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Ayoub LJ, Zhu J, Lee SJ, Mugisha N, Patel K, Duerden EG, Stinson J, Verriotis M, Noel M, Kong D, Moayedi M, McAndrews MP. Age-related effects on the anterior and posterior hippocampal volumes in 6-21 year olds: A model selection approach. Hippocampus 2023; 33:37-46. [PMID: 36519826 DOI: 10.1002/hipo.23487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022]
Abstract
Although recent studies support significant differences in intrinsic structure, function, and connectivity along the longitudinal axis of the hippocampus, few studies have investigated the normative development of this dimension. In addition, factors known to influence hippocampal structure, such as sex or puberty, have yet to be characterized when assessing age-related effects on its subregions. This study addresses this gap by investigating the relationship of the anterior (antHC) and posterior (postHC) hippocampus volumes with age, and how these are moderated by sex or puberty, in structural magnetic resonance imaging scans from 183 typically developing participants aged 6-21 years. Based on previous literature, we first anticipated that non-linear models would best represent the relationship between age and the antHC and postHC volumes. We found that age-related effects are region-specific, such that the antHC volume remains stable with increasing age, while the postHC shows a cubic function characterized by overall volume increase with age but a slower rate during adolescence. Second, we hypothesized that models, which include biological sex or pubertal status would best describe these relationships. Contrary to expectation, models comprising either biological sex or pubertal status did not significantly improve model performance. Further longitudinal research is needed to evaluate their effects on the antHC and postHC development.
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Affiliation(s)
- Lizbeth J Ayoub
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.,University of Toronto Centre for the Study of Pain, Toronto, Ontario, Canada.,Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
| | - Junhao Zhu
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Steven J Lee
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
| | - Nancy Mugisha
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
| | - Kyle Patel
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
| | - Emma G Duerden
- Applied Psychology, Faculty of Education, Western University, London, Ontario, Canada
| | - Jennifer Stinson
- Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Madeleine Verriotis
- Pain Research, Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, UK.,Department of Anaesthesia and Pain Management, Great Ormond Street Hospital NHS Foundation Trust, London, UK
| | - Melanie Noel
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Dehan Kong
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.,University of Toronto Centre for the Study of Pain, Toronto, Ontario, Canada.,Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.,Department of Dentistry, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Mary Pat McAndrews
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada
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2
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Keresztes A, Raffington L, Bender AR, Bögl K, Heim C, Shing YL. Longitudinal Developmental Trajectories Do Not Follow Cross-Sectional Age Associations in Hippocampal Subfield and Memory Development. Dev Cogn Neurosci 2022; 54:101085. [PMID: 35278767 PMCID: PMC8917271 DOI: 10.1016/j.dcn.2022.101085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 11/03/2022] Open
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3
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Zavaliangos‐Petropulu A, Tubi MA, Haddad E, Zhu A, Braskie MN, Jahanshad N, Thompson PM, Liew S. Testing a convolutional neural network-based hippocampal segmentation method in a stroke population. Hum Brain Mapp 2022; 43:234-243. [PMID: 33067842 PMCID: PMC8675423 DOI: 10.1002/hbm.25210] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/03/2020] [Accepted: 09/05/2020] [Indexed: 12/22/2022] Open
Abstract
As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.
