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N-acetyl-aspartate and Myo-inositol as Markers of White Matter Microstructural Organization in Mild Cognitive Impairment: Evidence from a DTI- 1H-MRS Pilot Study. Diagnostics (Basel) 2023; 13:diagnostics13040654. [PMID: 36832141 PMCID: PMC9955118 DOI: 10.3390/diagnostics13040654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
We implemented a multimodal approach to examine associations between structural and neurochemical changes that could signify neurodegenerative processes related to mild cognitive impairment (MCI). Fifty-nine older adults (60-85 years; 22 MCI) underwent whole-brain structural 3T MRI (T1W, T2W, DTI) and proton magnetic resonance spectroscopy (1H-MRS). The regions of interest (ROIs) for 1H-MRS measurements were the dorsal posterior cingulate cortex, left hippocampal cortex, left medial temporal cortex, left primary sensorimotor cortex, and right dorsolateral prefrontal cortex. The findings revealed that subjects in the MCI group showed moderate to strong positive associations between the total N-acetylaspartate to total creatine and the total N-acetylaspartate to myo-inositol ratios in the hippocampus and dorsal posterior cingulate cortex and fractional anisotropy (FA) of WM tracts crossing these regions-specifically, the left temporal tapetum, right corona radiata, and right posterior cingulate gyri. In addition, negative associations between the myo-inositol to total creatine ratio and FA of the left temporal tapetum and right posterior cingulate gyri were observed. These observations suggest that the biochemical integrity of the hippocampus and cingulate cortex is associated with a microstructural organization of ipsilateral WM tracts originating in the hippocampus. Specifically, elevated myo-inositol might be an underlying mechanism for decreased connectivity between the hippocampus and the prefrontal/cingulate cortex in MCI.
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Chiang GC, Cho J, Dyke J, Zhang H, Zhang Q, Tokov M, Nguyen T, Kovanlikaya I, Amoashiy M, de Leon M, Wang Y. Brain oxygen extraction and neural tissue susceptibility are associated with cognitive impairment in older individuals. J Neuroimaging 2022; 32:697-709. [PMID: 35294075 DOI: 10.1111/jon.12990] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/02/2022] [Accepted: 03/02/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND AND PURPOSE We investigated the effects of aging, white matter hyperintensities (WMH), and cognitive impairment on brain iron levels and cerebral oxygen metabolism, known to be altered in Alzheimer's disease (AD), using quantitative susceptibility mapping and MR-based cerebral oxygen extraction fraction (OEF). METHODS In 100 individuals over the age of 50 (68/32 cognitively impaired/intact), OEF and neural tissue susceptibility (χn ) were computed retrospectively from MRI multi-echo gradient echo data, obtained on a 3 Tesla MRI scanner. The effects of age and WMH on OEF and χn were assessed within groups, and OEF and χn were assessed between groups, using multivariate regression analyses. RESULTS Cognitively impaired subjects were found to have 19% higher OEF and 34% higher χn than cognitively intact subjects in the cortical gray matter and several frontal, temporal, and parietal regions (p < .05). Increased WMH burden was significantly associated with decreased OEF in the cognitively impaired, but not in the cognitively intact. Older age had a stronger association with decreased OEF in the cognitively intact group. Both older age and increased WMH burden were significantly associated with increased χn in temporoparietal regions in the cognitively impaired. CONCLUSIONS Higher brain OEF and χn in cognitively impaired older individuals may reflect altered oxygen metabolism and iron in areas with underlying AD pathology. Both age and WMH have associations with OEF and χn but are modified by the presence of cognitive impairment.
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Affiliation(s)
- Gloria C Chiang
- Department of Radiology, Division of Neuroradiology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Junghun Cho
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jonathan Dyke
- Citigroup Biomedical Imaging Center, Weill Cornell Medicine, New York, New York, USA
| | - Hang Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Qihao Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Michael Tokov
- New York Institute of Technology College of Osteopathic Medicine, Glen Head, New York, USA
| | - Thanh Nguyen
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Ilhami Kovanlikaya
- Department of Radiology, Division of Neuroradiology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Michael Amoashiy
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Mony de Leon
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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Schmahmann JD, Vangel MG, Hoche F, Guell X, Sherman JC. Reply: Reference values for the Cerebellar Cognitive Affective Syndrome Scale: age and education matter. Brain 2021; 144:e21. [PMID: 33313688 DOI: 10.1093/brain/awaa419] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Jeremy D Schmahmann
- Ataxia Center, Cognitive Behavioral Neurology Unit, Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Mark G Vangel
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Franziska Hoche
- Ataxia Center, Cognitive Behavioral Neurology Unit, Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Xavier Guell
- Ataxia Center, Cognitive Behavioral Neurology Unit, Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Janet C Sherman
- Psychology Assessment Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Disturbances in brain energy metabolism in insulin resistance and diabetes and Alzheimer's disease - Learnings from brain imaging biomarkers. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 154:111-130. [PMID: 32739001 DOI: 10.1016/bs.irn.2020.02.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Medical imaging techniques, such as structural and functional magnetic resonance imaging and positron emission tomography, have been used to gain a better understanding of the alterations of the metabolic processes in the brain relating to type 2 diabetes melltius, insulin resistance and Alzheimer's disease. These studies have shown that there are several similarities in the effects that these seemingly disparate diseases have on the brain, and that some of the abnormalities are reversed by metabolic interventions. This review provides an overview of the overlap between these diseases using medical imaging, focusing on glucose metabolism, mitochondrial function and lipid metabolism.
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Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8915961. [PMID: 32549888 PMCID: PMC7256687 DOI: 10.1155/2020/8915961] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 05/06/2020] [Indexed: 12/20/2022]
Abstract
Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N = 9), mild cognitive impairment (N = 19), and subjective cognitive complaints (N = 4) and healthy controls (N = 18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline.
