analysis (DEA) was performed for genes and miRNAs based on microarray and RNA-Seq data. Global gene profile of healthy individuals, early and late mild cognitive impairment (EMCI and LMCI, respectively), and AD was obtained from ADNI Cohort. miRNA global profile of healthy individuals and AD patients was extracted from public RNA-Seq data. DEA performed with limma package on ADNI Cohort data highlighted eight differential expressed (DE) genes (AGER, LINC00483, MMP19, CATSPER1, ARFGAP1, GPER1, PHLPP2, TRPM2) (false discovery rate (FDR) p-value < 0.05) between EMCI and LMCI patients. Previous molecular studies showed associations between these genes with dementia and neurological-related pathways. Five dysregulated miRNAs were identified by DEA performed with RNA-Seq data and edgeR (FDR p-value < 0.002). All reported miRNAs in AD interact with the aforementioned genes. Our integrative transcriptomic analysis was able to identify a set of miRNA-gene interactions that may be involved in cognitive and neurodegeneration processes.
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Affiliation(s)
- Leonardo Miranda Brito
- Laboratório de Genética Humana e Médica, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil; (L.M.B.); (Â.R.-d.-S.); (A.F.V.)
- Programa de Pós-Graduação em Genética e Biologia Molecular, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil
| | - Ândrea Ribeiro-dos-Santos
- Laboratório de Genética Humana e Médica, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil; (L.M.B.); (Â.R.-d.-S.); (A.F.V.)
- Programa de Pós-Graduação em Genética e Biologia Molecular, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil
| | - Amanda Ferreira Vidal
- Laboratório de Genética Humana e Médica, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil; (L.M.B.); (Â.R.-d.-S.); (A.F.V.)
- Programa de Pós-Graduação em Genética e Biologia Molecular, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil
| | - Gilderlanio Santana de Araújo
- Laboratório de Genética Humana e Médica, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil; (L.M.B.); (Â.R.-d.-S.); (A.F.V.)
- Programa de Pós-Graduação em Genética e Biologia Molecular, Instituto de Ciêncas Biológicas, Universidade Federal do Pará, Belém 66075-110, Brazil
- Correspondence:
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Li B, Zhang M, Riphagen J, Morrison Yochim K, Li B, Liu J, Salat DH. Prediction of clinical and biomarker conformed Alzheimer's disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample. Neuroimage Clin 2020; 28:102387. [PMID: 32871388 PMCID: PMC7476071 DOI: 10.1016/j.nicl.2020.102387] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/13/2020] [Accepted: 08/14/2020] [Indexed: 02/06/2023]
Abstract
Structural neuroimaging has been applied to the identification of individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, these methods are greatly impacted by age limiting their utility for detection of preclinical pathology. We built linear models for age based on multiple combined structural features using a large independent lifespan sample of 272 healthy adults across a wide age range from the Human Connectome Project Aging study. These models were then used to create a new support vector machine (SVM) training model in 136 AD and 268 control participants based on residues of fit from the expected age-effects relationship. Subsequent validation assessed the accuracy of the SVM model in new datasets. Finally, we applied the classifier to 276 individuals with MCI to evaluate prediction for early impairment and longitudinal cognitive change. The optimal 10-fold cross-validation accuracy was 93.07%, compared to 91.83% without age detrending. In the validation dataset, the classifier for AD obtained an accuracy of 84.85% (56/66), sensitivity of 85.36% (35/41) and specificity of 84% (21/25). Classification accuracy was improved when using the lifespan sample as opposed to the classification sample. Importantly, we observed cross-sectional greater AD specific biomarkers, as well as faster cognitive decline in MCI who were classified as more 'AD-like' (MCI-AD), and these effects were pronounced in individuals who were late MCI. The top five contributive features were volumes of left hippocampus, right hippocampus, left amygdala, the thickness of left and right middle temporal & parahippocampus gyrus. Linear detrending for age in SVM for combined structural features resulted in good performance for recognition of AD and AD-specific biomarkers, as well as prediction of MCI progression. Such procedures may be used in future work to enhance prediction in samples with atypical age distributions.
