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Zhang S, Yuan J, Sun Y, Wu F, Liu Z, Zhai F, Zhang Y, Somekh J, Peleg M, Zhu YC, Huang Z. Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer's disease progression. iScience 2024; 27:110263. [PMID: 39040055 PMCID: PMC11261013 DOI: 10.1016/j.isci.2024.110263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/01/2024] [Accepted: 06/11/2024] [Indexed: 07/24/2024] Open
Abstract
Alzheimer's disease (AD) is a complex pathophysiological disease. Allowing for heterogeneity, not only in disease manifestations but also in different progression patterns, is critical for developing effective disease models that can be used in clinical and research settings. We introduce a machine learning model for identifying underlying patterns in Alzheimer's disease (AD) trajectory using longitudinal multi-modal data from the ADNI cohort and the AIBL cohort. Ten biologically and clinically meaningful disease-related states were identified from data, which constitute three non-overlapping stages (i.e., neocortical atrophy [NCA], medial temporal atrophy [MTA], and whole brain atrophy [WBA]) and two distinct disease progression patterns (i.e., NCA → WBA and MTA → WBA). The index of disease-related states provided a remarkable performance in predicting the time to conversion to AD dementia (C-Index: 0.923 ± 0.007). Our model shows potential for promoting the understanding of heterogeneous disease progression and early predicting the conversion time to AD dementia.
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Affiliation(s)
- Suixia Zhang
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Yu Sun
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
| | - Fei Wu
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
| | - Ziyue Liu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Feifei Zhai
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Yaoyun Zhang
- DAMO Academy, Alibaba Group, 969 Wenyixi Rd, Hangzhou 310058, P.R. China
| | - Judith Somekh
- Department of Information Systems, University of Haifa, Haifa 3303220, Israel
| | - Mor Peleg
- Department of Information Systems, University of Haifa, Haifa 3303220, Israel
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Zhengxing Huang
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
| | - for the Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Study of Aging
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
- DAMO Academy, Alibaba Group, 969 Wenyixi Rd, Hangzhou 310058, P.R. China
- Department of Information Systems, University of Haifa, Haifa 3303220, Israel
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
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Marzi C, Giannelli M, Barucci A, Tessa C, Mascalchi M, Diciotti S. Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets. Sci Data 2024; 11:115. [PMID: 38263181 PMCID: PMC10805868 DOI: 10.1038/s41597-023-02421-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 07/27/2023] [Indexed: 01/25/2024] Open
Abstract
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science and Applications "Giuseppe Parenti", University of Florence, 50134, Florence, Italy
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126, Pisa, Italy
| | - Andrea Barucci
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Carlo Tessa
- Radiology Unit Apuane e Lunigiana, Azienda USL Toscana Nord Ovest, 54100, Massa, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50139, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and netwoRk in Oncology (ISPRO), 50139, Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, 47522, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, 40121, Bologna, Italy.
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Knights H, Coleman A, Hobbs NZ, Tabrizi SJ, Scahill RI. Freesurfer Software Update Significantly Impacts Striatal Volumes in the Huntington's Disease Young Adult Study and Will Influence HD-ISS Staging. J Huntingtons Dis 2024; 13:77-90. [PMID: 38489194 DOI: 10.3233/jhd-231512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background The Huntington's Disease Integrated Staging System (HD-ISS) defined disease onset using volumetric cut-offs for caudate and putamen derived from FreeSurfer 6 (FS6). The impact of the latest software update (FS7) on volumes remains unknown. The Huntington's Disease Young Adult Study (HD-YAS) is appropriately positioned to explore differences in FS bias when detecting early atrophy. Objective Explore the relationships and differences between raw caudate and putamen volumes, calculated total intracranial volumes (cTICV), and adjusted caudate and putamen volumes, derived from FS6 and FS7, in HD-YAS. Methods Images from 123 participants were segmented and quality controlled. Relationships and differences between volumes were explored using intraclass correlation (ICC) and Bland-Altman analysis. Results Across the whole cohort, ICC for raw caudate and putamen was 0.99, cTICV 0.93, adjusted caudate 0.87, and adjusted putamen 0.86 (all p < 0.0005). Compared to FS6, FS7 calculated: i) larger raw caudate (+0.8%, p < 0.00005) and putamen (+1.9%, p < 0.00005), with greater difference for larger volumes; and ii) smaller cTICV (-5.1%, p < 0.00005), with greater difference for smaller volumes. The systematic and proportional difference in cTICV was greater than raw volumes. When raw volumes were adjusted for cTICV, these effects compounded (adjusted caudate +7.0%, p < 0.00005; adjusted putamen +8.2%, p < 0.00005), with greater difference for larger volumes. Conclusions As new software is released, it is critical that biases are explored since differences have the potential to significantly alter the findings of HD trials. Until conversion factors are defined, the HD-ISS must be applied using FS6. This should be incorporated into the HD-ISS online calculator.
