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Wen C, Zeng Q, Zhou R, Xie L, Yu J, Zhang C, Wang J, Yu Y, Gu Y, Cao G, Feng Y, Wang M. Characterization of local white matter microstructural alterations in Alzheimer's disease: A reproducible study. Comput Biol Med 2024; 179:108750. [PMID: 38996551 DOI: 10.1016/j.compbiomed.2024.108750] [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: 09/02/2023] [Revised: 05/24/2024] [Accepted: 06/08/2024] [Indexed: 07/14/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with a close association with microstructural alterations in white matter (WM). Current studies lack the characterization and further validation of specific regions in WM fiber tracts in AD. This study subdivided fiber tracts into multiple fiber clusters on the basis of automated fiber clustering and performed quantitative analysis along the fiber clusters to identify local WM microstructural alterations in AD. Diffusion tensor imaging data from a public dataset (53 patients with AD and 70 healthy controls [HCs]) and a clinical dataset (27 patients with AD and 19 HCs) were included for mutual validation. Whole-brain tractograms were automatically subdivided into 800 clusters through the automatic fiber clustering approach. Then, 100 segments were divided along the clusters, and the diffusion properties of each segment were calculated. Results showed that patients with AD had significantly lower fraction anisotropy (FA) and significantly higher mean diffusivity (MD) in some regions of the fiber clusters in the cingulum bundle, uncinate fasciculus, external capsule, and corpus callosum than HCs. Importantly, these changes were reproducible across the two datasets. Correlation analysis revealed a positive correlation between FA and Mini-Mental State Examination (MMSE) scores and a negative correlation between MD and MMSE in these clusters. The accuracy of the constructed classifier reached 89.76% with an area under the curve of 0.93. This finding indicates that this study can effectively identify local WM microstructural changes in AD and provides new insight into the analysis and diagnosis of WM abnormalities in patients with AD.
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Affiliation(s)
- Caiyun Wen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Qingrun Zeng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ronghui Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Lei Xie
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jiangli Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Chengzhe Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jingqiang Wang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yan Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yixin Gu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guoquan Cao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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2
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Tchetchenian A, Zekelman L, Chen Y, Rushmore J, Zhang F, Yeterian EH, Makris N, Rathi Y, Meijering E, Song Y, O'Donnell LJ. Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning. Hum Brain Mapp 2024; 45:e70008. [PMID: 39185598 PMCID: PMC11345609 DOI: 10.1002/hbm.70008] [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: 01/29/2024] [Revised: 07/18/2024] [Accepted: 08/10/2024] [Indexed: 08/27/2024] Open
Abstract
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.
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Affiliation(s)
- Ari Tchetchenian
- Biomedical Image Computing Group, School of Computer Science and EngineeringUniversity of New South Wales (UNSW)SydneyNew South WalesAustralia
| | - Leo Zekelman
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Harvard UniversityCambridgeMassachusettsUSA
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jarrett Rushmore
- Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Fan Zhang
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | - Nikos Makris
- Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Erik Meijering
- Biomedical Image Computing Group, School of Computer Science and EngineeringUniversity of New South Wales (UNSW)SydneyNew South WalesAustralia
| | - Yang Song
- Biomedical Image Computing Group, School of Computer Science and EngineeringUniversity of New South Wales (UNSW)SydneyNew South WalesAustralia
| | - Lauren J. O'Donnell
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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3
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Li Y, Zhang W, Wu Y, Yin L, Zhu C, Chen Y, Cetin-Karayumak S, Cho KIK, Zekelman LR, Rushmore J, Rathi Y, Makris N, O'Donnell LJ, Zhang F. A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations. Sci Data 2024; 11:787. [PMID: 39019877 PMCID: PMC11255335 DOI: 10.1038/s41597-024-03624-2] [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: 04/09/2024] [Accepted: 07/08/2024] [Indexed: 07/19/2024] Open
Abstract
The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.
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Affiliation(s)
- Yijie Li
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Li Yin
- West China Hospital of Medical Science, Sichuan University, Chengdu, China
| | - Ce Zhu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Leo R Zekelman
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jarrett Rushmore
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Fan Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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4
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Chauvel M, Pascucci M, Uszynski I, Herlin B, Mangin JF, Hopkins WD, Poupon C. Comparative analysis of the chimpanzee and human brain superficial structural connectivities. Brain Struct Funct 2024:10.1007/s00429-024-02823-2. [PMID: 39020215 DOI: 10.1007/s00429-024-02823-2] [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: 12/18/2023] [Accepted: 06/16/2024] [Indexed: 07/19/2024]
Abstract
Diffusion MRI tractography (dMRI) has fundamentally transformed our ability to investigate white matter pathways in the human brain. While long-range connections have extensively been studied, superficial white matter bundles (SWMBs) have remained a relatively underexplored aspect of brain connectivity. This study undertakes a comprehensive examination of SWMB connectivity in both the human and chimpanzee brains, employing a novel combination of empirical and geometric methodologies to classify SWMB morphology in an objective manner. Leveraging two anatomical atlases, the Ginkgo Chauvel chimpanzee atlas and the Ginkgo Chauvel human atlas, comprising respectively 844 and 1375 superficial bundles, this research focuses on sparse representations of the morphology of SWMBs to explore the little-understood superficial connectivity of the chimpanzee brain and facilitate a deeper understanding of the variability in shape of these bundles. While similar, already well-known in human U-shape fibers were observed in both species, other shapes with more complex geometry such as 6 and J shapes were encountered. The localisation of the different bundle morphologies, putatively reflecting the brain gyrification process, was different between humans and chimpanzees using an isomap-based shape analysis approach. Ultimately, the analysis aims to uncover both commonalities and disparities in SWMBs between chimpanzees and humans, shedding light on the evolution and organization of these crucial neural structures.
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Affiliation(s)
- Maëlig Chauvel
- BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France.
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Marco Pascucci
- BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France
| | - Ivy Uszynski
- BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France
| | - Bastien Herlin
- BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France
- Rehabilitation Unit, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | | | - William D Hopkins
- Department of Comparative Medicine, Michale E Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Bastrop, TX, USA
| | - Cyril Poupon
- BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France
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5
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Zhang D, Zong F, Zhang Q, Yue Y, Zhang F, Zhao K, Wang D, Wang P, Zhang X, Liu Y. Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning. Med Image Anal 2024; 95:103165. [PMID: 38608510 DOI: 10.1016/j.media.2024.103165] [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: 09/29/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.
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Affiliation(s)
- Di Zhang
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Fangrong Zong
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Qichen Zhang
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yunhui Yue
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Fan Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Kun Zhao
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China; Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China; Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yong Liu
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
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6
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Xue T, Zhang F, Zekelman LR, Zhang C, Chen Y, Cetin-Karayumak S, Pieper S, Wells WM, Rathi Y, Makris N, Cai W, O'Donnell LJ. TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography. Front Neurosci 2024; 18:1411797. [PMID: 38988766 PMCID: PMC11233814 DOI: 10.3389/fnins.2024.1411797] [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: 04/03/2024] [Accepted: 06/10/2024] [Indexed: 07/12/2024] Open
Abstract
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.
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Affiliation(s)
- Tengfei Xue
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Leo R. Zekelman
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Chaoyi Zhang
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Yuqian Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Steve Pieper
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - William M. Wells
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Weidong Cai
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Lauren J. O'Donnell
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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7
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Jellema PEJ, Mannsdörfer LM, Visser F, De Luca A, Smit CLE, Hoving EW, van Baarsen KM, Lindner T, Mutsaerts HJMM, Dankbaar JW, Lequin MH, Wijnen JP. Improving advanced intraoperative MRI methods during pediatric neurosurgery. NMR IN BIOMEDICINE 2024; 37:e5124. [PMID: 38403798 DOI: 10.1002/nbm.5124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/27/2024]
Abstract
Advanced intraoperative MR images (ioMRI) acquired during the resection of pediatric brain tumors could offer additional physiological information to preserve healthy tissue. With this work, we aimed to develop a protocol for ioMRI with increased sensitivity for arterial spin labeling (ASL) and diffusion MRI (dMRI), optimized for patient positioning regularly used in the pediatric neurosurgery setting. For ethical reasons, ASL images were acquired in healthy adult subjects that were imaged in the prone and supine position. After this, the ASL cerebral blood flow (CBF) was quantified and compared between both positions. To evaluate the impact of the RF coils setups on image quality, we compared different setups (two vs. four RF coils) by looking at T1-weighted (T1w) signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), as well as undertaking a qualitative evaluation of T1w, T2w, ASL, and dMR images. Mean ASL CBF did not differ between the surgical prone and supine positions in any of the investigated regions of interest or the whole brain. T1w SNR (gray matter: p = 0.016, 34% increase; white matter: p = 0.016, 32% increase) and CNR were higher (p = 0.016) in the four versus two RF coils setups (18.0 ± 1.8 vs. 13.9 ± 1.8). Qualitative evaluation of T1w, T2w, ASL, and dMR images resulted in acceptable to good image quality and did not differ statistically significantly between setups. Only the nonweighted diffusion image maps and corticospinal tract reconstructions yielded higher image quality and reduced susceptibility artifacts with four RF coils. Advanced ioMRI metrics were more precise with four RF coils as the standard deviation decreased. Taken together, we have investigated the practical use of advanced ioMRI during pediatric neurosurgery. We conclude that ASL CBF quantification in the surgical prone position is valid and that ASL and dMRI acquisition with two RF coils can be performed adequately for clinical use. With four versus two RF coils, the SNR of the images increases, and the sensitivity to artifacts reduces.
