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Shahim P, Norato G, Sinaii N, Zetterberg H, Blennow K, Chan L, Grunseich C. Neurofilaments in Sporadic and Familial Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-Analysis. Genes (Basel) 2024; 15:496. [PMID: 38674431 PMCID: PMC11050235 DOI: 10.3390/genes15040496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 03/30/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Neurofilament proteins have been implicated to be altered in amyotrophic lateral sclerosis (ALS). The objectives of this study were to assess the diagnostic and prognostic utility of neurofilaments in ALS. METHODS Studies were conducted in electronic databases (PubMed/MEDLINE, Embase, Web of Science, and Cochrane CENTRAL) from inception to 17 August 2023, and investigated neurofilament light (NfL) or phosphorylated neurofilament heavy chain (pNfH) in ALS. The study design, enrolment criteria, neurofilament concentrations, test accuracy, relationship between neurofilaments in cerebrospinal fluid (CSF) and blood, and clinical outcome were recorded. The protocol was registered with PROSPERO, CRD42022376939. RESULTS Sixty studies with 8801 participants were included. Both NfL and pNfH measured in CSF showed high sensitivity and specificity in distinguishing ALS from disease mimics. Both NfL and pNfH measured in CSF correlated with their corresponding levels in blood (plasma or serum); however, there were stronger correlations between CSF NfL and blood NfL. NfL measured in blood exhibited high sensitivity and specificity in distinguishing ALS from controls. Both higher levels of NfL and pNfH either measured in blood or CSF were correlated with more severe symptoms as assessed by the ALS Functional Rating Scale Revised score and with a faster disease progression rate; however, only blood NfL levels were associated with shorter survival. DISCUSSION Both NfL and pNfH measured in CSF or blood show high diagnostic utility and association with ALS functional scores and disease progression, while CSF NfL correlates strongly with blood (either plasma or serum) and is also associated with survival, supporting its use in clinical diagnostics and prognosis. Future work must be conducted in a prospective manner with standardized bio-specimen collection methods and analytical platforms, further improvement in immunoassays for quantification of pNfH in blood, and the identification of cut-offs across the ALS spectrum and controls.
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Affiliation(s)
- Pashtun Shahim
- Rehabilitation Medicine Department, National Institutes of Health (NIH) Clinical Center, Bethesda, MD 20892, USA;
- National Institutes of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA; (G.N.); (C.G.)
- Department of Neurology, MedStar Georgetown University Hospital, Washington, DC 20007, USA
- The Military Traumatic Brain Injury Initiative (MTBI2), Bethesda, MD 20814, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA
| | - Gina Norato
- National Institutes of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA; (G.N.); (C.G.)
| | - Ninet Sinaii
- Biostatistics and Clinical Epidemiology Service, NIH, Bethesda, MD 20892, USA;
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 41 Molndal, Sweden; (H.Z.); (K.B.)
- Clinical Neurochemistry Laboratory, Sahglrenska University Hospital, 431 41 Molndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong 518172, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 41 Molndal, Sweden; (H.Z.); (K.B.)
- Clinical Neurochemistry Laboratory, Sahglrenska University Hospital, 431 41 Molndal, Sweden
| | - Leighton Chan
- Rehabilitation Medicine Department, National Institutes of Health (NIH) Clinical Center, Bethesda, MD 20892, USA;
| | - Christopher Grunseich
- National Institutes of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA; (G.N.); (C.G.)
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Satizabal CL, Beiser AS, Fletcher E, Seshadri S, DeCarli C. A novel neuroimaging signature for ADRD risk stratification in the community. Alzheimers Dement 2024; 20:1881-1893. [PMID: 38147416 PMCID: PMC10984488 DOI: 10.1002/alz.13600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023]
Abstract
INTRODUCTION Early risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR). METHODS An MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD-ADRC) cohort. RESULTS Participants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P < 0.001). Greater thickness in the signature was related to better general cognition (P < 0.01) and episodic memory (P = 0.01). Results replicated in UCD-ADRC. DISCUSSION We identified a robust neuroimaging biomarker for persons at increased risk of ADRD. Other cohorts will further test the validity of this biomarker.
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Affiliation(s)
- Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health Sciences CenterSan AntonioTexasUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
| | - Alexa S. Beiser
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Evan Fletcher
- IDeA LaboratoryDepartment of NeurologyUniversity of California DavisDavisCaliforniaUSA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health Sciences CenterSan AntonioTexasUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
| | - Charles DeCarli
- IDeA LaboratoryDepartment of NeurologyUniversity of California DavisDavisCaliforniaUSA
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3
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Fletcher E, Farias S, DeCarli C, Gavett B, Widaman K, De Leon F, Mungas D. Toward a statistical validation of brain signatures as robust measures of behavioral substrates. Hum Brain Mapp 2023; 44:3094-3111. [PMID: 36939069 PMCID: PMC10171525 DOI: 10.1002/hbm.26265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 02/10/2023] [Accepted: 02/21/2023] [Indexed: 03/21/2023] Open
Abstract
The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.
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Affiliation(s)
- Evan Fletcher
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Sarah Farias
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Brandon Gavett
- School of Psychological ScienceUniversity of Western AustraliaPerthAustralia
| | - Keith Widaman
- School of EducationUniversity of California, RiversideRiversideCaliforniaUSA
| | - Fransia De Leon
- School of MedicineUniversity of California, DavisDavisCaliforniaUSA
| | - Dan Mungas
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
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4
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Chen K, Guo X, Pan R, Xiong C, Harvey DJ, Chen Y, Yao L, Su Y, Reiman EM. Limitations of clinical trial sample size estimate by subtraction of two measurements. Stat Med 2022; 41:1137-1147. [PMID: 34725853 PMCID: PMC8916961 DOI: 10.1002/sim.9244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/16/2021] [Accepted: 10/13/2021] [Indexed: 11/10/2022]
Abstract
In planning randomized clinical trials (RCTs) for diseases such as Alzheimer's disease (AD), researchers frequently rely on the use of existing data obtained from only two time points to estimate sample size via the subtraction of baseline from follow-up measurements in each subject. However, the inadequacy of this method has not been reported. The aim of this study is to discuss the limitation of sample size estimation based on the subtraction of available data from only two time points for RCTs. Mathematical equations are derived to demonstrate the condition under which the obtained data pairs with variable time intervals could be used to adequately estimate sample size. The MRI-based hippocampal volume measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Monte Carlo simulations (MCS) were used to illustrate the existing bias and variability of estimates. MCS results support the theoretically derived condition under which the subtraction approach may work. MCS also show the systematically under- or over-estimated sample sizes by up to 32.27 % bias. Not used properly, such subtraction approach outputs the same sample size regardless of trial durations partly due to the way measurement errors are handled. Estimating sample size by subtracting two measurements should be treated with caution. Such estimates can be biased, the magnitude of which depends on the planned RCT duration. To estimate sample sizes, we recommend using more than two measurements and more comprehensive approaches such as linear mixed effect models.
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Affiliation(s)
- Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
- Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona, USA
- Department of Neurology, University of Arizona, Phoenix, Arizona, USA
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rong Pan
- Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona, USA
| | - Chengjie Xiong
- Knight Alzheimer’s Disease Research Center, St. Louis, Missouri, USA
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | | | - Yinghua Chen
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
- Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
- Division of Neurogenomics, Translational Genomics Research Institute, Phoenix, Arizona, USA
- Department of Psychiatry, University of Arizona, Tucson, Arizona, USA
- Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA
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5
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Akrami H, Leahy R, Irimia A, Kim P, Heck C, Joshi A. Neuroanatomic Markers of Posttraumatic Epilepsy Based on MR Imaging and Machine Learning. AJNR Am J Neuroradiol 2022; 43:347-353. [PMID: 35210268 PMCID: PMC8910810 DOI: 10.3174/ajnr.a7436] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 01/01/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Although posttraumatic epilepsy is a common complication of traumatic brain injury, the relationship between these conditions is unclear and early posttraumatic epilepsy detection and prevention remain major unmet clinical challenges. This study aimed to identify imaging biomarkers that predict posttraumatic epilepsy among survivors of traumatic brain injury on the basis of an MR imaging data set. MATERIALS AND METHODS We performed tensor-based morphometry to analyze brain-shape changes associated with traumatic brain injury and to derive imaging features for statistical group comparison. Additionally, machine learning was used to identify structural anomalies associated with brain lesions. Automatically generated brain lesion maps were used to identify brain regions where lesion load may indicate an increased incidence of posttraumatic epilepsy. We used 138 non-posttraumatic epilepsy subjects for training the machine learning method. Validation of lesion delineation was performed on 15 subjects. Group analysis of the relationship between traumatic brain injury and posttraumatic epilepsy was performed on an independent set of 74 subjects (37 subjects with and 37 randomly selected subjects without epilepsy). RESULTS We observed significant F-statistics related to tensor-based morphometry analysis at voxels close to the pial surface, which may indicate group differences in the locations of edema, hematoma, or hemorrhage. The results of the F-test on lesion data showed significant differences between groups in both the left and right temporal lobes. We also saw significant differences in the right occipital lobe and cerebellum. CONCLUSIONS Statistical analysis suggests that lesions in the temporal lobes, cerebellum, and the right occipital lobe are associated with an increased posttraumatic epilepsy incidence.
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Affiliation(s)
- H. Akrami
- From the Department of Biomedical Engineering (H.A., A.I.)
| | - R.M. Leahy
- Ming Hsieh Department of Electrical and Computer Engineering (R.M.L., A.A.J.)
| | - A. Irimia
- From the Department of Biomedical Engineering (H.A., A.I.),Leonard Davis School of Gerontology (A.I.)
| | - P.E. Kim
- Departments of Radiology (P.E.K.)
| | - C.N. Heck
- Neurology (C.N.H.), University of Southern California, Los Angeles, California
| | - A.A. Joshi
- Ming Hsieh Department of Electrical and Computer Engineering (R.M.L., A.A.J.)