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Seiler S, Fletcher E, Hassan-Ali K, Weinstein M, Beiser A, Himali JJ, Satizabal CL, Seshadri S, DeCarli C, Maillard P. Cerebral tract integrity relates to white matter hyperintensities, cortex volume, and cognition. Neurobiol Aging 2018; 72:14-22. [PMID: 30172922 PMCID: PMC6242702 DOI: 10.1016/j.neurobiolaging.2018.08.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 07/19/2018] [Accepted: 08/03/2018] [Indexed: 01/09/2023]
Abstract
We examined the relationship among white matter (WM) tract integrity, WM hyperintensities (WMH), lobar gray matter (GM) volumes, and cognition in the cross-sectional Framingham Offspring Study. Six hundred eighty participants (71.7 ± 7.7 years) completed cognitive testing and magnetic resonance imaging. Diffusion tensor imaging probabilistic tractography was used to reconstruct major WM tracts. We computed tract-specific mean fractional anisotropy (FA) and tract-specific WMH ratio. Linear regressions identified relations between tracts and lobar GM volumes. Partial least squares regression examined associations between integrity of combined tracts, lobar GM volumes and cognition, including scores of memory and processing speed. Five tracts were particularly vulnerable to WMH, and tract-specific WMH volumes were inversely associated with tract-specific FA (p values < 0.05). Tract-specific FA related to lobar GM volumes. Memory was associated with lobar GM, while processing speed related to both tract integrity and lobar GM volumes. We conclude that subtle microstructural WM tract degeneration relates to specific lobar GM atrophy. The integrity of associated WM tracts and GM lobes differentially impacts memory and processing speed.
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Affiliation(s)
- Stephan Seiler
- Department of Neurology, Center for Neurosciences, University of California at Davis, Davis, CA, USA; Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, USA; Department of Neurology, Medical University Graz, Graz, Austria.
| | - Evan Fletcher
- Department of Neurology, Center for Neurosciences, University of California at Davis, Davis, CA, USA; Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, USA
| | - Kinsy Hassan-Ali
- Department of Neurology, Center for Neurosciences, University of California at Davis, Davis, CA, USA; Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, USA
| | - Michelle Weinstein
- Department of Neurology, Center for Neurosciences, University of California at Davis, Davis, CA, USA; Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, USA
| | - Alexa Beiser
- The Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jayandra J Himali
- The Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Claudia L Satizabal
- The Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Charles DeCarli
- Department of Neurology, Center for Neurosciences, University of California at Davis, Davis, CA, USA; Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, USA
| | - Pauline Maillard
- Department of Neurology, Center for Neurosciences, University of California at Davis, Davis, CA, USA; Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, USA
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ten Kate M, Ingala S, Schwarz AJ, Fox NC, Chételat G, van Berckel BNM, Ewers M, Foley C, Gispert JD, Hill D, Irizarry MC, Lammertsma AA, Molinuevo JL, Ritchie C, Scheltens P, Schmidt ME, Visser PJ, Waldman A, Wardlaw J, Haller S, Barkhof F. Secondary prevention of Alzheimer's dementia: neuroimaging contributions. Alzheimers Res Ther 2018; 10:112. [PMID: 30376881 PMCID: PMC6208183 DOI: 10.1186/s13195-018-0438-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 10/10/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations. MAIN BODY Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation. CONCLUSION We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.
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Affiliation(s)
- Mara ten Kate
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Adam J. Schwarz
- Takeda Pharmaceuticals Comparny, Cambridge, MA USA
- Eli Lilly and Company, Indianapolis, Indiana USA
| | - Nick C. Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Gaël Chételat
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR-S U1237, Université de Caen-Normandie, GIP Cyceron, Caen, France
| | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | | | | | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Craig Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | | | - Pieter Jelle Visser
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Adam Waldman
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Sven Haller
- Affidea Centre de Diagnostic Radiologique de Carouge, Geneva, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Insititutes of Neurology and Healthcare Engineering, University College London, London, UK
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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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Gyebnár G, Szabó Á, Sirály E, Fodor Z, Sákovics A, Salacz P, Hidasi Z, Csibri É, Rudas G, Kozák LR, Csukly G. What can DTI tell about early cognitive impairment? - Differentiation between MCI subtypes and healthy controls by diffusion tensor imaging. Psychiatry Res Neuroimaging 2018; 272:46-57. [PMID: 29126669 DOI: 10.1016/j.pscychresns.2017.10.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 10/17/2017] [Accepted: 10/21/2017] [Indexed: 01/10/2023]
Abstract
Mild cognitive impairment (MCI) gained a lot of interest recently, especially that the conversion rate to Alzheimer Disease (AD) in the amnestic subtype (aMCI) is higher than in the non-amnestic subtype (naMCI). We aimed to determine whether and how diffusion-weighted MRI (DWI) using the diffusion tensor model (DTI) can differentiate MCI subtypes from healthy subjects. High resolution 3D T1W and DWI images of patients (aMCI, n = 18; naMCI, n = 20; according to Petersen criteria) and controls (n = 27) were acquired at 3T and processed using ExploreDTI and SPM. Voxel-wise and region of interest (ROI) analyses of fractional anisotropy (FA) and mean diffusivity (MD) were performed with ANCOVA; MD was higher in aMCI compared to controls or naMCI in several grey and white matter (GM, WM) regions (especially in the temporal pole and the inferior temporal lobes), while FA was lower in WM ROI-s (e.g. left Cingulum). Moreover, significant correlations were identified between verbal fluency, visual and verbal memory performance and DTI metrics. Logistic regression showed that measuring FA of the crus of fornix along GM volumetry improves the discrimination of aMCI from naMCI. Additional information from DWI/DTI aids preclinical detection of AD and may help detecting early non-Alzheimer type dementia, too.
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Affiliation(s)
- Gyula Gyebnár
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary.
| | - Ádám Szabó
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Enikő Sirály
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Anna Sákovics
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Pál Salacz
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary; Department of Neurology, Péterfy Hospital and Trauma Centre, Budapest, Hungary
| | - Zoltán Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Éva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gábor Rudas
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Lajos R Kozák
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
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Maksimovich IV. Differences in Cerebral Angioarchitectonics in Alzheimer's Disease in Comparison with Other Neurodegenerative and Ischemic Lesions. ACTA ACUST UNITED AC 2018. [DOI: 10.4236/wjns.2018.84036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Perrotin A, La Joie R, de La Sayette V, Barré L, Mézenge F, Mutlu J, Guilloteau D, Egret S, Eustache F, Chételat G. Subjective cognitive decline in cognitively normal elders from the community or from a memory clinic: Differential affective and imaging correlates. Alzheimers Dement 2016; 13:550-560. [PMID: 27693187 DOI: 10.1016/j.jalz.2016.08.011] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 08/05/2016] [Accepted: 08/17/2016] [Indexed: 01/21/2023]
Abstract
INTRODUCTION Subjective cognitive decline (SCD) could indicate preclinical Alzheimer's disease, but the existing literature is confounded by heterogeneous approaches to studying SCD. We assessed the differential cognitive, affective, and neuroimaging correlates of two aspects of SCD: reporting high cognitive difficulties on a self-rated questionnaire versus consulting at a memory clinic. METHODS We compared 28 patients from a memory clinic with isolated SCD, 35 community-recruited elders with similarly high levels of self-reported cognitive difficulties, and 35 community-recruited controls with low self-reported cognitive difficulties. RESULTS Increased anxiety and amyloid β deposition were observed in both groups with high self-reported difficulties, whereas subclinical depression and (hippocampal) atrophy were specifically associated with medical help seeking. Cognitive tests showed no group differences. DISCUSSION These results further validate the concept of SCD in both community- and clinic-based groups. Yet, recruitment methods influence associated biomarkers and affective symptomatology, highlighting the heterogeneous nature of SCD depending on study characteristics.