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Affiliation(s)
- Artemis Zavaliangos‐Petropulu
- Neural Plasticity and Neurorehabilitation LaboratoryUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Meral A. Tubi
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Elizabeth Haddad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Alyssa Zhu
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Meredith N. Braskie
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - Sook‐Lei Liew
- Neural Plasticity and Neurorehabilitation LaboratoryUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsKeck School of Medicine of USCMarina del ReyCaliforniaUSA
- Chan Division of Occupational Science and Occupational TherapyOstrow School of Dentistry, University of Southern CaliforniaLos AngelesCaliforniaUSA
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4
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Wisse LEM, Xie L, Das SR, De Flores R, Hansson O, Habes M, Doshi J, Davatzikos C, Yushkevich PA, Wolk DA. Tau pathology mediates age effects on medial temporal lobe structure. Neurobiol Aging 2022; 109:135-144. [PMID: 34740075 PMCID: PMC8800343 DOI: 10.1016/j.neurobiolaging.2021.09.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023]
Abstract
Hippocampal atrophy is endemic in 'normal aging' but it is unclear what factors drive age-related changes in medial temporal lobe (MTL) structural measures. We investigated cross-sectional (n = 191) and longitudinal (n = 164) MTL atrophy patterns in cognitively normal older adults from ADNI-GO/2 with no to low cerebral β-amyloid and assessed whether white matter hyperintensities (WMHs) and cerebrospinal fluid (CSF) phospho tau (p-tau) levels can explain age-related changes in the MTL. Age was significantly associated with hippocampal volumes and Brodmann Area (BA) 35 thickness, regions affected early by neurofibrillary tangle pathology, in the cross-sectional analysis and with anterior and/or posterior hippocampus, entorhinal cortex and BA35 in the longitudinal analysis. CSF p-tau was significantly associated with hippocampal volumes and atrophy rates. Mediation analyses showed that CSF p-tau levels partially mediated age effects on hippocampal atrophy rates. No significant associations were observed for WMHs. These findings point toward a role of tau pathology, potentially reflecting Primary Age-Related Tauopathy, in age-related MTL structural changes and suggests a potential role for tau-targeted interventions in age-associated neurodegeneration and memory decline.
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Affiliation(s)
- LEM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - L Xie
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - SR Das
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - R De Flores
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen, France
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - M Habes
- Biggs Alzheimer’s Institute, UT Health, San Antonio, USA
| | - J Doshi
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - C Davatzikos
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - PA Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - DA Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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Bussy A, Patel R, Plitman E, Tullo S, Salaciak A, Bedford SA, Farzin S, Béland ML, Valiquette V, Kazazian C, Tardif CL, Devenyi GA, Chakravarty MM. Hippocampal shape across the healthy lifespan and its relationship with cognition. Neurobiol Aging 2021; 106:153-168. [PMID: 34280848 DOI: 10.1016/j.neurobiolaging.2021.03.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/02/2021] [Accepted: 03/29/2021] [Indexed: 01/18/2023]
Abstract
The study of the hippocampus across the healthy adult lifespan has rendered inconsistent findings. While volumetric measurements have often been a popular technique for analysis, more advanced morphometric techniques have demonstrated compelling results that highlight the importance and improved specificity of shape-based measures. Here, the MAGeT Brain algorithm was applied on 134 healthy individuals aged 18-81 years old to extract hippocampal subfield volumes and hippocampal shape measurements, namely: local surface area (SA) and displacement. We used linear-, second- or third-order natural splines to examine the relationships between hippocampal measures and age. In addition, partial least squares analyses were performed to relate volume and shape measurements with cognitive and demographic information. Volumetric results indicated a relative preservation of the right cornus ammonis 1 with age and a global volume reduction linked with older age, female sex, lower levels of education and cognitive performance. Vertex-wise analysis demonstrated an SA preservation in the anterior hippocampus with a peak during the sixth decade, while the posterior hippocampal SA gradually decreased across lifespan. Overall, SA decrease was linked to older age, female sex and, to a lesser extent lower levels of education and cognitive performance. Outward displacement in the lateral hippocampus and inward displacement in the medial hippocampus were enlarged with older age, lower levels of cognition and education, indicating an accentuation of the hippocampal "C" shape with age. Taken together, our findings suggest that vertex-wise analyses have higher spatial specifity and that sex, education, and cognition are implicated in the differential impact of age on hippocampal subregions throughout its anteroposterior and medial-lateral axes. This article is part of the Virtual Special Issue titled COGNITIVE NEU- ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.
