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Panigrahi P, Das S, Chakrabarti S. CCADD: An online webserver for Alzheimer's disease detection from brain MRI. Comput Biol Med 2024; 177:108622. [PMID: 38781645 DOI: 10.1016/j.compbiomed.2024.108622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) imposes a growing burden on public health due to its impact on memory, cognition, behavior, and social skills. Early detection using non-invasive brain magnetic resonance images (MRI) is vital for disease management. We introduce CCADD (Corpus Callosum-based Alzheimer's Disease Detection), a user-friendly webserver that automatically identifies and segments the corpus callosum (CC) region from brain MRI slices. Extracted shape and size-based features of CC are fed into Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers to predict AD or Mild Cognitive Impairment (MCI). Exhaustive benchmarking on ADNI data reveals high prediction accuracies for different AD severity levels. CCADD empowers clinicians and researchers for AD detection. This server is available at: http://www.hpppi.iicb.res.in/add.
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Affiliation(s)
- Priyanka Panigrahi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India
| | - Subhrangshu Das
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India.
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India.
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2
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Arble EP, Steinert SW, Shankar S, Cerjanic A, Sutton BP, Daugherty AM. Ideational Slippage in Middle-Aged and Older Adults: A Preliminary Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:656. [PMID: 38928903 DOI: 10.3390/ijerph21060656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Ideational slippage-characterized by incorrect word usage and strained logic during dialogue-is common in aging and, at greater frequency, is an indicator of pre-clinical cognitive decline. Performance-based assessment of ideational slippage may be useful in the study of cognitive aging and Alzheimer's-disease-related pathology. In this preliminary study, we examine the association between corpus callosum volume and a performance-based assessment of ideational slippage in middle-aged and older adults (age 61-79 years). Ideational slippage was indexed from cognitive special scores using the Rorschach Inkblot Method (RIM), which are validated indices of deviant verbalization and logical inaccuracy (Sum6, WSum6). Among middle-aged and older adults, smaller splenium volume was associated with greater ideational slippage (ηp2 = 0.48), independent of processing speed and fluid intelligence. The observed negative associations are consistent with visuospatial perception and cognitive functions of the splenium. The effect was strongest with the splenium, and volumes of the genu and total white matter had small effects that were not statistically significant. Conclusions: Results are discussed with future application of RIM special scores for the assessment of pre-clinical cognitive decline and, based on observed effect sizes, power analyses are reported to inform future study planning.
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Affiliation(s)
- Eamonn P Arble
- Department of Psychology, Eastern Michigan University, Ypsilanti, MI 48197, USA
| | - Steven W Steinert
- Department of Psychology, Eastern Michigan University, Ypsilanti, MI 48197, USA
| | - Sneha Shankar
- Department of Psychology, Eastern Michigan University, Ypsilanti, MI 48197, USA
| | - Alex Cerjanic
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Bradley P Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Ana M Daugherty
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Psychology, Wayne State University, Detroit, MI 48202, USA
- Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
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3
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Zhang J, Zhao YJ, Wang JY, Cui H, Li S, Meng X, Cai RY, Xie J, Sun SY, Yao Y, Li J. Comprehensive assessment of fine motor movement and cognitive function among older adults in China: a cross-sectional study. BMC Geriatr 2024; 24:118. [PMID: 38297201 PMCID: PMC10832076 DOI: 10.1186/s12877-024-04725-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/19/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Fine motor skills are closely related to cognitive function. However, there is currently no comprehensive assessment of fine motor movement and how it corresponds with cognitive function. To conduct a complete assessment of fine motor and clarify the relationship between various dimensions of fine motor and cognitive function. METHODS We conducted a cross-sectional study with 267 community-based participants aged ≥ 60 years in Beijing, China. We assessed four tests performance and gathered detailed fine motor indicators using Micro-Electro-Mechanical System (MEMS) motion capture technology. The wearable MEMS device provided us with precise fine motion metrics, while Chinese version of the Montreal Cognitive Assessment (MoCA) was used to assess cognitive function. We adopted logistic regression to analyze the relationship between fine motor movement and cognitive function. RESULTS 129 (48.3%) of the participants had cognitive impairment. The vast majority of fine motor movements have independent linear correlations with MoCA-BJ scores. According to logistic regression analysis, completion time in the Same-pattern tapping test (OR = 1.033, 95%CI = 1.003-1.063), Completion time of non-dominant hand in the Pieces flipping test (OR = 1.006, 95%CI = 1.000-1.011), and trajectory distance of dominant hand in the Pegboard test (OR = 1.044, 95%CI = 1.010-1.068), which represents dexterity, are related to cognitive impairment. Coordination, represented by lag time between hands in the Same-pattern tapping (OR = 1.663, 95%CI = 1.131-2.444), is correlated with cognitive impairment. Coverage in the Dual-hand drawing test as an important indicator of stability is negatively correlated with cognitive function (OR = 0.709, 95%CI = 0.6501-0.959). Based on the above 5-feature model showed consistently high accuracy and sensitivity at the MoCA-BJ score (ACU = 0.80-0.87). CONCLUSIONS The results of a comprehensive fine-motor assessment that integrates dexterity, coordination, and stability are closely related to cognitive functioning. Fine motor movement has the potential to be a reliable predictor of cognitive impairment.
