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Wang L, Qin Y, Yang S, Jin D, Zhu Y, Li X, Li W, Wang Y, Jin C. Posterior default mode network is associated with the social performance in male children with autism spectrum disorder: A dynamic causal modeling analysis based on triple-network model. Hum Brain Mapp 2024; 45:e26750. [PMID: 38853710 PMCID: PMC11163228 DOI: 10.1002/hbm.26750] [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: 01/10/2024] [Revised: 05/12/2024] [Accepted: 05/22/2024] [Indexed: 06/11/2024] Open
Abstract
The triple-network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple-network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange-I datasets. Spatial reference-based independent component analysis was used to identify the anterior and posterior part of default-mode network (aDMN and pDMN), salience network (SN), and bilateral executive-control network (ECN) from resting-state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple-network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self-inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self-inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple-networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.
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Affiliation(s)
- Lei Wang
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Yue Qin
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Shuhan Yang
- Department of Disease Control and PreventionNinth Hospital of Xi'anXi'anChina
| | - Dayong Jin
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Yinhu Zhu
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Xin Li
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Wei Li
- Department of Radiology, Tangdu HospitalAir Force Military Medical UniversityXi'anChina
| | - Yarong Wang
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Chenwang Jin
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
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2
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Yin Z, Ding X, Zhang X, Wu Z, Wang L, Xu X, Li G. Early autism diagnosis based on path signature and Siamese unsupervised feature compressor. Cereb Cortex 2024; 34:72-83. [PMID: 38696605 DOI: 10.1093/cercor/bhae069] [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: 10/09/2023] [Revised: 01/07/2024] [Accepted: 02/07/2024] [Indexed: 05/04/2024] Open
Abstract
Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.
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Affiliation(s)
- Zhuowen Yin
- School of Electronics and Information Engineering, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Xinyao Ding
- School of Electronics and Information Engineering, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
- The Affiliated Shenzhen School of Guangdong Experimental High School, 518100 Shenzhen, Guangdong Province, China
| | - Xin Zhang
- School of Electronics and Information Engineering, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
- Pazhou Lab, 510330 Guangzhou, Guangdong Province, China
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Xiangmin Xu
- Pazhou Lab, 510330 Guangzhou, Guangdong Province, China
- School of Future Technology, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
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3
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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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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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5
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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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6
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Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [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: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
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Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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7
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [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: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Helmy E, Elnakib A, ElNakieb Y, Khudri M, Abdelrahim M, Yousaf J, Ghazal M, Contractor S, Barnes GN, El-Baz A. Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey. Biomedicines 2023; 11:1858. [PMID: 37509498 PMCID: PMC10376963 DOI: 10.3390/biomedicines11071858] [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: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.
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Affiliation(s)
- Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura 3512, Egypt;
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Mohamed Khudri
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Mostafa Abdelrahim
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | - Gregory Neal Barnes
- Department of Neurology, Pediatric Research Institute, University of Louisville, Louisville, KY 40202, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
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9
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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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10
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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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11
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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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12
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Jiang X, Shou XJ, Zhao Z, Chen Y, Meng FC, Le J, Song TJ, Xu XJ, Guo W, Ke X, Cai XE, Zhao W, Kou J, Huo R, Liu Y, Yuan HS, Xing Y, Han JS, Han SP, Li Y, Lai H, Zhang L, Jia MX, Liu J, Liu X, Kendrick KM, Zhang R. A brain structural connectivity biomarker for autism spectrum disorder diagnosis in early childhood. PSYCHORADIOLOGY 2023; 3:kkad005. [PMID: 38666122 PMCID: PMC11003421 DOI: 10.1093/psyrad/kkad005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/16/2023] [Accepted: 04/19/2023] [Indexed: 04/28/2024]
Abstract
Background Autism spectrum disorder (ASD) is associated with altered brain development, but it is unclear which specific structural changes may serve as potential diagnostic markers, particularly in young children at the age when symptoms become fully established. Furthermore, such brain markers need to meet the requirements of precision medicine and be accurate in aiding diagnosis at an individual rather than only a group level. Objective This study aimed to identify and model brain-wide differences in structural connectivity using diffusion tensor imaging (DTI) in young ASD and typically developing (TD) children. Methods A discovery cohort including 93 ASD and 26 TD children and two independent validation cohorts including 12 ASD and 9 TD children from three different cities in China were included. Brain-wide (294 regions) structural connectivity was measured using DTI (fractional anisotropy, FA) together with symptom severity and cognitive development. A connection matrix was constructed for each child for comparisons between ASD and TD groups. Pattern classification was performed on the discovery dataset and the resulting model was tested on the two independent validation datasets. Results Thirty-three structural connections showed increased FA in ASD compared to TD children and associated with both autistic symptom severity and impaired general cognitive development. The majority (29/33) involved the frontal lobe and comprised five different networks with functional relevance to default mode, motor control, social recognition, language and reward. Overall, classification achieved very high accuracy of 96.77% in the discovery dataset, and 91.67% and 88.89% in the two independent validation datasets. Conclusions Identified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish novel individual ASD from TD children and may therefore represent a robust early brain biomarker which can address the requirements of precision medicine.