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Affiliation(s)
- Pien E J Jellema
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, The Netherlands
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Lilli M Mannsdörfer
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, The Netherlands
| | - Fredy Visser
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, The Netherlands
- Philips HealthCare, Best, The Netherlands
| | - Alberto De Luca
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Neurology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cynthia L E Smit
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, The Netherlands
| | - Eelco W Hoving
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, The Netherlands
- Department of Neurosurgery, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Kirsten M van Baarsen
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, The Netherlands
- Department of Neurosurgery, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Henk-Jan M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Jan Willem Dankbaar
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Maarten H Lequin
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, The Netherlands
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Jannie P Wijnen
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, The Netherlands
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8
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Chen Y, Zekelman LR, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Golby AJ, Cai W, Zhang F, O'Donnell LJ. TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance. Med Image Anal 2024; 94:103120. [PMID: 38458095 PMCID: PMC11016451 DOI: 10.1016/j.media.2024.103120] [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: 07/09/2023] [Revised: 11/30/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024]
Abstract
We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.
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Affiliation(s)
- Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Leo R Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, USA
| | - Chaoyi Zhang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Tengfei Xue
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Nikos Makris
- Departments of Psychiatry and Neurology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Weidong Cai
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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9
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Calixto C, Soldatelli MD, Jaimes C, Warfield SK, Gholipour A, Karimi D. A detailed spatio-temporal atlas of the white matter tracts for the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590815. [PMID: 38712296 PMCID: PMC11071632 DOI: 10.1101/2024.04.26.590815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | | | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
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10
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Mendoza C, Román C, Mangin JF, Hernández C, Guevara P. Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter. Front Neurosci 2024; 18:1394681. [PMID: 38737100 PMCID: PMC11088237 DOI: 10.3389/fnins.2024.1394681] [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: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/14/2024] Open
Abstract
In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers. This paper presents an enhanced short fiber bundle segmentation based on a SWM bundle atlas and the filtering of noisy fibers. The method was tuned and evaluated over HCP test-retest probabilistic tractography datasets (44 subjects). We propose four fiber bundle filters to remove spurious fibers. Furthermore, we include the identification of the main fiber fascicle to obtain well-defined fiber bundles. First, we identified four main bundle shapes in the SWM atlas, and performed a filter tuning in a subset of 28 subjects. The filter based on the Convex Hull provided the highest similarity between corresponding test-retest fiber bundles. Subsequently, we applied the best filter in the 16 remaining subjects for all atlas bundles, showing that filtered fiber bundles significantly improve test-retest reproducibility indices when removing between ten and twenty percent of the fibers. Additionally, we applied the bundle segmentation with and without filtering to the ABIDE-II database. The fiber bundle filtering allowed us to obtain a higher number of bundles with significant differences in fractional anisotropy, mean diffusivity, and radial diffusivity of Autism Spectrum Disorder patients relative to controls.
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Affiliation(s)
- Cristóbal Mendoza
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Claudio Román
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | | | - Cecilia Hernández
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
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11
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Geisler M, de la Cruz F, Makris N, Billah T, Zhang F, Rathi Y, O'Donnell LJ, Bouix S, Herbsleb M, Bär KJ, Kikinis Z, Weiss T. Brains of endurance athletes differ in the association areas but not in the primary areas. Psychophysiology 2024; 61:e14483. [PMID: 37950391 DOI: 10.1111/psyp.14483] [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: 09/15/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023]
Abstract
Regular participation in sports results in a series of physiological adaptations. However, little is known about the brain adaptations to physical activity. Here we aimed to investigate whether young endurance athletes and non-athletes differ in the gray and white matter of the brain and whether cardiorespiratory fitness (CRF) is associated with these differences. We assessed the CRF, volumes of the gray and white matter of the brain using structural magnetic resonance imaging (sMRI), and brain white matter connections using diffusion magnetic resonance imaging (dMRI) in 20 young male endurance athletes and 21 healthy non-athletes. While total brain volume was similar in both groups, the white matter volume was larger and the gray matter volume was smaller in the athletes compared to non-athletes. The reduction of gray matter was located in the association areas of the brain that are specialized in processing of sensory stimuli. In the microstructure analysis, significant group differences were found only in the association tracts, for example, the inferior occipito-frontal fascicle (IOFF) showing higher fractional anisotropy and lower radial diffusivity, indicating stronger myelination in this tract. Additionally, gray and white matter brain volumes, as well as association tracts correlated with CRF. No changes were observed in other brain areas or tracts. In summary, the brain signature of the endurance athlete is characterized by changes in the integration of sensory and motor information in the association areas.
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Affiliation(s)
- Maria Geisler
- Department of Clinical Psychology, Friedrich Schiller University Jena, Jena, Germany
- Department of Psychosomatic Medicine, University Hospital Jena, Jena, Germany
| | | | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Somerville, Massachusetts, USA
| | - Tashrif Billah
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Somerville, Massachusetts, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Somerville, Massachusetts, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Somerville, Massachusetts, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Somerville, Massachusetts, USA
| | - Sylvain Bouix
- Département de génie logiciel et TI, École de Technologie Supérieure, Université du Québec, Montreal, Quebec, Canada
| | - Marco Herbsleb
- Department of Psychosomatic Medicine, University Hospital Jena, Jena, Germany
- Department of Sports Medicine and Health Promotion, Friedrich Schiller University Jena, Jena, Germany
| | - Karl-Jürgen Bär
- Department of Psychosomatic Medicine, University Hospital Jena, Jena, Germany
| | - Zora Kikinis
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Somerville, Massachusetts, USA
| | - Thomas Weiss
- Department of Clinical Psychology, Friedrich Schiller University Jena, Jena, Germany
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12
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Joshi A, Li H, Parikh NA, He L. A systematic review of automated methods to perform white matter tract segmentation. Front Neurosci 2024; 18:1376570. [PMID: 38567281 PMCID: PMC10985163 DOI: 10.3389/fnins.2024.1376570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.
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Affiliation(s)
- Ankita Joshi
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A. Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Computer Science, Biomedical Informatics, and Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
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13
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Betz AK, Cetin-Karayumak S, Bonke EM, Seitz-Holland J, Zhang F, Pieper S, O'Donnell LJ, Tripodis Y, Rathi Y, Shenton ME, Koerte IK. Executive functioning, behavior, and white matter microstructure in the chronic phase after pediatric mild traumatic brain injury: results from the adolescent brain cognitive development study. Psychol Med 2024:1-11. [PMID: 38497117 DOI: 10.1017/s0033291724000229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) is common in children. Long-term cognitive and behavioral outcomes as well as underlying structural brain alterations following pediatric mTBI have yet to be determined. In addition, the effect of age-at-injury on long-term outcomes is largely unknown. METHODS Children with a history of mTBI (n = 406; Mage = 10 years, SDage = 0.63 years) who participated in the Adolescent Brain Cognitive Development (ABCD) study were matched (1:2 ratio) with typically developing children (TDC; n = 812) and orthopedic injury (OI) controls (n = 812). Task-based executive functioning, parent-rated executive functioning and emotion-regulation, and self-reported impulsivity were assessed cross-sectionally. Regression models were used to examine the effect of mTBI on these domains. The effect of age-at-injury was assessed by comparing children with their first mTBI at either 0-3, 4-7, or 8-10 years to the respective matched TDC controls. Fractional anisotropy (FA) and mean diffusivity (MD), both MRI-based measures of white matter microstructure, were compared between children with mTBI and controls. RESULTS Children with a history of mTBI displayed higher parent-rated executive dysfunction, higher impulsivity, and poorer self-regulation compared to both control groups. At closer investigation, these differences to TDC were only present in one respective age-at-injury group. No alterations were found in task-based executive functioning or white matter microstructure. CONCLUSIONS Findings suggest that everyday executive function, impulsivity, and emotion-regulation are affected years after pediatric mTBI. Outcomes were specific to the age at which the injury occurred, suggesting that functioning is differently affected by pediatric mTBI during vulnerable periods. Groups did not differ in white matter microstructure.
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Affiliation(s)
- Anja K Betz
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena M Bonke
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yorghos Tripodis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Inga K Koerte
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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14
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González Rodríguez LL, Osorio I, Cofre G. A, Hernandez Larzabal H, Román C, Poupon C, Mangin JF, Hernández C, Guevara P. Phybers: a package for brain tractography analysis. Front Neurosci 2024; 18:1333243. [PMID: 38529266 PMCID: PMC10962387 DOI: 10.3389/fnins.2024.1333243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 02/09/2024] [Indexed: 03/27/2024] Open
Abstract
We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.
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Affiliation(s)
| | - Ignacio Osorio
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Alejandro Cofre G.
