1
|
Benavidez SM, Abaryan Z, Kim GS, Laltoo E, McCracken JT, Thompson PM, Lawrence KE. Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models. bioRxiv 2024:2024.02.02.578712. [PMID: 38352346 PMCID: PMC10862784 DOI: 10.1101/2024.02.02.578712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.
Collapse
|
2
|
Dhinagar NJ, Thomopoulos SI, Laltoo E, Thompson PM. Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer's Disease Effect Detection. bioRxiv 2024:2024.02.05.578983. [PMID: 38370616 PMCID: PMC10871297 DOI: 10.1101/2024.02.05.578983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Generative AI models have recently achieved mainstream attention with the advent of powerful approaches such as stable diffusion, DALL-E and MidJourney. The underlying breakthrough generative mechanism of denoising diffusion modeling can generate high quality synthetic images and can learn the underlying distribution of complex, high-dimensional data. Recent research has begun to extend these models to medical and specifically neuroimaging data. Typical neuroimaging tasks such as diagnostic classification and predictive modeling often rely on deep learning approaches based on convolutional neural networks (CNNs) and vision transformers (ViTs), with additional steps to help in interpreting the results. In our paper, we train conditional latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) to provide insight into Alzheimer's disease (AD) effects on the brain's anatomy at the individual level. We first created diffusion models that could generate synthetic MRIs, by training them on real 3D T1-weighted MRI scans, and conditioning the generative process on the clinical diagnosis as a context variable. We conducted experiments to overcome limitations in training dataset size, compute time and memory resources, testing different model sizes, effects of pretraining, training duration, and latent diffusion models. We tested the sampling quality of the disease-conditioned diffusion using metrics to assess realism and diversity of the generated synthetic MRIs. We also evaluated the ability of diffusion models to conditionally sample MRI brains using a 3D CNN-based disease classifier relative to real MRIs. In our experiments, the diffusion models generated synthetic data that helped to train an AD classifier (using only 500 real training scans) -and boosted its performance by over 3% when tested on real MRI scans. Further, we used implicit classifier-free guidance to alter the conditioning of an encoded individual scan to its counterfactual (representing a healthy subject of the same age and sex) while preserving subject-specific image details. From this counterfactual image (where the same person appears healthy), a personalized disease map was generated to identify possible disease effects on the brain. Our approach efficiently generates realistic and diverse synthetic data, and may create interpretable AI-based maps for neuroscience research and clinical diagnostic applications.
Collapse
|
3
|
Feng Y, Chandio BQ, Villalon-Reina JE, Benavidez S, Chattopadhyay T, Chehrzadeh S, Laltoo E, Thomopoulos SI, Joshi H, Venkatasubramanian G, John JP, Jahanshad N, Thompson PM. Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease. bioRxiv 2024:2024.02.05.578943. [PMID: 38370817 PMCID: PMC10871218 DOI: 10.1101/2024.02.05.578943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.
Collapse
Affiliation(s)
- Yixue Feng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Bramsh Q Chandio
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sebastian Benavidez
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Tamoghna Chattopadhyay
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sasha Chehrzadeh
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Himanshu Joshi
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Ganesan Venkatasubramanian
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - John P John
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| |
Collapse
|
4
|
Chattopadhyay T, Joshy NA, Ozarkar SS, Buwa K, Feng Y, Laltoo E, Thomopoulos SI, Villalon JE, Joshi H, Venkatasubramanian G, John JP, Thompson PM. Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts. bioRxiv 2024:2024.02.04.578829. [PMID: 38370641 PMCID: PMC10871286 DOI: 10.1101/2024.02.04.578829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting "brain age" - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.