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Affiliation(s)
- Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiao-Jing Shou
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
- State Key Laboratory of Cognitive Neuroscience and Learning; Beijing Key Laboratory of Brain Imaging and Connectomics; and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhongbo Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuzhong Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fan-Chao Meng
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Jiao Le
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tian-Jia Song
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Xin-Jie Xu
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Weitong Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiaoyan Ke
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing 210029, China
| | - Xiao-E Cai
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Weihua Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Juan Kou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ran Huo
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
- Radiology Department, Peking University Third Hospital, Beijing 100191, China
| | - Ying Liu
- Radiology Department, Peking University Third Hospital, Beijing 100191, China
| | - Hui-Shu Yuan
- Radiology Department, Peking University Third Hospital, Beijing 100191, China
| | - Yan Xing
- Department of Pediatrics, Peking University Third Hospital, Beijing 100191, China
| | - Ji-Sheng Han
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Song-Ping Han
- Wuxi Shenpingxintai Medical Technology Co., Ltd, Wuxi 214091, China
| | - Yun Li
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing 210029, China
| | - Hua Lai
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lan Zhang
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mei-Xiang Jia
- Mental Health Institute, Peking University, Key Laboratory of Ministry of Health, The Ministry of Public Health, Beijing 100191, China
| | - Jing Liu
- Mental Health Institute, Peking University, Key Laboratory of Ministry of Health, The Ministry of Public Health, Beijing 100191, China
| | - Xuan Liu
- Shandong Ke Luo Ni Ke (CLINIC) Medical Technology Co., Ltd, Dezhou 253011, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Rong Zhang
- Neuroscience Research Institute; Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
- Autism Research Center of Peking University Health Science Center, Beijing 100191, China
- Department of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, Peking University, 100191,Beijing, China
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13
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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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14
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Barik K, Watanabe K, Bhattacharya J, Saha G. Functional connectivity based machine learning approach for autism detection in young children using MEG signals. J Neural Eng 2023; 20. [PMID: 36812588 DOI: 10.1088/1741-2552/acbe1f] [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: 09/03/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023]
Abstract
Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.
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Affiliation(s)
- Kasturi Barik
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Katsumi Watanabe
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Joydeep Bhattacharya
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
| | - Goutam Saha
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
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15
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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16
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Zhang C, Yuan Y, Sang T, Yu L, Yu Y, Liu X, Zhou W, Zeng Q, Wang J, Peng G, Feng Y. Local white matter abnormalities in Parkinson's disease with mild cognitive impairment: Assessed with neurite orientation dispersion and density imaging. J Neurosci Res 2023; 101:1154-1169. [PMID: 36854050 DOI: 10.1002/jnr.25179] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/06/2022] [Accepted: 01/31/2023] [Indexed: 03/02/2023]
Abstract
Mild cognitive impairment is a nonmotor complication in Parkinson's disease (PD) that have a high risk of developing dementia. White matter is associated with cognitive function in PD and the alterations may occur before the symptoms of the disease. Previous diffusion tensor imaging (DTI) studies lacked specificity to characterize the concrete contributions of distinct white matter tissue properties. This may lead to inconsistent conclusions about the alteration of white matter microstructure. Here, we used neurite orientation dispersion and density imaging (NODDI) and white matter fiber clustering method to uncover local white matter microstructures in PD with mild cognitive impairment (PD-MCI). This study included 23 PD-MCI and 20 PD with normal cognition (PD-NC) and 21 healthy controls (HC). To probe specific and fine-grained differences, metrics of NODDI and DTI in white matter fiber clusters were evaluated using along-tract analysis. Our results showed that PD-MCI patients had significantly lower neurite density index (NDI) and orientation dispersion index (ODI) in white matter fiber clusters in the prefrontal region. Correlation analysis and receiver operating characteristic (ROC) analysis revealed that the diagnostic performance of NODDI-derived metrics in cingulum bundle (2 clusters) and thalamo-frontal (2 clusters) were superior to DTI metrics. Our study provides a more specific insight to uncover local white matter abnormalities in PD-MCI, which benefit understanding the underlying mechanism of cognitive decline in PD and predicting the disease in advance.
