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Liu YS, Baxi M, Madan CR, Zhan K, Makris N, Rosene DL, Killiany RJ, Cetin-Karayumak S, Pasternak O, Kubicki M, Cao B. Brain age of rhesus macaques over the lifespan. Neurobiol Aging 2024; 139:73-81. [PMID: 38643691 DOI: 10.1016/j.neurobiolaging.2024.02.014] [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: 09/11/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 04/23/2024]
Abstract
Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.
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Affiliation(s)
- Yang S Liu
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Madhura Baxi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Kevin Zhan
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Nikolaos Makris
- Department of Psychiatry, Center for Morphometric Analysis, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas L Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ronald J Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Center for Morphometric Analysis, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bo Cao
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada; Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
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Guo X, Ding Y, Xu W, Wang D, Yu H, Lin Y, Chang S, Zhang Q, Zhang Y. Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers. J Neuroradiol 2024; 51:265-273. [PMID: 37722591 DOI: 10.1016/j.neurad.2023.09.002] [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: 07/08/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/20/2023]
Abstract
The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a single marker and can not discover meaningful hidden information in radiographic images. This study focuses on the application of radiomics, an advanced imaging analysis technique, combined with automated machine learning to predict BAG. Our methods achieve a promising result with a mean absolute error of 1.509 using the Alzheimer's Disease Neuroimaging Initiative dataset. Furthermore, we find that the hippocampus and parahippocampal gyrus play a significant role in predicting age with interpretable method called SHapley Additive exPlanations. Additionally, our investigation of age prediction discrepancies between patients with Alzheimer's disease (AD) and those with mild cognitive impairment (MCI) reveals a notable correlation with clinical cognitive assessment scale scores. This suggests that BAG has the potential to serve as a biomarker to support the diagnosis of AD and MCI. Overall, this study presents valuable insights into the application of neuroimaging models in the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Xiaoliang Guo
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
| | - Weizhi Xu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Dong Wang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunication, Beijing, China
| | - Huiying Yu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yongkang Lin
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Shulei Chang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiqi Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yongxin Zhang
- School of Mathematics and Statistics, Shandong Normal University, Jinan, China.
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3
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Chang JR, Yao ZF, Hsieh S, Nordling TEM. Age Prediction Using Resting-State Functional MRI. Neuroinformatics 2024; 22:119-134. [PMID: 38341830 DOI: 10.1007/s12021-024-09653-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2023] [Indexed: 02/13/2024]
Abstract
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.
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Affiliation(s)
- Jose Ramon Chang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Zai-Fu Yao
- College of Education, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Research Center for Education and Mind Sciences, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Department of Kinesiology, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Basic Psychology Group, Department of Educational Psychology and Counseling, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Shulan Hsieh
- Department of Psychology, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Institute of Allied Health Sciences, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Department of Public Health, College of Medicine, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Torbjörn E M Nordling
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
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4
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de Moraes FHP, Sudo F, Carneiro Monteiro M, de Melo BRP, Mattos P, Mota B, Tovar-Moll F. Cortical folding correlates to aging and Alzheimer's Disease's cognitive and CSF biomarkers. Sci Rep 2024; 14:3222. [PMID: 38332140 PMCID: PMC10853184 DOI: 10.1038/s41598-023-50780-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/25/2023] [Indexed: 02/10/2024] Open
Abstract
This manuscript presents the quantification and correlation of three aspects of Alzheimer's Disease evolution, including structural, biochemical, and cognitive assessments. We aimed to test a novel structural biomarker for neurodegeneration based on a cortical folding model for mammals. Our central hypothesis is that the cortical folding variable, representative of axonal tension in white matter, is an optimal discriminator of pathological aging and correlates with altered loadings in Cerebrospinal Fluid samples and a decline in cognition and memory. We extracted morphological features from T1w 3T MRI acquisitions using FreeSurfer from 77 Healthy Controls (age = 66 ± 8.4, 69% females), 31 Mild Cognitive Impairment (age = 72 ± 4.8, 61% females), and 13 Alzheimer's Disease patients (age = 77 ± 6.1, 62% females) of recruited volunteers in Brazil to test its discriminative power using optimal cut-point analysis. Cortical folding distinguishes the groups with reasonable accuracy (Healthy Control-Alzheimer's Disease, accuracy = 0.82; Healthy Control-Mild Cognitive Impairment, accuracy = 0.56). Moreover, Cerebrospinal Fluid biomarkers (total Tau, A[Formula: see text]1-40, A[Formula: see text]1-42, and Lipoxin) and cognitive scores (Cognitive Index, Rey's Auditory Verbal Learning Test, Trail Making Test, Digit Span Backward) were correlated with the global neurodegeneration in MRI aiming to describe health, disease, and the transition between the two states using morphology.
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Affiliation(s)
- Fernanda Hansen P de Moraes
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
- Instituto de Física, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-909, Brazil
| | - Felipe Sudo
- Memory Clinic, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Marina Carneiro Monteiro
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Bruno R P de Melo
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Paulo Mattos
- Memory Clinic, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Bruno Mota
- Instituto de Física, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-909, Brazil
| | - Fernanda Tovar-Moll
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil.
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5
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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6
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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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7
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Soumya Kumari LK, Sundarrajan R. A review on brain age prediction models. Brain Res 2024; 1823:148668. [PMID: 37951563 DOI: 10.1016/j.brainres.2023.148668] [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: 06/16/2023] [Revised: 10/23/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain structure will change, and these changes will be exclusive to males and females and will differ for each. White matter and grey matter density have a deeper relationship with brain aging. Hence, if the white matter and grey matter concentrations vary, the rate at which the brain ages will also vary. Neurodegenerative illnesses can be detected using the biomarker known as brain age. The development of deep learning has made it possible to analyze structural neuroimaging data in new ways, notably by predicting brain ages. We introduce the techniques and possible therapeutic uses of brain age prediction in this cutting-edge review. Creating a machine learning regression model to analyze age-related changes in brain structure among healthy individuals is a typical procedure in studies focused on brain aging. Subsequently, this model is employed to forecast the aging of brains in new individuals. The concept of the "brain-age gap" refers to the difference between an individual's predicted brain age and their actual chronological age. This score may serve as a gauge of the general state of the brain's health while also reflecting neuroanatomical disorders. It may help differential diagnosis, prognosis, and therapy decisions as well as early identification of brain-based illnesses. The following is a summary of the many forecasting techniques utilized over the past 11 years to estimate brain age. The study's conundrums and potential outcomes of the brain age predicted by current models will both be covered.
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Affiliation(s)
- L K Soumya Kumari
- Computer Science Engineering, Mohandas College of Engineering and Technology, Anad, India.
| | - R Sundarrajan
- Information Technology, School of Computing, Kalasalingam Academy of Research and Education, India.
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8
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Díaz Beltrán L, Madan CR, Finke C, Krohn S, Di Ieva A, Esteban FJ. Fractal Dimension Analysis in Neurological Disorders: An Overview. ADVANCES IN NEUROBIOLOGY 2024; 36:313-328. [PMID: 38468040 DOI: 10.1007/978-3-031-47606-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.
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Affiliation(s)
- Leticia Díaz Beltrán
- Department of Medical Oncology, Clinical Research Unit, University Hospital of Jaén, Jaén, Spain
| | | | - Carsten Finke
- Department of Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | - Stephan Krohn
- Department of Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Francisco J Esteban
- Systems Biology Unit, Department of Experimental Biology, University of Jaén, Jaén, Spain.
