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Huang Y, Holcombe SA, Wang SC, Tang J. A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors. Comput Med Imaging Graph 2024; 115:102388. [PMID: 38692200 DOI: 10.1016/j.compmedimag.2024.102388] [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: 11/08/2023] [Revised: 04/06/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024]
Abstract
Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or their correlations to anthropometrics is a prerequisite to the development of numerical human body models representing target demographics. Variational autoencoders (VAE) as anatomical shape generators remain to be explored in terms of utilizing the latent vectors to control or interpret the representativeness of the generated results. In this paper, we propose a pipeline for developing a multi-rib cross-sectional shape generative model from CT images, which consists of the achievement of rib cross-sectional shape data from CT images using an anatomical indexing system and regular grids, and a unified framework to fit shape distributions and associate shapes to anthropometrics for different rib categories. Specifically, we collected CT images including 3193 ribs, surface regular grid is generated for each rib based on anatomical coordinates, the rib cross-sectional shapes are characterized by nodal coordinates and cortical bone thickness. The tensor structure of shape data based on regular grids enable the implementation of CNNs in the conditional variational autoencoder (CVAE). The CVAE is trained against an auxiliary classifier to decouple the low-dimensional representations of the inter- and intra- variations and fit each intra-variation by a Gaussian distribution simultaneously. Random tree regressors are further leveraged to associate each continuous intra-class space with the corresponding anthropometrics of the subjects, i.e., age, height and weight. As a result, with the rib class labels and the latent vectors sampled from Gaussian distributions or predicted from anthropometrics as the inputs, the decoder can generate valid rib cross-sectional shapes of given class labels (male/female, 2nd to 11th ribs) for arbitrary populational percentiles or specific age, height and weight, which paves the road for future biomedical and biomechanical studies considering the diversity of rib shapes across the population.
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Affiliation(s)
- Yuan Huang
- Research Investigator in International Center for Automotive Medicine (ICAM), University of Michigan, USA.
| | - Sven A Holcombe
- Research Scientist in International Center for Automotive Medicine (ICAM), University of Michigan, USA
| | - Stewart C Wang
- University of Michigan of Surgery and Director of International Center for Automotive Medicine (ICAM), USA
| | - Jisi Tang
- Key Laboratory of Biorheological Science and Technology, Bioengineering College, Chongqing University, China.
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Joo Y, Namgung E, Jeong H, Kang I, Kim J, Oh S, Lyoo IK, Yoon S, Hwang J. Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms. Sci Rep 2023; 13:22388. [PMID: 38104173 PMCID: PMC10725434 DOI: 10.1038/s41598-023-49514-2] [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: 04/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.
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Affiliation(s)
- Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Eun Namgung
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Hyeonseok Jeong
- Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ilhyang Kang
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Jinsol Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Sohyun Oh
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea.
| | - Jaeuk Hwang
- Department of Psychiatry, Soonchunhyang University College of Medicine, Seoul, South Korea.
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Guo Y, Chen Q, Choi GPT, Lui LM. Automatic landmark detection and registration of brain cortical surfaces via quasi-conformal geometry and convolutional neural networks. Comput Biol Med 2023; 163:107185. [PMID: 37418897 DOI: 10.1016/j.compbiomed.2023.107185] [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: 12/05/2022] [Revised: 05/24/2023] [Accepted: 06/13/2023] [Indexed: 07/09/2023]
Abstract
In medical imaging, surface registration is extensively used for performing systematic comparisons between anatomical structures, with a prime example being the highly convoluted brain cortical surfaces. To obtain a meaningful registration, a common approach is to identify prominent features on the surfaces and establish a low-distortion mapping between them with the feature correspondence encoded as landmark constraints. Prior registration works have primarily focused on using manually labeled landmarks and solving highly nonlinear optimization problems, which are time-consuming and hence hinder practical applications. In this work, we propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces using quasi-conformal geometry and convolutional neural networks. We first develop a landmark detection network (LD-Net) that allows for the automatic extraction of landmark curves given two prescribed starting and ending points based on the surface geometry. We then utilize the detected landmarks and quasi-conformal theory for achieving the surface registration. Specifically, we develop a coefficient prediction network (CP-Net) for predicting the Beltrami coefficients associated with the desired landmark-based registration and a mapping network called the disk Beltrami solver network (DBS-Net) for generating quasi-conformal mappings from the predicted Beltrami coefficients, with the bijectivity guaranteed by quasi-conformal theory. Experimental results are presented to demonstrate the effectiveness of our proposed framework. Altogether, our work paves a new way for surface-based morphometry and medical shape analysis.