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Affiliation(s)
- Chengzhe Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuan Yuan
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tian Sang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Lihua Yu
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Yu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenyang Zhou
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Guoping Peng
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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17
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Chien YL, Chen YJ, Tseng WL, Hsu YC, Wu CS, Tseng WYI, Gau SSF. Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2022; 27:1036-1052. [PMID: 36254873 DOI: 10.1177/13623613221125620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
LAY ABSTRACT White matter is the neural pathway that connects neurons in different brain regions. Although research has shown white matter differences between autistic and non-autistic people, little is known about the properties of white matter in non-autistic siblings. In addition, past studies often focused on the whole neural tracts; it is unclear where differences exist in specific segments of the tracts. This study identified neural segments that differed between autistic people, their non-autistic siblings, and the age- and non-autistic people. We found altered segments within the tracts connected to anterior brain regions corresponding to several higher cognitive functions (e.g. executive functions) in autistic people and non-autistic siblings. Segments connecting to regions for social cognition and Theory of Mind were altered only in autistic people, explaining a large portion of autistic traits and may serve as neuroimaging markers. Segments within the tracts associated with fewer autistic traits or connecting brain regions for diverse highly integrated functions showed compensatory increases in the microstructural properties in non-autistic siblings. Our findings suggest that differential white matter segments that are shared between autistic people and non-autistic siblings may serve as potential "intermediate phenotypes"-biological or neuropsychological characteristics in the causal link between genetics and symptoms-of autism. These findings shed light on a promising neuroimaging model to refine the intermediate phenotype of autism which may facilitate further identification of the genetic and biological bases of autism. Future research exploring links between compensatory segments and neurocognitive strengths in non-autistic siblings may help understand brain adaptation to autism.
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Affiliation(s)
- Yi-Ling Chien
- National Taiwan University Hospital and College of Medicine, Taiwan.,National Taiwan University, Taiwan
| | | | | | | | - Chi-Shin Wu
- National Taiwan University Hospital and College of Medicine, Taiwan
| | | | - Susan Shur-Fen Gau
- National Taiwan University Hospital and College of Medicine, Taiwan.,National Taiwan University, Taiwan
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18
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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19
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Kai J, Khan AR. Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults. Front Neuroinform 2022; 16:777853. [PMID: 35250526 PMCID: PMC8891507 DOI: 10.3389/fninf.2022.777853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/06/2022] [Indexed: 11/21/2022] Open
Abstract
Tractography is a non-invasive technique to investigate the brain’s structural pathways (also referred to as tracts) that connect different brain regions. A commonly used approach for identifying tracts is with template-based clustering, where unsupervised clustering is first performed on a template in order to label corresponding tracts in unseen data. However, the reliability of this approach has not been extensively studied. Here, an investigation into template-based clustering reliability was performed, assessing the output from two datasets: Human Connectome Project (HCP) and MyConnectome project. The effect of intersubject variability on template-based clustering reliability was investigated, as well as the reliability of both deep and superficial white matter tracts. Identified tracts were evaluated by assessing Euclidean distances from a dataset-specific tract average centroid, the volumetric overlap across corresponding tracts, and along-tract agreement of quantitative values. Further, two template-based techniques were employed to evaluate the reliability of different clustering approaches. Reliability assessment can increase the confidence of a tract identifying technique in future applications to study pathways of interest. The two different template-based approaches exhibited similar reliability for identifying both deep white matter tracts and the superficial white matter.