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Affiliation(s)
- Aurélie Bussy
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.
| | - Raihaan Patel
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Eric Plitman
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stephanie Tullo
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Saashi A Bedford
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sarah Farzin
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Marie-Lise Béland
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Vanessa Valiquette
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Christina Kazazian
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Christine L Tardif
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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6
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Canada KL, Hancock GR, Riggins T. Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood. Dev Cogn Neurosci 2021; 48:100947. [PMID: 33774332 PMCID: PMC8039550 DOI: 10.1016/j.dcn.2021.100947] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 03/17/2021] [Accepted: 03/21/2021] [Indexed: 01/25/2023] Open
Abstract
The hippocampus has been suggested to show protracted postnatal developmental growth across childhood. Most previous studies during this developmental period have been cross-sectional in nature and have focused on age-related differences in either hippocampal subregions or subfields, but not both, potentially missing localized changes. This study capitalized on a latent structural equation modeling approach to examine the longitudinal development of hippocampal subfields (cornu ammonis (CA) 2-4/dentate gyrus (DG), CA1, subiculum) in both the head and the body of the hippocampus, separately, in 165 typically developing 4- to 8-year-old children. Our findings document differential development of subfields within hippocampal head and body. Specifically, within hippocampal head, CA1 volume increased between 4-5 years and within hippocampal body, CA2-4/DG and subiculum volume increased between 5-6 years. Additionally, changes in CA1 volume in the head and changes in subiculum in the body between 4-5 years related to improvements in memory between 4-5 years. These findings demonstrate the protracted development of subfields in vivo during early- to mid-childhood, illustrate the importance of considering subfields separately in the head and body of the hippocampus, document co-occurring development of brain and behavior, and highlight the strength of longitudinal data and latent modeling when examining brain development.
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Affiliation(s)
- Kelsey L Canada
- Department of Psychology, University of Maryland, College Park, United States.
| | - Gregory R Hancock
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, United States
| | - Tracy Riggins
- Department of Psychology, University of Maryland, College Park, United States
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7
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Liu S, Hou B, Zhang Y, Lin T, Fan X, You H, Feng F. Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software. BMC Neurosci 2020; 21:35. [PMID: 32887546 PMCID: PMC7472704 DOI: 10.1186/s12868-020-00585-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 08/19/2020] [Indexed: 11/26/2022] Open
Abstract
Background The inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners. Methods We included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to 1 h. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Coefficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools. Results The mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946 to 12.29% (mean, 9.577%) for FreeSurfer, 7.245 to 20.98% (mean, 12.60%) for FSL and 1.348 to 8.800% (mean value, 3.546%) for AccuBrain®. In addition, AccuBrain® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818 to 5.958% for AccuBrain®, and from 0.903 to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603 to 16.310%). AccuBrain® also had the lowest mean values of region-specific CV between Siemens and Philips scanners (from 1.138 to 6.615%). Conclusion There is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.
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Affiliation(s)
- Sirui Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Yiwei Zhang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Tianye Lin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Xiaoyuan Fan
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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8
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Brown EM, Pierce ME, Clark DC, Fischl BR, Iglesias JE, Milberg WP, McGlinchey RE, Salat DH. Test-retest reliability of FreeSurfer automated hippocampal subfield segmentation within and across scanners. Neuroimage 2020; 210:116563. [DOI: 10.1016/j.neuroimage.2020.116563] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/13/2020] [Accepted: 01/15/2020] [Indexed: 11/26/2022] Open
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9
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Investigating microstructural variation in the human hippocampus using non-negative matrix factorization. Neuroimage 2020; 207:116348. [DOI: 10.1016/j.neuroimage.2019.116348] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 11/05/2019] [Accepted: 11/08/2019] [Indexed: 11/23/2022] Open
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10
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Bhagwat N, Pipitone J, Voineskos AN, Chakravarty MM. An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures. J Psychiatry Neurosci 2019; 44:246-260. [PMID: 30720260 PMCID: PMC6606432 DOI: 10.1503/jpn.180016] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 04/19/2018] [Accepted: 08/01/2018] [Indexed: 01/18/2023] Open
Abstract