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Affiliation(s)
- Yuchen Guo
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
| | - Qiguang Chen
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
| | - Gary P T Choi
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lok Ming Lui
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong.
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Rammohan N, Ho A, Besson P, Kruser TJ, Bandt SK. Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning. Neuro Oncol 2023; 25:1323-1330. [PMID: 36734195 PMCID: PMC10326473 DOI: 10.1093/neuonc/noad029] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitive deficits. While dose-dependent injury to individual areas such as the hippocampus has been demonstrated, global structural shape changes after WBRT remain to be studied. METHODS We studied healthy controls and patients with brain metastases and examined MRI brain anatomic surface data before and after WBRT. We implemented a validated graph convolutional neural network model to estimate patient's "brain age". We further developed a mixed-effects linear model to compare the estimated age of the whole brain and substructures before and after WBRT. RESULTS 4220 subjects were analyzed (4148 healthy controls and 72 patients). The median radiation dose was 30 Gy (range 25-37.5 Gy). The whole brain and substructures underwent structural change resembling rapid aging in radiated patients compared to healthy controls; the whole brain "aged" 9.32 times faster, the cortex 8.05 times faster, the subcortical structures 12.57 times faster, and the hippocampus 10.14 times faster. In a subset analysis, the hippocampus "aged" 8.88 times faster in patients after conventional WBRT versus after hippocampal avoidance (HA)-WBRT. CONCLUSIONS Our findings suggest that WBRT causes the brain and its substructures to undergo structural changes at a pace up to 13x of the normal aging pace, where hippocampal avoidance offers focal structural protection. Correlating these structural imaging changes with neurocognitive outcomes following WBRT or HA-WBRT would benefit from future analysis.
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Affiliation(s)
- Nikhil Rammohan
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexander Ho
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Pierre Besson
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Tim J Kruser
- SSM Health Dean Medical Group, Turville Bay Radiation Oncology Center, Madison, WI, USA
| | - S Kathleen Bandt
- Department of Neurologic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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Iporre-Rivas A, Saur D, Rohr K, Scheuermann G, Gillmann C. Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J Med Imaging (Bellingham) 2023; 10:044502. [PMID: 37465592 PMCID: PMC10350625 DOI: 10.1117/1.jmi.10.4.044502] [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: 06/14/2022] [Revised: 06/13/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023] Open
Abstract
Purpose The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient's outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters. Approach We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods. Results The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553 ± 0.0031 . Conclusions We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.
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Affiliation(s)
- Ariel Iporre-Rivas
- Leipzig University, Institute for Computer Science, Faculty of Mathematics and Computer Science, Signal and Image Processing Group, Leipzig, Germany
- Max-Plank-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- ScaDS.AI, Leipzig, Germany
| | - Dorothee Saur
- Leipzig University, Department of Neurology, Leipzig, Germany
| | - Karl Rohr
- Heidelberg University, BioQuant Center, IPMB and DKFZ, Biomedical Computer Vision Group, Heidelberg, Germany
| | - Gerik Scheuermann
- Leipzig University, Institute for Computer Science, Faculty of Mathematics and Computer Science, Signal and Image Processing Group, Leipzig, Germany
| | - Christina Gillmann
- Leipzig University, Institute for Computer Science, Faculty of Mathematics and Computer Science, Signal and Image Processing Group, Leipzig, Germany
- ScaDS.AI, Leipzig, Germany
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Bu WQ, Guo YX, Zhang D, Du SY, Han MQ, Wu ZX, Tang Y, Chen T, Guo YC, Meng HT. Automatic sex estimation using deep convolutional neural network based on orthopantomogram images. Forensic Sci Int 2023; 348:111704. [PMID: 37094502 DOI: 10.1016/j.forsciint.2023.111704] [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: 12/29/2022] [Revised: 04/09/2023] [Accepted: 04/19/2023] [Indexed: 04/26/2023]
Abstract
Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.