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Hernan Hernandez Larzabal
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
| | - Claudio Román
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | - Cyril Poupon
- CEA, CNRS, Baobab, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Cecilia Hernández
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
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15
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Zhang F, Cho KIK, Seitz-Holland J, Ning L, Legarreta JH, Rathi Y, Westin CF, O'Donnell LJ, Pasternak O. DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1191-1202. [PMID: 37943635 PMCID: PMC10994696 DOI: 10.1109/tmi.2023.3331691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Parcellation of anatomically segregated cortical and subcortical brain regions is required in diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical specificity of tractography. Most current dMRI parcellation approaches compute the parcellation from anatomical MRI (T1- or T2-weighted) data, using tools such as FreeSurfer or CAT12, and then register it to the diffusion space. However, the registration is challenging due to image distortions and low resolution of dMRI data, often resulting in mislabeling in the derived brain parcellation. Furthermore, these approaches are not applicable when anatomical MRI data is unavailable. As an alternative we developed the Deep Diffusion Parcellation (DDParcel), a deep learning method for fast and accurate parcellation of brain anatomical regions directly from dMRI data. The input to DDParcel are dMRI parameter maps and the output are labels for 101 anatomical regions corresponding to the FreeSurfer Desikan-Killiany (DK) parcellation. A multi-level fusion network leverages complementary information in the different input maps, at three network levels: input, intermediate layer, and output. DDParcel learns the registration of diffusion features to anatomical MRI from the high-quality Human Connectome Project data. Then, to predict brain parcellation for a new subject, the DDParcel network no longer requires anatomical MRI data but only the dMRI data. Comparing DDParcel's parcellation with T1w-based parcellation shows higher test-retest reproducibility and a higher regional homogeneity, while requiring much less computational time. Generalizability is demonstrated on a range of populations and dMRI acquisition protocols. Utility of DDParcel's parcellation is demonstrated on tractography analysis for fiber tract identification.
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16
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Nie X, Ruan J, Otaduy MCG, Grinberg LT, Ringman J, Shi Y. Surface-Based Probabilistic Fiber Tracking in Superficial White Matter. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1113-1124. [PMID: 37917515 PMCID: PMC10917128 DOI: 10.1109/tmi.2023.3329451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
The short association fibers or U-fibers travel in the superficial white matter (SWM) beneath the cortical layer. While the U-fibers play a crucial role in various brain disorders, there is a lack of effective tools to reconstruct their highly curved trajectory from diffusion MRI (dMRI). In this work, we propose a novel surface-based framework for the probabilistic tracking of fibers on the triangular mesh representation of the SWM. By deriving a closed-form solution to transform the spherical harmonics (SPHARM) coefficients of 3D fiber orientation distributions (FODs) to local coordinate systems on each triangle, we develop a novel approach to project the FODs onto the tangent space of the SWM. After that, we utilize parallel transport to realize the intrinsic propagation of streamlines on SWM following probabilistically sampled fiber directions. Our intrinsic and surface-based method eliminates the need to perform the necessary but challenging sharp turns in 3D compared with conventional volume-based tractography methods. Using data from the Human Connectome Project (HCP), we performed quantitative comparisons to demonstrate the proposed algorithm can more effectively reconstruct the U-fibers connecting the precentral and postcentral gyrus than previous methods. Quantitative validations were then performed on post-mortem MRIs to show the reconstructed U-fibers from our method more faithfully follow the SWM than volume-based tractography. Finally, we applied our algorithm to study the parietal U-fiber connectivity changes in autosomal dominant Alzheimer's disease (ADAD) patients and successfully detected significant associations between U-fiber connectivity and disease severity.
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17
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Cetin-Karayumak S, Zhang F, Zurrin R, Billah T, Zekelman L, Makris N, Pieper S, O'Donnell LJ, Rathi Y. Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study. Sci Data 2024; 11:249. [PMID: 38413633 PMCID: PMC10899197 DOI: 10.1038/s41597-024-03058-w] [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: 05/25/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan Zurrin
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leo Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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18
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Li S, Zhang W, Yao S, He J, Zhu C, Gao J, Xue T, Xie G, Chen Y, Torio EF, Feng Y, Bastos DC, Rathi Y, Makris N, Kikinis R, Bi WL, Golby AJ, O'Donnell LJ, Zhang F. Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.574115. [PMID: 38260369 PMCID: PMC10802389 DOI: 10.1101/2024.01.03.574115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.
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19
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Zheng G, Fei B, Ge A, Liu Y, Liu Y, Yang Z, Chen Z, Wang X, Wang H, Ding J. U-fiber analysis: a toolbox for automated quantification of U-fibers and white matter hyperintensities. Quant Imaging Med Surg 2024; 14:662-683. [PMID: 38223048 PMCID: PMC10784071 DOI: 10.21037/qims-23-847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024]
Abstract
Background Whether white matter hyperintensities (WMHs) involve U-fibers is of great value in understanding the different etiologies of cerebral white matter (WM) lesions. However, clinical practice currently relies only on the naked eye to determine whether WMHs are in the vicinity of U-fibers, and there is a lack of good neuroimaging tools to quantify WMHs and U-fibers. Methods Here, we developed a multimodal neuroimaging toolbox named U-fiber analysis (UFA) that can automatically extract WMHs and quantitatively characterize the volume and number of WMHs in different brain regions. In addition, we proposed an anatomically constrained U-fiber tracking scheme and quantitatively characterized the microstructure diffusion properties, fiber length, and number of U-fibers in different brain regions to help clinicians to quantitatively determine whether WMHs in the proximal cortex disrupt the microstructure of U-fibers. To validate the utility of the UFA toolbox, we analyzed the neuroimaging data from 246 patients with cerebral small vessel disease (cSVD) enrolled at Zhongshan Hospital between March 2018 and November 2019 in a cross-sectional study. Results According to the manual judgment of the clinician, the patients with cSVD were divided into a WMHs involved U-fiber group (U-fiber-involved group, 51 cases) and WMHs not involved U-fiber group (U-fiber-spared group, 163 cases). There were no significant differences between the U-fiber-spared group and the U-fiber-involved group in terms of age (P=0.143), gender (P=0.462), education (P=0.151), Mini-Mental State Examination (MMSE) scores (P=0.151), and Montreal Cognitive Assessment (MoCA) scores (P=0.411). However, patients in the U-fiber-involved group had higher Fazekas scores (P<0.001) and significantly higher whole brain WMHs (P=0.046) and deep WMH volumes (P<0.001) compared to patients in the U-fiber-spared group. Moreover, the U-fiber-involved group had higher WMH volumes in the bilateral frontal [P(left) <0.001, P(right) <0.001] and parietal lobes [P(left) <0.001, P(right) <0.001]. On the other hand, patients in the U-fiber-involved group had higher mean diffusivity (MD) and axial diffusivity (AD) in the bilateral parietal [P(left, MD) =0.048, P(right, MD) =0.045, P(left, AD) =0.015, P(right, AD) =0.015] and right frontal-parietal regions [P(MD) =0.048, P(AD) =0.027], and had significantly reduced mean fiber length and number in the right parietal [P(length) =0.013, P(number) =0.028] and right frontal-parietal regions [P(length) =0.048] compared to patients in the U-fiber-spared group. Conclusions Our results suggest that WMHs in the proximal cortex may disrupt the microstructure of U-fibers. Our tool may provide new insights into the understanding of WM lesions of different etiologies in the brain.
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Affiliation(s)
- Gaoxing Zheng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Beini Fei
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Anyan Ge
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuchen Liu
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Ying Liu
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zidong Yang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Zhensen Chen
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - He Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
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20
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Marawi T, Zhukovsky P, Rashidi-Ranjbar N, Bowie CR, Brooks H, Fischer CE, Flint AJ, Herrmann N, Mah L, Pollock BG, Rajji TK, Tartaglia MC, Voineskos AN, Mulsant BH. Brain-Cognition Associations in Older Patients With Remitted Major Depressive Disorder or Mild Cognitive Impairment: A Multivariate Analysis of Gray and White Matter Integrity. Biol Psychiatry 2023; 94:913-923. [PMID: 37271418 DOI: 10.1016/j.biopsych.2023.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/10/2023] [Accepted: 05/24/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND Almost half of older patients with major depressive disorder (MDD) present with cognitive impairment, and one-third meet diagnostic criteria for mild cognitive impairment (MCI). However, mechanisms linking MDD and MCI remain unclear. We investigated multivariate associations between brain structural alterations and cognition in 3 groups of older patients at risk for dementia, remitted MDD (rMDD), MCI, and rMDD+MCI, as well as cognitively healthy nondepressed control participants. METHODS We analyzed magnetic resonance imaging data and cognitive domain scores in participants from the PACt-MD (Prevention of Alzheimer's Disease With Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression) study. Following quality control, we measured cortical thickness and subcortical volumes of selected regions from 283 T1-weighted scans and fractional anisotropy of white matter tracts from 226 diffusion-weighted scans. We assessed brain-cognition associations using partial least squares regressions in the whole sample and in each subgroup. RESULTS In the entire sample, atrophy in the medial temporal lobe and subregions of the motor and prefrontal cortex was associated with deficits in verbal and visuospatial memory, language skills, and, to a lesser extent, processing speed (p < .0001; multivariate r = 0.30, 0.34, 0.26, and 0.18, respectively). Widespread reduced white matter integrity was associated with deficits in executive functioning, working memory, and processing speed (p = .008; multivariate r = 0.21, 0.26, 0.35, respectively). Overall, associations remained significant in the MCI and rMDD+MCI groups, but not the rMDD or healthy control groups. CONCLUSIONS We confirm findings of brain-cognition associations previously reported in MCI and extend them to rMDD+MCI, but similar associations in rMDD are not supported. Early-onset and treated MDD might not contribute to structural alterations associated with cognitive impairment.
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Affiliation(s)
- Tulip Marawi
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Neda Rashidi-Ranjbar
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychology, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Heather Brooks
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Corinne E Fischer
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Alastair J Flint
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Nathan Herrmann
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Linda Mah
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Baycrest Health Services, Rotman Research Institute, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Bruce G Pollock
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Tarek K Rajji
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Benoit H Mulsant
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada.
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21
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Astolfi P, Verhagen R, Petit L, Olivetti E, Sarubbo S, Masci J, Boscaini D, Avesani P. Supervised tractogram filtering using Geometric Deep Learning. Med Image Anal 2023; 90:102893. [PMID: 37741032 DOI: 10.1016/j.media.2023.102893] [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: 12/01/2022] [Revised: 04/18/2023] [Accepted: 07/07/2023] [Indexed: 09/25/2023]
Abstract
A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute.