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6
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Bruchhage MMK, Correia S, Malloy P, Salloway S, Deoni S. Machine Learning Classification Identifies Cerebellar Contributions to Early and Moderate Cognitive Decline in Alzheimer's Disease. Front Aging Neurosci 2020; 12:524024. [PMID: 33240072 PMCID: PMC7669549 DOI: 10.3389/fnagi.2020.524024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022] Open
Abstract
Alzheimer's disease (AD) is one of the most common forms of dementia, marked by progressively degrading cognitive function. Although cerebellar changes occur throughout AD progression, its involvement and predictive contribution in its earliest stages, as well as gray or white matter components involved, remains unclear. We used MRI machine learning-based classification to assess the contribution of two tissue components [volume fraction myelin (VFM), and gray matter (GM) volume] within the whole brain, the neocortex, the whole cerebellum as well as its anterior and posterior parts and their predictive contribution to the first two stages of AD and typically aging controls. While classification accuracy increased with AD stages, VFM was the best predictor for all early stages of dementia when compared with typically aging controls. However, we document overall higher cerebellar prediction accuracy when compared to the whole brain with distinct structural signatures of higher anterior cerebellar contribution to mild cognitive impairment (MCI) and higher posterior cerebellar contribution to mild/moderate stages of AD for each tissue property. Based on these different cerebellar profiles and their unique contribution to early disease stages, we propose a refined model of cerebellar contribution to early AD development.
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Affiliation(s)
- Muriel M. K. Bruchhage
- Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI, United States
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Stephen Correia
- Butler Hospital Memory and Aging Program, Providence, RI, United States
- Department of Human Behavior and Psychiatry, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Paul Malloy
- Butler Hospital Memory and Aging Program, Providence, RI, United States
- Department of Human Behavior and Psychiatry, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Stephen Salloway
- Butler Hospital Memory and Aging Program, Providence, RI, United States
- Department of Human Behavior and Psychiatry, Warren Alpert Medical School at Brown University, Providence, RI, United States
- Department of Neurology, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Sean Deoni
- Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI, United States
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, United States
- Maternal, Newborn and Child Health Discovery & Tools, Bill & Melinda Gates Foundation, Seattle, WA, United States
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7
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Moore EE, Gifford KA, Khan OA, Liu D, Pechman KR, Acosta LMY, Bell SP, Turchan M, Landman BA, Blennow K, Zetterberg H, Hohman TJ, Jefferson AL. Cerebrospinal fluid biomarkers of neurodegeneration, synaptic dysfunction, and axonal injury relate to atrophy in structural brain regions specific to Alzheimer's disease. Alzheimers Dement 2020; 16:883-895. [PMID: 32378327 DOI: 10.1002/alz.12087] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 01/09/2020] [Accepted: 01/15/2020] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Patterns of atrophy can distinguish normal cognition from Alzheimer's disease (AD), but neuropathological drivers of this pattern are unknown. This study examined associations between cerebrospinal fluid biomarkers of AD pathology, synaptic dysfunction, and neuroaxonal injury with two AD imaging signatures. METHODS Signatures were calculated using published guidelines. Linear regressions related each biomarker to both signatures, adjusting for demographic factors. Bootstrapped analyses tested if associations were stronger with one signature versus the other. RESULTS Increased phosphorylated tau (p-tau), total tau, and neurofilament light (P-values <.045) related to smaller signatures (indicating greater atrophy). Diagnosis and sex modified associations between p-tau and neurogranin (P-values<.05) and signatures, such that associations were stronger among participants with mild cognitive impairment and female participants. The strength of associations did not differ between signatures. DISCUSSION Increased evidence of neurodegeneration, axonopathy, and tau phosphorylation relate to greater AD-related atrophy. Tau phosphorylation and synaptic dysfunction may be more prominent in AD-affected regions in females.
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Affiliation(s)
- Elizabeth E Moore
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Katherine A Gifford
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Omair A Khan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dandan Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kimberly R Pechman
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lealani Mae Y Acosta
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Susan P Bell
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Maxim Turchan
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Bennett A Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Lab, 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 Lab, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK.,UK Dementia Research Institute at UCL, London, UK
| | - Timothy J Hohman
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Angela L Jefferson
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
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8
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Liu H, Liu T, Jiang J, Cheng J, Liu Y, Li D, Dong C, Niu H, Li S, Zhang J, Brodaty H, Sachdev P, Wen W. Differential longitudinal changes in structural complexity and volumetric measures in community-dwelling older individuals. Neurobiol Aging 2020; 91:26-35. [PMID: 32311608 DOI: 10.1016/j.neurobiolaging.2020.02.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 01/11/2020] [Accepted: 02/22/2020] [Indexed: 01/04/2023]
Abstract
Fractal geometry provides a method of analyzing natural and especially biological morphologies. To investigate the relationship between the complexity measure, which is indexed as fractal dimensionality (FD), and the traditional Euclidean metrics, such as the volume and thickness, of the brain in older age, we analyzed 483 MRI scans of 161 community-dwelling, nondemented individuals aged 70-90 years at the baseline and their 2-year and 6-year follow-ups. We quantified changes in neuroimaging metrics in cortical lobes and subcortical structures and investigated the effects of age, sex, hemisphere, and education on FD. We also analyzed the mediating effects of these metrics for further investigation. FD showed significant age-related decline in all structures, and its trajectory was best modeled quadratically in the bilateral frontal, parietal, and occipital lobes, as well as in the bilateral caudate, putamen, hippocampus, amygdala, and accumbens. FD showed specific mediating effects on aging and cognitive decline in some regions. Our findings suggest that FD is reliable yet shows a different pattern of decline compared with volumetric measures.
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Affiliation(s)
- Hao Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Tao Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, China.
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jian Cheng
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China
| | - Yan Liu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China
| | - Daqing Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China
| | - Chao Dong
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Haijun Niu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beijing, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beijing, China
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, China.
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Dementia Collaborative Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
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9
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Dutta CN, Christov-Moore L, Anderson A, Koch Z, Kaur P, Vasheghani-Farahani F, Douglas PK. Inter-hemispheric Asymmetry Patterns in the ADHD Brain: A Neuroimaging Replication Study. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 11330:113301C. [PMID: 36590311 PMCID: PMC9799963 DOI: 10.1117/12.2546895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Attention-deficit/ hyperactivity disorder (ADHD) is the most common neurodevelopment disorder in children, and many genetic markers have been linked to the behavioral phenotypes of this highly heritable disease. The neuroimaging correlates are similarly complex, with multiple combinations of structural and functional alterations associated with the disease presentations of hyperactivity and inattentiveness. Thus far, neuroimaging studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings. It is possible that hemispheric asymmetry differences may help reconcile the variability of these findings. We recently reported that inter-hemispheric asymmetry differences were more sensitive descriptors for identifying differences between ADHD and typically developing (TD) brains (n=849) across volumetric, morphometric, and white matter neuroimaging metrics. Here, we examined the replicability of these findings across a new data set (n=202) of TD and ADHD subjects at the time of diagnosis (medication naive) and after a six week course of either stimulant drugs, non-stimulant medications, or combination therapy. Our findings replicated our earlier work across a number of volumetric and white matter measures confirming that asymmetry is more robust at detecting differences between TD and ADHD brains. However, the effects of medication failed to produce significant alterations across either lateralized or symmetry measures. We suggest that the delay in brain volume maturation observed in ADHD youths may be hemisphere dependent. Future work may investigate the extent to which these inter-hemispheric asymmetry differences are causal or compensatory in nature.
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Affiliation(s)
| | | | | | - Zane Koch
- Department of Psychiatry and Biobehavioral Medicine, UCLA
| | - Pashmeen Kaur
- Department of Psychiatry and Biobehavioral Medicine, UCLA
| | | | - Pamela K. Douglas
- Modeling and Simulation Department, University of Central Florida,Department of Psychiatry and Biobehavioral Medicine, UCLA
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10
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Seo K, Pan R, Lee D, Thiyyagura P, Chen K. Visualizing Alzheimer's disease progression in low dimensional manifolds. Heliyon 2019; 5:e02216. [PMID: 31406946 PMCID: PMC6684517 DOI: 10.1016/j.heliyon.2019.e02216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 01/05/2019] [Accepted: 07/30/2019] [Indexed: 01/18/2023] Open
Abstract
While tomographic neuroimaging data is information rich, objective, and with high sensitivity in the study of brain diseases such as Alzheimer's disease (AD), its direct use in clinical practice and in regulated clinical trial (CT) still has many challenges. Taking CT as an example, unless the relevant policy and the perception of the primary outcome measures change, the need to construct univariate indices (out of the 3-D imaging data) to serve as CT's primary outcome measures will remain the focus of active research. More relevant to this current study, an overall global index that summarizes multiple complicated features from neuroimages should be developed in order to provide high diagnostic accuracy and sensitivity in tracking AD progression over time in clinical setting. Such index should also be practically intuitive and logically explainable to patients and their families. In this research, we propose a new visualization tool, derived from the manifold-based nonlinear dimension reduction of brain MRI features, to track AD progression over time. In specific, we investigate the locally linear embedding (LLE) method using a dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI), which includes the longitudinal MRIs from 562 subjects. About 20% of them progressed to the next stage of dementia. Using only the baseline data of cognitively unimpaired (CU) and AD subjects, LLE reduces the feature dimension to two and a subject's AD progression path can be plotted in this low dimensional LLE feature space. In addition, the likelihood of being categorized to AD is indicated by color. This LLE map is a new data visualization tool that can assist in tracking AD progression over time.
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Affiliation(s)
- Kangwon Seo
- Department of Industrial and Manufacturing Systems Engineering and Department of Statistics, University of Missouri, USA
| | - Rong Pan
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA
| | - Dongjin Lee
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA
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11
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Cole JH, Jolly A, de Simoni S, Bourke N, Patel MC, Scott G, Sharp DJ. Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury. Brain 2019; 141:822-836. [PMID: 29309542 PMCID: PMC5837530 DOI: 10.1093/brain/awx354] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 11/08/2017] [Indexed: 12/14/2022] Open
Abstract
Traumatic brain injury leads to significant loss of brain volume, which continues into the chronic stage. This can be sensitively measured using volumetric analysis of MRI. Here we: (i) investigated longitudinal patterns of brain atrophy; (ii) tested whether atrophy is greatest in sulcal cortical regions; and (iii) showed how atrophy could be used to power intervention trials aimed at slowing neurodegeneration. In 61 patients with moderate-severe traumatic brain injury (mean age = 41.55 years ± 12.77) and 32 healthy controls (mean age = 34.22 years ± 10.29), cross-sectional and longitudinal (1-year follow-up) brain structure was assessed using voxel-based morphometry on T1-weighted scans. Longitudinal brain volume changes were characterized using a novel neuroimaging analysis pipeline that generates a Jacobian determinant metric, reflecting spatial warping between baseline and follow-up scans. Jacobian determinant values were summarized regionally and compared with clinical and neuropsychological measures. Patients with traumatic brain injury showed lower grey and white matter volume in multiple brain regions compared to controls at baseline. Atrophy over 1 year was pronounced following traumatic brain injury. Patients with traumatic brain injury lost a mean (± standard deviation) of 1.55% ± 2.19 of grey matter volume per year, 1.49% ± 2.20 of white matter volume or 1.51% ± 1.60 of whole brain volume. Healthy controls lost 0.55% ± 1.13 of grey matter volume and gained 0.26% ± 1.11 of white matter volume; equating to a 0.22% ± 0.83 reduction in whole brain volume. Atrophy was greatest in white matter, where the majority (84%) of regions were affected. This effect was independent of and substantially greater than that of ageing. Increased atrophy was also seen in cortical sulci compared to gyri. There was no relationship between atrophy and time since injury or age at baseline. Atrophy rates were related to memory performance at the end of the follow-up period, as well as to changes in memory performance, prior to multiple comparison correction. In conclusion, traumatic brain injury results in progressive loss of brain tissue volume, which continues for many years post-injury. Atrophy is most prominent in the white matter, but is also more pronounced in cortical sulci compared to gyri. These findings suggest the Jacobian determinant provides a method of quantifying brain atrophy following a traumatic brain injury and is informative in determining the long-term neurodegenerative effects after injury. Power calculations indicate that Jacobian determinant images are an efficient surrogate marker in clinical trials of neuroprotective therapeutics.