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Affiliation(s)
- Harry Knights
- Department of Neurodegenerative Disease, Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Annabelle Coleman
- Department of Neurodegenerative Disease, Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nicola Z Hobbs
- Department of Neurodegenerative Disease, Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah J Tabrizi
- Department of Neurodegenerative Disease, Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rachael I Scahill
- Department of Neurodegenerative Disease, Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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Kim J, Young GS, Willett AS, Pitaro AT, Crotty GF, Mesidor M, Jones KA, Bay C, Zhang M, Feany MB, Xu X, Qin L, Khurana V. Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects. CEREBELLUM (LONDON, ENGLAND) 2023; 22:1098-1108. [PMID: 36156185 PMCID: PMC10657274 DOI: 10.1007/s12311-022-01472-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/27/2022] [Indexed: 06/16/2023]
Abstract
Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.
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Affiliation(s)
- Jisoo Kim
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Geoffrey S Young
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Andrew S Willett
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Ariana T Pitaro
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Grace F Crotty
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Merlyne Mesidor
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Kristie A Jones
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Camden Bay
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Min Zhang
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Xiaoyin Xu
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Lei Qin
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Vikram Khurana
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Hale Building for Transformative Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.
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Debiasi G, Mazzonetto I, Bertoldo A. The effect of processing pipelines, input images and age on automatic cortical morphology estimates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107825. [PMID: 37806120 DOI: 10.1016/j.cmpb.2023.107825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/01/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Magnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age. METHODS For 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision. RESULTS Using FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction. CONCLUSIONS Our study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.
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Affiliation(s)
- Giulia Debiasi
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy
| | - Ilaria Mazzonetto
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy; Padova Neuroscience Center (PNC), University of Padova, via Orus 2/b, Padova 35131, Italy.
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6
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Vahermaa V, Aydogan DB, Raij T, Armio RL, Laurikainen H, Saramäki J, Suvisaari J. FreeSurfer 7 quality control: Key problem areas and importance of manual corrections. Neuroimage 2023; 279:120306. [PMID: 37541458 DOI: 10.1016/j.neuroimage.2023.120306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/06/2023] Open
Abstract
We have studied the effects of manual quality control of brain Magnetic Resonance Imaging (MRI) images processed with Freesurfer. T1 images of first episode psychosis patients (N = 60) and healthy controls (N = 41) were inspected for gray matter boundary errors. The errors were fixed, and the effects of error correction on brain volume, thickness, and surface area were measured. It is commonplace to apply quality control to Freesurfer MRI recordings to ensure that the edges of gray and white matter are detected properly, as incorrect edge detection leads to changes in variables such as volume, cortical thickness, and cortical surface area. We find that while Freesurfer v7.1.1. does regularly make mistakes in identifying the edges of cortical gray matter, correcting these errors yields limited changes in the commonly measured variables listed above. We further find that the software makes fewer gray matter boundary errors when processing female brains. The results suggest that manually correcting gray matter boundary errors may not be worthwhile due to its small effect on the measurements, with potential exceptions for studies that focus on the areas that are more commonly affected by errors: the areas around the cerebellar tentorium, paracentral lobule, and the optic nerves, specifically the horizontal segment of the middle cerebral artery.