Background The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages. Many neuroimaging studies have focused on case–control classification and predicting conversion from mild cognitive impairment to Alzheimer disease, but predicting scores from clinical assessments (such as the Alzheimer’s Disease Assessment Scale or the Mini Mental State Examination) using MRI data has received less attention. Predicting clinical scores can be crucial in providing a nuanced prognosis and inferring symptomatic severity. Methods We predicted clinical scores at the individual level using a novel anatomically partitioned artificial neural network (APANN) model. The model combined input from 2 structural MRI measures relevant to the neurodegenerative patterns observed in Alzheimer disease: hippocampal segmentations and cortical thickness. We evaluated the performance of the APANN model with 10 rounds of 10-fold cross-validation in 3 experiments, using cohorts from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): ADNI1, ADNI2 and ADNI1 + 2. Results Pearson correlation and root mean square error between the actual and predicted scores on the Alzheimer’s Disease Assessment Scale (ADNI1: r = 0.60; ADNI2: r = 0.68; ADNI1 + 2: r = 0.63) and Mini Mental State Examination (ADNI1: r = 0.52; ADNI2: r = 0.55; ADNI1 + 2: r = 0.55) showed that APANN can accurately infer clinical severity from MRI data. Limitations To rigorously validate the model, we focused primarily on large cross-sectional baseline data sets with only proof-of-concept longitudinal results. Conclusion The APANN provides a highly robust and scalable framework for predicting clinical severity at the individual level using high-dimensional, multimodal neuroimaging data.
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Affiliation(s)
- Nikhil Bhagwat
- From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada
| | - Jon Pipitone
- From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada
| | - Aristotle N. Voineskos
- From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada
| | - M. Mallar Chakravarty
- From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada
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11
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Nogovitsyn N, Souza R, Muller M, Srajer A, Hassel S, Arnott SR, Davis AD, Hall GB, Harris JK, Zamyadi M, Metzak PD, Ismail Z, Bray SL, Lebel C, Addington JM, Milev R, Harkness KL, Frey BN, Lam RW, Strother SC, Goldstein BI, Rotzinger S, Kennedy SH, MacQueen GM. Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants. Neuroimage 2019; 197:589-597. [PMID: 31075395 DOI: 10.1016/j.neuroimage.2019.05.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/15/2019] [Accepted: 05/06/2019] [Indexed: 12/31/2022] Open
Abstract
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets. Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues - Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.
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Affiliation(s)
- Nikita Nogovitsyn
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Roberto Souza
- Department of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Meghan Muller
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Amelia Srajer
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Stefanie Hassel
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | | | - Andrew D Davis
- Department of Psychology, Neuroscience & Behaviour, McMaster University, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience & Behaviour, McMaster University, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | | | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Paul D Metzak
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Zahinoor Ismail
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Signe L Bray
- Department of Radiology, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Calgary, AB, Canada; Child & Adolescent Imaging Research (CAIR) Program, Calgary, AB, Canada
| | - Catherine Lebel
- Department of Radiology, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Calgary, AB, Canada; Child & Adolescent Imaging Research (CAIR) Program, Calgary, AB, Canada
| | - Jean M Addington
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roumen Milev
- Department of Psychiatry, Queen's University and Providence Care Hospital, Kingston, ON, Canada; Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Kate L Harkness
- Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, ON, Canada
| | - Raymond W Lam
- University of British Columbia and Vancouver Coastal Health Authority, Vancouver, BC, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest and Department of Medical Biophysics, University of Toronto, ON, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Departments of Psychiatry and Pharmacology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Susan Rotzinger
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
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Abstract
Brain imaging studies have shown that slow and progressive cerebral atrophy characterized the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated to AD, key questions about the lifespan evolution of AD biomarkers remain open. When does the AD model diverge from the normal aging model? What is the lifespan trajectory of imaging biomarkers for AD? How do the trajectories of biomarkers in AD differ from normal aging? To answer these questions, we proposed an innovative way by inferring brain structure model across the entire lifespan using a massive number of MRI (N = 4329). We compared the normal model based on 2944 control subjects with the pathological model based on 3262 patients (AD + Mild cognitive Impaired subjects) older than 55 years and controls younger than 55 years. Our study provides evidences of early divergence of the AD models from the normal aging trajectory before 40 years for the hippocampus, followed by the lateral ventricles and the amygdala around 40 years. Moreover, our lifespan model reveals the evolution of these biomarkers and suggests close abnormality evolution for the hippocampus and the amygdala, whereas trajectory of ventricular enlargement appears to follow an inverted U-shape. Finally, our models indicate that medial temporal lobe atrophy and ventricular enlargement are two mid-life physiopathological events characterizing AD brain.