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Affiliation(s)
- Binyin Li
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Neurology, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Joost Riphagen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Kathryn Morrison Yochim
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jun Liu
- Department of Neurology, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
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Wang M, Lian C, Yao D, Zhang D, Liu M, Shen D. Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network. IEEE Trans Biomed Eng 2020; 67:2241-2252. [PMID: 31825859 PMCID: PMC7439279 DOI: 10.1109/tbme.2019.2957921] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate the proposed method on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with results suggesting the effectiveness of our method in both tasks of disease progression prediction and AD-related hub detection.
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Affiliation(s)
- Mingliang Wang
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Chunfeng Lian
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Dongren Yao
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Daoqiang Zhang
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Mingxia Liu
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Dinggang Shen
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Moore EE, Liu D, Pechman KR, Acosta LMY, Bell SP, Davis LT, Blennow K, Zetterberg H, Landman BA, Schrag MS, Hohman TJ, Gifford KA, Jefferson AL. Mild Cognitive Impairment Staging Yields Genetic Susceptibility, Biomarker, and Neuroimaging Differences. Front Aging Neurosci 2020; 12:139. [PMID: 32581762 PMCID: PMC7289958 DOI: 10.3389/fnagi.2020.00139] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 04/27/2020] [Indexed: 01/22/2023] Open
Abstract
Introduction While Alzheimer’s disease (AD) is divided into severity stages, mild cognitive impairment (MCI) remains a solitary construct despite clinical and prognostic heterogeneity. This study aimed to characterize differences in genetic, cerebrospinal fluid (CSF), neuroimaging, and neuropsychological markers across clinician-derived MCI stages. Methods Vanderbilt Memory & Aging Project participants with MCI were categorized into 3 severity subtypes at screening based on neuropsychological assessment, functional assessment, and Clinical Dementia Rating interview, including mild (n = 18, 75 ± 8 years), moderate (n = 89 72 ± 7 years), and severe subtypes (n = 18, 78 ± 8 years). At enrollment, participants underwent neuropsychological testing, 3T brain magnetic resonance imaging (MRI), and optional fasting lumbar puncture to obtain CSF. Neuropsychological testing and MRI were repeated at 18-months, 3-years, and 5-years with a mean follow-up time of 3.3 years. Ordinary least square regressions examined cross-sectional associations between MCI severity and apolipoprotein E (APOE)-ε4 status, CSF biomarkers of amyloid beta (Aβ), phosphorylated tau, total tau, and synaptic dysfunction (neurogranin), baseline neuroimaging biomarkers, and baseline neuropsychological performance. Longitudinal associations between baseline MCI severity and neuroimaging and neuropsychological trajectory were assessed using linear mixed effects models with random intercepts and slopes and a follow-up time interaction. Analyses adjusted for baseline age, sex, race/ethnicity, education, and intracranial volume for MRI models. Results Stages differed at baseline on APOE-ε4 status (early < middle = late; p-values < 0.03) and CSF Aβ (early > middle = late), phosphorylated and total tau (early = middle < late; p-values < 0.05), and neurogranin concentrations (early = middle < late; p-values < 0.05). MCI stage related to greater longitudinal cognitive decline, hippocampal atrophy, and inferior lateral ventricle dilation (early < late; p-values < 0.03). Discussion Clinician staging of MCI severity yielded longitudinal cognitive trajectory and structural neuroimaging differences in regions susceptible to AD neuropathology and neurodegeneration. As expected, participants with more severe MCI symptoms at study entry had greater cognitive decline and gray matter atrophy over time. Differences are likely attributable to baseline differences in amyloidosis, tau, and synaptic dysfunction. MCI staging may provide insight into underlying pathology, prognosis, and therapeutic targets.