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Affiliation(s)
- Vesa Vahermaa
- Department of Computer Science, Aalto School of Science, Aalto University, Finland; Mental Health Unit, Finnish Institute for Health and Welfare, Finland.
| | - Dogu Baran Aydogan
- A.I. Virtanen Institute for Molecular Science, University of Eastern Finland, Kuopio, Finland; Department of Neuroscience and Biomedical Engineering, Aalto School of Science, Espoo, Finland
| | - Tuukka Raij
- Department of Neuroscience and Biomedical Engineering, Aalto School of Science, Espoo, Finland; University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | | | | | - Jari Saramäki
- Department of Computer Science, Aalto School of Science, Aalto University, Finland
| | - Jaana Suvisaari
- Mental Health Unit, Finnish Institute for Health and Welfare, Finland
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7
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Aberathne I, Kulasiri D, Samarasinghe S. Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning. Neural Regen Res 2023; 18:2134-2140. [PMID: 37056120 PMCID: PMC10328296 DOI: 10.4103/1673-5374.367840] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/08/2022] [Accepted: 01/12/2023] [Indexed: 02/17/2023] Open
Abstract
The scientists are dedicated to studying the detection of Alzheimer's disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer's disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer's disease onset.
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Affiliation(s)
- Iroshan Aberathne
- Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
| | - Don Kulasiri
- Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
| | - Sandhya Samarasinghe
- Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
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8
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Haddad E, Pizzagalli F, Zhu AH, Bhatt RR, Islam T, Ba Gari I, Dixon D, Thomopoulos SI, Thompson PM, Jahanshad N. Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3. Hum Brain Mapp 2023; 44:1515-1532. [PMID: 36437735 PMCID: PMC9921222 DOI: 10.1002/hbm.26147] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/29/2022] Open
Abstract
Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.
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Affiliation(s)
- Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.,Department of Neurosciences, University of Turin, Turin, Italy
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Ravi R Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
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9
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Hung CI, Wu CT, Chao YP. Differences in gray matter volumes of subcortical nuclei between major depressive disorder with and without persistent depressive disorder. J Affect Disord 2023; 321:161-166. [PMID: 36272460 DOI: 10.1016/j.jad.2022.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 10/01/2022] [Accepted: 10/17/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVE This study aimed to compare the differences in gray matter volumes (GMVs) of subcortical nuclei between major depressive disorder (MDD) patients with and without persistent depressive disorder (PDD) at long-term follow-up. METHODS 114 and 94 subjects with MDD, including 48 and 41 with comorbid PDD, were enrolled to undergo high-resolution T1-weighted imaging at first (FIP) and second (three years later, SIP) investigation points, respectively. FreeSurfer was used to extract the GMVs of seven subcortical nuclei, and Generalized Estimating Equation models were employed to estimate the differences in GMVs of subcortical nuclei between the two subgroups. RESULTS The PDD subgroup had a significantly greater depressive severity and a higher percentage of patients undergoing pharmacotherapy at the FIP as compared with the non-PDD subgroup. These differences became insignificant at the SIP. The PDD subgroup had a significantly (p < 0.003) smaller GMV in the right putamen at the SIP and in the right nucleus accumbens (NAc) at the FIP and SIP as compared with the non-PDD subgroup. After controlling for clinical variables, PDD was independently associated with smaller GMVs in the right putamen and NAc. LIMITATIONS Imaging was not performed at baseline and pharmacotherapy was not controlled at the FIP and SIP. CONCLUSIONS MDD with PDD was associated with smaller GMVs in the right putamen and NAc as compared with MDD without PDD. Whether the two regions are biomarkers related to a poor prognosis and the chronicity of depression requires further study.
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Affiliation(s)
- Ching-I Hung
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chen-Te Wu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Ping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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10
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Kannappan B, Gunasekaran TI, te Nijenhuis J, Gopal M, Velusami D, Kothandan G, Lee KH. Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes. PLoS One 2022; 17:e0270795. [PMID: 35830443 PMCID: PMC9278752 DOI: 10.1371/journal.pone.0270795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/20/2022] [Indexed: 01/18/2023] Open
Abstract
Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.