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Affiliation(s)
- Jie Zhang
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Ye-Jing Zhao
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | | | - Han Cui
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Shaojie Li
- China Center for Health Development Studies, Peking University, Beijing, China
| | - Xue Meng
- Office of the National Clinical Research Center for Geriatric Diseases, Beijing Hospital, Institute of Geriatric Medicine, National Center of Gerontology, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Rui-Yu Cai
- Department of Geriatric Medicine, Zhucheng People's Hospital, Weifang City, Shandong Province, China
| | - Juan Xie
- Geriatric Department, Hefei First People's Hospital, Hefei, China
| | - Su-Ya Sun
- Department of Geriatrics, Tangshan Gong Ren Hospital, Tangshan, Hebei, China
| | - Yao Yao
- China Center for Health Development Studies, Peking University, Beijing, China.
| | - Jing Li
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China.
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Suzumura S, Osawa A, Kanada Y, Keisuke M, Takano E, Sugioka J, Natsumi M, Nagahama T, Shiramoto K, Kuno K, Kizuka S, Satoh K, Sakurai H, Sano Y, Mizuguchi T, Kandori A, Kondo I. Finger Tapping Test for Assessing the Risk of Mild Cognitive Impairment. Hong Kong J Occup Ther 2022; 35:137-145. [PMID: 36467516 PMCID: PMC9716461 DOI: 10.1177/15691861221109872] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 06/11/2022] [Indexed: 09/12/2023] Open
Abstract
AIM A testing method for early diagnosis of Mild cognitive dementia (MCI) that can be easily applied in clinical practice was investigated in this study. We examined whether MCI risk can be determined through finger movements. METHODS Between 2013 and 2020, 1097 individuals were screened. After applying propensity-score matching to adjust for variability between the groups, 173 individuals each in the mild cognitive impairment and control groups were selected. Thereafter, differences between groups in mean values of parameters extracted from finger tap movements were determined using unpaired t-test and effect size. Furthermore, area under the curve, sensitivity, and specificity were calculated from the receiver operating characteristic curve for parameters with significant difference. RESULTS A significant difference was observed, especially in the number of taps in the MCI group compared with that in the control group (p < .001; 95% CI, -12.7 to -8.8; r = 0.51). A cut-off value of 30 taps was applied (sensitivity, 0.77; specificity, 0.67; AUC, 0.79). Significant differences were also observed in rhythm-related parameters. CONCLUSIONS These parameters might be useful for capturing MCI risk. Finger taps are easily measured and may be suitable for screening large populations. This tool might be used as a supplemental method to increase the sensitivity of traditional cognitive tests.
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Affiliation(s)
- Shota Suzumura
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
- Faculty of Rehabilitation, School of Health Sciences, Fujita Health University, Toyoake, Japan
| | - Aiko Osawa
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Yoshikiyo Kanada
- Faculty of Rehabilitation, School of Health Sciences, Fujita Health University, Toyoake, Japan
| | - Maeda Keisuke
- Department of Geriatric Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Eiko Takano
- Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Junpei Sugioka
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Maeda Natsumi
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Taishi Nagahama
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kenta Shiramoto
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Katsumi Kuno
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Shiori Kizuka
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kenji Satoh
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Hiroaki Sakurai
- Faculty of Rehabilitation, School of Health Sciences, Fujita Health University, Toyoake, Japan
| | - Yuko Sano
- Center for Technology Innovation, Artificial Intelligence, Research and Development Group, Hitachi Ltd, Kokubunji, Japan
| | | | - Akihiko Kandori
- Center for Exploratory Research, Research & Development Group, Hitachi Ltd, Kokubunji, Japan
| | - Izumi Kondo
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
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Kamal S, Park I, Kim YJ, Kim YJ, Lee U. Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment. PLoS One 2021; 16:e0259051. [PMID: 34941878 PMCID: PMC8700055 DOI: 10.1371/journal.pone.0259051] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 10/11/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer's disease. METHODS We used the Open Access Series of Imaging Studies (OASIS) dataset to investigate changes in the CC. The individuals were divided into three groups using the Clinical Dementia Rating (CDR); 94 normal controls (NC) were not demented (NC group, CDR = 0), 56 individuals had very mild dementia (VMD group, CDR = 0.5), and 17 individuals were defined as having mild and moderate dementia (MD group, CDR = 1 or 2). Deep learning technology using a convolutional neural network organized in a U-net architecture was used to segment the CC in the midsagittal plane. Total CC length and regional magnetic resonance imaging (MRI) measurements of the CC were made. RESULTS The total CC length was negatively associated with cognitive function. (beta = -0.139, p = 0.022) Among MRI measurements of the CC, the height of the anterior third (beta = 0.038, p <0.0001) and width of the body (beta = 0.077, p = 0.001) and the height (beta = 0.065, p = 0.001) and area of the splenium (beta = 0.059, p = 0.027) were associated with cognitive function. To distinguish MD from NC and VMD, the receiver operating characteristic analyses of these MRI measurements showed areas under the curves of 0.65-0.74. (total CC length = 0.705, height of the anterior third = 0.735, width of the body = 0.714, height of the splenium = 0.703, area of the splenium = 0.649). CONCLUSIONS Among MRI measurements, total CC length, the height of the anterior third and width of the body, and the height and area of the splenium were associated with cognitive decline. They had fair diagnostic validity in distinguishing MD from NC and VMD.