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Affiliation(s)
- Pietro Astolfi
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; PAVIS, Istituto Italiano di Tecnologia, Geonva, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | | | - Laurent Petit
- GIN, IMN, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Emanuele Olivetti
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | - Silvio Sarubbo
- Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy; Department of Neurosurgery, Azienda Provinciale per i Servizi Sanitari, "Santa Chiara" Hospital, Trento, Italy
| | | | | | - Paolo Avesani
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy.
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22
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He J, Zhang F, Pan Y, Feng Y, Rushmore J, Torio E, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods. Hum Brain Mapp 2023; 44:6055-6073. [PMID: 37792280 PMCID: PMC10619402 DOI: 10.1002/hbm.26497] [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: 06/07/2023] [Revised: 09/09/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023] Open
Abstract
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.
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Affiliation(s)
- Jianzhong He
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Fan Zhang
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- University of Electronic Science and Technology of ChinaChengduSichuanChina
| | - Yiang Pan
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Yuanjing Feng
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Jarrett Rushmore
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Erickson Torio
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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23
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Calarco N, Oliver LD, Joseph M, Hawco C, Dickie EW, DeRosse P, Gold JM, Foussias G, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls. Schizophr Bull 2023; 49:1518-1529. [PMID: 36869812 PMCID: PMC10686342 DOI: 10.1093/schbul/sbac216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
BACKGROUND AND HYPOTHESIS Neurocognitive and social cognitive abilities are important contributors to functional outcomes in schizophrenia spectrum disorders (SSDs). An unanswered question of considerable interest is whether neurocognitive and social cognitive deficits arise from overlapping or distinct white matter impairment(s). STUDY DESIGN We sought to fill this gap, by harnessing a large sample of individuals from the multi-center Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) dataset, unique in its collection of advanced diffusion imaging and an extensive battery of cognitive assessments. We applied canonical correlation analysis to estimates of white matter microstructure, and cognitive performance, across people with and without an SSD. STUDY RESULTS Our results established that white matter circuitry is dimensionally and strongly related to both neurocognition and social cognition, and that microstructure of the uncinate fasciculus and the rostral body of the corpus callosum may assume a "privileged role" subserving both. Further, we found that participant-wise estimates of white matter microstructure, weighted by cognitive performance, were largely consistent with participants' categorical diagnosis, and predictive of (cross-sectional) functional outcomes. CONCLUSIONS The demonstrated strength of the relationship between white matter circuitry and neurocognition and social cognition underscores the potential for using relationships among these variables to identify biomarkers of functioning, with potential prognostic and therapeutic implications.
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Affiliation(s)
- Navona Calarco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Michael Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Pamela DeRosse
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - James M Gold
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Miklos Argyelan
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Anil K Malhotra
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Robert W Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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24
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Zanao TA, Luethi MS, Goerigk S, Suen P, Diaz AP, Soares JC, Brunoni AR. White matter predicts tDCS antidepressant effects in a sham-controlled clinical trial study. Eur Arch Psychiatry Clin Neurosci 2023; 273:1421-1431. [PMID: 36336757 DOI: 10.1007/s00406-022-01504-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022]
Abstract
Transcranial direct current stimulation (tDCS) has been used as treatment for depression, but its effects are heterogeneous. We investigated, in a subsample of the clinical trial Escitalopram versus Electrical Direct Current Therapy for Depression Study (ELECTTDCS), whether white matter areas associated with depression disorder were associated with tDCS response. Baseline diffusion tensor imaging data were analyzed from 49 patients (34 females, mean age 41.9) randomized to escitalopram 20 mg/day, tDCS (2 mA, 30 min, 22 sessions), or placebo. Antidepressant outcomes were assessed by Hamilton Depression Rating Scale-17 (HDRS) after 10-week treatment. We used whole-brain tractography for extracting white matter measures for anterior corpus callosum, and bilaterally for cingulum bundle, striato-frontal, inferior occipito-frontal fasciculus and uncinate. For the rostral body, tDCS group showed higher MD associated with antidepressant effects (estimate = -5.13 ± 1.64, p = 0.002), and tDCS significantly differed from the placebo and the escitalopram group. The left striato-frontal tract showed higher FA associated with antidepressant effects (estimate = -2.14 ± 0.72, p = 0.003), and tDCS differed only from the placebo group. For the right uncinate, the tDCS group lower AD values were associated with higher HDRS decrease (estimate = -1.45 ± 0.67, p = 0.031). Abnormalities in white matter MDD-related areas are associated with tDCS antidepressant effects. Suggested better white matter microstructure of the left prefrontal cortex was associated with tDCS antidepressant effects. Future studies should investigate whether these findings are driven by electric field diffusion and density in these areas.
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Affiliation(s)
- Tamires A Zanao
- Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Matthias S Luethi
- Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Stephan Goerigk
- Department and Institute of Psychiatry, Faculdade de Medicina da Universidade de São Paulo, Laboratory of Neurosciences LIM-27), São Paulo, Brazil
- Department of Psychological Methodology and Assessment, LMU Munich, Munich, Germany
| | - Paulo Suen
- Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Alexandre P Diaz
- Hochschule Fresenius, University of Applied Sciences, Munich, Germany
| | - Jair C Soares
- Hochschule Fresenius, University of Applied Sciences, Munich, Germany
| | - Andre R Brunoni
- Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
- Hospital Universitário, Departamento de Clínica Médica, Faculdade de Medicina da USP, São Paulo, Brazil.
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Dumais F, Legarreta JH, Lemaire C, Poulin P, Rheault F, Petit L, Barakovic M, Magon S, Descoteaux M, Jodoin PM. FIESTA: Autoencoders for accurate fiber segmentation in tractography. Neuroimage 2023; 279:120288. [PMID: 37495198 DOI: 10.1016/j.neuroimage.2023.120288] [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: 06/02/2023] [Accepted: 07/20/2023] [Indexed: 07/28/2023] Open
Abstract
White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework.
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Affiliation(s)
- Félix Dumais
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada.
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, USA
| | - Carl Lemaire
- Centre de Calcul Scientifique, Université de Sherbrooke, Canada
| | - Philippe Poulin
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada
| | - François Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, Université de Sherbrooke, Canada
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN), CNRS, CEA, IMN, GIN, UMR 5293, F-33000 Bordeaux, Université de Bordeaux, France
| | - Muhamed Barakovic
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Stefano Magon
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
| | - Pierre-Marc Jodoin
- Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
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Valdes PA, Ng S, Bernstock JD, Duffau H. Development of an educational method to rethink and learn oncological brain surgery in an "a la carte" connectome-based perspective. Acta Neurochir (Wien) 2023; 165:2489-2500. [PMID: 37199758 DOI: 10.1007/s00701-023-05626-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/03/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Understanding the structural connectivity of white matter tracts (WMT) and their related functions is a prerequisite to implementing an "a la carte" "connectomic approach" to glioma surgery. However, accessible resources facilitating such an approach are lacking. Here we present an educational method that is readily accessible, simple, and reproducible that enables the visualization of WMTs on individual patient images via an atlas-based approach. METHODS Our method uses the patient's own magnetic resonance imaging (MRI) images and consists of three main steps: data conversion, normalization, and visualization; these are accomplished using accessible software packages and WMT atlases. We implement our method on three common cases encountered in glioma surgery: a right supplementary motor area tumor, a left insular tumor, and a left temporal tumor. RESULTS Using patient-specific perioperative MRIs with open-sourced and co-registered atlas-derived WMTs, we highlight the critical subnetworks requiring specific surgical monitoring identified intraoperatively using direct electrostimulation mapping with cognitive monitoring. The aim of this didactic method is to provide the neurosurgical oncology community with an accessible and ready-to-use educational tool, enabling neurosurgeons to improve their knowledge of WMTs and to better learn their oncologic cases, especially in glioma surgery using awake mapping. CONCLUSIONS Taking no more than 3-5 min per patient and irrespective of their resource settings, we believe that this method will enable junior surgeons to develop an intuition, and a robust 3-dimensional imagery of WMT by regularly applying it to their cases both before and after surgery to develop an "a la carte" connectome-based perspective to glioma surgery.
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Affiliation(s)
- Pablo A Valdes
- Department of Neurosurgery, University of Texas Medical Branch, Galveston, TX, 77555, USA.