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Affiliation(s)
- Balaji Kannappan
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Tamil Iniyan Gunasekaran
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Jan te Nijenhuis
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
- * E-mail: (JN); (KHL)
| | - Muthu Gopal
- Health Systems Research & MRHRU, ICMR-National Institute of Epidemiology, Tirunelveli, Tamil Nadu, India
| | - Deepika Velusami
- Department of Physiology, Sri Manakula Vinayagar Medical College and Hospital, Puducherry, Tamil Nadu, India
| | - Gugan Kothandan
- Biopolymer Modeling and Protein Chemistry Laboratory, Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Chennai, Tamil Nadu, India
| | - Kun Ho Lee
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
- Korea Brain Research Institute, Daegu, Republic of Korea
- * E-mail: (JN); (KHL)
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11
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Fitter MH, Stern JA, Straske MD, Allard T, Cassidy J, Riggins T. Mothers’ Attachment Representations and Children’s Brain Structure. Front Hum Neurosci 2022; 16:740195. [PMID: 35370579 PMCID: PMC8967255 DOI: 10.3389/fnhum.2022.740195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 01/24/2022] [Indexed: 12/16/2022] Open
Abstract
Ample research demonstrates that parents’ experience-based mental representations of attachment—cognitive models of close relationships—relate to their children’s social-emotional development. However, no research to date has examined how parents’ attachment representations relate to another crucial domain of children’s development: brain development. The present study is the first to integrate the separate literatures on attachment and developmental social cognitive neuroscience to examine the link between mothers’ attachment representations and 3- to 8-year-old children’s brain structure. We hypothesized that mothers’ attachment representations would relate to individual differences in children’s brain structures involved in stress regulation—specifically, amygdala and hippocampal volumes—in part via mothers’ responses to children’s distress. We assessed 52 mothers’ attachment representations (secure base script knowledge on the Attachment Script Assessment and self-reported attachment avoidance and anxiety on the Experiences in Close Relationships scale) and children’s brain structure. Mothers’ secure base script knowledge was significantly related to children’s smaller left amygdala volume but was unrelated to hippocampal volume; we found no indirect links via maternal responses to children’s distress. Exploratory analyses showed associations between mothers’ attachment representations and white matter and thalamus volumes. Together, these preliminary results suggest that mothers’ attachment representations may be linked to the development of children’s neural circuitry related to stress regulation.
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Affiliation(s)
- Megan H. Fitter
- Maryland Child and Family Development Lab, University of Maryland, College Park, College Park, MD, United States
- *Correspondence: Megan H. Fitter,
| | - Jessica A. Stern
- BabyLab, University of Virginia, Charlottesville, VA, United States
| | - Martha D. Straske
- Maryland Child and Family Development Lab, University of Maryland, College Park, College Park, MD, United States
| | - Tamara Allard
- Neurocognitive Development Lab, University of Maryland, College Park, College Park, MD, United States
| | - Jude Cassidy
- Maryland Child and Family Development Lab, University of Maryland, College Park, College Park, MD, United States
| | - Tracy Riggins
- Neurocognitive Development Lab, University of Maryland, College Park, College Park, MD, United States
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12
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Mangesius S, Haider L, Lenhart L, Steiger R, Prados Carrasco F, Scherfler C, Gizewski ER. Qualitative and Quantitative Comparison of Hippocampal Volumetric Software Applications: Do All Roads Lead to Rome? Biomedicines 2022; 10:biomedicines10020432. [PMID: 35203641 PMCID: PMC8962257 DOI: 10.3390/biomedicines10020432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/30/2022] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
Brain volumetric software is increasingly suggested for clinical routine. The present study quantifies the agreement across different software applications. Ten cases with and ten gender- and age-adjusted healthy controls without hippocampal atrophy (median age: 70; 25–75% range: 64–77 years and 74; 66–78 years) were retrospectively selected from a previously published cohort of Alzheimer’s dementia patients and normal ageing controls. Hippocampal volumes were computed based on 3 Tesla T1-MPRAGE-sequences with FreeSurfer (FS), Statistical-Parametric-Mapping (SPM; Neuromorphometrics and Hammers atlases), Geodesic-Information-Flows (GIF), Similarity-and-Truth-Estimation-for-Propagated-Segmentations (STEPS), and Quantib™. MTA (medial temporal lobe atrophy) scores were manually rated. Volumetric measures of each individual were compared against the mean of all applications with intraclass correlation coefficients (ICC) and Bland–Altman plots. Comparing against the mean of all methods, moderate to low agreement was present considering categorization of hippocampal volumes into quartiles. ICCs ranged noticeably between applications (left hippocampus (LH): from 0.42 (STEPS) to 0.88 (FS); right hippocampus (RH): from 0.36 (Quantib™) to 0.86 (FS). Mean differences between individual methods and the mean of all methods [mm3] were considerable (LH: FS −209, SPM-Neuromorphometrics −820; SPM-Hammers −1474; Quantib™ −680; GIF 891; STEPS 2218; RH: FS −232, SPM-Neuromorphometrics −745; SPM-Hammers −1547; Quantib™ −723; GIF 982; STEPS 2188). In this clinically relevant sample size with large spread in data ranging from normal aging to severe atrophy, hippocampal volumes derived by well-accepted applications were quantitatively different. Thus, interchangeable use is not recommended.