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Affiliation(s)
- James H Cole
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Amy Jolly
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Sara de Simoni
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Niall Bourke
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Maneesh C Patel
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Gregory Scott
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - David J Sharp
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
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12
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Kueper JK, Speechley M, Montero-Odasso M. The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review. J Alzheimers Dis 2019; 63:423-444. [PMID: 29660938 PMCID: PMC5929311 DOI: 10.3233/jad-170991] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) was developed in the 1980s to assess the level of cognitive dysfunction in Alzheimer’s disease. Advancements in the research field have shifted focus toward pre-dementia populations, and use of the ADAS-Cog has extended into these pre-dementia studies despite concerns about its ability to detect important changes at these milder stages of disease progression. If the ADAS-Cog cannot detect important changes, our understanding of pre-dementia disease progression may be compromised and trials may incorrectly conclude that a novel treatment approach is not beneficial. The purpose of this review was to assess the performance of the ADAS-Cog in pre-dementia populations, and to review all modifications that have been made to the ADAS-Cog to improve its measurement performance in dementia or pre-dementia populations. The contents of this review are based on bibliographic searches of electronic databases to locate all studies using the ADAS-Cog in pre-dementia samples or subsamples, and to locate all modified versions. Citations from relevant articles were also consulted. Overall, our results suggest the original ADAS-Cog is not an optimal outcome measure for pre-dementia studies; however, given the prominence of the ADAS-Cog, care must be taken when considering the use of alternative outcome measures. Thirty-one modified versions of the ADAS-Cog were found. Modification approaches that appear most beneficial include altering scoring methodology or adding tests of memory, executive function, and/or daily functioning. Although modifications improve the performance of the ADAS-Cog, this is at the cost of introducing heterogeneity that may limit between-study comparison.
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Affiliation(s)
- Jacqueline K Kueper
- Department of Epidemiology and Biostatistics, The University of Western Ontario, London, ON, Canada
| | - Mark Speechley
- Department of Epidemiology and Biostatistics, The University of Western Ontario, London, ON, Canada.,Schulich Interfaculty Program in Public Health, The University of Western Ontario, London, ON, Canada
| | - Manuel Montero-Odasso
- Department of Epidemiology and Biostatistics, The University of Western Ontario, London, ON, Canada.,Department of Medicine, Division of Geriatric Medicine, The University of Western Ontario, London, ON, Canada.,Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, ON, Canada
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13
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Zavaliangos-Petropulu A, Nir TM, Thomopoulos SI, Reid RI, Bernstein MA, Borowski B, Jack CR, Weiner MW, Jahanshad N, Thompson PM. Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3. Front Neuroinform 2019; 13:2. [PMID: 30837858 PMCID: PMC6390411 DOI: 10.3389/fninf.2019.00002] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 01/21/2019] [Indexed: 12/14/2022] Open
Abstract
Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.
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Affiliation(s)
- Artemis Zavaliangos-Petropulu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Talia M Nir
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Bret Borowski
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Michael W Weiner
- Department of Radiology, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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14
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Femminella GD, Thayanandan T, Calsolaro V, Komici K, Rengo G, Corbi G, Ferrara N. Imaging and Molecular Mechanisms of Alzheimer's Disease: A Review. Int J Mol Sci 2018; 19:E3702. [PMID: 30469491 PMCID: PMC6321449 DOI: 10.3390/ijms19123702] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/13/2018] [Accepted: 11/14/2018] [Indexed: 02/07/2023] Open
Abstract
Alzheimer's disease is the most common form of dementia and is a significant burden for affected patients, carers, and health systems. Great advances have been made in understanding its pathophysiology, to a point that we are moving from a purely clinical diagnosis to a biological one based on the use of biomarkers. Among those, imaging biomarkers are invaluable in Alzheimer's, as they provide an in vivo window to the pathological processes occurring in Alzheimer's brain. While some imaging techniques are still under evaluation in the research setting, some have reached widespread clinical use. In this review, we provide an overview of the most commonly used imaging biomarkers in Alzheimer's disease, from molecular PET imaging to structural MRI, emphasising the concept that multimodal imaging would likely prove to be the optimal tool in the future of Alzheimer's research and clinical practice.
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Affiliation(s)
| | - Tony Thayanandan
- Imperial Memory Unit, Charing Cross Hospital, Imperial College London, London W6 8RF, UK.
| | - Valeria Calsolaro
- Neurology Imaging Unit, Imperial College London, London W12 0NN, UK.
| | - Klara Komici
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
| | - Giuseppe Rengo
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy.
- Istituti Clinici Scientifici Maugeri SPA-Società Benefit, IRCCS, 82037 Telese Terme, Italy.
| | - Graziamaria Corbi
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
| | - Nicola Ferrara
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy.
- Istituti Clinici Scientifici Maugeri SPA-Società Benefit, IRCCS, 82037 Telese Terme, Italy.
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15
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Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM. A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers. J Alzheimers Dis 2018; 59:1359-1379. [PMID: 28759968 PMCID: PMC5611893 DOI: 10.3233/jad-170261] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.
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Affiliation(s)
- Emma Lawrence
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Alison Ower
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | | | - Frank De Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Janssen Prevention Center, Leiden, The Netherlands
| | - Roy M Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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16
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Douglas PK, Gutman B, Anderson A, Larios C, Lawrence KE, Narr K, Sengupta B, Cooray G, Douglas DB, Thompson PM, McGough JJ, Bookheimer SY. Hemispheric brain asymmetry differences in youths with attention-deficit/hyperactivity disorder. Neuroimage Clin 2018; 18:744-752. [PMID: 29876263 PMCID: PMC5988460 DOI: 10.1016/j.nicl.2018.02.020] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 02/16/2018] [Accepted: 02/21/2018] [Indexed: 12/05/2022]
Abstract
Introduction Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings. Methods We studied 849 children and adolescents (ages 6-21 y.o.) diagnosed with ADHD (n = 341) and age-matched typically developing (TD) controls with structural brain MRI. We calculated volumetric measures from 34 cortical and 14 non-cortical brain regions per hemisphere, and detailed shape morphometry of subcortical nuclei. Diffusion tensor imaging (DTI) data were collected for a subset of 104 subjects; from these, we calculated mean diffusivity and fractional anisotropy of white matter tracts. Group comparisons were made for within-hemisphere (right/left) and between hemisphere asymmetry indices (AI) for each measure. Results DTI mean diffusivity AI group differences were significant in cingulum, inferior and superior longitudinal fasciculus, and cortico-spinal tracts (p < 0.001) with the effect of stimulant treatment tending to reduce these patterns of asymmetry differences. Gray matter volumes were more asymmetric in medication free ADHD individuals compared to TD in twelve cortical regions and two non-cortical volumes studied (p < 0.05). Morphometric analyses revealed that caudate, hippocampus, thalamus, and amygdala were more asymmetric (p < 0.0001) in ADHD individuals compared to TD, and that asymmetry differences were more significant than lateralized comparisons. Conclusions Brain asymmetry measures allow each individual to serve as their own control, diminishing variability between individuals and when pooling data across sites. Asymmetry group differences were more significant than lateralized comparisons between ADHD and TD subjects across morphometric, volumetric, and DTI comparisons.
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Affiliation(s)
- P K Douglas
- University of Central Florida, IST, Modeling and Simulation Department, FL, USA; Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, UCLA, CA, USA.
| | - Boris Gutman
- Imaging Genetics Center, USC Keck School of Medicine, Marina del Rey, CA, USA
| | - Ariana Anderson
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, UCLA, CA, USA
| | - C Larios
- University of Central Florida, IST, Modeling and Simulation Department, FL, USA
| | | | | | - Biswa Sengupta
- Wellcome Trust Centre for Neuroimaging, 12 Queen Square, UCL, London, UK
| | - Gerald Cooray
- Wellcome Trust Centre for Neuroimaging, 12 Queen Square, UCL, London, UK
| | - David B Douglas
- Nuclear Medicine and Molecular Imaging, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, USC Keck School of Medicine, Marina del Rey, CA, USA
| | - James J McGough
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, UCLA, CA, USA
| | - Susan Y Bookheimer
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, UCLA, CA, USA
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17
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Tsao S, Gajawelli N, Zhou J, Shi J, Ye J, Wang Y, Leporé N. Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry. Brain Behav 2017; 7:e00733. [PMID: 28729939 PMCID: PMC5516607 DOI: 10.1002/brb3.733] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 04/10/2017] [Accepted: 04/14/2017] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. METHODS Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces. RESULTS We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted. CONCLUSIONS By combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.