- Department of Neurosurgery, Hôpital Gui de Chauliac, CHU Montpellier, 80 Av Augustin Fliche, 34295, Montpellier, France.
| | - Sam Ng
- Department of Neurosurgery, Hôpital Gui de Chauliac, CHU Montpellier, 80 Av Augustin Fliche, 34295, Montpellier, France
- Team "Plasticity of Central Nervous System, Human Stem Cells and Glial Tumors", Institute of Functional Genomics, INSERM U1191, University of Montpellier, 141 Rue de la cardonille, 34091, Montpellier, France
| | - Joshua D Bernstock
- Department of Neurosurgery, Harvard Medical School/Brigham and Women's Hospital, Boston, MA, 02115, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hugues Duffau
- Department of Neurosurgery, Hôpital Gui de Chauliac, CHU Montpellier, 80 Av Augustin Fliche, 34295, Montpellier, France
- Team "Plasticity of Central Nervous System, Human Stem Cells and Glial Tumors", Institute of Functional Genomics, INSERM U1191, University of Montpellier, 141 Rue de la cardonille, 34091, Montpellier, France
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Zanao TA, Seitz‐Holland J, O'Donnell LJ, Zhang F, Rathi Y, Lopes TM, Pimentel‐Silva LR, Yassuda CL, Makris N, Shenton ME, Bouix S, Lyall AE, Cendes F. Exploring the impact of hippocampal sclerosis on white matter tracts and memory in individuals with mesial temporal lobe epilepsy. Epilepsia Open 2023; 8:1111-1122. [PMID: 37469213 PMCID: PMC10472386 DOI: 10.1002/epi4.12793] [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: 10/05/2022] [Accepted: 07/14/2023] [Indexed: 07/21/2023] Open
Abstract
OBJECTIVE To investigate how the presence/side of hippocampal sclerosis (HS) are related to the white matter structure of cingulum bundle (CB), arcuate fasciculus (AF), and inferior longitudinal fasciculus (ILF) in mesial temporal lobe epilepsy (MTLE). METHODS We acquired diffusion-weighted magnetic resonance imaging (MRI) from 86 healthy and 71 individuals with MTLE (22 righ-HS; right-HS, 34 left-HS; left-HS, and 15 nonlesional MTLE). We utilized two-tensor tractography and fiber clustering to compare fractional anisotropy (FA) of each side/tract between groups. Additionally, we examined the association between FA and nonverbal (WMS-R) and verbal (WMS-R, RAVLT codification) memory performance for MTLE individuals. RESULTS White matter abnormalities depended on the side and presence of HS. The left-HS demonstrated widespread abnormalities for all tracts, the right-HS showed lower FA for ipsilateral tracts and the nonlesional MTLE group did not differ from healthy individuals. Results indicate no differences in verbal/nonverbal memory performance between the groups, but trend-level associations between higher FA of visual memory and the left CB (r = 0.286, P = 0.018), verbal memory (RAVLT) and -left CB (r = 0.335, P = 0.005), -right CB (r = 0.286, P = 0.016), and -left AF (r = 0.287, P = 0.017). SIGNIFICANCE Our results highlight that the presence and side of HS are crucial to understand the pathophysiology of MTLE. Specifically, left-sided HS seems to be related to widespread bilateral white matter abnormalities. Future longitudinal studies should focus on developing diagnostic and treatment strategies dependent on HS's presence/side.
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Affiliation(s)
- Tamires A. Zanao
- Psychiatry Neuroimaging Laboratory, Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Neuroimaging Laboratory, School of Medical SciencesUniversity of CampinasCampinasSão PauloBrazil
| | - Johanna Seitz‐Holland
- Psychiatry Neuroimaging Laboratory, Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Psychiatry Neuroimaging Laboratory, Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Tátila M. Lopes
- Neuroimaging Laboratory, School of Medical SciencesUniversity of CampinasCampinasSão PauloBrazil
| | | | - Clarissa L. Yassuda
- Neuroimaging Laboratory, School of Medical SciencesUniversity of CampinasCampinasSão PauloBrazil
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Sylvain Bouix
- Département de génie logiciel et TI, École de technologie supérieureUniversité du QuébecMontrealQuebecCanada
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fernando Cendes
- Neuroimaging Laboratory, School of Medical SciencesUniversity of CampinasCampinasSão PauloBrazil
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Joo SW, Jo YT, Ahn S, Choi YJ, Choi W, Kim SK, Joe S, Lee J. Structural impairment in superficial and deep white matter in schizophrenia. Acta Neuropsychiatr 2023:1-10. [PMID: 37620164 DOI: 10.1017/neu.2023.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects. METHODS We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing. RESULTS Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing. CONCLUSION We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for schizophrenia.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Kyoung Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Zhang R, Oliver LD, Voineskos AN, Park JY. RELIEF: A structured multivariate approach for removal of latent inter-scanner effects. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-16. [PMID: 37719839 PMCID: PMC10503485 DOI: 10.1162/imag_a_00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/02/2023] [Indexed: 09/19/2023]
Abstract
Combining data collected from multiple study sites is becoming common and is advantageous to researchers to increase the generalizability and replicability of scientific discoveries. However, at the same time, unwanted inter-scanner biases are commonly observed across neuroimaging data collected from multiple study sites or scanners, rendering difficulties in integrating such data to obtain reliable findings. While several methods for handling such unwanted variations have been proposed, most of them use univariate approaches that could be too simple to capture all sources of scanner-specific variations. To address these challenges, we propose a novel multivariate harmonization method called RELIEF (REmoval of Latent Inter-scanner Effects through Factorization) for estimating and removing both explicit and latent scanner effects. Our method is the first approach to introduce the simultaneous dimension reduction and factorization of interlinked matrices to a data harmonization context, which provides a new direction in methodological research for correcting inter-scanner biases. Analyzing diffusion tensor imaging (DTI) data from the Social Processes Initiative in Neurobiology of the Schizophrenia (SPINS) study and conducting extensive simulation studies, we show that RELIEF outperforms existing harmonization methods in mitigating inter-scanner biases and retaining biological associations of interest to increase statistical power. RELIEF is publicly available as an R package.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
| | | | - Aristotle N. Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
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30
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Ghazi N, Aarabi MH, Soltanian-Zadeh H. Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective. Neuroinformatics 2023; 21:517-548. [PMID: 37328715 DOI: 10.1007/s12021-023-09636-4] [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] [Accepted: 05/20/2023] [Indexed: 06/18/2023]
Abstract
Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.
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Affiliation(s)
- Nayereh Ghazi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14399, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14399, Iran.
- Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, 48202, USA.
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31
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He J, Yao S, Zeng Q, Chen J, Sang T, Xie L, Pan Y, Feng Y. A unified global tractography framework for automatic visual pathway reconstruction. NMR IN BIOMEDICINE 2023; 36:e4904. [PMID: 36633539 DOI: 10.1002/nbm.4904] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/05/2023] [Accepted: 01/10/2023] [Indexed: 06/15/2023]
Abstract
The human visual pathway starts from the retina, passes through the retinogeniculate visual pathway, the optic radiation, and finally connects to the primary visual cortex. Diffusion MRI tractography is the only technology that can noninvasively reconstruct the visual pathway. However, complete and accurate visual pathway reconstruction is challenging because of the skull base environment and complex fiber geometries. Specifically, the optic nerve within the complex skull base environment can cause abnormal diffusion signals. The crossing and fanning fibers at the optic chiasm, and a sharp turn of Meyer's loop at the optic radiation, contribute to complex fiber geometries of the visual pathway. A fiber trajectory distribution (FTD) function-based tractography method of our previous work and several high sensitivity tractography methods can reveal these complex fiber geometries, but are accompanied by false-positive fibers. Thus, the related studies of the visual pathway mostly applied the expert region of interest selection strategy. However, interobserver variability is an issue in reconstructing an accurate visual pathway. In this paper, we propose a unified global tractography framework to automatically reconstruct the visual pathway. We first extend the FTD function to a high-order streamline differential equation for global trajectory estimation. At the global level, the tractography process is simplified as the estimation of global trajectory distribution coefficients by minimizing the cost between trajectory distribution and the selected directions under the prior guidance by introducing the tractography template as anatomic priors. Furthermore, we use a deep learning-based method and tractography template prior information to automatically generate the mask for tractography. The experimental results demonstrate that our proposed method can successfully reconstruct the visual pathway with high accuracy.
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Affiliation(s)
- Jianzhong He
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Shun Yao
- Center for Pituitary Tumor Surgery, Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Qingrun Zeng
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Jinping Chen
- Center for Pituitary Tumor Surgery, Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tian Sang
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Lei Xie
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Yiang Pan
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Yuanjing Feng
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
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32
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Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O'Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Mol Psychiatry 2023; 28:2301-2311. [PMID: 37173451 DOI: 10.1038/s41380-023-02031-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/13/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. METHODS Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. RESULTS We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions. CONCLUSIONS In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.
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Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - F Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Vangel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - P G Nestor
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, University of Massachusetts, Boston, MA, 02125, USA
| | - Y Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - S Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M J Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - K E Lewandowski
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - S Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Software Engineering and Information Technology, École de technologie supérieure, Université du Québec, Montréal, QC, H3C 1K3, Canada
| | - D Öngür
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - A Breier
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - M E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - L J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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Chen Y, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Cai W, Zhang F, O'Donnell LJ. Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation. Neuroimage 2023; 273:120086. [PMID: 37019346 PMCID: PMC10958986 DOI: 10.1016/j.neuroimage.2023.120086] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/02/2023] [Indexed: 04/05/2023] Open
Abstract
White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.
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Affiliation(s)
- Yuqian Chen
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | | | - Tengfei Xue
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | - Yang Song
- The University of New South Wales, NSW, Australia
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34
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Langhein M, Lyall AE, Steinmann S, Seitz-Holland J, Nägele FL, Cetin-Karayumak S, Zhang F, Rauh J, Mußmann M, Billah T, Makris N, Pasternak O, O’Donnell LJ, Rathi Y, Leicht G, Kubicki M, Shenton ME, Mulert C. The decoupling of structural and functional connectivity of auditory networks in individuals at clinical high-risk for psychosis. World J Biol Psychiatry 2023; 24:387-399. [PMID: 36083108 PMCID: PMC10399965 DOI: 10.1080/15622975.2022.2112974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 10/14/2022]
Abstract
OBJECTIVES Disrupted auditory networks play an important role in the pathophysiology of psychosis, with abnormalities already observed in individuals at clinical high-risk for psychosis (CHR). Here, we examine structural and functional connectivity of an auditory network in CHR utilising state-of-the-art electroencephalography and diffusion imaging techniques. METHODS Twenty-six CHR subjects and 13 healthy controls (HC) underwent diffusion MRI and electroencephalography while performing an auditory task. We investigated structural connectivity, measured as fractional anisotropy in the Arcuate Fasciculus (AF), Cingulum Bundle, and Superior Longitudinal Fasciculus-II. Gamma-band lagged-phase synchronisation, a functional connectivity measure, was calculated between cortical regions connected by these tracts. RESULTS CHR subjects showed significantly higher structural connectivity in the right AF than HC (p < .001). Although non-significant, functional connectivity between cortical areas connected by the AF was lower in CHR than HC (p = .078). Structural and functional connectivity were correlated in HC (p = .056) but not in CHR (p = .29). CONCLUSIONS We observe significant differences in structural connectivity of the AF, without a concomitant significant change in functional connectivity in CHR subjects. This may suggest that the CHR state is characterised by a decoupling of structural and functional connectivity, possibly due to abnormal white matter maturation.