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Affiliation(s)
- Jason Kai
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, ON, Canada
| | - Ali R. Khan
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, ON, Canada
- *Correspondence: Ali R. Khan,
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20
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Xu S, Li M, Yang C, Fang X, Ye M, Wu Y, Yang B, Huang W, Li P, Ma X, Fu S, Yin Y, Tian J, Gan Y, Jiang G. Abnormal Degree Centrality in Children with Low-Function Autism Spectrum Disorders: A Sleeping-State Functional Magnetic Resonance Imaging Study. Neuropsychiatr Dis Treat 2022; 18:1363-1374. [PMID: 35818374 PMCID: PMC9270980 DOI: 10.2147/ndt.s367104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/23/2022] [Indexed: 12/04/2022] Open
Abstract
PURPOSE This study used the graph-theory approach, degree centrality (DC) to analyze whole-brain functional networks at the voxel level in children with ASD, and investigated whether DC changes were correlated with any clinical variables in ASD children. METHODS The current study included 86 children with ASD and 54 matched healthy subjects Aged 2-5.5 years. Next, chloral hydrate induced sleeping-state functional magnetic resonance imaging (ss-fMRI) datasets were acquired from these ASD and healthy subjects. For a given voxel, the DC was calculated by calculating the number of functional connections with significantly positive correlations at the individual level. Group differences were tested using two-sample t-tests (p < 0.01, AlphaSim corrected). Finally, relationships between abnormal DCs and clinical variables were investigated via Pearson's correlation analysis. RESULTS Children with ASD exhibited low DC values in the right middle frontal gyrus (MFG) (p < 0.01, AlphaSim corrected). Furthermore, significantly negative correlations were established between the decreased average DC values within the right MFG in ASD children and the total ABC scores, as well as with two ABC subscales measuring highly relevant impairments in ASD (ie, stereotypes and object-use behaviors and difficulties in language). CONCLUSION Taken together, the results of our ss-fMRI study suggest that abnormal DC may represent an important contribution to elucidation of the neuropathophysiological mechanisms of preschoolers with ASD.
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Affiliation(s)
- Shoujun Xu
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Meng Li
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Chunlan Yang
- Department of Hematology and Oncology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Xiangling Fang
- Department of Department of Children Healthcare, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Miaoting Ye
- Department of Department of Children Healthcare, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Yunfan Wu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Binrang Yang
- Department of Department of Children Healthcare, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Wenxian Huang
- Department of Department of Children Healthcare, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Peng Li
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Xiaofen Ma
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Shishun Fu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Yi Yin
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Junzhang Tian
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Yungen Gan
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
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21
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Li M, Yeh FC, Zeng Q, Wu X, Wang X, Zhu Z, Liu X, Liang J, Chen G, Zhang H, Feng Y, Li M. The trajectory of the medial longitudinal fasciculus in the human brain: A diffusion imaging-based tractography study. Hum Brain Mapp 2021; 42:6070-6086. [PMID: 34597450 PMCID: PMC8596984 DOI: 10.1002/hbm.25670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/04/2021] [Accepted: 09/10/2021] [Indexed: 01/23/2023] Open
Abstract
The aim of this study is to investigate the trajectory of medial longitudinal fasciculus (MLF) and explore its anatomical relationship with the oculomotor nerve using tractography technique. The MLF and oculomotor nerve were reconstructed at the same time with preset three region of interests (ROIs): one set at the area of rostral midbrain, one placed on the MLF area at the upper pons, and one placed at the cisternal part of the oculomotor nerve. This mapping protocol was tested in an HCP‐1065 template, 35 health subjects from Massachusetts General Hospital (MGH), 20 healthy adults and 6 brainstem cavernous malformation (BCM) patients with generalized q‐sampling imaging (GQI)‐based tractography. Finally, the 200 μm brainstem template from Center for In Vivo Microscopy, Duke University (Duke CIVM), was used to validate the trajectory of reconstructed MLF. The MLF and oculomotor nerve were reconstructed in the HCP‐1065 template, 35 MGH health subjects, 20 healthy adults and 6 BCM patients. The MLF was in conjunction with the ipsilateral mesencephalic part of the oculomotor nerve. The displacement of MLF was identified in all BCM patients. Decreased QA, RDI and FA were found in the MLF of lesion side, indicating axonal loss and/or edema of displaced MLF. The reconstructed MLF in Duke CIVM brainstem 200 μm template corresponded well with histological anatomy. The MLF and oculomotor nerve were visualized accurately with our protocol using GQI‐based fiber tracking. This GQI‐based tractography is an important tool in the reconstruction and evaluation of MLF.