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9
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Schinz D, Schmitz-Koep B, Tahedl M, Teckenberg T, Schultz V, Schulz J, Zimmer C, Sorg C, Gaser C, Hedderich DM. Lower cortical thickness and increased brain aging in adults with cocaine use disorder. Front Psychiatry 2023; 14:1266770. [PMID: 38025412 PMCID: PMC10679447 DOI: 10.3389/fpsyt.2023.1266770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Background Cocaine use disorder (CUD) is a global health issue with severe behavioral and cognitive sequelae. While previous evidence suggests a variety of structural and age-related brain changes in CUD, the impact on both, cortical thickness and brain age measures remains unclear. Methods Derived from a publicly available data set (SUDMEX_CONN), 74 CUD patients and 62 matched healthy controls underwent brain MRI and behavioral-clinical assessment. We determined cortical thickness by surface-based morphometry using CAT12 and Brain Age Gap Estimate (BrainAGE) via relevance vector regression. Associations between structural brain changes and behavioral-clinical variables of patients with CUD were investigated by correlation analyses. Results We found significantly lower cortical thickness in bilateral prefrontal cortices, posterior cingulate cortices, and the temporoparietal junction and significantly increased BrainAGE in patients with CUD [mean (SD) = 1.97 (±3.53)] compared to healthy controls (p < 0.001, Cohen's d = 0.58). Increased BrainAGE was associated with longer cocaine abuse duration. Conclusion Results demonstrate structural brain abnormalities in CUD, particularly lower cortical thickness in association cortices and dose-dependent, increased brain age.
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Affiliation(s)
- David Schinz
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen- (FAU), Nürnberg, Germany
| | - Benita Schmitz-Koep
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Marlene Tahedl
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Timo Teckenberg
- Digital Management & Transformation, SRH Fernhochschule - The Mobile University, Riedlingen, Germany
| | - Vivian Schultz
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julia Schulz
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Neurology, Jena University Hospital, Jena, Germany
- German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Dennis M. Hedderich
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
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10
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Pfarr JK, Meller T, Brosch K, Stein F, Thomas-Odenthal F, Evermann U, Wroblewski A, Ringwald KG, Hahn T, Meinert S, Winter A, Thiel K, Flinkenflügel K, Jansen A, Krug A, Dannlowski U, Kircher T, Gaser C, Nenadić I. Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach. Neuroimage 2023; 281:120349. [PMID: 37683808 DOI: 10.1016/j.neuroimage.2023.120349] [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: 04/17/2023] [Revised: 07/14/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. METHODS In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. RESULTS Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. CONCLUSIONS Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.
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Affiliation(s)
- Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Department of Psychology, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany.
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Kai G Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Germany; Institute for Translational Neuroscience, University of Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Germany
| | | | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany; Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Germany
| | - Axel Krug
- Department of Psychiatry und Psychotherapy, University Hospital Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Christian Gaser
- Department of Neurology, Jena University Hospital, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany; German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
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11
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Marzi C, Scheda R, Salvadori E, Giorgio A, De Stefano N, Poggesi A, Inzitari D, Pantoni L, Mascalchi M, Diciotti S. Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis. Front Hum Neurosci 2023; 17:1231513. [PMID: 37822707 PMCID: PMC10562576 DOI: 10.3389/fnhum.2023.1231513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
Background The relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis. Methods Sixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model. Results After 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia. Discussion Our findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science, Applications "Giuseppe Parenti, " University of Florence, Florence, Italy
| | - Riccardo Scheda
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
| | - Emilia Salvadori
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Antonio Giorgio
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Anna Poggesi
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Domenico Inzitari
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio, " University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and Network in Oncology (ISPRO), Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
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12
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Liu X, Tyler LK, Cam-Can, Davis SW, Rowe JB, Tsvetanov KA. Cognition's dependence on functional network integrity with age is conditional on structural network integrity. Neurobiol Aging 2023; 129:195-208. [PMID: 37392579 DOI: 10.1016/j.neurobiolaging.2023.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 07/03/2023]
Abstract
Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain's structural connectivity.
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Affiliation(s)
- Xulin Liu
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Lorraine K Tyler
- The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
| | - Cam-Can
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Simon W Davis
- Department of Neurology, Duke University, School of Medicine, Durham, NC, USA
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
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13
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Cui D, Wang D, Jin J, Liu X, Wang Y, Cao W, Liu Z, Yin T. Age- and sex-related differences in cortical morphology and their relationships with cognitive performance in healthy middle-aged and older adults. Quant Imaging Med Surg 2023; 13:1083-1099. [PMID: 36819243 PMCID: PMC9929420 DOI: 10.21037/qims-22-583] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022]
Abstract
Background The impacts of age and sex on brain structures related to cognitive function may be important for understanding the role of aging in Alzheimer disease for both sexes. We intended to investigate the age and sex differences of cortical morphology in middle-aged and older adults and their relationships with the decline of cognitive function. Methods In this cross-sectional study, we examined the cortical morphology in 204 healthy middle-aged and older adult participants aged 45 to 89 years using structural magnetic resonance imaging (sMRI) data from the Dallas Lifespan Brain Study data set. Brain cortical thickness, surface complexity, and gyrification index were analyzed through a completely automated surface-based morphometric analysis using the CAT12 toolbox. Furthermore, we explored the correlation between cortical morphology differences and test scores for processing speed and working memory. Results There were no significant interactions of age and sex with cortical thickness, fractal dimension, or gyrification index. Rather, we found that both males and females showed age-related decreases in cortical thickness, fractal dimension, and gyrification index. There were significant sex differences in the fractal dimension in middle-aged participants and the gyrification index in older adult participants. In addition, there were significant positive correlations between the cortical thickness of the right superior frontal gyrus and Wechsler Adult Intelligence Scale (WAIS)-III Letter-Number Sequencing test scores in males (r=0.394; P<0.001; 95% CI for r values 0.216-0.577) and females (r=0.344; P<0.001; 95% CI for r values 0.197-0.491), respectively. Furthermore, a significant relationship between the gyrification index of the right supramarginal gyrus (SupraMG) and WAIS-III Digit Symbol test scores was observed in older adult participants (r=0.375; P<0.001; 95% CI for r values 0.203-0.522). Conclusions The results suggest that, compared with males, females have more extensive differences in cortical morphology. The gyrification index of the right SupraMG can be used as an imaging marker of sexual cognitive differences between males and females in older adults. This study helps to further understand sex differences in the aging of the brain and cognition.
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Affiliation(s)
- Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China;,School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Dianyu Wang
- Institute of Radiation Medicine, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Jingna Jin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Xu Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Yuheng Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China;,School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Zhipeng Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Tao Yin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China;,Neuroscience Center, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
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14
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Malakshan SR, Daneshvarfard F, Abrishami Moghaddam H. A correlational study between microstructural, macrostructural and functional age-related changes in the human visual cortex. PLoS One 2023; 18:e0266206. [PMID: 36662780 PMCID: PMC9858032 DOI: 10.1371/journal.pone.0266206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 12/27/2022] [Indexed: 01/21/2023] Open
Abstract
Age-related changes in the human brain can be investigated from either structural or functional perspectives. Analysis of structural and functional age-related changes throughout the lifespan may help to understand the normal brain development process and monitor the structural and functional pathology of the brain. This study, combining dedicated electroencephalography (EEG) and magnetic resonance imaging (MRI) approaches in adults (20-78 years), highlights the complex relationship between micro/macrostructural properties and the functional responses to visual stimuli. Here, we aimed to relate age-related changes of the latency of visual evoked potentials (VEPs) to micro/macrostructural indexes and find any correlation between micro/macrostructural features, as well. We studied age-related structural changes in the brain, by using the MRI and diffusion-weighted imaging (DWI) as preferred imaging methods for extracting brain macrostructural parameters such as the cortical thickness, surface area, folding and curvature index, gray matter volume, and microstructural parameters such as mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). All the mentioned features were significantly correlated with age in V1 and V2 regions of the visual cortex. Furthermore, we highlighted, negative correlations between structural features extracted from T1-weighted images and DWI. The latency and amplitude of the three dominants peaks (C1, P1, N1) of the VEP were considered as the brain functional features to be examined for correlation with age and structural features of the corresponding age. We observed significant correlations between mean C1 latency and GM volume averaged in V1 and V2. In hierarchical regression analysis, the structural index did not contribute to significant variance in the C1 latency after regressing out the effect of age. However, the age explained significant variance in the model after regressing out the effect of structural feature.