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Affiliation(s)
- Audrey Perrotin
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, U1077, Caen, France
| | - Renaud La Joie
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, U1077, Caen, France.
| | - Vincent de La Sayette
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, Service de Neurologie, Caen, France
| | - Louisa Barré
- Université de Caen Normandie, UMR-S1077, Caen, France; CEA, DRF/I2BM, LDM-TEP Group, Caen, France; CNRS, UMR ISTCT 6301, LDM-TEP Group, Caen, France
| | - Florence Mézenge
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, U1077, Caen, France
| | - Justine Mutlu
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, U1077, Caen, France
| | - Denis Guilloteau
- INSERM U930, Université François Rabelais de Tours, CHRU de Tours, Tours, France
| | - Stéphanie Egret
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, U1077, Caen, France
| | - Francis Eustache
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, U1077, Caen, France
| | - Gaël Chételat
- INSERM, U1077, Caen, France; Université de Caen Normandie, UMR-S1077, Caen, France; Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France; CHU de Caen, U1077, Caen, France
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Schultz SA, Larson J, Oh J, Koscik R, Dowling MN, Gallagher CL, Carlsson CM, Rowley HA, Bendlin BB, Asthana S, Hermann BP, Johnson SC, Sager M, LaRue A, Okonkwo OC. Participation in cognitively-stimulating activities is associated with brain structure and cognitive function in preclinical Alzheimer's disease. Brain Imaging Behav 2016; 9:729-36. [PMID: 25358750 DOI: 10.1007/s11682-014-9329-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
This study tested the hypothesis that frequent participation in cognitively-stimulating activities, specifically those related to playing games and puzzles, is beneficial to brain health and cognition among middle-aged adults at increased risk for Alzheimer's disease (AD). Three hundred twenty-nine cognitively normal, middle-aged adults (age range, 43.2-73.8 years) enrolled in the Wisconsin Registry for Alzheimer's Prevention (WRAP) participated in this study. They reported their current engagement in cognitive activities using a modified version of the Cognitive Activity Scale (CAS), underwent a structural MRI scan, and completed a comprehensive cognitive battery. FreeSurfer was used to derive gray matter (GM) volumes from AD-related regions of interest (ROIs), and composite measures of episodic memory and executive function were obtained from the cognitive tests. Covariate-adjusted least squares analyses were used to examine the association between the Games item on the CAS (CAS-Games) and both GM volumes and cognitive composites. Higher scores on CAS-Games were associated with greater GM volumes in several ROIs including the hippocampus, posterior cingulate, anterior cingulate, and middle frontal gyrus. Similarly, CAS-Games scores were positively associated with scores on the Immediate Memory, Verbal Learning & Memory, and Speed & Flexibility domains. These findings were not modified by known risk factors for AD. In addition, the Total score on the CAS was not as sensitive as CAS-Games to the examined brain and cognitive measures. For some individuals, participation in cognitive activities pertinent to game playing may help prevent AD by preserving brain structures and cognitive functions vulnerable to AD pathophysiology.
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Affiliation(s)
- Stephanie A Schultz
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Jordan Larson
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Jennifer Oh
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Rebecca Koscik
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Maritza N Dowling
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Department of Biostatistics & Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Catherine L Gallagher
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Cynthia M Carlsson
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Howard A Rowley
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Barbara B Bendlin
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Sanjay Asthana
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Bruce P Hermann
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Sterling C Johnson
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Mark Sager
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Asenath LaRue
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Ozioma C Okonkwo
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705, USA.
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA.
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA.
- Department of Medicine and Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792, USA.
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13
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Knight MJ, McCann B, Kauppinen RA, Coulthard EJ. Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease. Front Aging Neurosci 2016; 8:139. [PMID: 27378911 PMCID: PMC4909770 DOI: 10.3389/fnagi.2016.00139] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Accepted: 05/27/2016] [Indexed: 11/13/2022] Open
Abstract
Recent pharmaceutical trials have demonstrated that slowing or reversing pathology in Alzheimer's disease is likely to be possible only in the earliest stages of disease, perhaps even before significant symptoms develop. Pathology in Alzheimer's disease accumulates for well over a decade before symptoms are detected giving a large potential window of opportunity for intervention. It is therefore important that imaging techniques detect subtle changes in brain tissue before significant macroscopic brain atrophy. Current diagnostic techniques often do not permit early diagnosis or are too expensive for routine clinical use. Magnetic Resonance Imaging (MRI) is the most versatile, affordable, and powerful imaging modality currently available, being able to deliver detailed analyses of anatomy, tissue volumes, and tissue state. In this mini-review, we consider how MRI might detect patients at risk of future dementia in the early stages of pathological change when symptoms are mild. We consider the contributions made by the various modalities of MRI (structural, diffusion, perfusion, relaxometry) in identifying not just atrophy (a late-stage AD symptom) but more subtle changes reflective of early dementia pathology. The sensitivity of MRI not just to gross anatomy but to the underlying "health" at the cellular (and even molecular) scales, makes it very well suited to this task.