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Affiliation(s)
- Elizabeth E Moore
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Dandan Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Kimberly R Pechman
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Lealani Mae Y Acosta
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Susan P Bell
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States.,Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - L Taylor Davis
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom.,UK Dementia Research Institute at UCL, London, United Kingdom
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Matthew S Schrag
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Timothy J Hohman
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Katherine A Gifford
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Angela L Jefferson
- Vanderbilt Memory & Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States.,Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
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Zajac L, Koo BB, Tripodis Y, Mian A, Steinberg E, Mez J, Alosco ML, Cervantes-Arslanian A, Stern R, Killiany R. Hippocampal Resting-State Functional Connectivity Patterns are More Closely Associated with Severity of Subjective Memory Decline than Whole Hippocampal and Subfield Volumes. Cereb Cortex Commun 2020; 1:tgaa019. [PMID: 32905008 PMCID: PMC7463163 DOI: 10.1093/texcom/tgaa019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/18/2020] [Accepted: 05/18/2020] [Indexed: 12/02/2022] Open
Abstract
The goal of this study was to examine whether hippocampal volume or resting-state functional connectivity (rsFC) patterns are associated with subjective memory decline (SMD) in cognitively normal aged adults. Magnetic resonance imaging data from 53 participants (mean age: 71.9 years) of the Boston University Alzheimer’s Disease Center registry were used in this cross-sectional study. Separate analyses treating SMD as a binary and continuous variable were performed. Subfield volumes were generated using FreeSurfer v6.0, and rsFC strength between the head and body of the hippocampus and the rest of the brain was calculated. Decreased left whole hippocampal volume and weaker rsFC strength between the right body of the hippocampus and the default mode network (DMN) were found in SMD+. Cognitive Change Index score was not correlated with volumetric measures but was inversely correlated with rsFC strength between the right body of the hippocampus and 6 brain networks, including the DMN, task control, and attentional networks. These findings suggest that hippocampal rsFC patterns reflect the current state of SMD in cognitively normal adults and may reflect subtle memory changes that standard neuropsychological tests are unable to capture.
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Affiliation(s)
- Lauren Zajac
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Bang-Bon Koo
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Yorghos Tripodis
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Asim Mian
- Department of Radiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Eric Steinberg
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Jesse Mez
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Michael L Alosco
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA 02118, USA
| | | | - Robert Stern
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Ronald Killiany
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
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Dicks E, van der Flier WM, Scheltens P, Barkhof F, Tijms BM. Single-subject gray matter networks predict future cortical atrophy in preclinical Alzheimer's disease. Neurobiol Aging 2020; 94:71-80. [PMID: 32585492 DOI: 10.1016/j.neurobiolaging.2020.05.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 05/08/2020] [Accepted: 05/10/2020] [Indexed: 02/07/2023]
Abstract
The development of preventive strategies in early-stage Alzheimer's disease (AD) requires measures that can predict future brain atrophy. Gray matter network measures are related to amyloid burden in cognitively normal older individuals and predict clinical progression in preclinical AD. Here, we show that within individuals with preclinical AD, gray matter network measures predict hippocampal atrophy rates, whereas other AD biomarkers (total gray matter volume, cerebrospinal fluid total tau, and Mini-Mental State Examination) do not. Furthermore, in brain areas where amyloid is known to start aggregating (i.e. anterior cingulate and precuneus), disrupted network measures predict faster atrophy in other distant areas, mostly involving temporal regions, which are associated with AD. When repeating analyses in age-matched, cognitively unimpaired individuals without amyloid or tau pathology, we did not find any associations between network measures and hippocampal atrophy, suggesting that the associations are specific for preclinical AD. Our findings suggest that disrupted gray matter networks may indicate a treatment opportunity in preclinical AD individuals but before the onset of irreversible atrophy and cognitive impairment.