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Affiliation(s)
- Sadia Kamal
- Department of Electronic Engineering, Hallym University, Chuncheon, Korea
| | - Ingyu Park
- Department of Electronic Engineering, Hallym University, Chuncheon, Korea
| | - Yeo Jin Kim
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Korea
| | - Yun Joong Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea
| | - Unjoo Lee
- Department of Electronic Engineering, Hallym University, Chuncheon, Korea
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Ardesch DJ, Scholtens LH, de Lange SC, Roumazeilles L, Khrapitchev AA, Preuss TM, Rilling JK, Mars RB, van den Heuvel MP. Scaling Principles of White Matter Connectivity in the Human and Nonhuman Primate Brain. Cereb Cortex 2021; 32:2831-2842. [PMID: 34849623 PMCID: PMC9247419 DOI: 10.1093/cercor/bhab384] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 11/14/2022] Open
Abstract
Brains come in many shapes and sizes. Nature has endowed big-brained primate species like humans with a proportionally large cerebral cortex. Comparative studies have suggested, however, that the total volume allocated to white matter connectivity-the brain's infrastructure for long-range interregional communication-does not keep pace with the cortex. We investigated the consequences of this allometric scaling on brain connectivity and network organization. We collated structural and diffusion magnetic resonance imaging data across 14 primate species, describing a comprehensive 350-fold range in brain size across species. We show volumetric scaling relationships that indeed point toward a restriction of macroscale connectivity in bigger brains. We report cortical surface area to outpace white matter volume, with larger brains showing lower levels of overall connectedness particularly through sparser long-range connectivity. We show that these constraints on white matter connectivity are associated with longer communication paths, higher local network clustering, and higher levels of asymmetry in connectivity patterns between homologous areas across the left and right hemispheres. Our findings reveal conserved scaling relationships of major brain components and show consequences for macroscale brain circuitry, providing insights into the connectome architecture that could be expected in larger brains such as the human brain.
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Affiliation(s)
- Dirk Jan Ardesch
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an institute of the Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK
| | - Alexandre A Khrapitchev
- Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford, Oxford OX3 7DQ, UK
| | - Todd M Preuss
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30329, USA.,Center for Translational Social Neuroscience, Emory University, Atlanta, GA 30329, USA.,Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30307, USA
| | - James K Rilling
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30329, USA.,Center for Translational Social Neuroscience, Emory University, Atlanta, GA 30329, USA.,Department of Anthropology, Emory University, Atlanta, GA 30322, USA.,Silvio O. Conte Center for Oxytocin and Social Cognition, Emory University, Atlanta, GA 30322, USA.,Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322, USA
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, AJ 6525, Nijmegen, the Netherlands.,Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Child Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
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7
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Shi H, Ma D, Faisal Beg M, Cao J. A functional proportional hazard cure rate model for interval-censored data. Stat Methods Med Res 2021; 31:154-168. [PMID: 34806480 DOI: 10.1177/09622802211052972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Existing survival models involving functional covariates typically rely on the Cox proportional hazards structure and the assumption of right censorship. Motivated by the aim of predicting the time of conversion to Alzheimer's disease from sparse biomarker trajectories in patients with mild cognitive impairment, we propose a functional mixture cure rate model with both functional and scalar covariates for interval censoring and sparsely sampled functional data. To estimate the nonparametric coefficient function that depicts the effect of the shape of the trajectories on the survival outcome and cure probability, we utilize the functional principal component analysis to extract the functional features from the sparsely and irregularly sampled trajectories. To obtain parameter estimates from the mixture cure rate model with interval censoring, we apply the expectation-maximization algorithm based on Poisson data augmentation. The estimation accuracy of our method is assessed via a simulation study and we apply our model on Alzheimer's disease Neuroimaging Initiative data set.
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Affiliation(s)
- Haolun Shi
- Department of Statistics and Actuarial Science, 1763Simon Fraser University, Burnaby, BC, Canada
| | - Da Ma
- School of Engineering, 1763Simon Fraser University, Burnaby, BC, Canada
| | - Mirza Faisal Beg
- School of Engineering, 1763Simon Fraser University, Burnaby, BC, Canada
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, 1763Simon Fraser University, Burnaby, BC, Canada
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8
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Aziz MNM, Kumar J, Muhammad Nawawi KN, Raja Ali RA, Mokhtar NM. Irritable Bowel Syndrome, Depression, and Neurodegeneration: A Bidirectional Communication from Gut to Brain. Nutrients 2021; 13:nu13093061. [PMID: 34578939 PMCID: PMC8468817 DOI: 10.3390/nu13093061] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/19/2021] [Accepted: 08/23/2021] [Indexed: 12/13/2022] Open
Abstract
Patients with irritable bowel syndrome (IBS) are increasingly presenting with a wide range of neuropsychiatric symptoms, such as deterioration in gastroenteric physiology, including visceral hypersensitivity, altered intestinal membrane permeability, and gastrointestinal motor dysfunction. Functional imaging of IBS patients has revealed several abnormalities in various brain regions, such as significant activation of amygdala, thinning of insular and anterior cingulate cortex, and increase in hypothalamic gray matter, which results in poor psychiatric and cognitive outcomes. Interrelations between the enteric and central events in IBS-related gastrointestinal, neurological, and psychiatric pathologies have compelled researchers to study the gut-brain axis-a bidirectional communication that maintains the homeostasis of the gastrointestinal and central nervous system with gut microbiota as the protagonist. Thus, it can be disrupted by any alteration owing to the gut dysbiosis or loss of diversity in microbial composition. Available evidence indicates that the use of probiotics as a part of a balanced diet is effective in the management of IBS and IBS-associated neurodegenerative and psychiatric comorbidities. In this review, we delineate the pathogenesis and complications of IBS from gastrointestinal and neuropsychiatric standpoints while also discussing the neurodegenerative events in enteric and central nervous systems of IBS patients and the therapeutic potential of gut microbiota-based therapy established on clinical and preclinical data.
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Affiliation(s)
- Muhammad Nazirul Mubin Aziz
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (M.N.M.A.); (J.K.)
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (M.N.M.A.); (J.K.)