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Affiliation(s)
- Michael J Knight
- School of Experimental Psychology, University of Bristol Bristol, UK
| | - Bryony McCann
- School of Experimental Psychology, University of Bristol Bristol, UK
| | - Risto A Kauppinen
- School of Experimental Psychology, University of BristolBristol, UK; Clinical Research and Imaging Centre, University of BristolBristol, UK
| | - Elizabeth J Coulthard
- Research into Memory the Brain and Dementia Group, Institute of Clinical Neuroscience, University of BristolBristol, UK; North Bristol NHS TrustBristol, UK
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14
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Jack CR, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, Dale AM, Carmichael O, Ching C, DeCarli C, Desikan RS, Fennema-Notestine C, Fjell AM, Fletcher E, Fox NC, Gunter J, Gutman BA, Holland D, Hua X, Insel P, Kantarci K, Killiany RJ, Krueger G, Leung KK, Mackin S, Maillard P, Malone IB, Mattsson N, McEvoy L, Modat M, Mueller S, Nosheny R, Ourselin S, Schuff N, Senjem ML, Simonson A, Thompson PM, Rettmann D, Vemuri P, Walhovd K, Zhao Y, Zuk S, Weiner M. Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2. Alzheimers Dement 2016; 11:740-56. [PMID: 26194310 DOI: 10.1016/j.jalz.2015.05.002] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 04/28/2015] [Accepted: 05/05/2015] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
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Affiliation(s)
| | - Josephine Barnes
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | | | | | - James Brewer
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Shona Clegg
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anders M Dale
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Owen Carmichael
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - Christopher Ching
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Rahul S Desikan
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California at San Diego, La Jolla, CA, USA
| | - Anders M Fjell
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Evan Fletcher
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Nick C Fox
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jeff Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Boris A Gutman
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dominic Holland
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Xue Hua
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Philip Insel
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ron J Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | | | - Kelvin K Leung
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Scott Mackin
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Ian B Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Niklas Mattsson
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
| | - Linda McEvoy
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Marc Modat
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Susanne Mueller
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Rachel Nosheny
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Sebastien Ourselin
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | | | - Alix Simonson
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, MN, USA
| | | | | | | | - Samantha Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Weiner
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA; Department of Medicine, University of California at San Francisco, San Francisco, CA, USA; Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
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15
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Csukly G, Sirály E, Fodor Z, Horváth A, Salacz P, Hidasi Z, Csibri É, Rudas G, Szabó Á. The Differentiation of Amnestic Type MCI from the Non-Amnestic Types by Structural MRI. Front Aging Neurosci 2016; 8:52. [PMID: 27065855 PMCID: PMC4811920 DOI: 10.3389/fnagi.2016.00052] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 02/29/2016] [Indexed: 11/18/2022] Open
Abstract
Introduction: While amnestic mild cognitive impairment (aMCI) and non-amnestic mild cognitive impairment (naMCI) are theoretically different entities, only a few investigations studied the structural brain differences between these subtypes of mild cognitive impairment. The aim of the study was to find the structural differences between aMCI and naMCI, and to replicate previous findings on the differentiation between aMCI and healthy controls. Methods: Altogether 62 aMCI, naMCI, and healthy control subjects were included into the study based on the Petersen criteria. All patients underwent a routine brain MR examination, and a detailed neuropsychological examination. Results: The sizes of the hippocampus, the entorhinal cortex and the amygdala were decreased in aMCI relative to naMCI and to controls. Furthermore the cortical thickness of the entorhinal cortex, the fusiform gyrus, the precuneus and the isthmus of the cingulate gyrus were significantly decreased in aMCI relative to naMCI and healthy controls. The largest differences relative to controls were detected for the volume of the hippocampus (18% decrease vs. controls) and the cortical thickness (20% decrease vs. controls) of the entorhinal cortex: 1.6 and 1.4 in terms of Cohen's d. Only the volume of the precuneus were decreased in the naMCI group (5% decrease) compared to the control subjects: 0.9 in terms of Cohen's d. Significant between group differences were also found in the neuropsychological test results: a decreased anterograde, retrograde memory, and category fluency performance was detected in the aMCI group relative to controls and naMCI subjects. Subjects with naMCI showed decreased letter fluency relative to controls, while both MCI groups showed decreased executive functioning relative to controls as measured by the Trail Making test part B. Memory performance in the aMCI group and in the entire sample correlated with the thickness of the entorhinal cortex and with the volume of the amygdala. Conclusion: The amnestic mild cognitive impairment/non-amnestic mild cognitive impairment separation is not only theoretical but backed by structural imaging methods and neuropsychological tests. A better knowledge of the MCI subtypes can help to predict the direction of progression and create targeted prevention.
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Affiliation(s)
- Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University Budapest, Hungary
| | - Enikő Sirály
- Department of Psychiatry and Psychotherapy, Semmelweis University Budapest, Hungary
| | - Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University Budapest, Hungary
| | - András Horváth
- Department of Neurology, National Institute of Clinical Neurosciences Budapest, Hungary
| | - Pál Salacz
- Department of Psychiatry and Psychotherapy, Semmelweis UniversityBudapest, Hungary; Department of Neurology, Hospital at Péterfy Sándor StreetBudapest, Hungary
| | - Zoltán Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University Budapest, Hungary
| | - Éva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University Budapest, Hungary
| | - Gábor Rudas
- Magnetic Resonance Imaging Research Center, Semmelweis University Budapest, Hungary
| | - Ádám Szabó
- Magnetic Resonance Imaging Research Center, Semmelweis University Budapest, Hungary
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16
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Masdeu JC, Pascual B. Genetic and degenerative disorders primarily causing dementia. HANDBOOK OF CLINICAL NEUROLOGY 2016; 135:525-564. [PMID: 27432682 DOI: 10.1016/b978-0-444-53485-9.00026-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Neuroimaging comprises a powerful set of instruments to diagnose the different causes of dementia, clarify their neurobiology, and monitor their treatment. Magnetic resonance imaging (MRI) depicts volume changes with neurodegeneration and inflammation, as well as abnormalities in functional and structural connectivity. MRI arterial spin labeling allows for the quantification of regional cerebral blood flow, characteristically altered in Alzheimer's disease, diffuse Lewy-body disease, and the frontotemporal dementias. Positron emission tomography allows for the determination of regional metabolism, with similar abnormalities as flow, and for the measurement of β-amyloid and abnormal tau deposition in the brain, as well as regional inflammation. These instruments allow for the quantification in vivo of most of the pathologic features observed in disorders causing dementia. Importantly, they allow for the longitudinal study of these abnormalities, having revealed, for instance, that the deposition of β-amyloid in the brain can antecede by decades the onset of dementia. Thus, a therapeutic window has been opened and the efficacy of immunotherapies directed at removing β-amyloid from the brain of asymptomatic individuals is currently being tested. Tau and inflammation imaging, still in their infancy, combined with genomics, should provide powerful insights into these disorders and facilitate their treatment.