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Affiliation(s)
- Sinchai Tsao
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Niharika Gajawelli
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering Michigan State University East Lansing MI USA
| | - Jie Shi
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics & Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Natasha Leporé
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
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18
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Binney RJ, Pankov A, Marx G, He X, McKenna F, Staffaroni AM, Kornak J, Attygalle S, Boxer AL, Schuff N, Gorno‐Tempini M, Weiner MW, Kramer JH, Miller BL, Rosen HJ. Data-driven regions of interest for longitudinal change in three variants of frontotemporal lobar degeneration. Brain Behav 2017; 7:e00675. [PMID: 28413716 PMCID: PMC5390848 DOI: 10.1002/brb3.675] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 02/04/2017] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Longitudinal imaging of neurodegenerative disorders is a potentially powerful biomarker for use in clinical trials. In Alzheimer's disease, studies have demonstrated that empirically derived regions of interest (ROIs) can provide more reliable measurement of disease progression compared with anatomically defined ROIs. METHODS We set out to derive ROIs with optimal effect size for quantifying longitudinal change in a hypothetical clinical trial by comparing atrophy rates in 44 patients with behavioral variant of frontotemporal dementia (bvFTD), 30 with the semantic variant primary progressive aphasia (svPPA), and 26 with the nonfluent variant PPA (nfvPPA) to atrophy in 97 cognitively healthy controls. RESULTS The regions identified for each variant were generally what would be expected from prior studies of frontotemporal lobar degeneration (FTLD). Sample size estimates for detecting a 40% reduction in annual rate of ROI atrophy varied substantially across groups, being 103 per arm in bvFTD, 31 in nfvPPA, and 10 in svPPA, but in all groups were less than those estimated for a priori ROIs and clinical measures. The variability in location of peak regions of atrophy across individuals was highest in bvFTD and lowest in svPPA, likely relating to the differences in effect size. CONCLUSIONS These findings suggest that, while cross-validated maps of change can improve sensitivity to change in FTLD compared with a priori regions, the reliability of these maps differs considerably across syndromes. Future studies can utilize these maps to design clinical trials, and should try to identify factors accounting for the variability in patterns of atrophy across individuals, particularly those with bvFTD.
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Affiliation(s)
- Richard J. Binney
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Aleksandr Pankov
- Department of Epidemiology and BiostatisticsUniversity of California, San FranciscoSan FranciscoCAUSA
- Department of Neurological SurgeryUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Gabriel Marx
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Xuanzie He
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Faye McKenna
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Adam M. Staffaroni
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - John Kornak
- Department of Epidemiology and BiostatisticsUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Suneth Attygalle
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Adam L. Boxer
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Norbert Schuff
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Maria‐Luisa Gorno‐Tempini
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Michael W. Weiner
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Joel H. Kramer
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Bruce L. Miller
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Howard J. Rosen
- Department of NeurologyMemory and Aging CenterUniversity of California, San FranciscoSan FranciscoCAUSA
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19
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Hu C, Hua X, Ying J, Thompson PM, Fakhri GE, Li Q. Localizing Sources of Brain Disease Progression with Network Diffusion Model. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2016; 10:1214-1225. [PMID: 28503250 PMCID: PMC5423678 DOI: 10.1109/jstsp.2016.2601695] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Pinpointing the sources of dementia is crucial to the effective treatment of neurodegenerative diseases. In this paper, we propose a diffusion model with impulsive sources over the brain connectivity network to model the progression of brain atrophy. To reliably estimate the atrophy sources, we impose sparse regularization on the source distribution and solve the inverse problem with an efficient gradient descent method. We localize the possible origins of Alzheimer's disease (AD) based on a large set of repeated magnetic resonance imaging (MRI) scans in Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The distribution of the sources averaged over the sample population is evaluated. We find that the dementia sources have different concentrations in the brain lobes for AD patients and mild cognitive impairment (MCI) subjects, indicating possible switch of the dementia driving mechanism. Moreover, we demonstrate that we can effectively predict changes of brain atrophy patterns with the proposed model. Our work could help understand the dynamics and origin of dementia, as well as monitor the progression of the diseases in an early stage.
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Affiliation(s)
| | - Xue Hua
- M3 Biotechnology, Seattle, WA, 98195 USA
| | - Jun Ying
- Chinese PLA General Hospital (301 Hospital), Haidian, Beijing, 100853 China
| | - Paul M Thompson
- Neurology & Psychiatry, Imaging Genetics Center, University of Southern California, Los Angeles, CA, 90032 USA
| | - Georges E Fakhri
- Center for Advanced Medical Imaging Science, Massachusetts General Hospital, Boston, MA 02114 USA
| | - Quanzheng Li
- Center for Advanced Medical Imaging Science, Massachusetts General Hospital, Boston, MA 02114 USA
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20
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Aksman LM, Lythgoe DJ, Williams SCR, Jokisch M, Mönninghoff C, Streffer J, Jöckel KH, Weimar C, Marquand AF. Making use of longitudinal information in pattern recognition. Hum Brain Mapp 2016; 37:4385-4404. [PMID: 27451934 PMCID: PMC5111621 DOI: 10.1002/hbm.23317] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 06/20/2016] [Accepted: 07/05/2016] [Indexed: 12/31/2022] Open
Abstract
Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies in neuroimaging has been the adoption of pattern recognition algorithms for making individualized predictions of disease. However, at present few pattern recognition methods exist to make full use of neuroimaging data that have been collected longitudinally, with most methods relying instead on cross-sectional style analysis. This article presents a principal component analysis-based feature construction method that uses longitudinal high-dimensional data to improve predictive performance of pattern recognition algorithms. The method can be applied to data from a wide range of longitudinal study designs and permits an arbitrary number of time-points per subject. We apply the method to two longitudinal datasets, one containing subjects with mild cognitive impairment along with healthy controls, the other with early dementia subjects and healthy controls. Across both datasets, we show improvements in predictive accuracy relative to cross-sectional classifiers for discriminating disease subjects from healthy controls on the basis of whole-brain structural magnetic resonance image-based voxels. In addition, we can transfer longitudinal information from one set of subjects to make disease predictions in another set of subjects. The proposed method is simple and, as a feature construction method, flexible with respect to the choice of classifier and image registration algorithm. Hum Brain Mapp 37:4385-4404, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Leon M Aksman
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Martha Jokisch
- Department of Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christoph Mönninghoff
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Streffer
- Janssen-Pharmaceutical Companies of Johnson & Johnson, Janssen Research and Development, Beerse, Belgium
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Germany
| | - Christian Weimar
- Department of Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Andre F Marquand
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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21
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Löwe LC, Gaser C, Franke K. The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease. PLoS One 2016; 11:e0157514. [PMID: 27410431 PMCID: PMC4943637 DOI: 10.1371/journal.pone.0157514] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 05/30/2016] [Indexed: 01/28/2023] Open
Abstract
In our aging society, diseases in the elderly come more and more into focus. An important issue in research is Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) with their causes, diagnosis, treatment, and disease prediction. We applied the Brain Age Gap Estimation (BrainAGE) method to examine the impact of the Apolipoprotein E (APOE) genotype on structural brain aging, utilizing longitudinal magnetic resonance image (MRI) data of 405 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We tested for differences in neuroanatomical aging between carrier and non-carrier of APOE ε4 within the diagnostic groups and for longitudinal changes in individual brain aging during about three years follow-up. We further examined whether a combination of BrainAGE and APOE status could improve prediction accuracy of conversion to AD in MCI patients. The influence of the APOE status on conversion from MCI to AD was analyzed within all allelic subgroups as well as for ε4 carriers and non-carriers. The BrainAGE scores differed significantly between normal controls, stable MCI (sMCI) and progressive MCI (pMCI) as well as AD patients. Differences in BrainAGE changing rates over time were observed for APOE ε4 carrier status as well as in the pMCI and AD groups. At baseline and during follow-up, BrainAGE scores correlated significantly with neuropsychological test scores in APOE ε4 carriers and non-carriers, especially in pMCI and AD patients. Prediction of conversion was most accurate using the BrainAGE score as compared to neuropsychological test scores, even when the patient’s APOE status was unknown. For assessing the individual risk of coming down with AD as well as predicting conversion from MCI to AD, the BrainAGE method proves to be a useful and accurate tool even if the information of the patient’s APOE status is missing.
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Affiliation(s)
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, University Hospital Jena, Jena, Germany
- Department of Psychiatry, University Hospital Jena, Jena, Germany
| | - Katja Franke
- Structural Brain Mapping Group, Department of Neurology, University Hospital Jena, Jena, Germany
- * E-mail:
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22
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Migliaccio R, Agosta F, Possin KL, Canu E, Filippi M, Rabinovici GD, Rosen HJ, Miller BL, Gorno-Tempini ML. Mapping the Progression of Atrophy in Early- and Late-Onset Alzheimer's Disease. J Alzheimers Dis 2016; 46:351-64. [PMID: 25737041 DOI: 10.3233/jad-142292] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The term early-onset Alzheimer's disease (EOAD) identifies patients who meet criteria for AD, but show onset of symptoms before the age of 65. We map progression of gray matter atrophy in EOAD patients compared to late-onset AD (LOAD). T1-weighted MRI scans were obtained at diagnosis and one-year follow-up from 15 EOAD, 10 LOAD, and 38 age-matched controls. Voxel-based and tensor-based morphometry were used, respectively, to assess the baseline and progression of atrophy. At baseline, EOAD patients already showed a widespread atrophy in temporal, parietal, occipital, and frontal cortices. After one year, EOAD had atrophy progression in medial temporal and medial parietal cortices. At baseline, LOAD patients showed atrophy in the medial temporal regions only, and, after one year, an extensive pattern of atrophy progression in the same neocortical cortices of EOAD. Although atrophy mainly involved different lateral neocortical or medial temporal hubs at baseline, it eventually progressed along the same brain default-network regions in both groups. The cortical region showing a significant progression in both groups was the medial precuneus/posterior cingulate.
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23
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Schwarz CG, Gunter JL, Wiste HJ, Przybelski SA, Weigand SD, Ward CP, Senjem ML, Vemuri P, Murray ME, Dickson DW, Parisi JE, Kantarci K, Weiner MW, Petersen RC, Jack CR. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. NEUROIMAGE-CLINICAL 2016; 11:802-812. [PMID: 28050342 PMCID: PMC5187496 DOI: 10.1016/j.nicl.2016.05.017] [Citation(s) in RCA: 224] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 04/29/2016] [Accepted: 05/27/2016] [Indexed: 01/07/2023]
Abstract
Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, “AD signature” measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12. Evaluate thickness- and volume-based quantitative measures of AD severity Volume- and thickness-based measures perform similarly for separating by diagnosis. Volume-based measures are correlated with head size; thickness-based mostly aren't. We recommend an AD signature measure formed from cortical thickness measures. We recommend thicknesses using ANTs software with input segmentations from SPM12.