Collapse
Affiliation(s)
- Tamoghna Chattopadhyay
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Neha Ann Joshy
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Saket S Ozarkar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Ketaki Buwa
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Yixue Feng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Julio E Villalon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Himanshu Joshi
- Multimodal Brain Image Analysis Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Ganesan Venkatasubramanian
- Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - John P John
- Multimodal Brain Image Analysis Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| |
Collapse
|
5
|
Dhinagar NJ, Santhalingam V, Lawrence KE, Laltoo E, Thompson PM. Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-6. [PMID: 38082874 DOI: 10.1109/embc40787.2023.10340852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framework to adapt it to learn across multiple sites. We tested our meta-learning model for classifying ASD versus typically developing controls in 2,201 T1-weighted (T1-w) MRI scans collected from 38 imaging sites as part of Autism Brain Imaging Data Exchange (ABIDE) [age: 5.2 -64.0 years]. The method was trained to find a good initialization state for our model that can quickly adapt to data from new unseen sites by fine-tuning on the limited data that is available. The proposed method achieved an area under the receiver operating characteristic curve (ROC-AUC)=0.857 on 370 scans from 7 unseen sites in ABIDE using a few-shot setting of 2-way 20-shot i.e., 20 training samples per site. Our results outperformed a transfer learning baseline by generalizing across a wider range of sites as well as other related prior work. We also tested our model in a zero-shot setting on an independent test site without any additional fine-tuning. Our experiments show the promise of the proposed site-agnostic meta-learning framework for challenging neuroimaging tasks involving multi-site heterogeneity with limited availability of training data.Clinical Relevance- We propose a learning framework that accommodates multi-site heterogeneity and limited data to assist in challenging neuroimaging tasks.
Collapse
|
6
|
Dhinagar NJ, Thomopoulos SI, Laltoo E, Thompson PM. Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-6. [PMID: 38083552 DOI: 10.1109/embc40787.2023.10341190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to brain images to perform diagnostic and prognostic tasks by learning robust features. Vision transformers (ViT) - a new class of deep learning architectures - have emerged in recent years as an alternative to CNNs for several computer vision applications. Here we tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty, in this case for sex and Alzheimer's disease (AD) classification based on 3D brain MRI. In our experiments, two vision transformer architecture variants achieved an AUC of 0.987 for sex and 0.892 for AD classification, respectively. We independently evaluated our models on data from two benchmark AD datasets. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic (generated by a latent diffusion model) and real MRI scans, respectively. Our main contributions include testing the effects of different ViT training strategies including pre-training, data augmentation and learning rate warm-ups followed by annealing, as pertaining to the neuroimaging domain. These techniques are essential for training ViT-like models for neuroimaging applications where training data is usually limited. We also analyzed the effect of the amount of training data utilized on the test-time performance of the ViT via data-model scaling curves.Clinical Relevance- The models evaluated in this work could be trained on neuroimaging data to assist in diagnosis, subtyping and prognosis of Alzheimer's disease.
Collapse
|
7
|
Nabulsi L, Chandio BQ, Dhinagar N, Laltoo E, McPhilemy G, Martyn FM, Hallahan B, McDonald C, Thompson PM, Cannon DM. Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-7. [PMID: 38083303 DOI: 10.1109/embc40787.2023.10339964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN)-a recently developed analytic approach for tractography-to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic and interhemispheric pathways and higher mean FA in posterior bundles relative to controls.Clinical Relevance- BUAN combines tractography and anatomical information to capture distinct along-tract effects on WM microstructure that may aid in classifying diseases based on anatomical differences.
Collapse
|
8
|
Chattopadhyay T, Singh A, Laltoo E, Boyle CP, Owens-Walton C, Chen YL, Cook P, McMillan C, Tsai CC, Wang JJ, Wu YR, van der Werf Y, Thompson PM. Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-6. [PMID: 38083460 DOI: 10.1109/embc40787.2023.10340792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.Clinical Relevance- This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson's disease.
Collapse
|
9
|
Chattopadhyay T, Singh A, Laltoo E, Boyle CP, Owens-Walton C, Chen YL, Cook P, McMillan C, Tsai CC, Wang JJ, Wu YR, van der Werf Y, Thompson PM. Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network. bioRxiv 2023:2023.05.01.538952. [PMID: 37205416 PMCID: PMC10187193 DOI: 10.1101/2023.05.01.538952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification. Clinical Relevance This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson's disease.