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Affiliation(s)
- Joseph C Masdeu
- Department of Neurology, Houston Methodist Hospital, Houston, TX, USA.
| | - Belen Pascual
- Department of Neurology, Houston Methodist Hospital, Houston, TX, USA
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17
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McCarthy CS, Ramprashad A, Thompson C, Botti JA, Coman IL, Kates WR. A comparison of FreeSurfer-generated data with and without manual intervention. Front Neurosci 2015; 9:379. [PMID: 26539075 PMCID: PMC4612506 DOI: 10.3389/fnins.2015.00379] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 09/29/2015] [Indexed: 01/18/2023] Open
Abstract
This paper examined whether FreeSurfer-generated data differed between a fully-automated, unedited pipeline and an edited pipeline that included the application of control points to correct errors in white matter segmentation. In a sample of 30 individuals, we compared the summary statistics of surface area, white matter volumes, and cortical thickness derived from edited and unedited datasets for the 34 regions of interest (ROIs) that FreeSurfer (FS) generates. To determine whether applying control points would alter the detection of significant differences between patient and typical groups, effect sizes between edited and unedited conditions in individuals with the genetic disorder, 22q11.2 deletion syndrome (22q11DS) were compared to neurotypical controls. Analyses were conducted with data that were generated from both a 1.5 tesla and a 3 tesla scanner. For 1.5 tesla data, mean area, volume, and thickness measures did not differ significantly between edited and unedited regions, with the exception of rostral anterior cingulate thickness, lateral orbitofrontal white matter, superior parietal white matter, and precentral gyral thickness. Results were similar for surface area and white matter volumes generated from the 3 tesla scanner. For cortical thickness measures however, seven edited ROI measures, primarily in frontal and temporal regions, differed significantly from their unedited counterparts, and three additional ROI measures approached significance. Mean effect sizes for edited ROIs did not differ from most unedited ROIs for either 1.5 or 3 tesla data. Taken together, these results suggest that although the application of control points may increase the validity of intensity normalization and, ultimately, segmentation, it may not affect the final, extracted metrics that FS generates. Potential exceptions to and limitations of these conclusions are discussed.
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Affiliation(s)
- Christopher S McCarthy
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Avinash Ramprashad
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Carlie Thompson
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Jo-Anna Botti
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Ioana L Coman
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
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18
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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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Sirály E, Szabó Á, Szita B, Kovács V, Fodor Z, Marosi C, Salacz P, Hidasi Z, Maros V, Hanák P, Csibri É, Csukly G. Monitoring the early signs of cognitive decline in elderly by computer games: an MRI study. PLoS One 2015; 10:e0117918. [PMID: 25706380 PMCID: PMC4338307 DOI: 10.1371/journal.pone.0117918] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2014] [Accepted: 12/31/2014] [Indexed: 11/18/2022] Open
Abstract
Background It is anticipated that current and future preventive therapies will likely be more effective in the early stages of dementia, when everyday functioning is not affected. Accordingly the early identification of people at risk is particularly important. In most cases, when subjects visit an expert and are examined using neuropsychological tests, the disease has already been developed. Contrary to this cognitive games are played by healthy, well functioning elderly people, subjects who should be monitored for early signs. Further advantages of cognitive games are their accessibility and their cost-effectiveness. Purpose The aim of the investigation was to show that computer games can help to identify those who are at risk. In order to validate games analysis was completed which measured the correlations between results of the 'Find the Pairs' memory game and the volumes of the temporal brain regions previously found to be good predictors of later cognitive decline. Participants and Methods 34 healthy elderly subjects were enrolled in the study. The volume of the cerebral structures was measured by MRI. Cortical reconstruction and volumetric segmentation were performed by Freesurfer. Results There was a correlation between the number of attempts and the time required to complete the memory game and the volume of the entorhinal cortex, the temporal pole, and the hippocampus. There was also a correlation between the results of the Paired Associates Learning (PAL) test and the memory game. Conclusions The results gathered support the initial hypothesis that healthy elderly subjects achieving lower scores in the memory game have increased level of atrophy in the temporal brain structures and showed a decreased performance in the PAL test. Based on these results it can be concluded that memory games may be useful in early screening for cognitive decline.
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Affiliation(s)
- Enikő Sirály
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Ádám Szabó
- Semmelweis University, Magnetic Resonance Imaging Research Center, Budapest, Hungary
- * E-mail:
| | - Bernadett Szita
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Vivienne Kovács
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Csilla Marosi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Pál Salacz
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zoltán Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Viktor Maros
- Healthcare Technologies Knowledge Center, Budapest University of Technology and Economics, Budapest, Hungary
| | - Péter Hanák
- Healthcare Technologies Knowledge Center, Budapest University of Technology and Economics, Budapest, Hungary
| | - Éva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
- * E-mail:
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20
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Zhou Q, Goryawala M, Cabrerizo M, Barker W, Loewenstein D, Duara R, Adjouadi M. Multivariate analysis of structural MRI and PET (FDG and 18F-AV-45) for Alzheimer's disease and its prodromal stages. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:1051-4. [PMID: 25570142 DOI: 10.1109/embc.2014.6943774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A multivariate analysis method, orthogonal partial least squares to latent structures (OPLS), was used to discriminate Alzheimer's disease (AD), early and late mild cognitive impairment (EMCI and LMCI) from cognitively normal control (CN) using MRI and PET measures. FreeSurfer 5.1 generated 271 MRI features including 49 subcortical volumes, 68 cortical volumes, 68 cortical thicknesses, 70 surface areas and 16 hippocampus subfields. Subjects with all aforementioned MRI measures passing quality control and valid Fludeoxyglucose (18F) (FDG) and Florbetapir (18F) PET scans were selected from ADNI database, resulting in a total of 524 participants (137 CN, 214 EMCI, 103 LMCI and 70 AD) for the study. Altogether 286 features including 15 significant PET uptake features (7 for FDG and 8 for AV-45) were utilized for OPLS analysis. Predictive power was evaluated by Q2(Y), a quantifier of the statistical significance for class separation. The results show that MRI features (Q2(Y) =0.645), and PET features (Q2(Y) = 0.636) has comparable predictive power in separating AD from CN, and MRI features are better predictor of LMCI (Q2(Y) = 0.282) than PET (Q2(Y) = 0.294). Combination of PET and MRI has the most predictive power for LMCI and AD with Q2(Y) of 0.294 and 0.721, respectively. While for EMCI, cortical thickness was found to be the best predictor with a Q2(Y) of 0.108, suggesting cortical thickness may be the first structural change ahead of others and should be prioritized in prediction of very mild cognitive impairment.