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Affiliation(s)
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Heather J Wiste
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Stephen D Weigand
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Chadwick P Ward
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Melissa E Murray
- Department of Neuroscience (Neuropathology), Mayo Clinic and Foundation, Jacksonville, FL, USA
| | - Dennis W Dickson
- Department of Neuroscience (Neuropathology), Mayo Clinic and Foundation, Jacksonville, FL, USA
| | - Joseph E Parisi
- Department of Laboratory Medicine, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michael W Weiner
- Veterans Affairs, University of California, San Francisco, CA, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
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Foley JM, Salat DH, Stricker NH, McGlinchey RE, Milberg WP, Grande LJ, Leritz EC. Glucose Dysregulation Interacts With APOE-∊4 to Potentiate Temporoparietal Cortical Thinning. Am J Alzheimers Dis Other Demen 2016; 31:76-86. [PMID: 26006791 PMCID: PMC4913470 DOI: 10.1177/1533317515587084] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
We examined the interactive effects of apolipoprotein ∊4 (APOE-∊4), a risk factor for Alzheimer's disease (AD), and diabetes risk on cortical thickness among 107 healthy elderly participants; in particular, participants included 27 APOE-∊4+ and 80 APOE-∊4- controls using T1-weighted structural magnetic resonance imaging. Regions of interests included select frontal, temporal, and parietal cortical regions. Among APOE-∊4, glucose abnormalities independently predicted reduced cortical thickness among temporoparietal regions but failed to predict changes for noncarriers. However, among noncarriers, age independently predicted reduced cortical thickness among temporoparietal and frontal regions. Diabetes risk is particularly important for the integrity of cortical gray matter in APOE-∊4 and demonstrates a pattern of thinning that is expected in preclinical AD. However, in the absence of this genetic factor, age confers risk for reduced cortical thickness among regions of expected compromise. This study supports aggressive management of cerebrovascular factors and earlier preclinical detection of AD among individuals presenting with genetic and metabolic risks.
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Affiliation(s)
- Jessica M Foley
- Department of Psychiatry, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - David H Salat
- Department of Psychiatry, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Nikki H Stricker
- Department of Psychiatry, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Regina E McGlinchey
- Department of Psychiatry, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - William P Milberg
- Department of Psychiatry, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Laura J Grande
- Department of Psychiatry, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Elizabeth C Leritz
- Department of Psychiatry, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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25
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Cerebral atrophy in mild cognitive impairment: A systematic review with meta-analysis. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2015; 1:487-504. [PMID: 27239527 PMCID: PMC4879488 DOI: 10.1016/j.dadm.2015.11.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Although mild cognitive impairment (MCI) diagnosis is mainly based on cognitive assessment, reliable estimates of structural changes in specific brain regions, that could be contrasted against normal brain aging and inform diagnosis, are lacking. This study aimed to systematically review the literature reporting on MCI-related brain changes. METHODS The MEDLINE database was searched for studies investigating longitudinal structural changes in MCI. Studies with compatible data were included in the meta-analyses. A qualitative review was conducted for studies excluded from meta-analyses. RESULTS The analyses revealed a 2.2-fold higher volume loss in the hippocampus, 1.8-fold in the whole brain, and 1.5-fold in the entorhinal cortex in MCI participants. DISCUSSION Although the medial temporal lobe is likely to be more vulnerable to MCI pathology, atrophy in this brain area represents a relatively small proportion of whole brain loss, suggesting that future investigations are needed to identify the source of unaccounted volume loss in MCI.
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26
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Pankov A, Binney RJ, Staffaroni AM, Kornak J, Attygalle S, Schuff N, Weiner MW, Kramer JH, Dickerson BC, Miller BL, Rosen HJ. Data-driven regions of interest for longitudinal change in frontotemporal lobar degeneration. NEUROIMAGE-CLINICAL 2015; 12:332-40. [PMID: 27547726 PMCID: PMC4983147 DOI: 10.1016/j.nicl.2015.08.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Current research is investigating the potential utility of longitudinal measurement of brain structure as a marker of drug effect in clinical trials for neurodegenerative disease. Recent studies in Alzheimer's disease (AD) have shown that measurement of change in empirically derived regions of interest (ROIs) allows more reliable measurement of change over time compared with regions chosen a-priori based on known effects of AD on brain anatomy. Frontotemporal lobar degeneration (FTLD) is a devastating neurodegenerative disorder for which there are no approved treatments. The goal of this study was to identify an empirical ROI that maximizes the effect size for the annual rate of brain atrophy in FTLD compared with healthy age matched controls, and to estimate the effect size and associated power estimates for a theoretical study that would use change within this ROI as an outcome measure. Eighty six patients with FTLD were studied, including 43 who were imaged twice at 1.5 T and 43 at 3 T, along with 105 controls (37 imaged at 1.5 T and 67 at 3 T). Empirically-derived maps of change were generated separately for each field strength and included the bilateral insula, dorsolateral, medial and orbital frontal, basal ganglia and lateral and inferior temporal regions. The extent of regions included in the 3 T map was larger than that in the 1.5 T map. At both field strengths, the effect sizes for imaging were larger than for any clinical measures. At 3 T, the effect size for longitudinal change measured within the empirically derived ROI was larger than the effect sizes derived from frontal lobe, temporal lobe or whole brain ROIs. The effect size derived from the data-driven 1.5 T map was smaller than at 3 T, and was not larger than the effect size derived from a-priori ROIs. It was estimated that measurement of longitudinal change using 1.5 T MR systems requires approximately a 3-fold increase in sample size to obtain effect sizes equivalent to those seen at 3 T. While the results should be confirmed in additional datasets, these results indicate that empirically derived ROIs can reduce the number of subjects needed for a longitudinal study of drug effects in FTLD compared with a-priori ROIs. Field strength may have a significant impact on the utility of imaging for measuring longitudinal change.
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Affiliation(s)
- Aleksandr Pankov
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Richard J Binney
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - John Kornak
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Suneth Attygalle
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Norbert Schuff
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Michael W Weiner
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Joel H Kramer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | | | - Bruce L Miller
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
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27
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Gorji HT, Haddadnia J. A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI. Neuroscience 2015; 305:361-71. [PMID: 26265552 DOI: 10.1016/j.neuroscience.2015.08.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Revised: 08/04/2015] [Accepted: 08/05/2015] [Indexed: 12/31/2022]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia among older people. The number of patients with AD will grow rapidly each year and AD is the fifth leading cause of death for those aged 65 and older. In recent years, one of the main challenges for medical investigators has been the early diagnosis of patients with AD because an early diagnosis can provide greater opportunities for patients to be eligible for more clinical trials and they will have enough time to plan for future, medical and financial decisions. An established risk factor for AD is mild cognitive impairment (MCI) which is described as a transitional state between normal aging and AD patients. Hence an accurate and reliable diagnosis of MCI can be very effective and helpful for early diagnosis of AD. Therefore in this paper we present a novel and efficient method based on pseudo Zernike moments (PZMs) for the diagnosis of MCI individuals from AD and healthy control (HC) groups using structural MRI. The proposed method uses PZMs to extract discriminative information from the MR images of the AD, MCI, and HC groups. Two types of artificial neural networks, which are based on pattern recognition and learning vector quantization (LVQ) networks, were used to classify the information extracted from the MRIs. We worked with 500 MRIs from the database of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1 1.5T). The 1 slice of 500 MRIs used in this study included 180 AD patients, 172 MCI patients, and 148 HC individuals. We selected 50 percent of the MRIs randomly for use in training the classifiers, 25 percent for validation and we used 25 percent for the testing phase. The technique proposed here yielded the best overall classification results between AD and MCI (accuracy 94.88%, sensitivity 94.18%, and specificity 95.55%), and for pairs of the MCI and HC (accuracy 95.59%, sensitivity 95.89% and specificity 95.34%). These results were achieved using maximum order 30 of PZM and the pattern recognition network with the scaled conjugate gradient (SCG) back-propagation training algorithm as a classifier.
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Affiliation(s)
- H T Gorji
- Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran.
| | - J Haddadnia
- Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran
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28
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Liang Z, He X, Ceritoglu C, Tang X, Li Y, Kutten KS, Oishi K, Miller MI, Mori S, Faria AV. Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool. PLoS One 2015. [PMID: 26208327 PMCID: PMC4514626 DOI: 10.1371/journal.pone.0133533] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer’s disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.
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Affiliation(s)
- Zifei Liang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Xiaohai He
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Can Ceritoglu
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Xiaoying Tang
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Yue Li
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Kwame S. Kutten
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Michael I. Miller
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Andreia V. Faria
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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29
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Whitwell JL, Duffy JR, Strand EA, Machulda MM, Tosakulwong N, Weigand SD, Senjem ML, Spychalla AJ, Gunter JL, Petersen RC, Jack CR, Josephs KA. Sample size calculations for clinical trials targeting tauopathies: a new potential disease target. J Neurol 2015; 262:2064-72. [PMID: 26076744 DOI: 10.1007/s00415-015-7821-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 06/08/2015] [Accepted: 06/09/2015] [Indexed: 12/12/2022]
Abstract
Disease-modifying therapies are being developed to target tau pathology, and should, therefore, be tested in primary tauopathies. We propose that progressive apraxia of speech should be considered one such target group. In this study, we investigate potential neuroimaging and clinical outcome measures for progressive apraxia of speech and determine sample size estimates for clinical trials. We prospectively recruited 24 patients with progressive apraxia of speech who underwent two serial MRI with an interval of approximately 2 years. Detailed speech and language assessments included the Apraxia of Speech Rating Scale and Motor Speech Disorders severity scale. Rates of ventricular expansion and rates of whole brain, striatal and midbrain atrophy were calculated. Atrophy rates across 38 cortical regions were also calculated and the regions that best differentiated patients from controls were selected. Sample size estimates required to power placebo-controlled treatment trials were calculated. The smallest sample size estimates were obtained with rates of atrophy of the precentral gyrus and supplementary motor area, with both measures requiring less than 50 subjects per arm to detect a 25% treatment effect with 80% power. These measures outperformed the other regional and global MRI measures and the clinical scales. Regional rates of cortical atrophy, therefore, provide the best outcome measures in progressive apraxia of speech. The small sample size estimates demonstrate feasibility for including progressive apraxia of speech in future clinical treatment trials targeting tau.
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Affiliation(s)
| | - Joseph R Duffy
- Division of Speech Pathology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Edythe A Strand
- Division of Speech Pathology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Division of Neuropsychology, Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nirubol Tosakulwong
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Stephen D Weigand
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.,Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.,Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Ronald C Petersen
- Division of Behavioral Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Keith A Josephs
- Division of Behavioral Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
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30
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2015; 11:e1-120. [PMID: 26073027 PMCID: PMC5469297 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Farzan A, Mashohor S, Ramli AR, Mahmud R. Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns. Behav Brain Res 2015; 290:124-30. [PMID: 25889456 DOI: 10.1016/j.bbr.2015.04.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Revised: 04/04/2015] [Accepted: 04/06/2015] [Indexed: 02/01/2023]
Abstract
OBJECTIVE Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI). METHOD Longitudinal percentage of brain volume changes (PBVC) in two-year follow up and its intermediate counterparts in early 6-month and late 18-month are used as features in supervised and unsupervised classification procedures based on K-mean, fuzzy clustering method (FCM) and support vector machine (SVM). The most relevant features for classification are selected using discriminative analysis (DA) of features and their principal components (PC). Accuracy of the proposed method is evaluated in a group of 30 patients with AD (16 males, 14 females, age±standard-deviation (SD)=75±1.36 years) and 30 normal controls (15 males, 15 females, age±SD=77±0.88 years) using leave-one-out cross-validation. RESULTS Results indicate superiority of supervised machine learning techniques over unsupervised ones in diagnosing AD and withal, predominance of RBF kernel over lineal one. Accuracies of 83.3%, 83.3%, 90% and 91.7% are achieved in classification by K-mean, FCM, linear SVM and SVM with radial based function (RBF) respectively. CONCLUSION Evidence that SVM classification of longitudinal atrophy rates may results in high accuracy is given. Additionally, it is realized that use of intermediate atrophy rates and their principal components improves diagnostic accuracy.