- Gut Research Group, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (K.N.M.N.); (R.A.R.A.)
| | - Khairul Najmi Muhammad Nawawi
- Gut Research Group, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (K.N.M.N.); (R.A.R.A.)
- Gastroenterology Unit, Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Raja Affendi Raja Ali
- Gut Research Group, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (K.N.M.N.); (R.A.R.A.)
- Gastroenterology Unit, Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Norfilza M. Mokhtar
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (M.N.M.A.); (J.K.)
- Gut Research Group, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (K.N.M.N.); (R.A.R.A.)
- Correspondence:
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9
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Tao B, Xiao Y, Yang B, Zeng J, Zhang W, Hu N, Yang C, Lencer R, Gong Q, Sweeney JA, Lui S. Morphological alterations of the corpus callosum in antipsychotic-naive first-episode schizophrenia before and 1-year after treatment. Schizophr Res 2021; 231:115-121. [PMID: 33839369 DOI: 10.1016/j.schres.2021.03.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 03/28/2021] [Accepted: 03/28/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The corpus callosum (CC) is known to be altered in patients with schizophrenia. However, its morphologic characteristics are less well studied in treatment-naive first-episode schizophrenia patients, as is the effect of antipsychotic treatment on this structure. METHODS T-1 weighted MRI scans were obtained from 160 antipsychotic-naïve first-episode schizophrenia patients (AN-FES) and 155 healthy controls (HCs) before treatment initiation. Among the patients, forty-four were available for follow-up studies after one year of antipsychotic treatment, and were divided into good-outcome (n = 31) and poor-outcome subgroups (n = 13) based on whether there was a 50% reduction in Positive and Negative Symptom Scale (PANSS) total scores from baseline. A computer algorithm was applied to automatically identify the mid-sagittal plane (MSP) and obtain morphological measurement parameters of the CC. RESULTS Compared with HCs, AN-FES patients showed a significant reduction of thickness in the posterior midbody of the CC. This deficit was correlated with severity of negative symptoms. After one year of antipsychotic treatment, there was no significant change in CC morphological measurements in schizophrenia patients, nor was there a significant difference of CC morphological measurements between good-outcome and poor-outcome subgroups at baseline or at 1-year follow-up. CONCLUSION Thickness of the posterior midbody of the CC is reduced in the early course of schizophrenia before treatment. This alteration was not affected by antipsychotic treatment and was unrelated to treatment outcome at 1-year.
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Affiliation(s)
- Bo Tao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Beisheng Yang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jiaxin Zeng
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Na Hu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chengmin Yang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, United States
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
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Shi H, Ma D, Nie Y, Faisal Beg M, Pei J, Cao J, Neuroimaging Initiative TAD. Early diagnosis of Alzheimer's disease on ADNI data using novel longitudinal score based on functional principal component analysis. J Med Imaging (Bellingham) 2021; 8:024502. [PMID: 33898638 DOI: 10.1117/1.jmi.8.2.024502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 03/12/2021] [Indexed: 11/14/2022] Open
Abstract
Methods: Alzheimer's disease (AD) is a worldwide prevalent age-related neurodegenerative disease with no available cure yet. Early prognosis is therefore crucial for planning proper clinical intervention. It is especially true for people diagnosed with mild cognitive impairment, to whom the prediction of whether and when the future disease onset would happen is particularly valuable. However, such prognostic prediction has been proven to be challenging, and previous studies have only achieved limited success. Approach: In this study, we seek to extract the principal component of the longitudinal disease progression trajectory in the early stage of AD, measured as the magnetic resonance imaging (MRI)-derived structural volume, to predict the onset of AD for mild cognitive impaired patients two years ahead. Results: Cross-validation results of LASSO regression using the longitudinal functional principal component (FPC) features show significant improved predictive power compared to training using the baseline volume 12 months before AD conversion [area under the receiver operating characteristic curve (AUC) of 0.802 versus 0.732] and 24 months before AD conversion (AUC of 0.816 versus 0.717). Conclusions: We present a framework using the FPCA to extract features from MRI-derived information collected from multiple timepoints. The results of our study demonstrate the advantageous predictive power of the population-based longitudinal features to predict the disease onset compared with using only cross-sectional data-based on volumetric features extracted from a single timepoint, demonstrating the improved prediction power using FPC-derived longitudinal features.
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Affiliation(s)
- Haolun Shi
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada
| | - Da Ma
- Simon Fraser University, School of Engineering Science, Burnaby, BC, Canada
| | - Yunlong Nie
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada
| | - Mirza Faisal Beg
- Simon Fraser University, School of Engineering Science, Burnaby, BC, Canada
| | - Jian Pei
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada.,Simon Fraser University, School of Computing Science, Burnaby, BC, Canada
| | - Jiguo Cao
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada.,Simon Fraser University, School of Computing Science, Burnaby, BC, Canada
| | - The Alzheimer's Disease Neuroimaging Initiative
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada.,Simon Fraser University, School of Engineering Science, Burnaby, BC, Canada.,Simon Fraser University, School of Computing Science, Burnaby, BC, Canada
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11
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Raghavan S, Przybelski SA, Reid RI, Graff-Radford J, Lesnick TG, Zuk SM, Knopman DS, Machulda MM, Mielke MM, Petersen RC, Jack CR, Vemuri P. Reduced fractional anisotropy of the genu of the corpus callosum as a cerebrovascular disease marker and predictor of longitudinal cognition in MCI. Neurobiol Aging 2020; 96:176-183. [PMID: 33022474 PMCID: PMC7722208 DOI: 10.1016/j.neurobiolaging.2020.09.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/25/2020] [Accepted: 09/01/2020] [Indexed: 12/29/2022]
Abstract
Our goal was to evaluate the utility of diffusion tensor imaging (DTI) for predicting future cognitive decline in mild cognitive impairment (MCI) in conjunction with Alzheimer's disease (AD) biomarkers (amyloid positron emission tomography and AD signature neurodegeneration) in 132 MCI individuals ≥60 year old with structural magnetic resonance imaging, DTI, amyloid positron emission tomography, and at least one clinical follow-up. We used mixed-effect models to evaluate the prognostic ability of fractional anisotropy of the genu of the corpus callosum (FA-Genu), as a cerebrovascular disease marker, for predicting cognitive decline along with AD biomarkers. We contrasted the value of white matter hyperintensities, a traditional cerebrovascular disease marker as well as FA in the hippocampal cingulum bundle with the FA-Genu models. FA-Genu significantly predicted cognitive decline even after accounting for AD biomarkers. WMH was not associated with cognitive decline in the model with both WMH and FA-Genu. DTI specifically FA-Genu provides unique complementary information to AD biomarkers and has significant utility for prediction of cognitive decline in MCI.