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21
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Zhou Q, Goryawala M, Cabrerizo M, Wang J, Barker W, Loewenstein DA, Duara R, Adjouadi M. An Optimal Decisional Space for the Classification of Alzheimer's Disease and Mild Cognitive Impairment. IEEE Trans Biomed Eng 2014; 61:2245-53. [DOI: 10.1109/tbme.2014.2310709] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Braskie MN, Thompson PM. A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative. Biol Psychiatry 2014; 75:527-33. [PMID: 24367935 PMCID: PMC4019004 DOI: 10.1016/j.biopsych.2013.11.020] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Revised: 11/05/2013] [Accepted: 11/06/2013] [Indexed: 01/18/2023]
Abstract
In recent years, numerous laboratories and consortia have used neuroimaging to evaluate the risk for and progression of Alzheimer's disease (AD). The Alzheimer's Disease Neuroimaging Initiative is a longitudinal, multicenter study that is evaluating a range of biomarkers for use in diagnosis of AD, prediction of patient outcomes, and clinical trials. These biomarkers include brain metrics derived from magnetic resonance imaging (MRI) and positron emission tomography scans as well as metrics derived from blood and cerebrospinal fluid. We focus on Alzheimer's Disease Neuroimaging Initiative studies published between 2011 and March 2013 for which structural MRI was a major outcome measure. Our main goal was to review key articles offering insights into progression of AD and the relationships of structural MRI measures to cognition and to other biomarkers in AD. In Supplement 1, we also discuss genetic and environmental risk factors for AD and exciting new analysis tools for the efficient evaluation of large-scale structural MRI data sets such as the Alzheimer's Disease Neuroimaging Initiative data.
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Affiliation(s)
- Meredith N Braskie
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, California; Department of Neurology, University of Southern California, Los Angeles, California
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, California; Department of Neurology, University of Southern California, Los Angeles, California; Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, California; Department of Radiology, University of Southern California, Los Angeles, California; Department of Pediatrics, University of Southern California, Los Angeles, California; Department of Ophthalmology, University of Southern California, Los Angeles, California; Keck School of Medicine, and Viterbi School of Engineering, University of Southern California, Los Angeles, California.
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23
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Ewers M, Brendel M, Rizk-Jackson A, Rominger A, Bartenstein P, Schuff N, Weiner MW. Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects. NEUROIMAGE-CLINICAL 2013; 4:45-52. [PMID: 24286024 PMCID: PMC3841292 DOI: 10.1016/j.nicl.2013.10.018] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Revised: 10/12/2013] [Accepted: 10/27/2013] [Indexed: 02/03/2023]
Abstract
Brain changes reminiscent of Alzheimer disease (AD) have been previously reported in a substantial portion of elderly cognitive healthy (HC) subjects. The major aim was to evaluate the accuracy of MRI assessed regional gray matter (GM) volume, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), and neuropsychological test scores to identify those HC subjects who subsequently convert to mild cognitive impairment (MCI) or AD dementia. We obtained in 54 healthy control (HC) subjects a priori defined region of interest (ROI) values of medial temporal and parietal FDG-PET and medial temporal GM volume. In logistic regression analyses, these ROI values were tested together with neuropsychological test scores (free recall, trail making test B (TMT-B)) as predictors of HC conversion during a clinical follow-up between 3 and 4 years. In voxel-based analyses, FDG-PET and MRI GM maps were compared between HC converters and HC non-converters. Out of the 54 HC subjects, 11 subjects converted to MCI or AD dementia. Lower FDG-PET ROI values were associated with higher likelihood of conversion (p = 0.004), with the area under the curve (AUC) yielding 82.0% (95% CI = (95.5%, 68.5%)). The GM volume ROI was not a significant predictor (p = 0.07). TMT-B but not the free recall tests were a significant predictor (AUC = 71% (95% CI = 50.4%, 91.7%)). For the combination of FDG-PET and TMT-B, the AUC was 93.4% (sensitivity = 82%, specificity = 93%). Voxel-based group comparison showed reduced FDG-PET metabolism within the temporo-parietal and prefrontal cortex in HC converters. In conclusion, medial temporal and-parietal FDG-PET and executive function show a clinically acceptable accuracy for predicting clinical progression in elderly HC subjects.
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Affiliation(s)
- Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian-University, Munich, Germany
- Department of Radiology, University of California, San Francisco, CA, USA
- VA Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
- Corresponding author at: Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig Maximilian University, Max-Lebsche-Platz 30, 81377 Munich, Germany. Tel.: + 49 89 7095 8367; fax: + 49 89 7095 8369.
| | - Matthias Brendel
- Department of Nuclear Medicine, Klinikum der Universität München, Ludwig-Maximilian-University, Munich, Germany
| | - Angela Rizk-Jackson
- Clinical & Translational Science Institute, University of California, San Francisco, CA, USA
| | - Axel Rominger
- Clinical & Translational Science Institute, University of California, San Francisco, CA, USA
| | - Peter Bartenstein
- Clinical & Translational Science Institute, University of California, San Francisco, CA, USA
| | - Norbert Schuff
- Department of Radiology, University of California, San Francisco, CA, USA
- VA Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Michael W. Weiner
- Department of Radiology, University of California, San Francisco, CA, USA
- VA Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
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Understanding cognitive deficits in Alzheimer's disease based on neuroimaging findings. Trends Cogn Sci 2013; 17:510-6. [PMID: 24029445 DOI: 10.1016/j.tics.2013.08.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Accepted: 08/07/2013] [Indexed: 01/21/2023]
Abstract
Brain amyloid can be measured using positron emission tomography (PET). There are mixed reports regarding whether amyloid measures are correlated with measures of cognition (in particular memory), depending on the cohorts and cognitive domains assessed. In Alzheimer's disease (AD) patients and those at heightened risk for AD, cognitive performance may be related to the level and extent of classical AD pathology (amyloid plaques and neurofibrillary angles), but it is also influenced by neurodegeneration, neurocognitive reserve, and vascular health. We discuss what recent neuroimaging research has discovered about cognitive deficits in AD and offer suggestions for future research.