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Affiliation(s)
- Ellen Dicks
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, United Kingdom
| | - Betty M Tijms
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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Fernández-Cabello S, Kronbichler M, Van Dijk KRA, Goodman JA, Spreng RN, Schmitz TW. Basal forebrain volume reliably predicts the cortical spread of Alzheimer's degeneration. Brain 2020; 143:993-1009. [PMID: 32203580 PMCID: PMC7092749 DOI: 10.1093/brain/awaa012] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 11/21/2019] [Accepted: 12/04/2019] [Indexed: 12/25/2022] Open
Abstract
Alzheimer's disease neurodegeneration is thought to spread across anatomically and functionally connected brain regions. However, the precise sequence of spread remains ambiguous. The prevailing model used to guide in vivo human neuroimaging and non-human animal research assumes that Alzheimer's degeneration starts in the entorhinal cortices, before spreading to the temporoparietal cortex. Challenging this model, we previously provided evidence that in vivo markers of neurodegeneration within the nucleus basalis of Meynert (NbM), a subregion of the basal forebrain heavily populated by cortically projecting cholinergic neurons, precedes and predicts entorhinal degeneration. There have been few systematic attempts at directly comparing staging models using in vivo longitudinal biomarker data, and none to our knowledge testing if comparative evidence generalizes across independent samples. Here we addressed the sequence of pathological staging in Alzheimer's disease using two independent samples of the Alzheimer's Disease Neuroimaging Initiative (n1 = 284; n2 = 553) with harmonized CSF assays of amyloid-β and hyperphosphorylated tau (pTau), and longitudinal structural MRI data over 2 years. We derived measures of grey matter degeneration in a priori NbM and the entorhinal cortical regions of interest. To examine the spreading of degeneration, we used a predictive modelling strategy that tests whether baseline grey matter volume in a seed region accounts for longitudinal change in a target region. We demonstrated that predictive spread favoured the NbM→entorhinal over the entorhinal→NbM model. This evidence generalized across the independent samples. We also showed that CSF concentrations of pTau/amyloid-β moderated the observed predictive relationship, consistent with evidence in rodent models of an underlying trans-synaptic mechanism of pathophysiological spread. The moderating effect of CSF was robust to additional factors, including clinical diagnosis. We then applied our predictive modelling strategy to an exploratory whole-brain voxel-wise analysis to examine the spatial specificity of the NbM→entorhinal model. We found that smaller baseline NbM volumes predicted greater degeneration in localized regions of the entorhinal and perirhinal cortices. By contrast, smaller baseline entorhinal volumes predicted degeneration in the medial temporal cortex, recapitulating a prior influential staging model. Our findings suggest that degeneration of the basal forebrain cholinergic projection system is a robust and reliable upstream event of entorhinal and neocortical degeneration, calling into question a prevailing view of Alzheimer's disease pathogenesis.
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Affiliation(s)
- Sara Fernández-Cabello
- Department of Psychology, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
| | - Martin Kronbichler
- Department of Psychology, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Koene R A Van Dijk
- Clinical and Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, USA
| | - James A Goodman
- Clinical and Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, USA
| | - R Nathan Spreng
- Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Verdun, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - Taylor W Schmitz
- Brain and Mind Institute, Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western University, London, ON, Canada
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Blennow K, Shaw LM, Stomrud E, Mattsson N, Toledo JB, Buck K, Wahl S, Eichenlaub U, Lifke V, Simon M, Trojanowski JQ, Hansson O. Predicting clinical decline and conversion to Alzheimer's disease or dementia using novel Elecsys Aβ(1-42), pTau and tTau CSF immunoassays. Sci Rep 2019; 9:19024. [PMID: 31836810 PMCID: PMC6911086 DOI: 10.1038/s41598-019-54204-z] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 10/31/2019] [Indexed: 02/06/2023] Open
Abstract
We evaluated the performance of CSF biomarkers for predicting risk of clinical decline and conversion to dementia in non-demented patients with cognitive symptoms. CSF samples from patients in two multicentre longitudinal studies (ADNI, n = 619; BioFINDER, n = 431) were analysed. Aβ(1-42), tTau and pTau CSF concentrations were measured using Elecsys CSF immunoassays, and tTau/Aβ(1-42) and pTau/Aβ(1-42) ratios calculated. Patients were classified as biomarker (BM)-positive or BM-negative at baseline. Ability of biomarkers to predict risk of clinical decline and conversion to AD/dementia was assessed using pre-established cut-offs for Aβ(1-42) and ratios; tTau and pTau cut-offs were determined. BM-positive patients showed greater clinical decline than BM-negative patients, demonstrated by greater decreases in MMSE scores (all biomarkers: -2.10 to -0.70). Risk of conversion to AD/dementia was higher in BM-positive patients (HR: 1.67 to 11.48). Performance of Tau/Aβ(1-42) ratios was superior to single biomarkers, and consistent even when using cut-offs derived in a different cohort. Optimal pTau and tTau cut-offs were approximately 27 pg/mL and 300 pg/mL in both BioFINDER and ADNI. Elecsys pTau/Aβ(1-42) and tTau/Aβ(1-42) are robust biomarkers for predicting risk of clinical decline and conversion to dementia in non-demented patients, and may support AD diagnosis in clinical practice.