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Affiliation(s)
| | | | - Robert I Reid
- Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Samantha M Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Michelle M Mielke
- Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA
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12
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Mangalore S, Mukku SSR, Vankayalapati S, Sivakumar PT, Varghese M. Shape Profile of Corpus Callosum As a Signature to Phenotype Different Dementia. J Neurosci Rural Pract 2020; 12:185-192. [PMID: 33531781 PMCID: PMC7846348 DOI: 10.1055/s-0040-1716805] [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] [Indexed: 10/28/2022] Open
Abstract
Background Phenotyping dementia is always a complex task for a clinician. There is a need for more practical biomarkers to aid clinicians. Objective The aim of the study is to investigate the shape profile of corpus callosum (CC) in different phenotypes of dementia. Materials and Methods Our study included patients who underwent neuroimaging in our facility as a part of clinical evaluation for dementia referred from Geriatric Clinic (2017-2018). We have analyzed the shape of CC and interpreted the finding using a seven-segment division. Results The sample included MPRAGE images of Alzheimer' dementia (AD) ( n = 24), posterior cortical atrophy- Alzheimer' dementia (PCA-AD) ( n = 7), behavioral variant of frontotemporal dementia (Bv-FTD) ( n = 17), semantic variant frontotemporal dementia (Sv-FTD) ( n = 11), progressive nonfluent aphasia (PNFA) ( n = 4), Parkinson's disease dementia (PDD) ( n = 5), diffuse Lewy body dementia ( n = 7), progressive supranuclear palsy (PSP) ( n = 3), and corticobasal degeneration (CBD) ( n = 3). We found in posterior dementias such as AD and PCA-AD that there was predominant atrophy of splenium of CC. In Bv-FTD, the genu and anterior half of the body of CC was atrophied, whereas in PNFA, PSP, PDD, and CBD there was atrophy of the body of CC giving a dumbbell like profile. Conclusion Our study findings were in agreement with the anatomical cortical regions involved in different phenotypes of dementia. Our preliminary study highlighted potential usefulness of CC in the clinical setting for phenotyping dementia in addition to clinical history and robust biomarkers.
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Affiliation(s)
- Sandhya Mangalore
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Shiva Shanker Reddy Mukku
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Sriharish Vankayalapati
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Palanimuthu Thangaraju Sivakumar
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Mathew Varghese
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
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13
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Popuri K, Ma D, Wang L, Beg MF. Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases. Hum Brain Mapp 2020; 41:4127-4147. [PMID: 32614505 PMCID: PMC7469784 DOI: 10.1002/hbm.25115] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 04/15/2020] [Accepted: 06/08/2020] [Indexed: 12/29/2022] Open
Abstract
Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.
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Affiliation(s)
- Karteek Popuri
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Da Ma
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Lei Wang
- Feinberg School of MedicineNorthwestern UniversityEvanstonIllinoisUSA
| | - Mirza Faisal Beg
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
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14
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Utoomprurkporn N, Hardy CJD, Stott J, Costafreda SG, Warren J, Bamiou DE. "The Dichotic Digit Test" as an Index Indicator for Hearing Problem in Dementia: Systematic Review and Meta-Analysis. J Am Acad Audiol 2020; 31:646-655. [PMID: 33296935 DOI: 10.1055/s-0040-1718700] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Patients with dementia commonly have problems processing speech in the presence of competing background speech or noise. This difficulty can be present from the very early stages of dementia, and may be a preclinical feature of Alzheimer's disease. PURPOSE This study investigates whether people with dementia perform worse on the dichotic digit test (DDT), an experimental probe of speech processing in the presence of competing speech, and whether test performance may predict dementia onset. RESEARCH DESIGN Systematic review and meta-analysis. DATA COLLECTION AND ANALYSIS A literature search was conducted in Medline, Embase, Scopus, and Psycinfo. We included (1) studies that included people with a diagnosis of dementia and a healthy control group with no cognitive impairment; (2) studies that reported results from a DDT in a free-recall response task; and (3) studies that had the dichotic digit mean correct percentage score or right-ear advantage, as outcome measurements. RESULTS People with dementia had a lower DDT total score, with a pooled mean difference of 18.6% (95% confidence interval [CI]: 21.2-15.9). Patients with dementia had an increased right-ear advantage relative to controls with a pooled difference of 24.4% (95% CI: 21.8-27.0). CONCLUSION The DDT total scores are lower and the right-ear advantage increased in cognitively impaired versus normal control participants. The findings also suggest that the reduction of dichotic digit total score and increase of right-ear advantage progress as cognitive impairment increases. Whether abnormalities in dichotic digit scores could predict subsequent dementia onset should be examined in further longitudinal studies.