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Affiliation(s)
- Sahar Rahimi Malakshan
- Faculty of Electrical Engineering, Department of Biomedical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Farveh Daneshvarfard
- Faculty of Electrical Engineering, Department of Biomedical Engineering, K.N. Toosi University of Technology, Tehran, Iran
- INSERM U1105, Université de Picardie, CURS, Amiens, France
| | - Hamid Abrishami Moghaddam
- Faculty of Electrical Engineering, Department of Biomedical Engineering, K.N. Toosi University of Technology, Tehran, Iran
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15
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El Damaty S, Darcey VL, McQuaid GA, Picci G, Stoianova M, Mucciarone V, Chun Y, Laws ML, Campano V, Van Hecke K, Ryan M, Rose EJ, Fishbein DH, VanMeter AS. Introducing an adolescent cognitive maturity index. Front Psychol 2022; 13:1017317. [PMID: 36571021 PMCID: PMC9771453 DOI: 10.3389/fpsyg.2022.1017317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/25/2022] [Indexed: 12/12/2022] Open
Abstract
Children show substantial variation in the rate of physical, cognitive, and social maturation as they traverse adolescence and enter adulthood. Differences in developmental paths are thought to underlie individual differences in later life outcomes, however, there remains a lack of consensus on the normative trajectory of cognitive maturation in adolescence. To address this problem, we derive a Cognitive Maturity Index (CMI), to estimate the difference between chronological and cognitive age predicted with latent factor estimates of inhibitory control, risky decision-making and emotional processing measured with standard neuropsychological instruments. One hundred and forty-one children from the Adolescent Development Study (ADS) were followed longitudinally across three time points from ages 11-14, 13-16, and 14-18. Age prediction with latent factor estimates of cognitive skills approximated age within ±10 months (r = 0.71). Males in advanced puberty displayed lower cognitive maturity relative to peers of the same age; manifesting as weaker inhibitory control, greater risk-taking, desensitization to negative affect, and poor recognition of positive affect.
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Affiliation(s)
- Shady El Damaty
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States,Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States,*Correspondence: Shady El Damaty
| | - Valerie L. Darcey
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States,Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Goldie A. McQuaid
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Giorgia Picci
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States,Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Maria Stoianova
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Veronica Mucciarone
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Yewon Chun
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Marissa L. Laws
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States
| | - Victor Campano
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Kinney Van Hecke
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Mary Ryan
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Emma Jane Rose
- Department of Psychology, The Pennsylvania State University, University Park, PA, United States
| | - Diana H. Fishbein
- Translational Neuro-Prevention Research, Frank Porter Graham Child Development Institute, University of Northern Carolina, Chapel Hill, NC, United States
| | - Ashley S. VanMeter
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States,Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
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16
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Hupfeld KE, McGregor HR, Hass CJ, Pasternak O, Seidler RD. Sensory system-specific associations between brain structure and balance. Neurobiol Aging 2022; 119:102-116. [PMID: 36030560 PMCID: PMC9728121 DOI: 10.1016/j.neurobiolaging.2022.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/26/2022] [Accepted: 07/28/2022] [Indexed: 11/15/2022]
Abstract
Nearly 75% of older adults in the US report balance problems. Although it is known that aging results in widespread brain atrophy, less is known about how brain structure relates to balance in aging. We collected T1- and diffusion-weighted MRI scans and measured postural sway of 36 young (18-34 years) and 22 older (66-84 years) adults during eyes open, eyes closed, eyes open-foam, and eyes closed-foam conditions. We calculated summary measures indicating visual, proprioceptive, and vestibular contributions to balance. Across both age groups, thinner cortex in multisensory integration regions was associated with greater reliance on visual inputs for balance. Greater gyrification within sensorimotor and parietal cortices was associated with greater reliance on proprioceptive inputs. Poorer vestibular function was correlated with thinner vestibular cortex, greater gyrification within sensorimotor, parietal, and frontal cortices, and lower free water-corrected axial diffusivity across the corona radiata and corpus callosum. These results expand scientific understanding of how individual differences in brain structure relate to balance and have implications for developing brain stimulation interventions to improve balance.
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Affiliation(s)
- K E Hupfeld
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - H R McGregor
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - C J Hass
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - O Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R D Seidler
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA; University of Florida Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA.
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17
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Nazlee N, Waiter GD, Sandu A. Age-associated sex and asymmetry differentiation in hemispheric and lobar cortical ribbon complexity across adulthood: A UK Biobank imaging study. Hum Brain Mapp 2022; 44:49-65. [PMID: 36574599 PMCID: PMC9783444 DOI: 10.1002/hbm.26076] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 07/28/2022] [Accepted: 08/21/2022] [Indexed: 02/01/2023] Open
Abstract
Cortical morphology changes with ageing and age-related neurodegenerative diseases. Previous studies suggest that the age effect is more pronounced in the frontal lobe. However, our knowledge of structural complexity changes in male and female brains is still limited. We measured cortical ribbon complexity through fractal dimension (FD) analysis at the hemisphere and lobe level in 7010 individuals from the UK Biobank imaging cohort to study age-related sex differences (3332 males, age ranged 45-79 years). FD decreases significantly with age and sexual dimorphism exists. With correction for brain size, females showed higher complexity in the left hemisphere and left and right parietal lobes whereas males showed higher complexity in the right temporal and left and right occipital lobes. A nonlinear age effect was observed in the left and right frontal, and right temporal lobes. Differential patterns of age effects were observed in both sexes with relatively more age-affected regions in males. Significantly higher rightward asymmetries at hemisphere, frontal, parietal, and occipital lobe level and higher leftward asymmetry in temporal lobe were observed. There was no age-by-sex-by asymmetry interaction in any region. When controlling for brain size, the leftward hemispheric, and temporal lobe asymmetry decreased with age. Males had significantly lower asymmetry between hemispheres and higher asymmetry in the parietal and occipital lobes than females. This work provides distinct patterns of age-related sex and asymmetry differences that can aid in the future development of sex-specific models of the normal brain to ascribe cognitive functional significance of these patterns in ageing.
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Affiliation(s)
- Nafeesa Nazlee
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenScotland
| | - Gordon D. Waiter
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenScotland
| | - Anca‐Larisa Sandu
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenScotland
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18
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Dias MDF, de Carvalho P, Duarte JV, Castelo-Branco M. Deformation Fields: A new source of information to predict Brain Age. J Neural Eng 2022; 19. [PMID: 35576908 DOI: 10.1088/1741-2552/ac7003] [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: 11/30/2021] [Accepted: 05/16/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The modelling of healthy ageing critically requires the identification of methods that detect subtle changes in this process. In the last few years multiple machine learning models have been proposed that learn age patterns from Magnetic Resonance Images (MRI). Current standard information sources rely on local volumetric information of brain tissues, namely white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). Information about patterns of brain deformation remains underexplored. In this paper an assessment is performed to understand better the predictive value of the deformation fields. APPROACH A shallow approach was used to compare the predictive value of deformation fields with the brain tissues (GM, WM and CSF). Images were compressed into a lower dimension space using Principal Components Analysis and then, a Relevant Vector Regression (RVR) learned the age patterns from the components. A model was trained per modality (deformation fields, GM, WM and CSF) and the performance between the models was compared. To evaluate whether the deformation fields increased the predictive power of GM, a model fusion approach was explored in which the final estimator was an RVR. Each model was validated using a cross-validation approach and was also evaluated on an external dataset. MAIN RESULTS We found that models trained with deformation patterns have higher predictive value than the ones trained with WM or CSF. Furthermore, deformation fields had a significantly better performance on the test set and also yield the lower difference between the validation and test set. Moreover, the predictions based on the combination of deformation patterns with GM volume yields better results than GM volumetric information alone. SIGNIFICANCE These findings suggest that deformation fields have a higher predictive power than WM and CSF and are robustly invariant across a set of confounding variables. Therefore, deformation fields should be considered in BrainAge models.