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Affiliation(s)
- Ali Farzan
- Faculty of Computer Engineering, IAU, Shabestar Branch, Iran.
| | - Syansiah Mashohor
- Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia; Institute of Advanced Technology, UPM, Malaysia
| | - Abd Rahman Ramli
- Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Rozi Mahmud
- Faculty of Radiology, University Putra Malaysia (UPM), 43400 Serdang, Selangor D.E., Malaysia
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Suri S, Topiwala A, Mackay CE, Ebmeier KP, Filippini N. Using structural and diffusion magnetic resonance imaging to differentiate the dementias. Curr Neurol Neurosci Rep 2015; 14:475. [PMID: 25030502 DOI: 10.1007/s11910-014-0475-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Dementia is one of the major causes of personal, societal and financial dependence in older people and in today's ageing society there is a pressing need for early and accurate markers of cognitive decline. There are several subtypes of dementia but the four most common are Alzheimer's disease, Lewy body dementia, vascular dementia and frontotemporal dementia. These disorders can only be diagnosed at autopsy, and ante-mortem assessments of "probable dementia (e.g. of Alzheimer type)" are traditionally driven by clinical symptoms of cognitive or behavioural deficits. However, owing to the overlapping nature of symptoms and age of onset, a significant proportion of dementia cases remain incorrectly diagnosed. Misdiagnosis can have an extensive impact, both at the level of the individual, who may not be offered the appropriate treatment, and on a wider scale, by influencing the entry of patients into relevant clinical trials. Magnetic resonance imaging (MRI) may help to improve diagnosis by providing non-invasive and detailed disease-specific markers of cognitive decline. MRI-derived measurements of grey and white matter structural integrity are potential surrogate markers of disease progression, and may also provide valuable diagnostic information. This review summarises the latest evidence on the use of structural and diffusion MRI in differentiating between the four major dementia subtypes.
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Affiliation(s)
- Sana Suri
- Department of Psychiatry, Warneford Hospital, Warneford Lane, University of Oxford, Oxford, OX3 7JX, UK
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Horr T, Messinger-Rapport B, Pillai JA. Systematic review of strengths and limitations of randomized controlled trials for non-pharmacological interventions in mild cognitive impairment: focus on Alzheimer's disease. J Nutr Health Aging 2015; 19:141-53. [PMID: 25651439 DOI: 10.1007/s12603-014-0565-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Non-pharmacological interventions may improve cognition and quality of life, reduce disruptive behaviors, slow progression from Mild Cognitive Impairment (MCI) to dementia, and delay institutionalization. It is important to look at their trial designs as well as outcomes to understand the state of the evidence supporting non-pharmacological interventions in Alzheimer's disease (AD). An analysis of trial design strengths and limitations may help researchers clarify treatment effect and design future studies of non-pharmacological interventions for MCI related to AD. METHODS A systematic review of the methodology of Randomized Controlled Trials (RCTs) targeting physical activity, cognitive interventions, and socialization among subjects with MCI in AD reported until March 2014 was undertaken. The primary outcome was CONSORT 2010 reporting quality. Secondary outcomes were qualitative assessments of specific methodology problems. RESULTS 23 RCT studies met criteria for this review. Eight focused on physical activity, fourteen on cognitive interventions, and one on the effects of socialization. Most studies found a benefit with the intervention compared to control. CONSORT reporting quality of physical activity interventions was higher than that of cognitive interventions. Reporting quality of recent studies was higher than older studies, particularly with respect to sample size, control characteristics, and methodology of intervention training and delivery. However, the heterogeneity of subjects identified as having MCI and variability in interventions and outcomes continued to limit generalizability. CONCLUSIONS The role for non-pharmacological interventions targeting MCI is promising. Future studies of RCTs for non-pharmacological interventions targeting MCI related to AD may benefit by addressing design limitations.
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Affiliation(s)
- T Horr
- J.A. Pillai, MBBS, PhD, Staff Neurologist, Lou Ruvo Center for Brain Health, Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, 9500 Euclid Ave / U10, Cleveland, OH 44195, Tel: 216 636 9467, Fax: 216 445 7013, E-mail:
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Single time point high-dimensional morphometry in Alzheimer's disease: group statistics on longitudinally acquired data. Neurobiol Aging 2015; 36 Suppl 1:S11-22. [DOI: 10.1016/j.neurobiolaging.2014.06.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 06/10/2014] [Accepted: 06/14/2014] [Indexed: 12/21/2022]
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Gutman BA, Wang Y, Yanovsky I, Hua X, Toga AW, Jack CR, Weiner MW, Thompson PM. Empowering imaging biomarkers of Alzheimer's disease. Neurobiol Aging 2015; 36 Suppl 1:S69-80. [PMID: 25260848 PMCID: PMC4268333 DOI: 10.1016/j.neurobiolaging.2014.05.038] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2013] [Revised: 05/22/2014] [Accepted: 05/23/2014] [Indexed: 01/18/2023]
Abstract
In a previous report, we proposed a method for combining multiple markers of atrophy caused by Alzheimer's disease into a single atrophy score that is more powerful than any one feature. We applied the method to expansion rates of the lateral ventricles, achieving the most powerful ventricular atrophy measure to date. Here, we expand our method's application to tensor-based morphometry measures. We also combine the volumetric tensor-based morphometry measures with previously computed ventricular surface measures into a combined atrophy score. We show that our atrophy scores are longitudinally unbiased with the intercept bias estimated at 2 orders of magnitude below the mean atrophy of control subjects at 1 year. Both approaches yield the most powerful biomarker of atrophy not only for ventricular measures but also for all published unbiased imaging measures to date. A 2-year trial using our measures requires only 31 (22, 43) Alzheimer's disease subjects or 56 (44, 64) subjects with mild cognitive impairment to detect 25% slowing in atrophy with 80% power and 95% confidence.
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Affiliation(s)
- Boris A Gutman
- USC Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Igor Yanovsky
- UCLA Joint Institute for Regional Earth System Science and Engineering, Los Angeles, CA, USA
| | - Xue Hua
- USC Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA, USA; Department of Medicine, UC San Francisco, San Francisco, CA, USA; Department of Psychiatry, UC San Francisco, San Francisco, CA, USA
| | - Paul M Thompson
- USC Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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Abramson RG, Burton KR, Yu JPJ, Scalzetti EM, Yankeelov TE, Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, Ganeshan D, Lenchik L, Subramaniam RM. Methods and challenges in quantitative imaging biomarker development. Acad Radiol 2015; 22:25-32. [PMID: 25481515 PMCID: PMC4258641 DOI: 10.1016/j.acra.2014.09.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 09/03/2014] [Accepted: 09/03/2014] [Indexed: 12/18/2022]
Abstract
Academic radiology is poised to play an important role in the development and implementation of quantitative imaging (QI) tools. This article, drafted by the Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force, reviews current issues in QI biomarker research. We discuss motivations for advancing QI, define key terms, present a framework for QI biomarker research, and outline challenges in QI biomarker development. We conclude by describing where QI research and development is currently taking place and discussing the paramount role of academic radiology in this rapidly evolving field.
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Affiliation(s)
- Richard G. Abramson
- Department of Radiology and Radiological Sciences Vanderbilt University 1161 21 Ave. S, CCC-1121 MCN Nashville, TN 37232-2675 (615)322-6759 Fax (615) 322-3764
| | - Kirsteen R. Burton
- Dept. of Medical Imaging and Institute of Health Policy, Management and Evaluation University of Toronto 263 McCaul Street, 4th Floor Toronto, ON M5T1W7 (416) 978-6801
| | - John-Paul J. Yu
- Department of Radiology and Biomedical Imaging University of California, San Francisco 505 Parnassus Ave., M-391 Box 0628 San Francisco, CA 94143-0628
| | - Ernest M. Scalzetti
- Department of Radiology SUNY Upstate Medical University 750 E. Adams St. Syracuse NY 13210
| | - Thomas E. Yankeelov
- Institute of Imaging Science Vanderbilt University 1161 21 Ave. S, AA-1105 MCN Nashville, TN 37232-2310
| | - Andrew B. Rosenkrantz
- Department of Radiology NYU Langone Medical Center 550 First Avenue New York, NY 10016 (212) 263-0232 fax: (212) 263-6634
| | - Mishal Mendiratta-Lala
- Abdominal and Cross-sectional Interventional Radiology Henry Ford Hospital 2799 West Grand Blvd. Detroit, MI 48202 (313) 461-1648
| | - Brian J. Bartholmai
- Chair, Division of Radiology Informatics Mayo Clinic Rochester, MN Phone 507-284-4292 FAX: 507-284-8996
| | - Dhakshinamoorthy Ganeshan
- Department of Abdominal Imaging University of Texas MD Anderson Cancer Center Houston, TX 77030 713-792-2486 Fax: 713-745-1151
| | - Leon Lenchik
- Department of Radiology Wake Forest School of Medicine Medical Center Boulevard Winston-Salem, NC 27157 Phone: 336-716-4316 Fax: 336-716-1278
| | - Rathan M. Subramaniam
- Russell H Morgan Department of Radiology and Radiological Sciences Johns Hopkins School of Medicine Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Johns Hopkins University Baltimore, MD
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High-Dimensional Medial Lobe Morphometry: An Automated MRI Biomarker for the New AD Diagnostic Criteria. Int J Alzheimers Dis 2014; 2014:278096. [PMID: 25254139 PMCID: PMC4164123 DOI: 10.1155/2014/278096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 07/25/2014] [Indexed: 11/21/2022] Open
Abstract
Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer's disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. The study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer's Disease Neuroimaging Initiative, with data acquired on 58 different 1.5 T scanners. We measured the sensitivity and specificity of our technique in a hierarchical fashion, first testing the effect of intensity standardization, then between different volumes of interest, and finally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. The positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique's ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD.