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Affiliation(s)
- Nattawan Utoomprurkporn
- UCL Ear Institute, Faculty of Brain Science, University College London, London, United Kingdom
- Department of Otolaryngology, Faculty of Medicine, Chulalongkorn University, Pathumwan, Bangkok, Thailand
| | - Chris J D Hardy
- Dementia Research Centre, Faculty of Brain Science, University College London, London, United Kingdom
| | - Joshua Stott
- Division of Psychology and Language Science, Faculty of Brain Sciences, University College London, Gower Street, United Kingdom
| | - Sergi G Costafreda
- Division of Psychiatry, Faculty of Brain Sciences, University College London, Gower Street, United Kingdom
| | - Jason Warren
- Dementia Research Centre, Faculty of Brain Science, University College London, London, United Kingdom
| | - Doris Eva Bamiou
- UCL Ear Institute, Faculty of Brain Science, University College London, London, United Kingdom
- NIHR Biomedical Research Centre Hearing and Deafness, London, United Kingdom
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15
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Yuan S, Li H, Xie J, Sun X. Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease. Int J Mol Sci 2019; 20:E5912. [PMID: 31775305 PMCID: PMC6928939 DOI: 10.3390/ijms20235912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/21/2019] [Accepted: 11/22/2019] [Indexed: 01/02/2023] Open
Abstract
The pathological features of Alzheimer's Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson's correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus-cerebellum-brainstem-pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.
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Affiliation(s)
| | | | | | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; (S.Y.)
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16
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Marizzoni M, Ferrari C, Jovicich J, Albani D, Babiloni C, Cavaliere L, Didic M, Forloni G, Galluzzi S, Hoffmann KT, Molinuevo JL, Nobili F, Parnetti L, Payoux P, Ribaldi F, Rossini PM, Schönknecht P, Salvatore M, Soricelli A, Hensch T, Tsolaki M, Visser PJ, Wiltfang J, Richardson JC, Bordet R, Blin O, Frisoni GB. Predicting and Tracking Short Term Disease Progression in Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer’s Disease: Structural Brain Biomarkers. J Alzheimers Dis 2019; 69:3-14. [DOI: 10.3233/jad-180152] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Moira Marizzoni
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Clarissa Ferrari
- Unit of Statistics, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Italy
| | - Diego Albani
- Neuroscience Department, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy
- IRCCS San Raffaele Pisana of Rome, Rome, Italy
| | - Libera Cavaliere
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Mira Didic
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
- APHM, Timone, Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France
| | - Gianluigi Forloni
- Neuroscience Department, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Samantha Galluzzi
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | | | - José Luis Molinuevo
- Alzheimer’s Disease Unit and Other Cognitive Disorders Unit, Hospital Clínic de Barcelona, and Institut d’Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalunya, Spain
| | - Flavio Nobili
- Clinical Neurology, Dept. of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU SanMartino-IST, Genoa, Italy
| | - Lucilla Parnetti
- Clinica Neurologica, Università di Perugia, Ospedale Santa Mariadella Misericordia, Perugia, Italy
| | - Pierre Payoux
- INSERM; Imagerie cérébrale et handicapsneurologiques UMR 825, Toulouse, France
| | - Federica Ribaldi
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Maria Rossini
- Area of Neuroscience, Department of Gerontology, Neurosciences & Orthopedics, Catholic University, Policlinic A. Gemelli Foundation Rome, Italy
| | - Peter Schönknecht
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - Marco Salvatore
- SDN Istituto di Ricerca Diagnostica e Nucleare, Napoli, Italy
| | | | - Tilman Hensch
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - Magda Tsolaki
- 3rd Neurologic Clinic, Medical School, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Pieter Jelle Visser
- Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, The Netherlands
| | - Jens Wiltfang
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Duisburg-Essen, Essen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg-August-University, Goettingen, Germany
- iBiMED, Medical Sciences Department, University of Aveiro, Aveiro, Portugal
| | - Jill C. Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, United Kingdom
| | - Régis Bordet
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative and vascular cognitive disorders, Lille, France
| | - Olivier Blin
- Aix Marseille University, UMR-CNRS 7289, Service de Pharmacologie Clinique, AP-HM, Marseille, France
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
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17
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Adamson C, Beare R, Ball G, Walterfang M, Seal M. Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach. Brain Behav 2018; 8:e01142. [PMID: 30565884 PMCID: PMC6305917 DOI: 10.1002/brb3.1142] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 08/31/2018] [Accepted: 09/27/2018] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei. METHODS This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The corpus callosum is computationally less demanding to segment automatically compared to more complicated structures and a subject can be processed in a few minutes. We aimed to demonstrate that callosal shape and thickness measures provide a simple, effective, and accurate prognostication tool in ADNI dataset. Using longitudinal datasets, we classified MCI subjects based on conversion to AD assessed via cognitive testing. We evaluated the classification accuracy of callosal shape features in comparison with the existing "gold standard" cortical thickness and subcortical gray matter volume measures. RESULTS The callosal thickness measures were less accurate in classifying conversion status by cognitive scores compared to gray matter measures for AD. CONCLUSIONS While this paper presented a negative result, this method may be more suitable for a disease of the white matter.