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Affiliation(s)
- Stephanie Mangesius
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Lukas Haider
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London Institute of Neurology, Russell Square House, Russell Square 10-12, London WC1B 5EH, UK;
- Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
- Correspondence:
| | - Lukas Lenhart
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Ruth Steiger
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Ferran Prados Carrasco
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London Institute of Neurology, Russell Square House, Russell Square 10-12, London WC1B 5EH, UK;
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, UK
- e-Health Centre, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain
| | - Christoph Scherfler
- Department of Neurology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria;
| | - Elke R. Gizewski
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
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13
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Filip P, Bednarik P, Eberly LE, Moheet A, Svatkova A, Grohn H, Kumar AF, Seaquist ER, Mangia S. Different FreeSurfer versions might generate different statistical outcomes in case-control comparison studies. Neuroradiology 2022; 64:765-773. [PMID: 34988592 DOI: 10.1007/s00234-021-02862-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/12/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Neuroimaging pipelines have long been known to generate mildly differing results depending on various factors, including software version. While considered generally acceptable and within the margin of reasonable error, little is known about their effect in common research scenarios such as inter-group comparisons between healthy controls and various pathological conditions. The aim of the presented study was to explore the differences in the inferences and statistical significances in a model situation comparing volumetric parameters between healthy controls and type 1 diabetes patients using various FreeSurfer versions. METHODS T1- and T2-weighted structural scans of healthy controls and type 1 diabetes patients were processed with FreeSurfer 5.3, FreeSurfer 5.3 HCP, FreeSurfer 6.0 and FreeSurfer 7.1, followed by inter-group statistical comparison using outputs of individual FreeSurfer versions. RESULTS Worryingly, FreeSurfer 5.3 detected both cortical and subcortical volume differences out of the preselected regions of interest, but newer versions such as FreeSurfer 5.3 HCP and FreeSurfer 6.0 reported only subcortical differences of lower magnitude and FreeSurfer 7.1 failed to find any statistically significant inter-group differences. CONCLUSION Since group averages of individual FreeSurfer versions closely matched, in keeping with previous literature, the main origin of this disparity seemed to lie in substantially higher within-group variability in the model pathological condition. Ergo, until validation in common research scenarios as case-control comparison studies is included into the development process of new software suites, confirmatory analyses utilising a similar software based on analogous, but not fully equivalent principles, might be considered as supplement to careful quality control.
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Affiliation(s)
- Pavel Filip
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, 2021 Sixth St. SE, Minneapolis, MN, 55455, USA.,Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Petr Bednarik
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, 2021 Sixth St. SE, Minneapolis, MN, 55455, USA.,High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lynn E Eberly
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, 2021 Sixth St. SE, Minneapolis, MN, 55455, USA.,Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, MN, USA
| | - Amir Moheet
- Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Alena Svatkova
- Department of Medicine III, Clinical Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria.,Department of Imaging Methods, Faculty of Medicine, University of Ostrava, Ostrava, Czechia
| | - Heidi Grohn
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, 2021 Sixth St. SE, Minneapolis, MN, 55455, USA.,Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Anjali F Kumar
- Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | | | - Silvia Mangia
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, 2021 Sixth St. SE, Minneapolis, MN, 55455, USA.