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Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. Neuroimage 2014; 101:778-86. [PMID: 25111470 DOI: 10.1016/j.neuroimage.2014.08.003] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 07/18/2014] [Accepted: 08/02/2014] [Indexed: 11/22/2022] Open
Abstract
Cognitive decline in normal ageing and Alzheimer's disease (AD) emerges from functional disruption in the coordination of large-scale brain systems sustaining cognition. Integrity of these systems can be examined by correlation methods based on analysis of resting state functional magnetic resonance imaging (fMRI). Here we investigate functional connectivity within the default mode network (DMN) in normal ageing and AD using resting state fMRI. Images from young and elderly controls, and patients with AD were processed using spatial independent component analysis to identify the DMN. Functional connectivity was quantified using integration and indices derived from graph theory. Four DMN sub-systems were identified: Frontal (medial and superior), parietal (precuneus-posterior cingulate, lateral parietal), temporal (medial temporal), and hippocampal (bilateral). There was a decrease in antero-posterior interactions (lower global efficiency), but increased interactions within the frontal and parietal sub-systems (higher local clustering) in elderly compared to young controls. This decreased antero-posterior integration was more pronounced in AD patients compared to elderly controls, particularly in the precuneus-posterior cingulate region. Conjoint knowledge of integration measures and graph indices in the same data helps in the interpretation of functional connectivity results, as comprehension of one measure improves with understanding of the other. The approach allows for complete characterisation of connectivity changes and could be applied to other resting state networks and different pathologies.
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Tessa C, Lucetti C, Giannelli M, Diciotti S, Poletti M, Danti S, Baldacci F, Vignali C, Bonuccelli U, Mascalchi M, Toschi N. Progression of brain atrophy in the early stages of Parkinson's disease: a longitudinal tensor-based morphometry study in de novo patients without cognitive impairment. Hum Brain Mapp 2014; 35:3932-44. [PMID: 24453162 PMCID: PMC6868950 DOI: 10.1002/hbm.22449] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Revised: 11/06/2013] [Accepted: 11/29/2013] [Indexed: 11/11/2022] Open
Abstract
The presence of brain atrophy and its progression in early Parkinson's disease (PD) are still a matter of debate, particularly in patients without cognitive impairment. The aim of this longitudinal study was to assess whether PD patients who remain cognitively intact develop progressive atrophic changes in the early stages of the disease. For this purpose, we employed high-resolution T1-weighted MR imaging to compare 22 drug-naïve de novo PD patients without cognitive impairment to 17 age-matched control subjects, both at baseline and at three-year follow-up. We used tensor-based morphometry to explore the presence of atrophic changes at baseline and to compute yearly atrophy rates, after which we performed voxel-wise group comparisons using threshold-free cluster enhancement. At baseline, we did not observe significant differences in regional atrophy in PD patients with respect to control subjects. In contrast, PD patients showed significantly higher yearly atrophy rates in the prefrontal cortex, anterior cingulum, caudate nucleus, and thalamus when compared to control subjects. Our results indicate that even cognitively preserved PD patients show progressive cortical and subcortical atrophic changes in regions related to cognitive functions and that these changes are already detectable in the early stages of the disease.
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Affiliation(s)
- Carlo Tessa
- Division of RadiologyVersilia Hospital, AUSL 12 Viareggio, Lido di Camaiore (Lu)Italy
| | - Claudio Lucetti
- Division of NeurologyVersilia Hospital, AUSL 12 Viareggio, Lido di Camaiore (Lu)Italy
| | - Marco Giannelli
- Unit of Medical PhysicsPisa University Hospital “Azienda Ospedaliero‐Universitaria Pisana”PisaItaly
| | - Stefano Diciotti
- Quantitative and Functional Neuroradiology Research UnitDepartment of Experimental and Clinical Biomedical SciencesUniversity of FlorenceFlorenceItaly
| | - Michele Poletti
- Department of Mental Health and Pathological AddictionAUSL Reggio EmiliaReggio EmiliaItaly
| | - Sabrina Danti
- Division of PsychologyVersilia Hospital, AUSL 12 ViareggioLido di Camaiore (Lu)Italy
| | | | - Claudio Vignali
- Division of RadiologyVersilia Hospital, AUSL 12 Viareggio, Lido di Camaiore (Lu)Italy
| | | | - Mario Mascalchi
- Quantitative and Functional Neuroradiology Research UnitDepartment of Experimental and Clinical Biomedical SciencesUniversity of FlorenceFlorenceItaly
| | - Nicola Toschi
- Medical Physics SectionDepartment of Biomedicine and PreventionFaculty of MedicineUniversity of Rome “Tor Vergata”RomeItaly
- Department of RadiologyAthinoula A. Martinos Center for Biomedical ImagingBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
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Nedelska Z, Ferman TJ, Boeve BF, Przybelski SA, Lesnick TG, Murray ME, Gunter JL, Senjem ML, Vemuri P, Smith GE, Geda YE, Graff-Radford J, Knopman DS, Petersen RC, Parisi JE, Dickson DW, Jack CR, Kantarci K. Pattern of brain atrophy rates in autopsy-confirmed dementia with Lewy bodies. Neurobiol Aging 2014; 36:452-61. [PMID: 25128280 DOI: 10.1016/j.neurobiolaging.2014.07.005] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Revised: 06/16/2014] [Accepted: 07/08/2014] [Indexed: 11/27/2022]
Abstract
Dementia with Lewy bodies (DLB) is characterized by preserved whole brain and medial temporal lobe volumes compared with Alzheimer's disease dementia (AD) on magnetic resonance imaging. However, frequently coexistent AD-type pathology may influence the pattern of regional brain atrophy rates in DLB patients. We investigated the pattern and magnitude of the atrophy rates from 2 serial MRIs in autopsy-confirmed DLB patients (n = 20) and mixed DLB/AD patients (n = 22), compared with AD (n = 30) and elderly nondemented control subjects (n = 15), followed antemortem. DLB patients without significant AD-type pathology were characterized by lower global and regional rates of atrophy, similar to control subjects. The mixed DLB/AD patients displayed greater atrophy rates in the whole brain, temporoparietal cortices, hippocampus and amygdala, and ventricle expansion, similar to AD patients. In the DLB and DLB/AD patients, the atrophy rates correlated with Braak neurofibrillary tangle stage, cognitive decline, and progression of motor symptoms. Global and regional atrophy rates are associated with AD-type pathology in DLB, and these rates can be used as biomarkers of AD progression in patients with LB pathology.
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Affiliation(s)
- Zuzana Nedelska
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Department of Neurology, 2nd Faculty of Medicine and Motol University Hospital, Charles University in Prague, Prague, the Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Brno, the Czech Republic
| | - Tanis J Ferman
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | | | - Timothy G Lesnick
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Glenn E Smith
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Yonas E Geda
- Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, USA; Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA
| | | | | | | | - Joseph E Parisi
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, MN, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Neuropathology Laboratory, Mayo Clinic, Jacksonville, FL, USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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Progression of brain atrophy in spinocerebellar ataxia type 2: a longitudinal tensor-based morphometry study. PLoS One 2014; 9:e89410. [PMID: 24586758 PMCID: PMC3934889 DOI: 10.1371/journal.pone.0089410] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2013] [Accepted: 01/20/2014] [Indexed: 12/28/2022] Open
Abstract
Spinocerebellar ataxia type 2 (SCA2) is the second most frequent autosomal dominant inherited ataxia worldwide. We investigated the capability of magnetic resonance imaging (MRI) to track in vivo progression of brain atrophy in SCA2 by examining twice 10 SCA2 patients (mean interval 3.6 years) and 16 age- and gender-matched healthy controls (mean interval 3.3 years) on the same 1.5 T MRI scanner. We used T1-weighted images and tensor-based morphometry (TBM) to investigate volume changes and the Inherited Ataxia Clinical Rating Scale to assess the clinical deficit. With respect to controls, SCA2 patients showed significant higher atrophy rates in the midbrain, including substantia nigra, basis pontis, middle cerebellar peduncles and posterior medulla corresponding to the gracilis and cuneatus tracts and nuclei, cerebellar white matter (WM) and cortical gray matter (GM) in the inferior portions of the cerebellar hemisphers. No differences in WM or GM volume loss were observed in the supratentorial compartment. TBM findings did not correlate with modifications of the neurological deficit. In conclusion, MRI volumetry using TBM is capable of demonstrating the progression of pontocerebellar atrophy in SCA2, supporting a possible role of MRI as biomarker in future trials.
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de Marvao A, Dawes TJW, Shi W, Minas C, Keenan NG, Diamond T, Durighel G, Montana G, Rueckert D, Cook SA, O’Regan DP. Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power. J Cardiovasc Magn Reson 2014; 16:16. [PMID: 24490638 PMCID: PMC3914701 DOI: 10.1186/1532-429x-16-16] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2013] [Accepted: 01/29/2014] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Cardiac phenotypes, such as left ventricular (LV) mass, demonstrate high heritability although most genes associated with these complex traits remain unidentified. Genome-wide association studies (GWAS) have relied on conventional 2D cardiovascular magnetic resonance (CMR) as the gold-standard for phenotyping. However this technique is insensitive to the regional variations in wall thickness which are often associated with left ventricular hypertrophy and require large cohorts to reach significance. Here we test whether automated cardiac phenotyping using high spatial resolution CMR atlases can achieve improved precision for mapping wall thickness in healthy populations and whether smaller sample sizes are required compared to conventional methods. METHODS LV short-axis cine images were acquired in 138 healthy volunteers using standard 2D imaging and 3D high spatial resolution CMR. A multi-atlas technique was used to segment and co-register each image. The agreement between methods for end-diastolic volume and mass was made using Bland-Altman analysis in 20 subjects. The 3D and 2D segmentations of the LV were compared to manual labeling by the proportion of concordant voxels (Dice coefficient) and the distances separating corresponding points. Parametric and nonparametric data were analysed with paired t-tests and Wilcoxon signed-rank test respectively. Voxelwise power calculations used the interstudy variances of wall thickness. RESULTS The 3D volumetric measurements showed no bias compared to 2D imaging. The segmented 3D images were more accurate than 2D images for defining the epicardium (Dice: 0.95 vs 0.93, P<0.001; mean error 1.3 mm vs 2.2 mm, P<0.001) and endocardium (Dice 0.95 vs 0.93, P<0.001; mean error 1.1 mm vs 2.0 mm, P<0.001). The 3D technique resulted in significant differences in wall thickness assessment at the base, septum and apex of the LV compared to 2D (P<0.001). Fewer subjects were required for 3D imaging to detect a 1 mm difference in wall thickness (72 vs 56, P<0.001). CONCLUSIONS High spatial resolution CMR with automated phenotyping provides greater power for mapping wall thickness than conventional 2D imaging and enables a reduction in the sample size required for studies of environmental and genetic determinants of LV wall thickness.