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Affiliation(s)
- Maria de Fátima Dias
- CIBIT, ICNAS, University of Coimbra, Universidade de Coimbra, Pólo das Ciências da Saúde Azinhaga de, R. Santa Comba, Coimbra, 3000-548, PORTUGAL
| | - Paulo de Carvalho
- University of Coimbra, Pólo II, 3030-290, Coimbra, 3004-504, PORTUGAL
| | - João Valente Duarte
- CIBIT, ICNAS, University of Coimbra, Universidade de Coimbra, Pólo das Ciências da Saúde Azinhaga de, R. Santa Comba, Coimbra, 3000-548, PORTUGAL
| | - Miguel Castelo-Branco
- CIBIT, ICNAS, University of Coimbra, Universidade de Coimbra, Pólo das Ciências da Saúde Azinhaga de, R. Santa Comba, Coimbra, 3000-548, PORTUGAL
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19
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Longitudinal study of the effect of a 5-year exercise intervention on structural brain complexity in older adults. A Generation 100 substudy. Neuroimage 2022; 256:119226. [PMID: 35447353 DOI: 10.1016/j.neuroimage.2022.119226] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 03/15/2022] [Accepted: 04/16/2022] [Indexed: 12/17/2022] Open
Abstract
Physical inactivity has been identified as an important risk factor for dementia. High levels of cardiorespiratory fitness (CRF) have been shown to reduce the risk of dementia. However, the mechanism by which exercise affects brain health is still debated. Fractal dimension (FD) is an index that quantifies the structural complexity of the brain. The purpose of this study was to investigate the effects of a 5-year exercise intervention on the structural complexity of the brain, measured through the FD, in a subset of 105 healthy older adults participating in the randomized controlled trial Generation 100 Study. The subjects were randomized into control, moderate intensity continuous training, and high intensity interval training groups. Both brain MRI and CRF were acquired at baseline and at 1-, 3- and 5-years follow-ups. Cortical thickness and volume data were extracted with FreeSurfer, and FD of the cortical lobes, cerebral and cerebellar gray and white matter were computed. CRF was measured as peak oxygen uptake (VO2peak) using ergospirometry during graded maximal exercise testing. Linear mixed models were used to investigate exercise group differences and possible CRF effects on the brain's structural complexity. Associations between change over time in CRF and FD were performed if there was a significant association between CRF and FD. There were no effects of group membership on the structural complexity. However, we found a positive association between CRF and the cerebral gray matter FD (p < 0.001) and the temporal lobe gray matter FD (p < 0.001). This effect was not present for cortical thickness, suggesting that FD is a more sensitive index of structural changes. The change over time in CRF was associated with the change in temporal lobe gray matter FD from baseline to 5-year follow-up (p < 0.05). No association of the change was found between CRF and cerebral gray matter FD. These results demonstrated that entering old age with high and preserved CRF levels protected against loss of structural complexity in areas sensitive to aging and age-related pathology.
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20
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Hupfeld KE, Geraghty JM, McGregor HR, Hass CJ, Pasternak O, Seidler RD. Differential Relationships Between Brain Structure and Dual Task Walking in Young and Older Adults. Front Aging Neurosci 2022; 14:809281. [PMID: 35360214 PMCID: PMC8963788 DOI: 10.3389/fnagi.2022.809281] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/31/2022] [Indexed: 12/13/2022] Open
Abstract
Almost 25% of all older adults experience difficulty walking. Mobility difficulties for older adults are more pronounced when they perform a simultaneous cognitive task while walking (i.e., dual task walking). Although it is known that aging results in widespread brain atrophy, few studies have integrated across more than one neuroimaging modality to comprehensively examine the structural neural correlates that may underlie dual task walking in older age. We collected spatiotemporal gait data during single and dual task walking for 37 young (18–34 years) and 23 older adults (66–86 years). We also collected T1-weighted and diffusion-weighted MRI scans to determine how brain structure differs in older age and relates to dual task walking. We addressed two aims: (1) to characterize age differences in brain structure across a range of metrics including volumetric, surface, and white matter microstructure; and (2) to test for age group differences in the relationship between brain structure and the dual task cost (DTcost) of gait speed and variability. Key findings included widespread brain atrophy for the older adults, with the most pronounced age differences in brain regions related to sensorimotor processing. We also found multiple associations between regional brain atrophy and greater DTcost of gait speed and variability for the older adults. The older adults showed a relationship of both thinner temporal cortex and shallower sulcal depth in the frontal, sensorimotor, and parietal cortices with greater DTcost of gait. Additionally, the older adults showed a relationship of ventricular volume and superior longitudinal fasciculus free-water corrected axial and radial diffusivity with greater DTcost of gait. These relationships were not present for the young adults. Stepwise multiple regression found sulcal depth in the left precentral gyrus, axial diffusivity in the superior longitudinal fasciculus, and sex to best predict DTcost of gait speed, and cortical thickness in the superior temporal gyrus to best predict DTcost of gait variability for older adults. These results contribute to scientific understanding of how individual variations in brain structure are associated with mobility function in aging. This has implications for uncovering mechanisms of brain aging and for identifying target regions for mobility interventions for aging populations.
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Affiliation(s)
- Kathleen E. Hupfeld
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Justin M. Geraghty
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Heather R. McGregor
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - C. J. Hass
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Rachael D. Seidler
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
- University of Florida Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
- *Correspondence: Rachael D. Seidler
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21
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Meregalli V, Alberti F, Madan CR, Meneguzzo P, Miola A, Trevisan N, Sambataro F, Favaro A, Collantoni E. Cortical Complexity Estimation Using Fractal Dimension: A Systematic Review of the Literature on Clinical and Nonclinical Samples. Eur J Neurosci 2022; 55:1547-1583. [PMID: 35229388 PMCID: PMC9313853 DOI: 10.1111/ejn.15631] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/27/2022] [Accepted: 02/20/2022] [Indexed: 12/04/2022]
Abstract
Fractal geometry has recently been proposed as a useful tool for characterizing the complexity of the brain cortex, which is likely to derive from the recurrence of sulci–gyri convolution patterns. The index used to describe the cortical complexity is called fractal dimensional (FD) and was employed by different research exploring the neurobiological correlates of distinct pathological and nonpathological conditions. This review aims to describe the literature on the application of this index, summarize the heterogeneities between studies and inform future research on this topic. Sixty‐two studies were included in the systematic review. The main research lines concern neurodevelopment, aging and the neurobiology of specific psychiatric and neurological disorders. Overall, the included papers indicate that cortical complexity is likely to reduce during aging and in various pathological processes affecting the brain. Nevertheless, the high heterogeneity between studies strongly prevents the possibility of drawing conclusions. Further research considering this index besides other morphological values is needed to better clarify the role of FD in characterizing the cortical structure. Fractal dimension (FD) is a useful tool for the assessment of cortical complexity. In healthy controls, FD is associated with development, aging and cognition. Alterations in FD have been observed in different neurological and psychiatric disorders.
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Affiliation(s)
- Valentina Meregalli
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | | | | | - Paolo Meneguzzo
- Department of Neurosciences, University of Padua, Padova, Italy
| | - Alessandro Miola
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Nicolò Trevisan
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Fabio Sambataro
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Angela Favaro
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
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22
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Nemmi F, Levardon M, Péran P. Brain-age estimation accuracy is significantly increased using multishell free-water reconstruction. Hum Brain Mapp 2022; 43:2365-2376. [PMID: 35141974 PMCID: PMC8996361 DOI: 10.1002/hbm.25792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 12/27/2022] Open
Abstract
Although free-water diffusion reconstruction for diffusion-weighted imaging (DWI) data can be applied to both single-shell and multishell data, recent finding in synthetic data suggests that the free-water indices from single-shell acquisition should be interpreted with care, as they are heavily influenced by initialization parameters and cannot discriminate between free-water and mean diffusivity modifications. However, whether using a longer multishell acquisition protocol significantly improve reconstruction for real human MRI data is still an open question. In this study, we compare canonical diffusion tensor imaging (DTI), single-shell and multishell free-water imaging (FW) indices derived from a short, clinical compatible diffusion protocol (b = 500 s/mm2 , b = 1,000 s/mm2 , 32 directions each) on their power to predict brain age. Age was chosen as it is well-known to be related to widespread modification of the white matter and because brain-age estimation has recently been found to be relevant to several neurodegenerative diseases. We used a previously developed and validated data-driven whole-brain machine learning pipeline to directly compare the precision of brain-age estimates in a sample of 89 healthy males between 20 and 85 years old. We found that multishell FW outperform DTI indices in estimating brain age and that multishell FW, even when using low (500 ms2 ) b-values secondary shell, outperform single-shell FW. Single-shell FW led to lower brain-age estimation accuracy even of canonical DTI indices, suggesting that single-shell FW indices should be used with caution. For all considered reconstruction algorithms, the most discriminant indices were those measuring free diffusion of water in the white matter.