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14
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Shokri-Kojori E, Bennett IJ, Tomeldan ZA, Krawczyk DC, Rypma B. Estimates of brain age for gray matter and white matter in younger and older adults: Insights into human intelligence. Brain Res 2021; 1763:147431. [PMID: 33737067 PMCID: PMC8428193 DOI: 10.1016/j.brainres.2021.147431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 02/01/2021] [Accepted: 03/10/2021] [Indexed: 12/18/2022]
Abstract
Aging entails a multifaceted complex of changes in macro- and micro-structural properties of human brain gray matter (GM) and white matter (WM) tissues, as well as in intellectual abilities. To better capture tissue-specific brain aging, we combined volume and distribution properties of diffusivity indices to derive subject-specific age scores for each tissue. We compared age-related variance between younger and older adults for GM and WM age scores, and tested whether tissue-specific age scores could explain different effects of aging on fluid (Gf) and crystalized (Gc) intelligence in younger and older adults. Chronological age was strongly associated with GM (R2 = 0.73) and WM (R2 = 0.57) age scores. The GM age score accounted for significantly more variance in chronological age in younger relative to older adults (p < 0.001), whereas the WM age score accounted for significantly more variance in chronological age in older compared to younger adults (p < 0.025). Consistent with existing literature, younger adults outperformed older adults in Gf while older adults outperformed younger adults in Gc. The GM age score was negatively associated with Gf in younger adults (p < 0.02), whereas the WM age score was negatively associated with Gc in older adults (p < 0.02). Our results provide evidence for differences in the effects of age on GM and WM in younger versus older adults that may contribute to age-related differences in Gf and Gc.
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Affiliation(s)
- Ehsan Shokri-Kojori
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA.
| | - Ilana J Bennett
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA; Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Zuri A Tomeldan
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA
| | - Daniel C Krawczyk
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA; Department of Psychiatry, The University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA
| | - Bart Rypma
- Center for BrainHealth®, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA; Department of Psychiatry, The University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA
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15
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Zhao L, Batta I, Matloff W, O'Driscoll C, Hobel S, Toga AW. Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies. Neuroinformatics 2021; 19:285-303. [PMID: 32822005 DOI: 10.1007/s12021-020-09486-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.
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Affiliation(s)
- Lu Zhao
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ishaan Batta
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - William Matloff
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Caroline O'Driscoll
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Samuel Hobel
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
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16
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Weeland CJ, White T, Vriend C, Muetzel RL, Starreveld J, Hillegers MHJ, Tiemeier H, van den Heuvel OA. Brain Morphology Associated With Obsessive-Compulsive Symptoms in 2,551 Children From the General Population. J Am Acad Child Adolesc Psychiatry 2021; 60:470-478. [PMID: 32949714 DOI: 10.1016/j.jaac.2020.03.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 03/23/2020] [Accepted: 08/31/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Obsessive-compulsive (OC) symptoms are common in the general population, but it is unclear whether subclinical OC symptoms and obsessive-compulsive disorder (OCD) are part of a neuroanatomical continuum. The goal of this study was to investigate the relation between OC symptoms and subcortical and cortical morphology in a population-based sample of children. METHOD The study included 2,551 participants, aged 9-12 years, from the population-based Generation R Study. OC symptoms were measured using the 7-item caregiver-rated Short Obsessive-Compulsive Disorder Screener (SOCS). Structural (3T) magnetic resonance imaging scans were processed using FreeSurfer to study the thalamus and other subcortical volumes, intracranial volume, vertexwise cortical thickness, and surface area. We used linear regression models to investigate the association between OC symptoms and brain morphology. Emulating case-control studies from the literature, we compared children scoring above the clinical cutoff of the SOCS (probable OCD cases, n = 164) with matched children without symptoms. RESULTS Children with probable OCD had larger thalami compared with the control group (d 0.16, p = .044). Vertexwise analysis showed a positive association between OC symptoms and thickness of the right inferior parietal cortex, which disappeared after adjusting for total behavioral problems. SOCS scores correlated negatively with intracranial volume (B = -2444, p = .038). CONCLUSION Children with probable OCD showed thalamus alterations similar to those previously reported in unmedicated children with OCD. OC symptoms showed a stronger association with total intracranial volume than regional brain measures. Longitudinal studies are needed to further elucidate similarities and distinctions between neural correlates of subclinical and clinical OC symptoms.
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Affiliation(s)
- Cees J Weeland
- Amsterdam University Medical Centers, Amsterdam, The Netherlands; Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Tonya White
- Erasmus Medical Center, Rotterdam, The Netherlands
| | - Chris Vriend
- Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | | | | | | | - Henning Tiemeier
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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