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Affiliation(s)
- Antonio de Marvao
- From the Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
| | - Timothy JW Dawes
- From the Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
| | - Wenzhe Shi
- Department of Computing, Imperial College London, Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | - Christopher Minas
- Department of Mathematics, Imperial College London, South Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | - Niall G Keenan
- Department of Cardiology, Imperial College NHS Healthcare Trust, Du Cane Road, London W12 0HS, UK
| | - Tamara Diamond
- From the Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
| | - Giuliana Durighel
- From the Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
| | - Giovanni Montana
- Department of Mathematics, Imperial College London, South Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | - Stuart A Cook
- From the Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Department of Cardiology, National Heart Centre Singapore, 17 Third Hospital Ave, Singapore 168752, Singapore
- Duke-NUS, 8 College Road, Singapore 169857, Singapore
| | - Declan P O’Regan
- From the Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
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Liu CY, Iglesias JE, Tu Z. Deformable templates guided discriminative models for robust 3D brain MRI segmentation. Neuroinformatics 2013; 11:447-68. [PMID: 23836390 PMCID: PMC5966025 DOI: 10.1007/s12021-013-9190-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.
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Affiliation(s)
- Cheng-Yi Liu
- Laboratory of Neuro Imaging Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225, 90095, Los Angeles, CA, USA,
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Jiang J, Sachdev P, Lipnicki DM, Zhang H, Liu T, Zhu W, Suo C, Zhuang L, Crawford J, Reppermund S, Trollor J, Brodaty H, Wen W. A longitudinal study of brain atrophy over two years in community-dwelling older individuals. Neuroimage 2013; 86:203-11. [PMID: 23959201 DOI: 10.1016/j.neuroimage.2013.08.022] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Revised: 07/26/2013] [Accepted: 08/11/2013] [Indexed: 10/26/2022] Open
Abstract
Most previous neuroimaging studies of age-related brain structural changes in older individuals have been cross-sectional and/or restricted to clinical samples. The present study of 345 community-dwelling non-demented individuals aged 70-90years aimed to examine age-related brain volumetric changes over two years. T1-weighted magnetic resonance imaging scans were obtained at baseline and at 2-year follow-up and analyzed using the FMRIB Software Library and FreeSurfer to investigate cortical thickness and shape and volumetric changes of subcortical structures. The results showed significant atrophy across much of the cerebral cortex with bilateral transverse temporal regions shrinking the fastest. Atrophy was also found in a number of subcortical structures, including the CA1 and subiculum subfields of the hippocampus. In some regions, such as left and right entorhinal cortices, right hippocampus and right precentral area, the rate of atrophy increased with age. Our analysis also showed that rostral middle frontal regions were thicker bilaterally in older participants, which may indicate its ability to compensate for medial temporal lobe atrophy. Compared to men, women had thicker cortical regions but greater rates of cortical atrophy. Women also had smaller subcortical structures. A longer period of education was associated with greater thickness in a number of cortical regions. Our results suggest a pattern of brain atrophy with non-demented people that resembles a less extreme form of the changes associated with Alzheimer's disease (AD).
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Affiliation(s)
- Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Darren M Lipnicki
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Haobo Zhang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Tao Liu
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Wanlin Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Chao Suo
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Lin Zhuang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - John Crawford
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Simone Reppermund
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Julian Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Department of Development Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Aged Care Psychiatry, Prince of Wales Hospital, Randwick, NSW, Australia; Dementia Collaborative Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia.
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E, Morris JC, Petersen RC, Saykin AJ, Schmidt ME, Shaw L, Shen L, Siuciak JA, Soares H, Toga AW, Trojanowski JQ. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 2013; 9:e111-94. [PMID: 23932184 DOI: 10.1016/j.jalz.2013.05.1769] [Citation(s) in RCA: 308] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/19/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
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Nir TM, Jahanshad N, Villalon-Reina JE, Toga AW, Jack CR, Weiner MW, Thompson PM. Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging. Neuroimage Clin 2013; 3:180-95. [PMID: 24179862 PMCID: PMC3792746 DOI: 10.1016/j.nicl.2013.07.006] [Citation(s) in RCA: 223] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Revised: 07/03/2013] [Accepted: 07/21/2013] [Indexed: 01/08/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.
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Affiliation(s)
- Talia M. Nir
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Julio E. Villalon-Reina
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Arthur W. Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic and Foundation,
Rochester, MN, USA
| | - Michael W. Weiner
- Department of Radiology and Biomedical Imaging, UCSF School
of Medicine, San Francisco, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
- Deptartment of Psychiatry, Semel Institute, UCLA School of
Medicine, Los Angeles, CA, USA
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Lista S, Garaci FG, Ewers M, Teipel S, Zetterberg H, Blennow K, Hampel H. CSF Aβ1-42 combined with neuroimaging biomarkers in the early detection, diagnosis and prediction of Alzheimer's disease. Alzheimers Dement 2013; 10:381-92. [PMID: 23850330 DOI: 10.1016/j.jalz.2013.04.506] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 04/29/2013] [Indexed: 11/17/2022]
Abstract
The development of validated, qualified, and standardized biomarkers for Alzheimer's disease (AD) that allow for an early presymptomatic diagnosis and discrimination (classification) from other types of dementia and neurodegenerative diseases is warranted to accelerate the successful development of novel disease-modifying therapies. Here, we focus on the value of the 42-residue-long amyloid β isoform (Aβ1-42) peptide in the cerebrospinal fluid as the core, feasible neurobiochemical marker for the amyloidogenic mechanisms in early-onset familial and late-onset sporadic AD. We discuss the role and use of Aβ1-42 in combination with evolving neuroimaging biomarkers in AD detection and diagnosis. Multimodal neuroimaging techniques, directly providing structural-functional-metabolic aspects of brain pathophysiology, are supportive to predict and monitor the progression of the disease. Advances in multimodal neuroimaging provide new insights into brain organization and enable the detection of specific proteins and/or protein aggregates associated with AD. The combination of biomarkers from different methodologies is believed to be of incrementally added risk-value to accurately identify asymptomatic and prodromal individuals who will likely progress to dementia and represent rational biomarker candidates for preventive and symptomatic pharmacological intervention trials.
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Affiliation(s)
- Simone Lista
- Department of Psychiatry, Goethe-University, Frankfurt am Main, Germany.
| | - Francesco G Garaci
- Department of Diagnostic Imaging, Molecular Imaging, Interventional Radiology, and Radiotherapy, University of Rome "Tor Vergata," Rome, Italy; IRCCS San Raffaele Pisana, Rome, Italy
| | - Michael Ewers
- Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Stefan Teipel
- Department of Psychiatry, University of Rostock, Rostock, Germany DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; University College London Institute of Neurology, Queen Square, London, UK
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Harald Hampel
- Department of Psychiatry, Goethe-University, Frankfurt am Main, Germany
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Kohannim O, Hua X, Rajagopalan P, Hibar DP, Jahanshad N, Grill JD, Apostolova LG, Toga AW, Jack CR, Weiner MW, Thompson PM. Multilocus genetic profiling to empower drug trials and predict brain atrophy. NEUROIMAGE-CLINICAL 2013; 2:827-35. [PMID: 24179834 PMCID: PMC3777716 DOI: 10.1016/j.nicl.2013.05.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 04/14/2013] [Accepted: 05/11/2013] [Indexed: 12/16/2022]
Abstract
Designers of clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10–20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials. ApoE genotype status helps enrich MCI trials, using a structural MRI outcome measure. CLU, PICALM and CR1 risk genes boost potential MCI trial power beyond ApoE alone. CLU, PICALM and CR1 show significant, aggregate effects on TBM maps of brain atrophy.
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Affiliation(s)
- Omid Kohannim
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Priya Rajagopalan
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Derrek P. Hibar
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Joshua D. Grill
- Mary Easton Center for Alzheimer's Disease Research, UCLA School of Medicine, Los Angeles, CA, USA
| | - Liana G. Apostolova
- Mary Easton Center for Alzheimer's Disease Research, UCLA School of Medicine, Los Angeles, CA, USA
| | - Arthur W. Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | | | - Michael W. Weiner
- Depts. of Radiology, Medicine and Psychiatry, UCSF, San Francisco, CA, USA
- Dept. of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
- Corresponding author at: Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA. Tel.: + 1 310 206 2101; fax: + 1 310 206 5518.
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Fletcher E, Knaack A, Singh B, Lloyd E, Wu E, Carmichael O, DeCarli C. Combining boundary-based methods with tensor-based morphometry in the measurement of longitudinal brain change. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:223-236. [PMID: 23014714 PMCID: PMC3775845 DOI: 10.1109/tmi.2012.2220153] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Tensor-based morphometry is a powerful tool for automatically computing longitudinal change in brain structure. Because of bias in images and in the algorithm itself, however, a penalty term and inverse consistency are needed to control the over-reporting of nonbiological change. These may force a tradeoff between the intrinsic sensitivity and specificity, potentially leading to an under-reporting of authentic biological change with time. We propose a new method incorporating prior information about tissue boundaries (where biological change is likely to exist) that aims to keep the robustness and specificity contributed by the penalty term and inverse consistency while maintaining localization and sensitivity. Results indicate that this method has improved sensitivity without increased noise. Thus it will have enhanced power to detect differences within normal aging and along the spectrum of cognitive impairment.
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Affiliation(s)
- Evan Fletcher
- IDeA Laboratory, Department of Neurology, University of California-Davis, Davis, CA 95618, USA.
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50
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Gutman BA, Hua X, Rajagopalan P, Chou YY, Wang Y, Yanovsky I, Toga AW, Jack CR, Weiner MW, Thompson PM. Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features. Neuroimage 2013; 70:386-401. [PMID: 23296188 DOI: 10.1016/j.neuroimage.2012.12.052] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2012] [Revised: 12/15/2012] [Accepted: 12/18/2012] [Indexed: 01/20/2023] Open
Abstract
We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80-in two-fold nested cross-validation-is 104 MCI subjects (95% CI: [94,139]) for a 1-year drug trial, and 75AD subjects [64,102]. This compares favorably with other MRI analysis methods. The standard "statistical ROI" approach applied to the same ventricular surfaces requires 165 MCI or 94AD subjects. At 2 years, the best LDA measure needs only 67 MCI and 52AD subjects, versus 119 MCI and 80AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.
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Affiliation(s)
- Boris A Gutman
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA
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