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Affiliation(s)
- Federico Nemmi
- Inserm Unité ToNIC, UMR 1214, CHU PURPAN - Pavillon BAUDOT, Toulouse, France
| | - Mathilde Levardon
- Inserm Unité ToNIC, UMR 1214, CHU PURPAN - Pavillon BAUDOT, Toulouse, France
| | - Patrice Péran
- Inserm Unité ToNIC, UMR 1214, CHU PURPAN - Pavillon BAUDOT, Toulouse, France
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23
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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24
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Cheng J, Liu Z, Guan H, Wu Z, Zhu H, Jiang J, Wen W, Tao D, Liu T. Brain Age Estimation From MRI Using Cascade Networks With Ranking Loss. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3400-3412. [PMID: 34086565 DOI: 10.1109/tmi.2021.3085948] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain age more accurately from the discretized brain age by the first-stage network. Second, to our knowledge, TSAN is the first work to apply novel ranking losses in brain age estimation, together with the traditional mean square error (MSE) loss. Third, densely connected paths are used to combine feature maps with different scales. The experiments with 6586 MRIs showed that TSAN could provide accurate brain age estimation, yielding mean absolute error (MAE) of 2.428 and Pearson's correlation coefficient (PCC) of 0.985, between the estimated and chronological ages. Furthermore, using the brain age gap between brain age and chronological age as a biomarker, Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) can be distinguished from healthy control (HC) subjects by support vector machine (SVM). Classification AUC in AD/HC and MCI/HC was 0.904 and 0.823, respectively. It showed that brain age gap is an effective biomarker associated with risk of dementia, and has potential for early-stage dementia risk screening. The codes and trained models have been released on GitHub: https://github.com/Milan-BUAA/TSAN-brain-age-estimation.
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25
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Kritikos M, Clouston SAP, Huang C, Pellecchia AC, Mejia-Santiago S, Carr MA, Kotov R, Lucchini RG, Gandy SE, Bromet EJ, Luft BJ. Cortical complexity in world trade center responders with chronic posttraumatic stress disorder. Transl Psychiatry 2021; 11:597. [PMID: 34815383 PMCID: PMC8611009 DOI: 10.1038/s41398-021-01719-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/18/2021] [Accepted: 10/29/2021] [Indexed: 12/19/2022] Open
Abstract
Approximately 23% of World Trade Center (WTC) responders are experiencing chronic posttraumatic stress disorder (PTSD) associated with their exposures at the WTC following the terrorist attacks of 9/11/2001, which has been demonstrated to be a risk factor for cognitive impairment raising concerns regarding their brain health. Cortical complexity, as measured by analyzing Fractal Dimension (FD) from T1 MRI brain images, has been reported to be reduced in a variety of psychiatric and neurological conditions. In this report, we hypothesized that FD would be also reduced in a case-control sample of 99 WTC responders as a result of WTC-related PTSD. The results of our surface-based morphometry cluster analysis found alterations in vertex clusters of complexity in WTC responders with PTSD, with marked reductions in regions within the frontal, parietal, and temporal cortices, in addition to whole-brain absolute bilateral and unilateral complexity. Furthermore, region of interest analysis identified that the magnitude of changes in regional FD severity was associated with increased PTSD symptoms (reexperiencing, avoidance, hyperarousal, negative affect) severity. This study confirms prior findings on FD and psychiatric disorders and extends our understanding of FD associations with posttraumatic symptom severity. The complex and traumatic experiences that led to WTC-related PTSD were associated with reductions in cortical complexity. Future work is needed to determine whether reduced cortical complexity arose prior to, or concurrently with, onset of PTSD.
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Affiliation(s)
- Minos Kritikos
- Program in Public Health and Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Sean A P Clouston
- Program in Public Health and Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
| | - Chuan Huang
- Department of Radiology, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Alison C Pellecchia
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Stephanie Mejia-Santiago
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Melissa A Carr
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Roberto G Lucchini
- Department of Environmental Health Sciences, Robert Stempel School of Public Health, Florida International University, Miami, FL, USA
| | - Samuel E Gandy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry and Mount Sinai Alzheimer's Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evelyn J Bromet
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Benjamin J Luft
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
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26
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Collantoni E, Madan CR, Meregalli V, Meneguzzo P, Marzola E, Panero M, D'Agata F, Abbate-Daga G, Tenconi E, Manara R, Favaro A. Sulcal characteristics patterns and gyrification gradient at different stages of Anorexia Nervosa: A structural MRI evaluation. Psychiatry Res Neuroimaging 2021; 316:111350. [PMID: 34384959 DOI: 10.1016/j.pscychresns.2021.111350] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 05/04/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Previous research evidenced alterations of different cortical parameters in patients with acute Anorexia Nervosa (AN), but no study to date investigated the morphology of individual sulci and their relationship with other structural indices. Our study aims at exploring the depth and width of 16 major cortical sulci in AN at different stages of the disorder and their relationships with the gyrification gradient. Two samples were included in the study. The first involved 38 patients with acute AN, 20 who fully recovered from AN, and 38 healthy women (HW); the second included 16 patients with AN and 16 HW. Sulcal width and depth were estimated for 16 sulci and outlined with a factorial analysis. An anterior-posterior gradient of gyrification was also extracted. Compared to HW, patients with acute AN displayed higher width and depth values in specific cortical sulci, and an altered gyrification gradient in areas encompassing the Central Sulcus, and Parieto-Temporal and Frontal Lobe regions. Sulcal width negatively correlated with gyrification gradient in areas where these values are altered in AN patients. Our results suggest the presence of alterations in sulcal morphology with a pattern similar to the gyrification gradient one and which seems to be related with malnutrition.
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Affiliation(s)
| | | | | | - Paolo Meneguzzo
- Department of Neurosciences, University of Padua, Padova, Italy
| | - Enrica Marzola
- Department of Neuroscience, University of Turin, Torino, Italy
| | - Matteo Panero
- Department of Neuroscience, University of Turin, Torino, Italy
| | | | | | - Elena Tenconi
- Department of Neurosciences, University of Padua, Padova, Italy; Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Renzo Manara
- Department of Neurosciences, University of Padua, Padova, Italy
| | - Angela Favaro
- Department of Neurosciences, University of Padua, Padova, Italy; Padua Neuroscience Center, University of Padua, Padova, Italy
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27
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Hong J, Yun HJ, Park G, Kim S, Ou Y, Vasung L, Rollins CK, Ortinau CM, Takeoka E, Akiyama S, Tarui T, Estroff JA, Grant PE, Lee JM, Im K. Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging. Front Neurosci 2021; 15:714252. [PMID: 34707474 PMCID: PMC8542770 DOI: 10.3389/fnins.2021.714252] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/08/2021] [Indexed: 11/23/2022] Open
Abstract
The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.
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Affiliation(s)
- Jinwoo Hong
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea.,Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Division of Newborn Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Gilsoon Park
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Seonggyu Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Yangming Ou
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Division of Newborn Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Computational Health Informatics Program, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Lana Vasung
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Division of Newborn Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Cynthia M Ortinau
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, United States
| | - Emiko Takeoka
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, United States
| | - Shizuko Akiyama
- Center for Perinatal and Neonatal Medicine, Tohoku University Hospital, Sendai, Japan
| | - Tomo Tarui
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, United States
| | - Judy A Estroff
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Patricia Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Division of Newborn Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.,Division of Newborn Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
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28
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Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 2021; 72:103600. [PMID: 34614461 PMCID: PMC8498228 DOI: 10.1016/j.ebiom.2021.103600] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
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Affiliation(s)
- Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Department of General Psychology, University of Padua, Italy
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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29
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Song X, García-Saldivar P, Kindred N, Wang Y, Merchant H, Meguerditchian A, Yang Y, Stein EA, Bradberry CW, Ben Hamed S, Jedema HP, Poirier C. Strengths and challenges of longitudinal non-human primate neuroimaging. Neuroimage 2021; 236:118009. [PMID: 33794361 PMCID: PMC8270888 DOI: 10.1016/j.neuroimage.2021.118009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 01/20/2023] Open
Abstract
Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.