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Affiliation(s)
- Mengjun Li
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.,Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Xiaolong Wu
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Xu Wang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Zixin Zhu
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Xiaohai Liu
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Ge Chen
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Hongqi Zhang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.,Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Mingchu Li
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
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22
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Zekelman LR, Zhang F, Makris N, He J, Chen Y, Xue T, Liera D, Drane DL, Rathi Y, Golby AJ, O'Donnell LJ. White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project. Neuroimage 2021; 246:118739. [PMID: 34856375 PMCID: PMC8862285 DOI: 10.1016/j.neuroimage.2021.118739] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/20/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Language and theory of mind (ToM) are the cognitive capacities that allow for the successful interpretation and expression of meaning. While functional MRI investigations are able to consistently localize language and ToM to specific cortical regions, diffusion MRI investigations point to an inconsistent and sometimes overlapping set of white matter tracts associated with these two cognitive domains. To further examine the white matter tracts that may underlie these domains, we use a two-tensor tractography method to investigate the white matter microstructure of 809 participants from the Human Connectome Project. 20 association white matter tracts (10 in each hemisphere) are uniquely identified by leveraging a neuroanatomist-curated automated white matter tract atlas. The fractional anisotropy (FA), mean diffusivity (MD), and number of streamlines (NoS) are measured for each white matter tract. Performance on neuropsychological assessments of semantic memory (NIH Toolbox Picture Vocabulary Test, TPVT) and emotion perception (Penn Emotion Recognition Test, PERT) are used to measure critical subcomponents of the language and ToM networks, respectively. Regression models are constructed to examine how structural measurements of left and right white matter tracts influence performance across these two assessments. We find that semantic memory performance is influenced by the number of streamlines of the left superior longitudinal fasciculus III (SLF-III), and emotion perception performance is influenced by the number of streamlines of the right SLF-III. Additionally, we find that performance on both semantic memory & emotion perception is influenced by the FA of the left arcuate fasciculus (AF). The results point to multiple, overlapping white matter tracts that underlie the cognitive domains of language and ToM. Results are discussed in terms of hemispheric dominance and concordance with prior investigations.
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Affiliation(s)
- Leo R Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, USA.
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, USA; Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Psychiatric Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jianzhong He
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | - Tengfei Xue
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | | | - Daniel L Drane
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington School of Medicine, Seattle, WA, US
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, 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
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23
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Burstein O, Geva R. The Brainstem-Informed Autism Framework: Early Life Neurobehavioral Markers. Front Integr Neurosci 2021; 15:759614. [PMID: 34858145 PMCID: PMC8631363 DOI: 10.3389/fnint.2021.759614] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/18/2021] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum disorders (ASD) have long-term implications on functioning at multiple levels. In this perspective, we offer a brainstem-informed autism framework (BIAF) that traces the protracted neurobehavioral manifestations of ASD to early life brainstem dysfunctions. Early life brainstem-mediated markers involving functions of autonomic/arousal regulation, sleep-wake homeostasis, and sensorimotor integration are delineated. Their possible contributions to the early identification of susceptible infants are discussed. We suggest that the BIAF expands our multidimensional understanding of ASD by focusing on the early involvement of brainstem systems. Importantly, we propose an integrated BIAF screener that brings about the prospect of a sensitive and reliable early life diagnostic scheme for weighing the risk for ASD. The BIAF screener could provide clinicians substantial gains in the future and may carve customized interventions long before the current DSM ASD phenotype is manifested using dyadic co-regulation of brainstem-informed autism markers.
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Affiliation(s)
- Or Burstein
- Department of Psychology, Bar-Ilan University, Ramat Gan, Israel
| | - Ronny Geva
- Department of Psychology, Bar-Ilan University, Ramat Gan, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
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24
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Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Kubicki M, Shenton ME, O'Donnell LJ. The Organization of Frontostriatal Brain Wiring in Healthy Subjects Using a Novel Diffusion Imaging Fiber Cluster Analysis. Cereb Cortex 2021; 31:5308-5318. [PMID: 34180506 DOI: 10.1093/cercor/bhab159] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 11/14/2022] Open
Abstract
To assess normal organization of frontostriatal brain wiring, we analyzed diffusion magnetic resonance imaging (dMRI) scans in 100 young adult healthy subjects (HSs). We identified fiber clusters intersecting the frontal cortex and caudate, a core component of associative striatum, and quantified their degree of deviation from a strictly topographic pattern. Using whole brain dMRI tractography and an automated tract parcellation clustering method, we extracted 17 white matter fiber clusters per hemisphere connecting the frontal cortex and caudate. In a novel approach to quantify the geometric relationship among clusters, we measured intercluster endpoint distances between corresponding cluster pairs in the frontal cortex and caudate. We show first, the overall frontal cortex wiring pattern of the caudate deviates from a strictly topographic organization due to significantly greater convergence in regionally specific clusters; second, these significantly convergent clusters originate in subregions of ventrolateral, dorsolateral, and orbitofrontal prefrontal cortex (PFC); and, third, a similar organization in both hemispheres. Using a novel tractography method, we find PFC-caudate brain wiring in HSs deviates from a strictly topographic organization due to a regionally specific pattern of cluster convergence. We conjecture cortical subregions projecting to the caudate with greater convergence subserve functions that benefit from greater circuit integration.