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Affiliation(s)
- Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson
- Clinical Memory Research Unit, Lund University, Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Jon B Toledo
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Houston Methodist Hospital, Houston, TX, USA
| | | | | | | | | | - Maryline Simon
- Roche Diagnostics International Ltd, Rotkreuz, Switzerland
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Malmö, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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50
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Loera-Valencia R, Cedazo-Minguez A, Kenigsberg PA, Page G, Duarte AI, Giusti P, Zusso M, Robert P, Frisoni GB, Cattaneo A, Zille M, Boltze J, Cartier N, Buee L, Johansson G, Winblad B. Current and emerging avenues for Alzheimer's disease drug targets. J Intern Med 2019; 286:398-437. [PMID: 31286586 DOI: 10.1111/joim.12959] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Alzheimer's disease (AD), the most frequent cause of dementia, is escalating as a global epidemic, and so far, there is neither cure nor treatment to alter its progression. The most important feature of the disease is neuronal death and loss of cognitive functions, caused probably from several pathological processes in the brain. The main neuropathological features of AD are widely described as amyloid beta (Aβ) plaques and neurofibrillary tangles of the aggregated protein tau, which contribute to the disease. Nevertheless, AD brains suffer from a variety of alterations in function, such as energy metabolism, inflammation and synaptic activity. The latest decades have seen an explosion of genes and molecules that can be employed as targets aiming to improve brain physiology, which can result in preventive strategies for AD. Moreover, therapeutics using these targets can help AD brains to sustain function during the development of AD pathology. Here, we review broadly recent information for potential targets that can modify AD through diverse pharmacological and nonpharmacological approaches including gene therapy. We propose that AD could be tackled not only using combination therapies including Aβ and tau, but also considering insulin and cholesterol metabolism, vascular function, synaptic plasticity, epigenetics, neurovascular junction and blood-brain barrier targets that have been studied recently. We also make a case for the role of gut microbiota in AD. Our hope is to promote the continuing research of diverse targets affecting AD and promote diverse targeting as a near-future strategy.
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Affiliation(s)
- R Loera-Valencia
- Division of Neurogeriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - A Cedazo-Minguez
- Division of Neurogeriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | | | - G Page
- Neurovascular Unit and Cognitive impairments - EA3808, University of Poitiers, Poitiers, France
| | - A I Duarte
- CNC- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal.,Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal
| | - P Giusti
- Dipartimento di Scienze del Farmaco, Università degli Studi di Padova, Padova, Italy
| | - M Zusso
- Dipartimento di Scienze del Farmaco, Università degli Studi di Padova, Padova, Italy
| | - P Robert
- CoBTeK - lab, CHU Nice University Côte d'Azur, Nice, France
| | - G B Frisoni
- University Hospitals and University of Geneva, Geneva, Switzerland
| | - A Cattaneo
- University Hospitals and University of Geneva, Geneva, Switzerland
| | - M Zille
- Institute of Experimental and Clinical Pharmacology and Toxicology, Lübeck, Germany
| | - J Boltze
- School of Life Sciences, The University of Warwick, Coventry, UK
| | - N Cartier
- Preclinical research platform, INSERM U1169/MIRCen Commissariat à l'énergie atomique, Fontenay aux Roses, France.,Université Paris-Sud, Orsay, France
| | - L Buee
- Alzheimer & Tauopathies, LabEx DISTALZ, CHU-Lille, Inserm, Univ. Lille, Lille, France
| | - G Johansson
- Division of Neurogeriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - B Winblad
- Division of Neurogeriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden.,Theme Aging, Karolinska University Hospital, Stockholm, Sweden
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