Collapse
|
10
|
Dhinagar NJ, Thomopoulos SI, Laltoo E, Thompson PM. Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection. ArXiv 2023:arXiv:2303.08216v1. [PMID: 36994152 PMCID: PMC10055494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to brain images to perform diagnostic and prognostic tasks by learning robust features. Vision transformers (ViT) - a new class of deep learning architectures - have emerged in recent years as an alternative to CNNs for several computer vision applications. Here we tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty, in this case for sex and Alzheimer's disease (AD) classification based on 3D brain MRI. In our experiments, two vision transformer architecture variants achieved an AUC of 0.987 for sex and 0.892 for AD classification, respectively. We independently evaluated our models on data from two benchmark AD datasets. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic (generated by a latent diffusion model) and real MRI scans, respectively. Our main contributions include testing the effects of different ViT training strategies including pre-training, data augmentation and learning rate warm-ups followed by annealing, as pertaining to the neuroimaging domain. These techniques are essential for training ViT-like models for neuroimaging applications where training data is usually limited. We also analyzed the effect of the amount of training data utilized on the test-time performance of the ViT via data-model scaling curves.
Collapse
|
11
|
Dhinagar NJ, Santhalingam V, Lawrence KE, Laltoo E, Thompson PM. Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI. ArXiv 2023:arXiv:2303.08224v1. [PMID: 36994159 PMCID: PMC10055488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framework to adapt it to learn across multiple sites. We tested our meta-learning model for classifying ASD versus typically developing controls in 2,201 T1-weighted (T1-w) MRI scans collected from 38 imaging sites as part of Autism Brain Imaging Data Exchange (ABIDE) [age: 5.2-64.0 years]. The method was trained to find a good initialization state for our model that can quickly adapt to data from new unseen sites by fine-tuning on the limited data that is available. The proposed method achieved an ROC-AUC=0.857 on 370 scans from 7 unseen sites in ABIDE using a few-shot setting of 2-way 20-shot i.e., 20 training samples per site. Our results outperformed a transfer learning baseline by generalizing across a wider range of sites as well as other related prior work. We also tested our model in a zero-shot setting on an independent test site without any additional fine-tuning. Our experiments show the promise of the proposed site-agnostic meta-learning framework for challenging neuroimaging tasks involving multi-site heterogeneity with limited availability of training data.
Collapse
|
12
|
Nabulsi L, Chandio BQ, Dhinagar N, Laltoo E, McPhilemy G, Martyn FM, Hallahan B, McDonald C, Thompson PM, Cannon DM. Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study. bioRxiv 2023:2023.03.07.531585. [PMID: 36945403 PMCID: PMC10028925 DOI: 10.1101/2023.03.07.531585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN), a recently developed analytic approach for tractography, to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups. Using the BUAN pipeline, BD was associated with lower mean Fractional Anisotropy (FA) in fronto-limbic and interhemispheric pathways and higher mean FA in posterior bundles relative to controls. BUAN combines tractography and anatomical information to capture distinct along-tract effects on WM microstructure that may aid in classifying diseases based on anatomical differences.