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Affiliation(s)
- Mina Langhein
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Saskia Steinmann
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Felix L. Nägele
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonas Rauh
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marius Mußmann
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tashrif Billah
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, 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
| | - Lauren J O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, 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
| | - Gregor Leicht
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Centre for Psychiatry, Justus-Liebig-University, Giessen, Germany
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35
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Bingham KS, Calarco N, Dickie EW, Alexopoulos GS, Butters MA, Meyers BS, Marino P, Neufeld NH, Rothschild AJ, Whyte EM, Mulsant BH, Flint AJ, Voineskos AN. The relationship of white matter microstructure with psychomotor disturbance and relapse in remitted psychotic depression. J Affect Disord 2023; 334:317-324. [PMID: 37149056 DOI: 10.1016/j.jad.2023.04.136] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/06/2023] [Accepted: 04/29/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Psychomotor disturbance is common in psychotic depression and is associated with relapse. In this analysis, we examined whether white matter microstructure is associated with relapse probability in psychotic depression and, if so, whether white matter microstructure accounts for the association between psychomotor disturbance and relapse. METHODS We used tractography to characterize diffusion-weighted MRI data in 80 participants enrolled in a randomized clinical trial that compared efficacy and tolerability of sertraline plus olanzapine with sertraline plus placebo in the continuation treatment of remitted psychotic depression. Cox proportional hazard models tested the relationships between psychomotor disturbance (processing speed and CORE score) at baseline, white matter microstructure (fractional anisotropy [FA] and mean diffusivity [MD]) in 15 selected tracts at baseline, and relapse probability. RESULTS CORE was significantly associated with relapse. Higher mean MD was significantly associated with relapse in the each of the following tracts: corpus callosum, left striato-frontal, left thalamo-frontal, and right thalamo-frontal. CORE and MD were each associated with relapse in the final models. LIMITATIONS As a secondary analysis with a small sample size, this study was not powered for its aims, and is vulnerable to types I and II statistical errors. Further, the sample size was not sufficient to test the interaction of the independent variables and randomized treatment group with relapse probability. CONCLUSIONS While both psychomotor disturbance and MD were associated with psychotic depression relapse, MD did not account for the relationship between psychomotor disturbance and relapse. The mechanism by which of psychomotor disturbance increases the risk of relapse requires further investigation. CLINICAL TRIAL REGISTRATION Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II); NCT01427608. URL: https://clinicaltrials.gov/ct2/show/NCT01427608.
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Affiliation(s)
- Kathleen S Bingham
- Centre for Mental Health, University Health Network, Toronto, Canada; Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada.
| | - Navona Calarco
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Erin W Dickie
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - George S Alexopoulos
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - Barnett S Meyers
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Patricia Marino
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Nicholas H Neufeld
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Anthony J Rothschild
- University of Massachusetts Chan Medical School and UMass Memorial Health Care, Worcester, USA
| | - Ellen M Whyte
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - Benoit H Mulsant
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Alastair J Flint
- Centre for Mental Health, University Health Network, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
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36
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Cetin-Karayumak S, Zhang F, Billah T, Zekelman L, Makris N, Pieper S, O’Donnell LJ, Rathi Y. Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.04.535587. [PMID: 37066186 PMCID: PMC10104063 DOI: 10.1101/2023.04.04.535587] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The Adolescent Brain Cognitive Development (ABCD) study has collected data from over 10,000 children across 21 sites, providing valuable insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a database of harmonized and processed ABCD dMRI data has been created, comprising quality-controlled imaging data from 9345 subjects. This resource required significant computational effort, taking ~50,000 CPU hours to harmonize the data, perform white matter parcellation, and run whole brain tractography. The database includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts both in full-resolution (for analysis) and low-resolution (for visualization), and 804 different dMRI-derived measures per subject. It is available via the NIMH Data Archive and offers tremendous potential for scientific discoveries in structural connectivity studies of neurodevelopment in children and adolescents. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leo Zekelman
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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37
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Schilling KG, Archer D, Rheault F, Lyu I, Huo Y, Cai LY, Bunge SA, Weiner KS, Gore JC, Anderson AW, Landman BA. Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features. Brain Struct Funct 2023; 228:1019-1031. [PMID: 37074446 PMCID: PMC10320929 DOI: 10.1007/s00429-023-02642-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 04/08/2023] [Indexed: 04/20/2023]
Abstract
Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Ilwoo Lyu
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Yuankai Huo
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Silvia A Bunge
- Department of Psychology, University of California at Berkeley, Berkeley, USA
| | - Kevin S Weiner
- Department of Psychology, University of California at Berkeley, Berkeley, USA
- Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, USA
| | - John C Gore
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
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Wang Z, He M, Lv Y, Ge E, Zhang S, Qiang N, Liu T, Zhang F, Li X, Ge B. Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism. Cereb Cortex 2023:7133663. [PMID: 37083279 DOI: 10.1093/cercor/bhad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 04/22/2023] Open
Abstract
Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification. Defining the similarity feature is the basic premise and determines its potential reliability and application. In this study, we adopt geometric features for fiber tract segmentation and develop a novel descriptor (FiberGeoMap) for the corresponding representation, which can effectively depict fiber streamlines' shapes and positions. FiberGeoMap can differentiate fiber tracts within the same subject, meanwhile preserving the shape and position consistency across subjects, thus can identify common fiber tracts across brains. We also proposed a Transformer-based encoder network called FiberGeoMap Learner, to perform segmentation based on the geometric features. Experimental results showed that the proposed method can differentiate the 103 various fiber tracts, which outperformed the existing methods in both the number of categories and segmentation accuracy. Furthermore, the proposed method identified some fiber tracts that were statistically different on fractional anisotropy (FA), mean diffusion (MD), and fiber number ration in autism.
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Affiliation(s)
- Zhenwei Wang
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Mengshen He
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Yifan Lv
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Enjie Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Tianming Liu
- Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States
| | - Fan Zhang
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Bao Ge
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
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39
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Umminger LF, Rojczyk P, Seitz-Holland J, Sollmann N, Kaufmann E, Kinzel P, Zhang F, Kochsiek J, Langhein M, Kim CL, Wiegand TLT, Kilts JD, Naylor JC, Grant GA, Rathi Y, Coleman MJ, Bouix S, Tripodis Y, Pasternak O, George MS, McAllister TW, Zafonte R, Stein MB, O'Donnell LJ, Marx CE, Shenton ME, Koerte IK. White Matter Microstructure Is Associated with Serum Neuroactive Steroids and Psychological Functioning. J Neurotrauma 2023; 40:649-664. [PMID: 36324218 PMCID: PMC10061338 DOI: 10.1089/neu.2022.0111] [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] [Indexed: 11/06/2022] Open
Abstract
Military service members are at increased risk for mental health issues, and comorbidity with mild traumatic brain injury (mTBI) is common. Largely overlapping symptoms between conditions suggest a shared pathophysiology. The present work investigates the associations among white matter microstructure, psychological functioning, and serum neuroactive steroids that are part of the stress-response system. Diffusion-weighted brain imaging was acquired from 163 participants (with and without military affiliation) and free-water-corrected fractional anisotropy (FAT) was extracted. Associations between serum neurosteroid levels of allopregnanolone (ALLO) and pregnenolone (PREGNE), psychological functioning, and whole-brain white matter microstructure were assessed using regression models. Moderation models tested the effect of mTBI and comorbid post-traumatic stress disorder (PTSD) and mTBI on these associations. ALLO is associated with whole-brain white matter FAT (β = 0.24, t = 3.05, p = 0.006). This association is significantly modulated by PTSD+mTBI comorbidity (β = 0.00, t = 2.50, p = 0.027), although an mTBI diagnosis alone did not significantly impact this association (p = 0.088). There was no significant association between PREGNE and FAT (p = 0.380). Importantly, lower FAT is associated with poor psychological functioning (β = -0.19, t = -2.35, p = 0.020). This study provides novel insight into a potential common pathophysiological mechanism of neurosteroid dysregulation underlying the high risk for mental health issues in military service members. Further, comorbidity of PTSD and mTBI may bring the compensatory effects of the brain's stress response to their limit. Future research is needed to investigate whether neurosteroid regulation may be a promising tool for restoring brain health and improving psychological functioning.