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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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Weinstein G, Beiser AS, Decarli C, Au R, Wolf PA, Seshadri S. Brain imaging and cognitive predictors of stroke and Alzheimer disease in the Framingham Heart Study. Stroke 2013; 44:2787-94. [PMID: 23920020 DOI: 10.1161/strokeaha.113.000947] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Exposure to vascular risk factors has a gradual deleterious effect on brain MRI and cognitive measures. We explored whether a pattern of these measures exists that predicts stroke and Alzheimer disease (AD) risk. METHODS A cognitive battery was administered to 1679 dementia and stroke-free Framingham offspring (age, >55 years; mean, 65.7±7.0) between 1999 and 2004; participants were also free of other neurological conditions that could affect cognition and >90% also had brain MRI examination. We related cognitive and MRI measures to risks of incident stroke and AD ≤10 years of follow-up. As a secondary analysis, we explored these associations in The Framingham Heart Study original cohort (mean age, 67.5±7.3 and 84.8±3.3 years at the cognitive assessment and MRI examination, respectively). RESULTS A total of 55 Offspring participants sustained strokes and 31 developed AD. Offspring who scored <1.5 SD below predicted mean scores, for age and education, on an executive function test, had a higher risk of future stroke (hazard ratio [HR], 2.27; 95% confidence interval [CI], 1.06-4.85) and AD (HR, 3.60; 95% CI, 1.52-8.52); additional cognitive tests also predicted AD. Participants with low (<20 percentile) total brain volume and high (>20 percentile) white matter hyperintensity volume had a higher risk of stroke (HR, 1.97; 95% CI, 1.03-3.77 and HR, 2.74; 95% CI, 1.51-5.00, respectively) but not AD. Hippocampal volume at the bottom quintile predicted AD in the offspring and original cohorts (HR, 4.41; 95% CI, 2.00-9.72 and HR, 2.37; 95% CI, 1.12-5.00, respectively). A stepwise increase in stroke risk was apparent with increasing numbers of these cognitive and imaging markers. CONCLUSIONS Specific patterns of cognitive and brain structural measures observed even in early aging predict stroke risk and may serve as biomarkers for risk prediction.
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Affiliation(s)
- Galit Weinstein
- From the Department of Neurology, Boston University School of Medicine, Boston, MA (G.W., A.S.B., R.A., P.A.W., S.S.); The Framingham Heart Study, Boston, MA (G.W., A.S.B., R.A., P.A.W., S.S.); Department of Biostatistics, Boston University School of Public Health, Boston, MA (A.S.B.); and the Department of Neurology, University of California at Davis, Sacramento, CA (C.D.)
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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: 229] [Impact Index Per Article: 20.8] [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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Braskie MN, Toga AW, Thompson PM. Recent advances in imaging Alzheimer's disease. J Alzheimers Dis 2013; 33 Suppl 1:S313-27. [PMID: 22672880 DOI: 10.3233/jad-2012-129016] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in brain imaging technology in the past five years have contributed greatly to the understanding of Alzheimer's disease (AD). Here, we review recent research related to amyloid imaging, new methods for magnetic resonance imaging analyses, and statistical methods. We also review research that evaluates AD risk factors and brain imaging, in the context of AD prediction and progression. We selected a variety of illustrative studies, describing how they advanced the field and are leading AD research in promising new directions.
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Affiliation(s)
- Meredith N Braskie
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-7334, USA
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Nho K, Risacher SL, Crane PK, DeCarli C, Glymour MM, Habeck C, Kim S, Lee GJ, Mormino E, Mukherjee S, Shen L, West JD, Saykin AJ. Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer's disease. Brain Imaging Behav 2012; 6:551-67. [PMID: 23070747 PMCID: PMC3532574 DOI: 10.1007/s11682-012-9203-2] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are associated with a progressive loss of cognitive abilities. In the present report, we assessed the relationship of memory and executive function with brain structure in a sample of 810 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, including 188 AD, 396 MCI, and 226 healthy older adults (HC). Composite scores of memory (ADNI-Mem) and executive function (ADNI-Exec) were generated by applying modern psychometric theory to item-level data from ADNI's neuropsychological battery. We performed voxel-based morphometry (VBM) and surface-based association (SurfStat) analyses to evaluate relationships of ADNI-Mem and ADNI-Exec with grey matter (GM) density and cortical thickness across the whole brain in the combined sample and within diagnostic groups. We observed strong associations between ADNI-Mem and medial and lateral temporal lobe atrophy. Lower ADNI-Exec scores were associated with advanced GM and cortical atrophy across broadly distributed regions, most impressively in the bilateral parietal and temporal lobes. We also evaluated ADNI-Exec adjusted for ADNI-Mem, and found associations with GM density and cortical thickness primarily in the bilateral parietal, temporal, and frontal lobes. Within-group analyses suggest these associations are strongest in patients with MCI and AD. The present study provides insight into the spatially unbiased associations between brain atrophy and memory and executive function, and underscores the importance of structural brain changes in early cognitive decline.
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Affiliation(s)
- Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L. Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Charles DeCarli
- Department of Neurology and the Center for Neuroscience, School of Medicine, University of California Davis, Davis, CA, USA
| | - M. Maria Glymour
- Department of Society, Human Development and Health, Harvard School of Public Health, Boston, MA, USA
| | - Christian Habeck
- Cognitive Neuroscience Division of Taub Institute for the Study of Alzheimer’s Disease and Aging Brain, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Sungeun Kim
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Grace J. Lee
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Elizabeth Mormino
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | | | - Li Shen
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John D. West
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J. Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
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Abstract
PURPOSE OF REVIEW In 2011, a new set of new guidelines for the research diagnosis of three stages of Alzheimer disease was promulgated by the US National Institute of Aging and the Alzheimer Association. For the first time, they include the diagnosis of presymptomatic Alzheimer disease, recognizing that the disease process begins years before cognitive impairment develops. Awareness of this fact has largely been driven by neuroimaging, and particularly by imaging amyloid β (abeta) deposition in the brain, a procedure approved by the US Food and Drug Administration for clinical use in April 2012. RECENT FINDINGS In Alzheimer disease, abeta deposition antecedes, probably by decades, the onset of cognitive impairment. In brain regions with greatest abeta deposition, synaptic dysfunction can be imaged beginning at preclinical stages. In regions that are not identical with the ones with greatest abeta deposition but heavily connected with them, regional atrophy and loss of white-matter anisotropy can be detected later in the course of the disease, near the time when mild cognitive impairment supervenes. Together with neuropsychological testing, imaging can improve the prediction of worsening to Alzheimer disease among patients with mild cognitive impairment. SUMMARY These findings have huge implications for research on therapeutic approaches to Alzheimer disease. For instance, while so far only patients with the clinical diagnosis have been treated with immunotherapy targeting abeta removal, a consensus is building that to be effective, this therapy should be given in the preclinical stages of the disease, which are assessed most advantageously by means of neuroimaging.