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Voineskos D. After Electroconvulsive Therapy, Is a Bigger Hippocampus Always Better? Biol Psychiatry 2018; 84:e59-e60. [PMID: 30261978 DOI: 10.1016/j.biopsych.2018.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 07/17/2018] [Indexed: 11/25/2022]
Affiliation(s)
- Daphne Voineskos
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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14
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Veitch DP, Weiner MW, 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. Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement 2018; 15:106-152. [PMID: 30321505 DOI: 10.1016/j.jalz.2018.08.005] [Citation(s) in RCA: 241] [Impact Index Per Article: 40.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 08/21/2018] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. METHODS We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/). RESULTS (1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. DISCUSSION ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.
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Affiliation(s)
- Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Northern California Institute for Research and Education (NCIRE), Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - 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.
| | - 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
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, 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, 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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15
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Bhagwat N, Viviano JD, Voineskos AN, Chakravarty MM. Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. PLoS Comput Biol 2018; 14:e1006376. [PMID: 30216352 PMCID: PMC6157905 DOI: 10.1371/journal.pcbi.1006376] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 09/26/2018] [Accepted: 07/18/2018] [Indexed: 01/18/2023] Open
Abstract
Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). Individual prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer's Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). We model the prototypical symptom trajectory classes using clinical assessment scores from mini-mental state exam (MMSE) and Alzheimer's Disease Assessment Scale (ADAS-13) at nine timepoints spanned over six years based on a hierarchical clustering approach. Subsequently we predict these trajectory classes for a given subject using magnetic resonance (MR) imaging, genetic, and clinical variables from two timepoints (baseline + follow-up). For prediction, we present a longitudinal Siamese neural-network (LSN) with novel architectural modules for combining multimodal data from two timepoints. The trajectory modeling yields two (stable and decline) and three (stable, slow-decline, fast-decline) trajectory classes for MMSE and ADAS-13 assessments, respectively. For the predictive tasks, LSN offers highly accurate performance with 0.900 accuracy and 0.968 AUC for binary MMSE task and 0.760 accuracy for 3-way ADAS-13 task on ADNI datasets, as well as, 0.724 accuracy and 0.883 AUC for binary MMSE task on replication AIBL dataset.
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Affiliation(s)
- Nikhil Bhagwat
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Computational Brain Anatomy Laboratory, Brain Imaging Center, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Joseph D. Viviano
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Aristotle N. Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - M. Mallar Chakravarty
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Computational Brain Anatomy Laboratory, Brain Imaging Center, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
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16
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Kong V, Devenyi GA, Gallino D, Ayranci G, Germann J, Rollins C, Chakravarty MM. Early-in-life neuroanatomical and behavioural trajectories in a triple transgenic model of Alzheimer’s disease. Brain Struct Funct 2018; 223:3365-3382. [DOI: 10.1007/s00429-018-1691-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 05/26/2018] [Indexed: 11/29/2022]
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17
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Tardif CL, Devenyi GA, Amaral RSC, Pelleieux S, Poirier J, Rosa‐Neto P, Breitner J, Chakravarty MM. Regionally specific changes in the hippocampal circuitry accompany progression of cerebrospinal fluid biomarkers in preclinical Alzheimer's disease. Hum Brain Mapp 2018; 39:971-984. [PMID: 29164798 PMCID: PMC6866392 DOI: 10.1002/hbm.23897] [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: 07/03/2017] [Revised: 11/10/2017] [Accepted: 11/13/2017] [Indexed: 01/18/2023] Open
Abstract