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Affiliation(s)
- Wen-Qing Bu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu-Xin Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Dong Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China
| | - Shao-Yi Du
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China
| | - Meng-Qi Han
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Zi-Xuan Wu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu Tang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Teng Chen
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, 76 West Yanta Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu-Cheng Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China.
| | - Hao-Tian Meng
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China.
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Zhu Z, Huang T, Zhen Z, Wang B, Wu X, Li S. From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping. Med Image Anal 2023; 83:102681. [PMID: 36459804 DOI: 10.1016/j.media.2022.102681] [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: 10/30/2021] [Revised: 07/28/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022]
Abstract
Achieving predictions of brain functional activation patterns/task-fMRI maps from its underlying anatomy is an important yet challenging problem. Once successful, it will not only open up new ways to understand how brain anatomy influences functional organization of the brain, but also provide new technical support for the clinical use of anatomical information to guide the localization of cortical functional areas. However, due to the non-Euclidean complex architecture of brain anatomy and the inherent low signal-to-noise ratio (SNR) properties of fMRI signals, the key challenge in building such a cross-modal brain anatomo-functional mapping is how to effectively learn the context-aware information of brain anatomy and overcome the interference of noise-containing task-fMRI labels on the learning process. In this work, we propose a Unified Geometric Deep Learning framework (BrainUGDL) to perform the cross-modal brain anatomo-functional mapping task. Considering that both global and local structures of brain anatomy have an impact on brain functions from their respective perspectives, we innovatively propose the novel Global Graph Encoding (GGE) unit and Local Graph Attention (LGA) unit embedded into two parallel branches, focusing on learning the high-level global and local context information, respectively. Specifically, GGE learns the global context information of each mesh vertex by building and encoding global interactions, and LGA learns the local context information of each mesh vertex by selectively aggregating patch structure enhanced features from its spatial neighbors. The information learnt from the two branches is then fused to form a comprehensive representation of brain anatomical features for final brain function predictions. To address the inevitable measurement noise in task-fMRI labels, we further elaborate a novel uncertainty-filtered learning mechanism, which enables BrainUGDL to realize revised learning from the noise-containing labels through the estimated uncertainty. Experiments across seven open task-fMRI datasets from human connectome project (HCP) demonstrate the superiority of BrainUGDL. To our best knowledge, our proposed BrainUGDL is the first to achieve the prediction of individual task-fMRI maps solely based on brain sMRI data.
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Affiliation(s)
- Zhiyuan Zhu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China; Engineering Research Center of Intelligent Technology and Educational Application (Beijing Normal University), Ministry of Education, Beijing, China
| | - Taicheng Huang
- Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Zonglei Zhen
- Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Boyu Wang
- Department of Computer Science, Western University, ON, Canada
| | - Xia Wu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China; Engineering Research Center of Intelligent Technology and Educational Application (Beijing Normal University), Ministry of Education, Beijing, China.
| | - Shuo Li
- Department of Computer and Data Science, Case Western Reserve University, Ohio, USA; Department of Biomedical Engineering, Case Western Reserve University, Ohio, USA
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Besson P, Rogalski E, Gill NP, Zhang H, Martersteck A, Bandt SK. Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging. Front Aging Neurosci 2022; 14:895535. [PMID: 36081894 PMCID: PMC9445244 DOI: 10.3389/fnagi.2022.895535] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.
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Affiliation(s)
- Pierre Besson
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States
| | - Emily Rogalski
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Department of Psychiatry and Behavioral Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Nathan P. Gill
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Hui Zhang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Adam Martersteck
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - S. Kathleen Bandt
- Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States,Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,*Correspondence: S. Kathleen Bandt,
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Liu M, Amey RC, Backer RA, Simon JP, Forbes CE. Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations. Front Hum Neurosci 2022; 16:875201. [PMID: 35782044 PMCID: PMC9244405 DOI: 10.3389/fnhum.2022.875201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
- Mengting Liu,
| | - Rachel C. Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- *Correspondence: Rachel C. Amey,
| | - Robert A. Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Julia P. Simon
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Chad E. Forbes
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
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