Collapse
Affiliation(s)
- Leila Nabulsi
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Bramsh Q Chandio
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Nikhil Dhinagar
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Emily Laltoo
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Genevieve McPhilemy
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Fiona M Martyn
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Brian Hallahan
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Colm McDonald
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Dara M Cannon
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| |
Collapse
|
13
|
Nabulsi L, Lawrence KE, Laltoo E, Santhalingam V, Abaryan Z, Villalon‐Reina JE, Nir TM, Gari IB, Zhu AH, Haddad E, Muir AM, Jahanshad N, Thompson PM. Advanced diffusion‐weighted MRI sensitively detects age and sex effects in 34,423 adults. Alzheimers Dement 2022. [DOI: 10.1002/alz.064791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California Marina del Rey CA USA
| | - Katherine E Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California Marina del Rey CA USA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California Marina del Rey CA USA
| | - Vigneshwaran Santhalingam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California Marina del Rey CA USA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California Marina del Rey CA USA
| | - Julio E Villalon‐Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| | - Talia M Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| | - Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| | - Alexandra M Muir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California Marina del Rey CA USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Marina del Rey CA USA
| |
Collapse
|
14
|
Lawrence KE, Abaryan Z, Laltoo E, Hernandez LM, Gandal MJ, McCracken JT, Thompson PM. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children. Hum Brain Mapp 2022; 44:535-548. [PMID: 36177528 PMCID: PMC9842921 DOI: 10.1002/hbm.26079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
Collapse
Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA,Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA,Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - James T. McCracken
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| |
Collapse
|
15
|
Lawrence KE, Nabulsi L, Santhalingam V, Abaryan Z, Villalon‐Reina JE, Nir TM, Gari IB, Zhu AH, Haddad E, Muir AM, Laltoo E, John JP, Venkatasubramanian G, Jahanshad N, Thompson PM. Advanced diffusion‐weighted MRI methods demonstrate improved sensitivity to white matter aging: Percentile charts for over 15,000 UK Biobank participants. Alzheimers Dement 2021. [DOI: 10.1002/alz.051187] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging & Informatics Institute University of Southern California Marina del Rey CA USA
| | - Leila Nabulsi
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging & Informatics Institute University of Southern California Marina del Rey CA USA
| | - Vigneshwaran Santhalingam
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA
| | - Zvart Abaryan
- Department of Pediatrics Mercy St. Vincent Medical Center Toledo OH USA
| | - Julio E. Villalon‐Reina
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging & Informatics Institute University of Southern California Marina del Rey CA USA
| | - Talia M. Nir
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA
| | - Iyad Ba Gari
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging & Informatics Institute University of Southern California Marina del Rey CA USA
| | - Alyssa H. Zhu
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA
| | - Elizabeth Haddad
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA
| | - Alexandra M. Muir
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA
| | - Emily Laltoo
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging & Informatics Institute University of Southern California Marina del Rey CA USA
| | - John P. John
- National Institute of Mental Health and Neurosciences (NIMHANS) Bengaluru India
| | | | - Neda Jahanshad
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA
| | - Paul M. Thompson
- Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA
| |
Collapse
|
16
|
Lawrence KE, Nabulsi L, Santhalingam V, Abaryan Z, Villalon-Reina JE, Nir TM, Ba Gari I, Zhu AH, Haddad E, Muir AM, Laltoo E, Jahanshad N, Thompson PM. Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: A UK biobank study. Brain Imaging Behav 2021; 15:2813-2823. [PMID: 34537917 PMCID: PMC8761720 DOI: 10.1007/s11682-021-00548-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 12/19/2022]
Abstract
A comprehensive characterization of the brain’s white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45–80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.
Collapse
Affiliation(s)
- Katherine E Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Vigneshwaran Santhalingam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Alexandra M Muir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA.
| |
Collapse
|
17
|
Salardini A, Hillmer AT, Mecca AP, Hashemi‐Aghdam A, Laltoo E, Savoia S, O'Dell RS, Harris JE, Godek TA, Nabulsi NB, Lim K, Ropchan JR, Huang Y, Cosgrove K, Carson RE, Strittmatter SM, van Dyck CH. PBR28 Brain PET imaging with lipopolysaccharide challenge for the study of microglia function in Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.037792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
18
|
Salardini A, Shen X, Hashemi‐Aghdam A, Laltoo E, Savoia S, Tokoglu F, Constable T. Increased connectivity in several bilateral frontal and fronto‐parietal networks predicts depressive symptoms in mid‐ to late‐life diabetics. Alzheimers Dement 2020. [DOI: 10.1002/alz.043619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
| | - Xilin Shen
- Yale School of Medicine New Haven CT USA
| | | | | | | | | | | |
Collapse
|
19
|
Salardini A, Hillmer AT, Mecca AP, Hashemi‐Aghdam A, Laltoo E, Savoia S, O'Dell RS, Harris JE, Godek TA, Nabulsi NB, Lim K, Ropchan JR, Huang Y, Cosgrove K, Carson RE, Strittmatter SM, van Dyck CH. 11C‐PBR28 brain PET imaging with lipopolysaccharide challenge for the study of microglia function in Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.043584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|