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Affiliation(s)
- Chris Adamson
- Developmental ImagingMurdoch Children’s Research InstituteParkvilleVictoriaAustralia
| | - Richard Beare
- Developmental ImagingMurdoch Children’s Research InstituteParkvilleVictoriaAustralia
- Department of MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Gareth Ball
- Developmental ImagingMurdoch Children’s Research InstituteParkvilleVictoriaAustralia
| | - Mark Walterfang
- Neuropsychiatry UnitRoyal Melbourne HospitalMelbourneVictoriaAustralia
- Department of PsychiatryUniversity of MelbourneMelbourneVictoriaAustralia
- Florey Institute of Neuroscience and Mental HealthMelbourneVictoriaAustralia
| | - Marc Seal
- Developmental ImagingMurdoch Children’s Research InstituteParkvilleVictoriaAustralia
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18
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Park G, Kwak K, Seo SW, Lee JM. Automatic Segmentation of Corpus Callosum in Midsagittal Based on Bayesian Inference Consisting of Sparse Representation Error and Multi-Atlas Voting. Front Neurosci 2018; 12:629. [PMID: 30271320 PMCID: PMC6142891 DOI: 10.3389/fnins.2018.00629] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Accepted: 08/21/2018] [Indexed: 11/13/2022] Open
Abstract
In this paper, we introduce a novel automatic method for Corpus Callosum (CC) in midsagittal plane segmentation. The robust segmentation of CC in midsagittal plane is key role for quantitative study of structural features of CC associated with various neurological disorder such as epilepsy, autism, Alzheimer's disease, and so on. Our approach is based on Bayesian inference using sparse representation and multi-atlas voting which both methods are used in various medical imaging, and show outstanding performance. Prior information in the proposed Bayesian inference is obtained from probability map generated from multi-atlas voting. The probability map contains the information of shape and location of CC of target image. Likelihood in the proposed Bayesian inference is obtained from gamma distribution function, generated from reconstruction errors (or sparse representation error), which are calculated in sparse representation of target patch using foreground dictionary and background dictionary each. Unlike the usual sparse representation method, we added gradient magnitude and gradient direction information to the patches of dictionaries and target, which had better segmentation performance than when not added. We compared three main segmentation results as follow: (1) the joint label fusion (JLF) method which is state-of-art method in multi-atlas voting based segmentation for evaluation of our method; (2) prior information estimated from multi-atlas voting only; (3) likelihood estimated from comparison of the reconstruction errors from sparse representation error only; (4) the proposed Bayesian inference. The methods were evaluated using two data sets of T1-weighted images, which one data set consists of 100 normal young subjects and the other data set consist of 25 normal old subjects and 22 old subjects with heavy drinker. In both data sets, the proposed Bayesian inference method has significantly the best segmentation performance than using each method separately.
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Affiliation(s)
- Gilsoon Park
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Kichang Kwak
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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19
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Prendergast DM, Karlsgodt KH, Fales CL, Ardekani BA, Szeszko PR. Corpus callosum shape and morphology in youth across the psychosis Spectrum. Schizophr Res 2018; 199:266-273. [PMID: 29656909 DOI: 10.1016/j.schres.2018.04.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 03/12/2018] [Accepted: 04/03/2018] [Indexed: 11/16/2022]
Abstract
The corpus callosum is the largest white matter tract in the human brain connecting and coordinating homologous regions of the right and left hemispheres and has been strongly implicated in the pathogenesis of psychosis. We investigated corpus callosum morphology in a large community cohort of 917 individuals (aged 8-21), including 267 endorsing subsyndromal or threshold psychotic symptoms (207 on the psychosis spectrum and 60 with limited psychosis based on previously published criteria) and 650 non-psychotic volunteers. We used a highly reliable and previously published algorithm to automatically identify the midsagittal plane and to align the corpus callosum along the anterior and posterior commissures for segmentation, thereby eliminating these sources of error variance in dependent measures, which included perimeter, length, mean thickness and shape (circularity). The parcellation scheme divided the corpus callosum into 7 subregions that consisted of the rostrum, genu, rostral body, anterior midbody, posterior midbody, isthmus, and splenium. Both individuals endorsing psychotic symptoms and those with limited psychosis had significantly (p<.05) smaller area and lower thickness measures compared to healthy volunteers, but did not differ significantly from each other. Findings were relatively widespread indicating a relatively global effect not circumscribed to any particular corpus callosum subregion. These data are consistent with the hypothesis that corpus callosum abnormalities may be evident early in the course of illness and predate the onset of frank psychosis. Given that these measures can be easily obtained and are highly reliable they may assist in the identification of individuals at future risk for psychosis.
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Affiliation(s)
| | - K H Karlsgodt
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA, USA
| | - C L Fales
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - B A Ardekani
- Center for Brain Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - P R Szeszko
- James J. Peters VA Medical Center, Mental Health Patient Care Center and Mental Illness Research Education Clinical Center (MIRECC), Bronx, NY, USA; Icahn School of Medicine at Mount Sinai, Department of Psychiatry, New York, NY, USA
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20
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Suzumura S, Osawa A, Maeda N, Sano Y, Kandori A, Mizuguchi T, Yin Y, Kondo I. Differences among patients with Alzheimer's disease, older adults with mild cognitive impairment and healthy older adults in finger dexterity. Geriatr Gerontol Int 2018; 18:907-914. [DOI: 10.1111/ggi.13277] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 01/09/2018] [Accepted: 01/22/2018] [Indexed: 11/28/2022]
Affiliation(s)
- Shota Suzumura
- Department of Rehabilitation Medicine; National Center for Geriatrics and Gerontology; Morioka Japan
| | - Aiko Osawa
- Department of Rehabilitation Medicine; National Center for Geriatrics and Gerontology; Morioka Japan
| | - Natsumi Maeda
- Department of Rehabilitation Medicine; National Center for Geriatrics and Gerontology; Morioka Japan
| | - Yuko Sano
- Healthcare Innovation Center, Hitachi, Research and Development Group; Tokyo Japan
| | - Akihiko Kandori
- Healthcare Innovation Center, Hitachi, Research and Development Group; Tokyo Japan
| | | | - Ying Yin
- Production Technology Research Department; Hitachi (China) Research & Development Corporation; Shanghai China
| | - Izumi Kondo
- Department of Rehabilitation Medicine; National Center for Geriatrics and Gerontology; Morioka Japan
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Ardekani BA, Bermudez E, Mubeen AM, Bachman AH. Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment. J Alzheimers Dis 2018; 55:269-281. [PMID: 27662309 DOI: 10.3233/jad-160594] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose. OBJECTIVE To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm. METHODS We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD. RESULTS The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI. CONCLUSION The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.