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Affiliation(s)
- Lisa F. Umminger
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Philine Rojczyk
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nico Sollmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Elisabeth Kaufmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Neurology, Epilepsy Center, Ludwig-Maximilians-Universität, Munich, Germany
| | - Philipp Kinzel
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Fan Zhang
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Janna Kochsiek
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mina Langhein
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Cara L. Kim
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Tim L. T. Wiegand
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Jason D. Kilts
- VA Mid-Atlantic Mental Illness Research and Clinical Center (MIRECC) and Durham VA Medical Center, Durham, NorthCarolina, USA
- Department of Psychiatry and Behavior Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jennifer C. Naylor
- VA Mid-Atlantic Mental Illness Research and Clinical Center (MIRECC) and Durham VA Medical Center, Durham, NorthCarolina, USA
- Department of Psychiatry and Behavior Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Gerald A. Grant
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael J. Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yorghos Tripodis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark S. George
- Psychiatry Department, Medical University of South Carolina, Charleston, South Carolina, USA
- Ralph H. Johnson VA Medical Center, Charleston, South Carolina, USA
| | - Thomas W. McAllister
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ross Zafonte
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Murray B. Stein
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
- School of Public Health, University of California San Diego, La Jolla, California, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Lauren J. O'Donnell
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christine E. Marx
- VA Mid-Atlantic Mental Illness Research and Clinical Center (MIRECC) and Durham VA Medical Center, Durham, NorthCarolina, USA
- Department of Psychiatry and Behavior Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Inga K. Koerte
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Graduate School of Systemic Neuroscience, Ludwig-Maximilians-Universität, Munich, Germany
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40
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Xue T, Zhang F, Zhang C, Chen Y, Song Y, Golby AJ, Makris N, Rathi Y, Cai W, O'Donnell LJ. Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions. Med Image Anal 2023; 85:102759. [PMID: 36706638 PMCID: PMC9975054 DOI: 10.1016/j.media.2023.102759] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/05/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023]
Abstract
Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.
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Affiliation(s)
- Tengfei Xue
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, Sydney, Australia
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Chaoyi Zhang
- School of Computer Science, University of Sydney, Sydney, Australia
| | - Yuqian Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, Sydney, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | | | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Center for Morphometric Analysis, Massachusetts General Hospital, Boston, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Weidong Cai
- School of Computer Science, University of Sydney, Sydney, Australia
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41
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Ahtam B, Solti M, Doo JM, Feldman HA, Vyas R, Zhang F, O'Donnell LJ, Rathi Y, Smith ER, Orbach D, See AP, Grant PE, Lehman LL. Diffusion-Weighted Magnetic Resonance Imaging Demonstrates White Matter Alterations in Watershed Regions in Children With Moyamoya Without Stroke or Silent Infarct. Pediatr Neurol 2023; 143:89-94. [PMID: 37054515 DOI: 10.1016/j.pediatrneurol.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/25/2023] [Accepted: 03/12/2023] [Indexed: 04/15/2023]
Abstract
BACKGROUND Moyamoya is a disease with progressive cerebral arterial stenosis leading to stroke and silent infarct. Diffusion-weighted magnetic resonance imaging (dMRI) studies show that adults with moyamoya have significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) compared with controls, which raises concern for unrecognized white matter injury. Children with moyamoya have significantly lower FA and higher MD in their white matter compared with controls. However, it is unknown which white matter tracts are affected in children with moyamoya. METHODS We present a cohort of 15 children with moyamoya with 24 affected hemispheres without stroke or silent infarct compared with 25 controls. We analyzed dMRI data using unscented Kalman filter tractography and extracted major white matter pathways with a fiber clustering method. We compared the FA, MD, AD, and RD in each segmented white matter tract and combined white matter tracts found within the watershed region using analysis of variance. RESULTS Age and sex were not significantly different between children with moyamoya and controls. Specific white matter tracts affected included inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, thalamofrontal, uncinate fasciculus, and arcuate fasciculus. Combined watershed region white matter tracts in children with moyamoya had significantly lower FA (-7.7% ± 3.2%, P = 0.02) and higher MD (4.8% ± 1.9%, P = 0.01) and RD (8.7% ± 2.8%, P = 0.002). CONCLUSIONS Lower FA with higher MD and RD is concerning for unrecognized white matter injury. Affected tracts were located in watershed regions suggesting that the findings may be due to chronic hypoperfusion. These findings support the concern that children with moyamoya without overt stroke or silent infarction are sustaining ongoing injury to their white matter microstructure and provide practitioners with a noninvasive method of more accurately assessing disease burden in children with moyamoya.
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Affiliation(s)
- Banu Ahtam
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Marina Solti
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts
| | - Justin M Doo
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts
| | - Henry A Feldman
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Rutvi Vyas
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts
| | - Fan Zhang
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Lauren J O'Donnell
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Yogesh Rathi
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Edward R Smith
- Harvard Medical School, Boston, Massachusetts; Department of Neurosurgery, Boston Children's Hospital, Boston, Massachusetts
| | - Darren Orbach
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - Alfred P See
- Harvard Medical School, Boston, Massachusetts; Department of Neurosurgery, Boston Children's Hospital, Boston, Massachusetts; Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - P Ellen Grant
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Laura L Lehman
- Harvard Medical School, Boston, Massachusetts; Department of Neurology, Boston Children's Hospital, Boston, Massachusetts.
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Rojczyk P, Seitz-Holland J, Kaufmann E, Sydnor VJ, Kim CL, Umminger LF, Wiegand TLT, Guenette JP, Zhang F, Rathi Y, Bouix S, Pasternak O, Fortier CB, Salat D, Hinds SR, Heinen F, O’Donnell LJ, Milberg WP, McGlinchey RE, Shenton ME, Koerte IK. Sleep Quality Disturbances Are Associated with White Matter Alterations in Veterans with Post-Traumatic Stress Disorder and Mild Traumatic Brain Injury. J Clin Med 2023; 12:2079. [PMID: 36902865 PMCID: PMC10004675 DOI: 10.3390/jcm12052079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/09/2023] Open
Abstract
Sleep disturbances are strongly associated with mild traumatic brain injury (mTBI) and post-traumatic stress disorder (PTSD). PTSD and mTBI have been linked to alterations in white matter (WM) microstructure, but whether poor sleep quality has a compounding effect on WM remains largely unknown. We evaluated sleep and diffusion magnetic resonance imaging (dMRI) data from 180 male post-9/11 veterans diagnosed with (1) PTSD (n = 38), (2) mTBI (n = 25), (3) comorbid PTSD+mTBI (n = 94), and (4) a control group with neither PTSD nor mTBI (n = 23). We compared sleep quality (Pittsburgh Sleep Quality Index, PSQI) between groups using ANCOVAs and calculated regression and mediation models to assess associations between PTSD, mTBI, sleep quality, and WM. Veterans with PTSD and comorbid PTSD+mTBI reported poorer sleep quality than those with mTBI or no history of PTSD or mTBI (p = 0.012 to <0.001). Poor sleep quality was associated with abnormal WM microstructure in veterans with comorbid PTSD+mTBI (p < 0.001). Most importantly, poor sleep quality fully mediated the association between greater PTSD symptom severity and impaired WM microstructure (p < 0.001). Our findings highlight the significant impact of sleep disturbances on brain health in veterans with PTSD+mTBI, calling for sleep-targeted interventions.
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Affiliation(s)
- Philine Rojczyk
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Elisabeth Kaufmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
- Department of Neurology, Ludwig-Maximilians-University, 81377 Munich, Germany
| | - Valerie J. Sydnor
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
| | - Cara L. Kim
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Lisa F. Umminger
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Tim L. T. Wiegand
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Jeffrey P. Guenette
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Software Engineering and IT, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Catherine B. Fortier
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
| | - David Salat
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Department of Radiology, Boston, MA 02129, USA
| | - Sidney R. Hinds
- Department of Neurology, Uniformed Services University, Bethesda, MD 20814, USA
| | - Florian Heinen
- Department of Pediatric Neurology and Developmental Medicine and LMU Center for Children with Medical Complexity, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-University, 80337 Munich, Germany
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - William P. Milberg
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
| | - Regina E. McGlinchey
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Inga K. Koerte
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-University, 82152 Munich, Germany
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Jha RR, Kumar BVR, Pathak SK, Schneider W, Bhavsar A, Nigam A. Undersampled single-shell to MSMT fODF reconstruction using CNN-based ODE solver. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107339. [PMID: 36682110 DOI: 10.1016/j.cmpb.2023.107339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/27/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Diffusion MRI (dMRI) has been considered one of the most popular non-invasive techniques for studying the human brain's white matter (WM). dMRI is used to delineate the brain's microstructure by approximating the WM region's fiber tracts. The achieved fiber tracts can be utilized to assess mental diseases like Multiple sclerosis, ADHD, Seizures, Intellectual disability, and others. New techniques such as high angular resolution diffusion-weighted imaging (HARDI) have been developed, providing precise fiber directions, and overcoming the limitation of traditional DTI. Unlike Single-shell, Multi-shell HARDI provides tissue fractions for white matter, gray matter, and cerebrospinal fluid, resulting in a Multi-shell Multi-tissue fiber orientation distribution function (MSMT fODF). This MSMT fODF comes up with more precise fiber directions than a Single-shell, which helps to get correct fiber tracts. In addition, various multi-compartment diffusion models, including as CHARMED and NODDI, have been developed to describe the brain tissue microstructural information. This type of model requires multi-shell data to obtain more specific tissue microstructural information. However, a major concern with multi-shell is that it takes a longer scanning time restricting its use in clinical applications. In addition, most of the existing dMRI scanners with low gradient strengths commonly acquire a single b-value (shell) upto b=1000s/mm2 due to SNR (Signal-to-noise ratio) reasons and severe imaging artifacts. METHODS To address this issue, we propose a CNN-based ordinary differential equations solver for the reconstruction of MSMT fODF from under-sampled and fully sampled Single-shell (b=1000s/mm2) dMRI. The proposed architecture consists of CNN-based Adams-Bash-forth and Runge-Kutta modules along with two loss functions, including L1 and total variation. RESULTS We have shown quantitative results and visualization of fODF, fiber tracts, and structural connectivity for several brain regions on the publicly available HCP dataset. In addition, the obtained angular correlation coefficients for white matter and full brain are high, showing the proposed network's utility.Finally, we have also demonstrated the effect of noise by adjusting SNR from 5 to 50 and observed the network robustness. CONCLUSION We can conclude that our model can accurately predict MSMT fODF from under-sampled or fully sampled Single-shell dMRI volumes.