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Affiliation(s)
- Xiaowei Song
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Pamela García-Saldivar
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Nathan Kindred
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, United Kingdom
| | - Hugo Merchant
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, UMR7290, Université Aix-Marseille/CNRS, Institut Language, Communication and the Brain 13331 Marseille, France
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Charles W Bradberry
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France
| | - Hank P Jedema
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA.
| | - Colline Poirier
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom.
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30
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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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Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space. Neuroimage 2021; 239:118274. [PMID: 34146709 DOI: 10.1016/j.neuroimage.2021.118274] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/14/2021] [Accepted: 06/15/2021] [Indexed: 11/23/2022] Open
Abstract
The parcellation of the brain's cortical surface into anatomically and/or functionally distinct areas is a topic of ongoing investigation and interest. We provide digital versions of six classical human brain atlases in common MRI space. The cortical atlases represent a range of modalities, including cyto- and myeloarchitecture (Campbell, Smith, Brodmann and Von Economo), myelogenesis (Flechsig), and mappings of symptomatic information in relation to the spatial location of brain lesions (Kleist). Digital reconstructions of these important cortical atlases widen the range of modalities for which cortex-wide imaging atlases are currently available and offer the opportunity to compare and combine microstructural and lesion-based functional atlases with in-vivo imaging-based atlases.
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Boyle R, Jollans L, Rueda-Delgado LM, Rizzo R, Yener GG, McMorrow JP, Knight SP, Carey D, Robertson IH, Emek-Savaş DD, Stern Y, Kenny RA, Whelan R. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behav 2021; 15:327-345. [PMID: 32141032 DOI: 10.1007/s11682-020-00260-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.
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Affiliation(s)
- Rory Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Lee Jollans
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, München, Germany
| | - Laura M Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Rossella Rizzo
- Physics Department, University of Calabria, Rende, CS, Italy
| | - Görsev G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Department of Neurology, Dokuz Eylul University Medical School, İzmir, Turkey
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, İzmir, Turkey
| | - Jason P McMorrow
- Centre for Advanced Medical Imaging, St. James's Hospital, Dublin 8, Ireland
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Silvin P Knight
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
| | - Daniel Carey
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
| | - Ian H Robertson
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Derya D Emek-Savaş
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, İzmir, Turkey
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | - Rose Anne Kenny
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Mercer's Institute for Successful Ageing, St. James's Hospital, Dublin 8, Ireland
| | - Robert Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
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Contribution of muscle proprioception to limb movement perception and proprioceptive decline with ageing. CURRENT OPINION IN PHYSIOLOGY 2021. [DOI: 10.1016/j.cophys.2021.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Baecker L, Dafflon J, da Costa PF, Garcia-Dias R, Vieira S, Scarpazza C, Calhoun VD, Sato JR, Mechelli A, Pinaya WHL. Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data. Hum Brain Mapp 2021; 42:2332-2346. [PMID: 33738883 PMCID: PMC8090783 DOI: 10.1002/hbm.25368] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/27/2021] [Accepted: 01/31/2021] [Indexed: 12/26/2022] Open
Abstract
Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such “brain age prediction” vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47–73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole‐brain region‐based or voxel‐based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross‐validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel‐level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.
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Affiliation(s)
- Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jessica Dafflon
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pedro F da Costa
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of General Psychology, University of Padua, Padua, Italy
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, Georgia, USA.,Georgia Institute of Technology, Emory University, Georgia, USA
| | - João R Sato
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, São Paulo, Brazil
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, São Paulo, Brazil.,Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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Tsvetanov KA, Gazzina S, Simon Jones P, van Swieten J, Borroni B, Sanchez-Valle R, Moreno F, LaforceJr R, Graff C, Synofzik M, Galimberti D, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Santana I, Ducharme S, Butler C, Gerhard A, Danek A, Levin J, Otto M, Frisoni G, Ghidoni R, Sorbi S, Rohrer JD, Rowe JB. Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia. Alzheimers Dement 2021; 17:500-514. [PMID: 33215845 PMCID: PMC7611220 DOI: 10.1002/alz.12209] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 09/07/2020] [Accepted: 09/12/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD). METHODS We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data-driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset. RESULTS There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure-function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to the expected onset of disease. DISCUSSION Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance.
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Affiliation(s)
- Kamen A. Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, UK
| | - Stefano Gazzina
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - P. Simon Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - John van Swieten
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Raquel Sanchez-Valle
- Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións iomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Hospital Universitario Donostia, San Sebastian, Gipuzkoa, Spain
- Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain
| | - Robert LaforceJr
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Québec, Canada
| | - Caroline Graff
- Karolinska Institutet, Department NVS, Center for Alzheimer Research, Division of Neurogenetics, Stockholm, Sweden
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research & Center of Neurology, University of Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Daniela Galimberti
- University of Milan, Centro Dino Ferrari, Milan, Italy
- Fondazione IRCSS Ca’ Granda, Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy
| | - Mario Masellis
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, Ontario, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospitals Leuven, Belgium, Laboratory for Neurobiology, VIB-KU
| | - Alexandre de Mendonça
- Laboratory of Neurosciences, Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Fabrizio Tagliavini
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, Ital
| | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centre of Neurosciences and Cell biology, Universidade de Coimbra, Coimbra, Portugal
| | - Simon Ducharme
- Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Chris Butler
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Alexander Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Germany
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Markus Otto
- Department of Neurology, University Hospital Ulm, Ulm, Germany
| | - Giovanni Frisoni
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) “Don Gnocchi”, Florence, Italy
| | - Jonathan D. Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - James B. Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, UK
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Bhagwat N, Barry A, Dickie EW, Brown ST, Devenyi GA, Hatano K, DuPre E, Dagher A, Chakravarty M, Greenwood CMT, Misic B, Kennedy DN, Poline JB. Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses. Gigascience 2021; 10:giaa155. [PMID: 33481004 PMCID: PMC7821710 DOI: 10.1093/gigascience/giaa155] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 11/01/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. METHODS Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction. RESULTS Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. CONCLUSIONS This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
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Affiliation(s)
- Nikhil Bhagwat
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Amadou Barry
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada
| | - Erin W Dickie
- Kimel Family Translational Imaging-Genetics Research Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Shawn T Brown
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Koji Hatano
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Elizabeth DuPre
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Alain Dagher
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology; Department of Epidemiology, Biostatistics & Occupational Health Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - David N Kennedy
- Child and Adolescent Neurodevelopment Initiative, University of Massachusetts, Worcester, MA, USA
| | - Jean-Baptiste Poline
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
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Madan CR. Beyond volumetry: Considering age-related changes in brain shape complexity using fractal dimensionality. AGING BRAIN 2021; 1:100016. [PMID: 36911503 PMCID: PMC9997150 DOI: 10.1016/j.nbas.2021.100016] [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: 04/28/2021] [Revised: 05/07/2021] [Accepted: 05/09/2021] [Indexed: 10/21/2022] Open
Abstract
Gray matter volume for cortical, subcortical, and ventricles all vary with age. However, these volumetric changes do not happen on their own, there are also age-related changes in cortical folding and other measures of brain shape. Fractal dimensionality has emerged as a more sensitive measure of brain structure, capturing both volumetric and shape-related differences. For subcortical structures it is readily apparent that segmented structures do not differ in volume in isolation-adjacent regions must also vary in shape. Fractal dimensionality here also appears to be more sensitive to these age-related differences than volume. Given these differences in structure are quite prominent in structure, caution should be used when examining comparisons across age in brain function measures, as standard normalisation methods are not robust enough to adjust for these inter-individual differences in cortical structure.