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Affiliation(s)
- Babak A Ardekani
- The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.,Department of Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Elaine Bermudez
- The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.,Department of Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Asim M Mubeen
- The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Alvin H Bachman
- The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
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Mubeen AM, Asaei A, Bachman AH, Sidtis JJ, Ardekani BA. A six-month longitudinal evaluation significantly improves accuracy of predicting incipient Alzheimer's disease in mild cognitive impairment. J Neuroradiol 2017; 44:381-387. [PMID: 28676345 DOI: 10.1016/j.neurad.2017.05.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 05/05/2017] [Accepted: 05/20/2017] [Indexed: 01/08/2023]
Abstract
RATIONALE AND OBJECTIVES Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD. MATERIALS AND METHODS Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change. RESULTS The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%). CONCLUSION Short-term longitudinal assessments significantly enhance the performance of AD prediction models.
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Affiliation(s)
- Asim M Mubeen
- The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA
| | - Ali Asaei
- The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA
| | - Alvin H Bachman
- The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA
| | - John J Sidtis
- The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA; Department of psychiatry, New York university school of medicine, New York, USA
| | - Babak A Ardekani
- The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA; Department of psychiatry, New York university school of medicine, New York, USA.
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 165] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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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
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, 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
| | - 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
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, 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 M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 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
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 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
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Sun Z, van de Giessen M, Lelieveldt BPF, Staring M. Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI. Front Neuroinform 2017; 11:16. [PMID: 28286479 PMCID: PMC5323395 DOI: 10.3389/fninf.2017.00016] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 02/08/2017] [Indexed: 01/18/2023] Open
Abstract
Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer's disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer's Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures. In this study we propose to exploit the anatomical development within the entire brain, as found by a non-rigid registration approach. Specifically, this anatomical development is represented by the Stationary Velocity Field (SVF) from registration between the baseline and follow-up images. To make the SVFs comparable among subjects, we use the parallel transport method to align them in a common space. The normalized SVF together with derived features are then used to distinguish between MCIc and MCIs subjects. This novel feature space is reduced using a Kernel Principal Component Analysis method, and a linear support vector machine is used as a classifier. Extensive comparative experiments are performed to inspect the influence of several aspects of our method on classification performance, specifically the feature choice, the smoothing parameter in the registration and the use of dimensionality reduction. The optimal result from a 10-fold cross-validation using 36 month follow-up data shows competitive results: accuracy 92%, sensitivity 95%, specificity 90%, and AUC 94%. Based on the same dataset, the proposed approach outperforms two alternative ones that either depends on the baseline image only, or uses longitudinal information from larger brain areas. Good results were also obtained when scans at 6, 12, or 24 months were used for training the classifier. Besides the classification power, the proposed method can quantitatively compare brain regions that have a significant difference in development between the MCIc and MCIs groups.
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Affiliation(s)
- Zhuo Sun
- Division of Image Processing, Department of Radiology, Leiden University Medical CenterLeiden, Netherlands
| | - Martijn van de Giessen
- Division of Image Processing, Department of Radiology, Leiden University Medical CenterLeiden, Netherlands
| | - Boudewijn P. F. Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical CenterLeiden, Netherlands
- Department of Intelligent Systems, Delft University of TechnologyDelft, Netherlands
| | - Marius Staring
- Division of Image Processing, Department of Radiology, Leiden University Medical CenterLeiden, Netherlands
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Lee SH, Bachman AH, Yu D, Lim J, Ardekani BA. Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2016; 2:68-74. [PMID: 27239537 PMCID: PMC4879655 DOI: 10.1016/j.dadm.2016.01.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Introduction We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Methods Longitudinal (baseline + 1-year follow-up) MRI scans of 132 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used. A total of 54 subjects did not convert to AD over an average (±SD) follow-up of 5.46 (±1.63) years, whereas 78 converted to AD with an average conversion time of 2.56 (±1.65) years. Annual change in the corpus callosum thickness profile was calculated from the baseline and 1-year follow-up MRI. A logistic regression model with fused lasso regularization for prediction was applied to the annual changes. Results We found a sex difference. The accuracy of prediction was 84% in females and 61% in males. The discriminating regions of corpus callosum differed between sexes. In females, the genu, rostrum, and posterior body had predictive power, whereas the genu and splenium were relevant in males. Discussion Annual callosal atrophy predicts MCI-to-AD conversion in females more accurately than in males.
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Affiliation(s)
- Sang Han Lee
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Alvin H Bachman
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Donghyeon Yu
- Department of Statistics, Keimyung University, Daegu, South Korea
| | - Johan Lim
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Babak A Ardekani
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, New York University School of Medicine, New York, NY, USA
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