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Affiliation(s)
- Ranjeet Ranjan Jha
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India.
| | - B V Rathish Kumar
- Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, India
| | - Sudhir K Pathak
- Learning Research and Development Center, University of Pittsburgh, USA
| | - Walter Schneider
- Learning Research and Development Center, University of Pittsburgh, USA
| | - Arnav Bhavsar
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
| | - Aditya Nigam
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
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44
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Jha RR, Kumar BR, Pathak SK, Bhavsar A, Nigam A. TrGANet: Transforming 3T to 7T dMRI using Trapezoidal Rule and Graph based Attention Modules. Med Image Anal 2023; 87:102806. [PMID: 37030056 DOI: 10.1016/j.media.2023.102806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 01/12/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Diffusion MRI (dMRI) is a non-invasive tool for assessing the white matter region of the brain by approximating the fiber streamlines, structural connectivity, and estimation of microstructure. This modality can yield useful information for diagnosing several mental diseases as well as for surgical planning. The higher angular resolution diffusion imaging (HARDI) technique is helpful in obtaining more robust fiber tracts by getting a good approximation of regions where fibers cross. Moreover, HARDI is more sensitive to tissue changes and can accurately represent anatomical details in the human brain at higher magnetic strengths. In other words, magnetic strengths affect the quality of the image, and hence high magnetic strength has good tissue contrast with better spatial resolution. However, a higher magnetic strength scanner (like 7T) is costly and unaffordable to most hospitals. Hence, in this work, we have proposed a novel CNN architecture for the transformation of 3T to 7T dMRI. Additionally, we have also reconstructed the multi-shell multi-tissue fiber orientation distribution function (MSMT fODF) at 7T from single-shell 3T. The proposed architecture consists of a CNN-based ODE solver utilizing the Trapezoidal rule and graph-based attention layer alongwith L1 and total variation loss. Finally, the model has been validated on the HCP data set quantitatively and qualitatively.
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45
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Schilling KG, Archer D, Yeh FC, Rheault F, Cai LY, Shafer A, Resnick SM, Hohman T, Jefferson A, Anderson AW, Kang H, Landman BA. Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions. AGING BRAIN 2023; 3:100067. [PMID: 36817413 PMCID: PMC9937516 DOI: 10.1016/j.nbas.2023.100067] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Leon Y Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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Wang S, Zhang F, Huang P, Hong H, Jiaerken Y, Yu X, Zhang R, Zeng Q, Zhang Y, Kikinis R, Rathi Y, Makris N, Lou M, Pasternak O, Zhang M, O'Donnell LJ. Superficial white matter microstructure affects processing speed in cerebral small vessel disease. Hum Brain Mapp 2022; 43:5310-5325. [PMID: 35822593 PMCID: PMC9812245 DOI: 10.1002/hbm.26004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/15/2023] Open
Abstract
White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.
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Affiliation(s)
- Shuyue Wang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Peiyu Huang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Hui Hong
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yeerfan Jiaerken
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Xinfeng Yu
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ruiting Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Qingze Zeng
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yao Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ron Kikinis
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Center for Morphometric AnalysisMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Min Lou
- Department of Neurologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Minming Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
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Irimia A, Ngo V, Chaudhari NN, Zhang F, Joshi SH, Penkova AN, O'Donnell LJ, Sheikh-Bahaei N, Zheng X, Chui HC. White matter degradation near cerebral microbleeds is associated with cognitive change after mild traumatic brain injury. Neurobiol Aging 2022; 120:68-80. [PMID: 36116396 PMCID: PMC9759713 DOI: 10.1016/j.neurobiolaging.2022.08.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 08/17/2022] [Accepted: 08/20/2022] [Indexed: 11/28/2022]
Abstract
To explore how cerebral microbleeds (CMBs) accompanying mild traumatic brain injury (mTBI) reflect white matter (WM) degradation and cognitive decline, magnetic resonance images were acquired from 62 mTBI adults (imaged ∼7 days and ∼6 months post-injury) and 203 matched healthy controls. On average, mTBI participants had a count of 2.7 ± 2.6 traumatic CMBs in WM, located 6.1 ± 4.4 mm from cortex. At ∼6-month follow-up, 97% of CMBs were associated with significant reductions (34% ± 11%, q < 0.05) in the fractional anisotropy of WM streamlines within ∼1 cm of CMB locations. Male sex and older age were significant risk factors for larger reductions (q < 0.05). For CMBs in the corpus callosum, cingulum bundle, inferior and middle longitudinal fasciculi, fractional anisotropy changes were significantly and positively associated with changes in cognitive functions mediated by these structures (q < 0.05). Our findings distinguish traumatic from non-traumatic CMBs by virtue of surrounding WM alterations and challenge the assumption that traumatic CMBs are neurocognitively silent. Thus, mTBI with CMB findings can be described as a clinical endophenotype warranting longitudinal cognitive assessment.
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Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
| | - Van Ngo
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Nikhil N Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Fan Zhang
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shantanu H Joshi
- Ahmanson Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Anita N Penkova
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Lauren J O'Donnell
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nasim Sheikh-Bahaei
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Zheng
- Department of Materials Science & Engineering, University of California, Berkeley, CA, USA
| | - Helena C Chui
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Sokolowski HM, Levine B. Common neural substrates of diverse neurodevelopmental disorders. Brain 2022; 146:438-447. [PMID: 36299249 PMCID: PMC9924912 DOI: 10.1093/brain/awac387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 09/02/2022] [Accepted: 09/19/2022] [Indexed: 11/14/2022] Open
Abstract
Neurodevelopmental disorders are categorized and studied according to their manifestations as distinct syndromes. For instance, congenital prosopagnosia and dyslexia have largely non-overlapping research literatures and clinical pathways for diagnosis and intervention. On the other hand, the high incidence of neurodevelopmental comorbidities or co-existing extreme strengths and weaknesses suggest that transdiagnostic commonalities may be greater than currently appreciated. The core-periphery model holds that brain regions within the stable core perceptual and motor regions are more densely connected to one another compared to regions in the flexible periphery comprising multimodal association regions. This model provides a framework for the interpretation of neural data in normal development and clinical disorders. Considering network-level commonalities reported in studies of neurodevelopmental disorders, variability in multimodal association cortex connectivity may reflect a shared origin of seemingly distinct neurodevelopmental disorders. This framework helps to explain both comorbidities in neurodevelopmental disorders and profiles of strengths and weaknesses attributable to competitive processing between cognitive systems within an individual.
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Affiliation(s)
- H Moriah Sokolowski
- Correspondence may also be addressed to: H. Moriah Sokolowski E-mail: Twitter: https://twitter.com/hm_sokolowski
| | - Brian Levine
- Correspondence to: Brian Levine 3560 Bathurst St, North York, ON M6A 2E1, Canada E-mail: Website: www.LevineLab.ca Twitter: https://twitter.com/briantlevine
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49
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Shastin D, Genc S, Parker GD, Koller K, Tax CMW, Evans J, Hamandi K, Gray WP, Jones DK, Chamberland M. Surface-based tracking for short association fibre tractography. Neuroimage 2022; 260:119423. [PMID: 35809886 PMCID: PMC10009610 DOI: 10.1016/j.neuroimage.2022.119423] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/30/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.
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Affiliation(s)
- Dmitri Shastin
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom.
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Kristin Koller
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom; Department of Neurology, University Hospital of Wales, Cardiff, United Kingdom
| | - William P Gray
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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50
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Heller C, Kimmig ACS, Kubicki MR, Derntl B, Kikinis Z. Imaging the human brain on oral contraceptives: A review of structural imaging methods and implications for future research goals. Front Neuroendocrinol 2022; 67:101031. [PMID: 35998859 DOI: 10.1016/j.yfrne.2022.101031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/30/2022] [Accepted: 08/15/2022] [Indexed: 12/21/2022]
Abstract
Worldwide over 150 million women use oral contraceptives (OCs), which are the most prescribed form of contraception in both the United States and in European countries. Sex hormones, such as estradiol and progesterone, are important endogenous hormones known for shaping the brain across the life span. Synthetic hormones, which are present in OCs, interfere with the natural hormonal balance by reducing the endogenous hormone levels. Little is known how this affects the brain, especially during the most vulnerable times of brain maturation. Here, we review studies that investigate differences in brain gray and white matter in women using OCs in comparison to naturally cycling women. We focus on two neuroimaging methods used to quantify structural gray and white matter changes, namely structural MRI and diffusion MRI. Finally, we discuss the potential of these imaging techniques to advance knowledge about the effects of OCs on the brain and wellbeing in women.
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Affiliation(s)
- Carina Heller
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany; Department of Clinical Psychology, Friedrich Schiller University Jena, Germany.
| | - Ann-Christin S Kimmig
- Department of Psychiatry and Psychotherapy, Innovative Neuroimaging, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany; Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Marek R Kubicki
- Psychiatry Neuroimaging Laboratory, 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
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Innovative Neuroimaging, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany; Lead Graduate School, University of Tübingen, Tübingen, Germany
| | - Zora Kikinis
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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