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Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton MA 02301, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA.,Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, 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.,Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA.,Department of Psychology, University of Massachusetts, Boston, MA 02125, USA
| | - Y Rathi
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - M Kubicki
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, 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 E Shenton
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, 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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Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis. Neuroradiology 2021; 63:2057-2072. [PMID: 34420058 DOI: 10.1007/s00234-021-02774-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 07/14/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE Autism Spectrum Disorder (ASD) is diagnosed through observation or interview assessments, which is time-consuming, subjective, and with questionable validity and reliability. Thus, we aimed to evaluate the role of machine learning (ML) with neuroimaging data to provide a reliable classification of ASD. METHODS A systematic search of PubMed, Scopus, and Embase was conducted to identify relevant publications. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the studies' quality. A bivariate random-effects model meta-analysis was employed to evaluate the pooled sensitivity, the pooled specificity, and the diagnostic performance through the hierarchical summary receiver operating characteristic (HSROC) curve of ML with neuroimaging data in classifying ASD. Meta-regression was also performed. RESULTS Forty-four studies (5697 ASD and 6013 typically developing individuals [TD] in total) were included in the quantitative analysis. The pooled sensitivity for differentiating ASD from TD individuals was 86.25 95% confidence interval [CI] (81.24, 90.08), while the pooled specificity was 83.31 95% CI (78.12, 87.48) with a combined area under the HSROC (AUC) of 0.889. Higgins I2 (> 90%) and Cochran's Q (p < 0.0001) suggest a high degree of heterogeneity. In the bivariate model meta-regression, a higher pooled specificity was observed in studies not using a brain atlas (90.91 95% CI [80.67, 96.00], p = 0.032). In addition, a greater pooled sensitivity was seen in studies recruiting both males and females (89.04 95% CI [83.84, 92.72], p = 0.021), and combining imaging modalities (94.12 95% [85.43, 97.76], p = 0.036). CONCLUSION ML with neuroimaging data is an exciting prospect in detecting individuals with ASD but further studies are required to improve its reliability for usage in clinical practice.
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Xu M, Calhoun V, Jiang R, Yan W, Sui J. Brain imaging-based machine learning in autism spectrum disorder: methods and applications. J Neurosci Methods 2021; 361:109271. [PMID: 34174282 DOI: 10.1016/j.jneumeth.2021.109271] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/25/2021] [Accepted: 06/19/2021] [Indexed: 01/09/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain. Many efforts have been devoted to developing imaging-based diagnostic tools for ASD based on machine learning (ML) technologies. In this survey, we review recent advances that utilize machine learning approaches to classify individuals with and without ASD. First, we provide a brief overview of neuroimaging-based ASD classification studies, including the analysis of publications and general classification pipeline. Next, representative studies are highlighted and discussed in detail regarding different imaging modalities, methods and sample sizes. Finally, we highlight several common challenges and provide recommendations on future directions. In summary, identifying discriminative biomarkers for ASD diagnosis is challenging, and further establishing more comprehensive datasets and dissecting the individual and group heterogeneity will be critical to achieve better ADS diagnosis performance. Machine learning methods will continue to be developed and are poised to help advance the field in this regard.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 100049
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 30303
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190
| | - Weizheng Yan
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 30303
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China 100088.
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27
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Structurally constrained effective brain connectivity. Neuroimage 2021; 239:118288. [PMID: 34147631 DOI: 10.1016/j.neuroimage.2021.118288] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 05/01/2021] [Accepted: 06/17/2021] [Indexed: 11/23/2022] Open
Abstract
The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal. The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task.
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Feng Y, Song J, Yan W, Wang J, Zhao C, Zeng Q. Investigation of Local White Matter Properties in Professional Chess Player: A Diffusion Magnetic Resonance Imaging Study Based on Automatic Annotation Fiber Clustering. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2968116] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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29
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Burkhardt E, Kinoshita M, Herbet G. Functional anatomy of the frontal aslant tract and surgical perspectives. J Neurosurg Sci 2021; 65:566-580. [PMID: 33870673 DOI: 10.23736/s0390-5616.21.05344-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The frontal aslant tract (FAT) is an intralobar white matter fasciculus providing dense connections between the medial part of the superior frontal gyrus, in particular the pre-supplementary motor area (SMA) and the SMA proper, and the lateral part of the frontal lobe, especially the inferior frontal gyrus. Although this tract has been characterized belatedly, it has received important attention in recent years due notably to its increasingly evidenced role in the speech and language networks. As cerebral tumors frequently affect the frontal lobe, an improved knowledge of the functional anatomy of the FAT is mandatory to refine the way neurosurgeries are performed and to give the patients the best opportunities to recover after surgery. In this work, we first describe the spatial arrangement of the FAT and detail its cortical projections. We then provide a comprehensive review of the functions supposedly mediated by this transverse frontal connectivity. It is structured following a tripartite organization where the linguistic (i.e. speech and language), supralinguistic (i.e. functions that interact with speech and language: executive functions, working memory, and social communication) and extralinguistic implications (i.e. functions outside the linguistic domain: visuospatial processing, praxis and motor skills) are successively addressed. We lastly discussed this knowledge in the context of wide-awake neurosurgeries for brain tumors. We emphasize the need to evaluate thoroughly the functions conveyed by FAT by means of longitudinally-designed studies to first estimate its plasticity potential and then to determine which tasks should be selected to avoid lasting impairments due to its disconnective breakdown.