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HIV and aging: state of knowledge and areas of critical need for research. A report to the NIH Office of AIDS Research by the HIV and Aging Working Group. J Acquir Immune Defic Syndr 2012; 60 Suppl 1:S1-18. [PMID: 22688010 DOI: 10.1097/qai.0b013e31825a3668] [Citation(s) in RCA: 413] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
HIV risk behaviors, susceptibility to HIV acquisition, progression of disease after infection, and response to antiretroviral therapy all vary by age. In those living with HIV, current effective treatment has increased the median life expectancy to >70 years of age. Biologic, medical, individual, social, and societal issues change as one ages with HIV infection, but there has been only a small amount of research in this field. Therefore, the Office of AIDS Research of the National Institutes of Health commissioned a working group to develop an outline of the current state of knowledge and areas of critical need for research in HIV and Aging; the working groups' findings and recommendations are summarized in this report. Key overarching themes identified by the group included the following: multimorbidity, polypharmacy, and the need to emphasize maintenance of function; the complexity of assessing HIV versus treatment effects versus aging versus concurrent disease; the inter-related mechanisms of immune senescence, inflammation, and hypercoagulability; the utility of multivariable indices for predicting outcomes; a need to emphasize human studies to account for complexity; and a required focus on issues of community support, caregivers, and systems infrastructure. Critical resources are needed to enact this research agenda and include expanded review panel expertise in aging, functional measures, and multimorbidity, and facilitated use and continued funding to allow long-term follow-up of cohorts aging with HIV.
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Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012; 61:1402-18. [PMID: 22430496 PMCID: PMC3389460 DOI: 10.1016/j.neuroimage.2012.02.084] [Citation(s) in RCA: 1672] [Impact Index Per Article: 139.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2011] [Revised: 02/24/2012] [Accepted: 02/27/2012] [Indexed: 01/11/2023] Open
Abstract
Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects.
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Affiliation(s)
- Martin Reuter
- Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
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Jack CR, Knopman DS, Weigand SD, Wiste HJ, Vemuri P, Lowe V, Kantarci K, Gunter JL, Senjem ML, Ivnik RJ, Roberts RO, Rocca WA, Boeve BF, Petersen RC. An operational approach to National Institute on Aging-Alzheimer's Association criteria for preclinical Alzheimer disease. Ann Neurol 2012; 71:765-75. [PMID: 22488240 DOI: 10.1002/ana.22628] [Citation(s) in RCA: 482] [Impact Index Per Article: 40.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2011] [Revised: 08/24/2011] [Accepted: 09/02/2011] [Indexed: 11/07/2022]
Abstract
OBJECTIVE A workgroup commissioned by the Alzheimer's Association (AA) and the National Institute on Aging (NIA) recently published research criteria for preclinical Alzheimer disease (AD). We performed a preliminary assessment of these guidelines. METHODS We employed Pittsburgh compound B positron emission tomography (PET) imaging as our biomarker of cerebral amyloidosis, and (18) fluorodeoxyglucose PET imaging and hippocampal volume as biomarkers of neurodegeneration. A group of 42 clinically diagnosed AD subjects was used to create imaging biomarker cutpoints. A group of 450 cognitively normal (CN) subjects from a population-based sample was used to develop cognitive cutpoints and to assess population frequencies of the different preclinical AD stages using different cutpoint criteria. RESULTS The new criteria subdivide the preclinical phase of AD into stages 1 to 3. To classify our CN subjects, 2 additional categories were needed. Stage 0 denotes subjects with normal AD biomarkers and no evidence of subtle cognitive impairment. Suspected non-AD pathophysiology (SNAP) denotes subjects with normal amyloid PET imaging, but abnormal neurodegeneration biomarker studies. At fixed cutpoints corresponding to 90% sensitivity for diagnosing AD and the 10th percentile of CN cognitive scores, 43% of our sample was classified as stage 0, 16% stage 1, 12 % stage 2, 3% stage 3, and 23% SNAP. INTERPRETATION This cross-sectional evaluation of the NIA-AA criteria for preclinical AD indicates that the 1-3 staging criteria coupled with stage 0 and SNAP categories classify 97% of CN subjects from a population-based sample, leaving only 3% unclassified. Future longitudinal validation of the criteria will be important.
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Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester, MN 55905, USA.
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Maksimovich IV. The tomography dementia rating scale (TDR)—The rating scale of Alzheimer’s disease stages. Health (London) 2012. [DOI: 10.4236/health.2012.429111] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Smith CD, Andersen AH, Gold BT. Structural brain alterations before mild cognitive impairment in ADNI: validation of volume loss in a predefined antero-temporal region. J Alzheimers Dis 2012; 31 Suppl 3:S49-58. [PMID: 22460332 PMCID: PMC3652624 DOI: 10.3233/jad-2012-120157] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Volume losses in the medial temporal lobe, posterior cingulated, and orbitofrontal region have been observed in Alzheimer's disease (AD). Smaller reductions in similar regions have also been reported in amnestic mild cognitive impairment (aMCI), a canonical precursor to AD. We previously demonstrated that volume loss in bilateral anteromedial temporal lobe is present at baseline in longitudinally followed normal subjects who later developed MCI or AD. In this study we compared grey matter volumes within this predefined anteromedial temporal region (AMTR) at baseline between: 1) normal subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who subsequently developed cognitive complaints as reflected in a CDR memory box score of 0.5; and 2) normal subjects who remained normal over a median of 48 months of follow-up (CDR sum of boxes 0). We found significantly decreased volume within AMTR in the ADNI memory complainers. To relate AMTR results to those from conventional anatomy, we demonstrate that volumes extracted with the ICBM amygdala region had the best correspondence with AMTR volumes. In contrast, regions that have demonstrated volume loss in frank MCI and AD in ADNI, e.g., the posterior cingulate, did not show volume loss. These findings provide independent confirmation that volume changes preceding MCI occur in AMTR, a region of overlap between amygdala and anterior hippocampus.
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Affiliation(s)
- Charles D Smith
- Department of Neurology, Chandler Medical Center, University of Kentucky, Lexington, KY 40536-0098, USA.
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