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Madan CR. Age-related decrements in cortical gyrification: Evidence from an accelerated longitudinal dataset. Eur J Neurosci 2020; 53:1661-1671. [PMID: 33171528 DOI: 10.1111/ejn.15039] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/25/2020] [Accepted: 10/31/2020] [Indexed: 01/05/2023]
Abstract
Cortical gyrification has been found to decrease due to aging, but thus far this has only been examined in cross-sectional samples. Interestingly, the topography of these age-related differences in gyrification follows a distinct gradient along the cortex relative to age effects on cortical thickness, likely suggesting a different underlying neurobiological mechanism. Here I examined several aspects of gyrification in an accelerated longitudinal dataset of 280 healthy adults aged 45-92 with an interval between first and last MRI sessions of up to 10 years (total of 815 MRI sessions). Results suggest that age changes in sulcal morphology underlie these changes in gyrification.
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McDonough IM, Madan CR. Structural complexity is negatively associated with brain activity: a novel multimodal test of compensation theories of aging. Neurobiol Aging 2020; 98:185-196. [PMID: 33302180 DOI: 10.1016/j.neurobiolaging.2020.10.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 10/13/2020] [Accepted: 10/24/2020] [Indexed: 12/17/2022]
Abstract
Fractal dimensionality (FD) measures the complexity within the folds and ridges of cortical and subcortical structures. We tested the degree that FD might provide a new perspective on the atrophy-compensation hypothesis: age or disease-related atrophy causes a compensatory neural response in the form of increased brain activity in the prefrontal cortex to maintain cognition. Brain structural and functional data were collected from 63 middle-aged and older adults and 18 young-adult controls. Two distinct patterns of FD were found that separated cortical from subcortical structures. Subcortical FD was more strongly negatively correlated with age than cortical FD, and cortical FD was negatively associated with brain activity during memory retrieval in medial and lateral parietal cortices uniquely in middle-aged and older adults. Multivariate analyses revealed that the lower FD/higher brain activity pattern was associated with poorer cognition-patterns not present in young adults, consistent with compensation. Bayesian analyses provide further evidence against the modal interpretation of the atrophy-compensation hypothesis in the prefrontal cortex-a key principle found in some neurocognitive theories of aging.
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Affiliation(s)
- Ian M McDonough
- Department of Psychology, The University of Alabama, Tuscaloosa, AL, USA.
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40
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Bellantuono L, Marzano L, La Rocca M, Duncan D, Lombardi A, Maggipinto T, Monaco A, Tangaro S, Amoroso N, Bellotti R. Predicting brain age with complex networks: From adolescence to adulthood. Neuroimage 2020; 225:117458. [PMID: 33099008 DOI: 10.1016/j.neuroimage.2020.117458] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 10/13/2020] [Indexed: 01/21/2023] Open
Abstract
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Luca Marzano
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- University of Southern California, Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Dominique Duncan
- University of Southern California, Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy
| | - Tommaso Maggipinto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy.
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy; Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia - Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy; Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
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41
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Marzi C, Giannelli M, Tessa C, Mascalchi M, Diciotti S. Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan. Sci Rep 2020; 10:16957. [PMID: 33046812 PMCID: PMC7550568 DOI: 10.1038/s41598-020-73961-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 09/14/2020] [Indexed: 01/12/2023] Open
Abstract
The cerebral cortex manifests an inherent structural complexity of folding. The fractal geometry describes the complexity of structures which show self-similarity in a proper interval of spatial scales. In this study, we aimed at evaluating in-vivo the effect of different criteria for selecting the interval of spatial scales in the estimation of the fractal dimension (FD) of the cerebral cortex in T1-weighted magnetic resonance imaging (MRI). We compared four different strategies, including two a priori selections of the interval of spatial scales, an automated selection of the spatial scales within which the cerebral cortex manifests the highest statistical self-similarity, and an improved approach, based on the search of the interval of spatial scales which presents the highest rounded R2adj coefficient and, in case of equal rounded R2adj coefficient, preferring the widest interval in the log–log plot. We employed two public and international datasets of in-vivo MRI scans for a total of 159 healthy subjects (age range 6–85 years). The improved approach showed strong associations of FD with age and yielded the most accurate machine learning models for individual age prediction in both datasets. Our results indicate that the selection of the interval of spatial scales of the cerebral cortex is thus critical in the estimation of FD.
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Affiliation(s)
- Chiara Marzi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Carlo Tessa
- Division of Radiology, Versilia Hospital, Azienda USL Toscana Nord Ovest, Lido di Camaiore (Lu), Italy
| | - Mario Mascalchi
- "Mario Serio" Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy.
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42
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Dinsdale NK, Bluemke E, Smith SM, Arya Z, Vidaurre D, Jenkinson M, Namburete AIL. Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage 2020; 224:117401. [PMID: 32979523 DOI: 10.1016/j.neuroimage.2020.117401] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 08/17/2020] [Accepted: 09/15/2020] [Indexed: 10/23/2022] Open
Abstract
Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors ΔBrainAge=AgePredicted-AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between ΔBrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the ΔBrainAge from models such as this network were predictive of any health outcomes.
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Affiliation(s)
- Nicola K Dinsdale
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
| | - Emma Bluemke
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Zobair Arya
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Diego Vidaurre
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Department of Psychiatry, University of Oxford, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Ana I L Namburete
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom
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43
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Davis NJ. Variance in cortical depth across the brain surface: Implications for transcranial stimulation of the brain. Eur J Neurosci 2020; 53:996-1007. [PMID: 32877015 DOI: 10.1111/ejn.14957] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 08/20/2020] [Indexed: 01/12/2023]
Abstract
The distance between the surface of the scalp and the surface of the grey matter of the brain is a key factor in determining the effective dose of non-invasive brain stimulation for an individual person. The highly folded nature of the cortical surface means that the depth of a particular brain area is likely to vary between individuals. The question addressed here is: what is the variability of this measure of cortical depth? Ninety-four anatomical MRI images were taken from the OASIS database. For each image, the minimum distance from each point in the grey matter to the scalp surface was determined. Transforming these estimates into standard space meant that the coefficient of variation could be determined across the sample. The results indicated that depth variability is high across the cortical surface, even when taking sulcal depth into account. This was true even for the primary visual and motor areas, which are often used in setting TMS dosage. The correlation of the depth of these areas and the depth of other brain areas was low. The results suggest that dose setting of TMS based on visual or evoked potentials may offer poor reliability, and that individual brain images should be used when targeting non-primary brain areas.
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Affiliation(s)
- Nick J Davis
- Department of Psychology, Manchester Metropolitan University, Manchester, UK
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44
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Dafflon J, Pinaya WHL, Turkheimer F, Cole JH, Leech R, Harris MA, Cox SR, Whalley HC, McIntosh AM, Hellyer PJ. An automated machine learning approach to predict brain age from cortical anatomical measures. Hum Brain Mapp 2020; 41:3555-3566. [PMID: 32415917 PMCID: PMC7416036 DOI: 10.1002/hbm.25028] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/10/2020] [Accepted: 04/21/2020] [Indexed: 12/31/2022] Open
Abstract
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.