Neuropathological and in vivo brain imaging studies agree that the cornu ammonis 1 and subiculum subfields of the hippocampus are most vulnerable to atrophy in the prodromal phases of Alzheimer's disease (AD). However, there has been limited investigation of the structural integrity of the components of the hippocampal circuit, including subfields and extra-hippocampal white matter structure, in relation to the progression of well-accepted cerebrospinal fluid (CSF) biomarkers of AD, amyloid-β 1-42 (Aβ) and total-tau (tau). We investigated these relationships in 88 aging asymptomatic individuals with a parental or multiple-sibling familial history of AD. Apolipoprotein (APOE) ɛ4 risk allele carriers were identified, and all participants underwent cognitive testing, structural magnetic resonance imaging, and lumbar puncture for CSF assays of tau, phosphorylated-tau (p-tau) and Aβ. Individuals with a reduction in CSF Aβ levels (an indicator of amyloid accretion into neuritic plaques) as well as evident tau pathology (believed to be linked to neurodegeneration) exhibited lower subiculum volume, lower fornix microstructural integrity, and a trend towards lower cognitive score than individuals who showed only reduction in CSF Aβ. In contrast, persons with normal levels of tau showed an increase in structural MR markers in relation to declining levels of CSF Aβ. These results suggest that hippocampal subfield volume and extra-hippocampal white matter microstructure demonstrate a complex pattern where an initial volume increase is followed by decline among asymptomatic individuals who, in some instances, may be a decade or more away from onset of cognitive or functional impairment.
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Affiliation(s)
- Christine L. Tardif
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
- Montreal Neurological InstituteMontrealQuebecCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
| | - Robert S. C. Amaral
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
| | - Sandra Pelleieux
- Centre for the Studies on the Prevention of AD, Douglas Mental Health University InstituteVerdunQuebecCanada
| | - Judes Poirier
- Centre for the Studies on the Prevention of AD, Douglas Mental Health University InstituteVerdunQuebecCanada
| | - Pedro Rosa‐Neto
- Montreal Neurological InstituteMontrealQuebecCanada
- McGill University, Research Centre for Studies in AgingMontreal QuebecCanada
| | | | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
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18
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Velasco-Annis C, Akhondi-Asl A, Stamm A, Warfield SK. Reproducibility of Brain MRI Segmentation Algorithms: Empirical Comparison of Local MAP PSTAPLE, FreeSurfer, and FSL-FIRST. J Neuroimaging 2017; 28:162-172. [PMID: 29134725 DOI: 10.1111/jon.12483] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 10/06/2017] [Accepted: 10/16/2017] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Segmentation of human brain structures is crucial for the volumetric quantification of brain disease. Advances in algorithmic approaches have led to automated techniques that save time compared to interactive methods. Recently, the utility and accuracy of template library fusion algorithms, such as Local MAP PSTAPLE (PSTAPLE), have been demonstrated but there is little guidance regarding its reproducibility compared to single template-based algorithms such as FreeSurfer and FSL-FIRST. METHODS Eight repeated magnetic resonance imagings of 20 subjects were segmented using FreeSurfer, FSL-FIRST, and PSTAPLE. We reported the reproducibility of segmentation-derived volume measurements for brain structures and calculated sample size estimates for detecting hypothetical rates of tissue atrophy given the observed variances. RESULTS PSTAPLE had the most reproducible volume measurements for hippocampus, putamen, thalamus, caudate, pallidum, amygdala, Accumbens area, and cortical regions. FreeSurfer was most reproducible for brainstem. PSTAPLE was the most accurate algorithm in terms of several metrics include Dice's coefficient. The sample size estimates showed that a study utilizing PSTAPLE would require tens to hundreds less subjects than the other algorithms for detecting atrophy rates typically observed in brain disease. CONCLUSIONS PSTAPLE is a useful tool for automatic human brain segmentation due to its precision and accuracy, which enable the detection of the size of the effect typically reported for neurological disorders with a substantially reduced sample size, in comparison to the other tools we assessed. This enables randomized controlled trials to be executed with reduced cost and duration, in turn, facilitating the assessment of new therapeutic interventions.
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Affiliation(s)
- Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA
| | - Alireza Akhondi-Asl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA
| | - Aymeric Stamm
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA
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