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Affiliation(s)
- Eléonor Burkhardt
- Praxiling, CNRS UMR 5267, Paul Valéry Montpellier 3 University, Montpellier, France
| | - Masashi Kinoshita
- Department of Neurosurgery, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Guillaume Herbet
- University of Montpellier, CNRS UMR5203, INSERM U1191, Institute of Functional Genomics, Montpellier, France - .,Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France
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30
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Abou-Abbas L, van Noordt S, Desjardins JA, Cichonski M, Elsabbagh M. Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder. Brain Sci 2021; 11:brainsci11040409. [PMID: 33804986 PMCID: PMC8063929 DOI: 10.3390/brainsci11040409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/01/2022] Open
Abstract
Event-related potentials (ERPs) activated by faces and gaze processing are found in individuals with autism spectrum disorder (ASD) in the early stages of their development and may serve as a putative biomarker to supplement behavioral diagnosis. We present a novel approach to the classification of visual ERPs collected from 6-month-old infants using intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD). Selected features were used as inputs to two machine learning methods (support vector machines and k-nearest neighbors (k-NN)) using nested cross validation. Different runs were executed for the modelling and classification of the participants in the control and high-risk (HR) groups and the classification of diagnosis outcome within the high-risk group: HR-ASD and HR-noASD. The highest accuracy in the classification of familial risk was 88.44%, achieved using a support vector machine (SVM). A maximum accuracy of 74.00% for classifying infants at risk who go on to develop ASD vs. those who do not was achieved through k-NN. IMF-based extracted features were highly effective in classifying infants by risk status, but less effective by diagnostic outcome. Advanced signal analysis of ERPs integrated with machine learning may be considered a first step toward the development of an early biomarker for ASD.
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Affiliation(s)
- Lina Abou-Abbas
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada; (S.v.N.); (M.E.)
- Correspondence:
| | - Stefon van Noordt
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada; (S.v.N.); (M.E.)
| | - James A. Desjardins
- Cognitive and Affective Neuroscience Lab, Brock University, St. Catharines, ON L2S 3A1, Canada; (J.A.D.); (M.C.)
| | - Mike Cichonski
- Cognitive and Affective Neuroscience Lab, Brock University, St. Catharines, ON L2S 3A1, Canada; (J.A.D.); (M.C.)
| | - Mayada Elsabbagh
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada; (S.v.N.); (M.E.)
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31
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Zhang F, Xie G, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, Golby AJ, O'Donnell LJ. Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification. Neuroimage 2020; 220:117063. [PMID: 32574805 PMCID: PMC7572753 DOI: 10.1016/j.neuroimage.2020.117063] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/07/2020] [Accepted: 06/14/2020] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.
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Affiliation(s)
- Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Guoqiang Xie
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, China
| | - Laura Leung
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Michael A Mooney
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lorenz Epprecht
- Department of Otolaryngology, Head and Neck Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Isaiah Norton
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ossama Al-Mefty
- Department of Neurosurgery, 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; Departments of Psychiatry, Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, 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
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Zhang F, Cetin Karayumak S, Hoffmann N, Rathi Y, Golby AJ, O'Donnell LJ. Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation. Med Image Anal 2020; 65:101761. [PMID: 32622304 PMCID: PMC7483951 DOI: 10.1016/j.media.2020.101761] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 06/16/2020] [Accepted: 06/18/2020] [Indexed: 02/07/2023]
Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | | | - Nico Hoffmann
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Nogay HS, Adeli H. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 2020; 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. [PMID: 32866134 DOI: 10.1515/revneuro-2020-0043] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/25/2020] [Indexed: 02/24/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
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Affiliation(s)
- Hidir Selcuk Nogay
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- The Ohio State University, Mathematical Bioscience Institute, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, US
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34
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Hodge SM, Haselgrove C, Honor L, Kennedy DN, Frazier JA. An assessment of the autism neuroimaging literature for the prospects of re-executability. F1000Res 2020; 9:1031. [PMID: 33796274 PMCID: PMC7968525 DOI: 10.12688/f1000research.25306.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/22/2021] [Indexed: 11/20/2022] Open
Abstract
Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if the technical aspects of our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available. Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.