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Affiliation(s)
- Jessica Dafflon
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Walter H. L. Pinaya
- Department of Psychosis StudiesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
- Center of Mathematics, Computation and CognitionUniversidade Federal do ABCSanto AndréBrazil
| | - Federico Turkheimer
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - James H. Cole
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Robert Leech
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | | | - Simon R. Cox
- Lothian Birth Cohorts group, Department of PsychologyUniversity of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | | | | | - Peter J. Hellyer
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
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45
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Carradus AJ, Mougin O, Hunt BAE, Tewarie PK, Geades N, Morris PG, Brookes MJ, Gowland PA, Madan CR. Age-related differences in myeloarchitecture measured at 7 T. Neurobiol Aging 2020; 96:246-254. [PMID: 33049517 DOI: 10.1016/j.neurobiolaging.2020.08.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 01/01/2023]
Abstract
We have used the magnetisation transfer (MT) MRI measure as a primary measure of myelination in both the gray matter (GM) of the 78 cortical automated anatomical labeling (AAL) regions of the brain, and the underlying white matter in each region, in a cohort of healthy adults (aged 19-62 year old). The results revealed a significant quadratic trend in myelination with age, with average global myelination peaking at 42.9 year old in gray matter, and at 41.7 year old in white matter. We also explored the possibility of using the Nuclear Overhauser Enhancement (NOE) effect, which is acquired in a similar method to MT, as an additional measure of myelination. We found that the MT and NOE signals were strongly correlated in the brain and that the NOE effects displayed similar (albeit weaker) parabolic trends with age. We also investigated differences in cortical thickness with age, and confirmed a previous result of a linear decline of 4.5 ± 1.2 μm/y.
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Affiliation(s)
- Andrew J Carradus
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Olivier Mougin
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Prejaas K Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Nicolas Geades
- Philips Clinical Science, Philips Healthcare, Eindhoven, the Netherlands
| | - Peter G Morris
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Penny A Gowland
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Christopher R Madan
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK; School of Psychology, University of Nottingham, Nottingham, UK.
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46
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Bashyam VM, Erus G, Doshi J, Habes M, Nasrallah IM, Truelove-Hill M, Srinivasan D, Mamourian L, Pomponio R, Fan Y, Launer LJ, Masters CL, Maruff P, Zhuo C, Völzke H, Johnson SC, Fripp J, Koutsouleris N, Satterthwaite TD, Wolf D, Gur RE, Gur RC, Morris J, Albert MS, Grabe HJ, Resnick S, Bryan RN, Wolk DA, Shou H, Davatzikos C. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain 2020; 143:2312-2324. [PMID: 32591831 PMCID: PMC7364766 DOI: 10.1093/brain/awaa160] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 03/17/2020] [Accepted: 03/31/2020] [Indexed: 01/21/2023] Open
Abstract
Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.
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Affiliation(s)
- Vishnu M Bashyam
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Correspondence to: Vishnu Bashyam3700 Hamilton Walk, 7th FloorCenter of Biomedical Image Computing and Analytics, University of PennsylvaniaPhiladelphia, PA 19104, USA E-mail: Website: https://www.med.upenn.edu/cbica/
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Monica Truelove-Hill
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Liz Mamourian
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, USA
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Chuanjun Zhuo
- Tianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China,Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Germany,German Centre for Cardiovascular Research, Partner Sit Greifswald, Germany
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, USA
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Daniel Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Raquel E Gur
- Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ruben C Gur
- Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - John Morris
- Department of Neurology, Washington University in St. Louis, St Louis, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, Ernst-Moritz-Arndt University, Greifswald, Mecklenburg-Vorpommern, Germany
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Bethesda, USA
| | - R Nick Bryan
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadephia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Correspondence may also be addressed to: Christos DavatzikosE-mail:
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47
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Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction. Brain Sci 2020; 10:brainsci10060364. [PMID: 32545374 PMCID: PMC7349402 DOI: 10.3390/brainsci10060364] [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: 05/05/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 02/06/2023] Open
Abstract
Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. Initiatives to share open access morphological data contributed significantly to the advance in brain structure characterization. Indeed, such initiatives allow large brain morphology multi-site datasets to be shared, which increases the statistical sensitivity of the outcomes. However, using neuroimaging data from multi-site studies requires harmonizing data across the site to avoid bias. In this work we evaluated three different harmonization techniques on the Autism Brain Imaging Data Exchange (ABIDE) dataset for age prediction analysis in two groups of subjects (i.e., controls and autism spectrum disorder). We extracted the morphological features from T1-weighted images of a mixed cohort of 654 subjects acquired from 17 sites to predict the biological age of the subjects using three machine learning regression models. A machine learning framework was developed to quantify the effects of the different harmonization strategies on the final performance of the models and on the set of morphological features that are relevant to the age prediction problem in both the presence and absence of pathology. The results show that, even if two harmonization strategies exhibit similar accuracy of predictive models, a greater mismatch occurs between the sets of most age-related predictive regions for the Autism Spectrum Disorder (ASD) subjects. Thus, we propose to use a stability index to extract meaningful features for a robust clinical validation of the outcomes of multiple harmonization strategies.
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48
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Cortical Complexity in Anorexia Nervosa: A Fractal Dimension Analysis. J Clin Med 2020; 9:jcm9030833. [PMID: 32204343 PMCID: PMC7141241 DOI: 10.3390/jcm9030833] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 11/17/2022] Open
Abstract
Fractal Dimension (FD) has shown to be a promising means to describe the morphology of cortical structures across different neurologic and psychiatric conditions, displaying a good sensitivity in capturing atrophy processes. In this study, we aimed at exploring the morphology of cortical areas by means of FD in 58 female patients with Anorexia Nervosa (AN) (38 currently underweight and 20 fully recovered) and 38 healthy controls (HC). All participants underwent high-resolution MRI. Surface extraction was completed using FreeSurfer, FD was computed using the calcFD toolbox. The whole cortex mean FD value was lower in acute AN patients compared to HC (p < 0.001). Recovered AN patients did not show differences in the global FD when compared to HC. However, some brain areas showed higher FD in patients than controls, while others showed the opposite pattern. Parietal regions showed lower FD in both AN groups. In acute AN patients, the FD correlated with age (p < 0.001), body mass index (p = 0.019) and duration of illness (p = 0.011). FD seems to represent a feasible method to explore cortical complexity in patients with AN since it demonstrated to be sensitive to the effects of both severity and duration of malnutrition.
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49
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Lombardi A, Monaco A, Donvito G, Amoroso N, Bellotti R, Tangaro S. Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019. Front Psychiatry 2020; 11:619629. [PMID: 33551880 PMCID: PMC7854554 DOI: 10.3389/fpsyt.2020.619629] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 12/18/2020] [Indexed: 12/05/2022] Open
Abstract
Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.
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Affiliation(s)
- Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | | | | | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento di Farmacia - Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Bari, Italy.,Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
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50
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Del Maschio N, Fedeli D, Sulpizio S, Abutalebi J. The relationship between bilingual experience and gyrification in adulthood: A cross-sectional surface-based morphometry study. BRAIN AND LANGUAGE 2019; 198:104680. [PMID: 31465990 DOI: 10.1016/j.bandl.2019.104680] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 08/13/2019] [Accepted: 08/14/2019] [Indexed: 06/10/2023]
Abstract
Neuroimaging evidence suggests that bilingualism may act as a source of neural plasticity. However, prior work has mostly focused on bilingualism-induced alterations in gray matter volume and white matter tract microstructure, with additional effects related to other neurostructural indices that might have remained undetected. The degree of cortical folding or gyrification is a morphometric parameter which provides information about changes on the brain's surface during development, aging and disease. We used Surface-based Morphometry (SBM) to investigate the contribution of bilingual experience to gyrification from early adulthood to old age in a sample of bilinguals and monolingual controls. Despite widespread cortical folding reductions for all participants with increasing age, preserved gyrification exclusive to bilinguals was detected in the right cingulate and entorhinal cortices, regions vulnerable with normal and pathological brain aging. Our results provide novel insights on experience-related cortical reshaping and bilingualism-induced cortical plasticity in adulthood.
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Affiliation(s)
- Nicola Del Maschio
- Centre for Neurolinguistics and Psycholinguistics (CNPL), University Vita-Salute San Raffaele, Milano 20132, Italy
| | - Davide Fedeli
- Centre for Neurolinguistics and Psycholinguistics (CNPL), University Vita-Salute San Raffaele, Milano 20132, Italy
| | - Simone Sulpizio
- Centre for Neurolinguistics and Psycholinguistics (CNPL), University Vita-Salute San Raffaele, Milano 20132, Italy
| | - Jubin Abutalebi
- Centre for Neurolinguistics and Psycholinguistics (CNPL), University Vita-Salute San Raffaele, Milano 20132, Italy.
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