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Affiliation(s)
- Steven M. Hodge
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Christian Haselgrove
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Leah Honor
- Lamar Soutter Library, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - David N. Kennedy
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Jean A. Frazier
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
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35
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Hodge SM, Haselgrove C, Honor L, Kennedy DN, Frazier JA. An assessment of the autism neuroimaging literature for the prospects of re-executability. F1000Res 2020; 9:1031. [PMID: 33796274 PMCID: PMC7968525 DOI: 10.12688/f1000research.25306.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/04/2020] [Indexed: 05/04/2024] Open
Abstract
Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available. Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.
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Affiliation(s)
- Steven M. Hodge
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Christian Haselgrove
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Leah Honor
- Lamar Soutter Library, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - David N. Kennedy
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Jean A. Frazier
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
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36
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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Guevara M, Guevara P, Román C, Mangin JF. Superficial white matter: A review on the dMRI analysis methods and applications. Neuroimage 2020; 212:116673. [DOI: 10.1016/j.neuroimage.2020.116673] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 12/12/2022] Open
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Tunç B, Yankowitz LD, Parker D, Alappatt JA, Pandey J, Schultz RT, Verma R. Deviation from normative brain development is associated with symptom severity in autism spectrum disorder. Mol Autism 2019; 10:46. [PMID: 31867092 PMCID: PMC6907209 DOI: 10.1186/s13229-019-0301-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 11/29/2019] [Indexed: 12/13/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6–25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity. Results Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = − 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.
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Affiliation(s)
- Birkan Tunç
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,2Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,3Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA.,4Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Lisa D Yankowitz
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,5Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Drew Parker
- 6DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Jacob A Alappatt
- 6DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Juhi Pandey
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,3Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Robert T Schultz
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,3Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA.,7Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ragini Verma
- 4Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104 USA.,6DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
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Zhang F, Wu Y, Norton I, Rathi Y, Golby AJ, O'Donnell LJ. Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Hum Brain Mapp 2019; 40:3041-3057. [PMID: 30875144 PMCID: PMC6548665 DOI: 10.1002/hbm.24579] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/28/2019] [Accepted: 03/07/2019] [Indexed: 01/22/2023] Open
Abstract
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Ye Wu
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Isaiah Norton
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Yogesh Rathi
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
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Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review. REVIEW JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2019. [DOI: 10.1007/s40489-019-00158-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Hrdlicka M, Sanda J, Urbanek T, Kudr M, Dudova I, Kickova S, Pospisilova L, Mohaplova M, Maulisova A, Krsek P, Kyncl M, Blatny M, Komarek V. Diffusion Tensor Imaging And Tractography In Autistic, Dysphasic, And Healthy Control Children. Neuropsychiatr Dis Treat 2019; 15:2843-2852. [PMID: 31632032 PMCID: PMC6781738 DOI: 10.2147/ndt.s219545] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 09/01/2019] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Diffusion tensor imaging (DTI) is a powerful tool for investigating brain anatomical connectivity. The aim of our study was to compare brain connectivity among children with autism spectrum disorders (ASD), developmental dysphasia (DD), and healthy controls (HC) in the following tracts: the arcuate fasciculus (AF), inferior frontal occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), and uncinate fasciculus (UF). METHODS Our sample consisted of 113 children with a mean age 8.7±2.2 years (77 boys, 36 girls), divided into three subgroups: ASD (n=39), DD (n=36), and HC (n=38). The International Classification of Diseases, 10th ed. was used to make clinical diagnoses. DTI images were collected using a 1.5 T Phillips Achieva MR imaging system. RESULTS Detailed analyses of fractional anisotropy (FA) revealed significant differences among the ASD, DD, and HC groups in the left AF (p=0.014) and right AF (p=0.001), the left IFOF (p<0.001) and right IFOF (p<0.001), the left ILF (p<0.001) and right ILF (p<0.001), but not in the UF. Post-hoc analyses revealed three patterns of FA differences among the groups: (1) in the right AF, right IFOF, and right ILF, FA was significantly lower in the ASD group compared to the DD and HC groups; however, there was no difference in FA between DD and HC; (2) in the left AF and left IFOF, FA was significantly lower in the ASD than in the HC group, but there were no differences between DD vs HC nor DD vs ASD; and (3) in the left ILF, no difference in FA was seen between ASD and DD, but FA in both was significantly lower than in the HC. CONCLUSION Microstructural white matter properties differed between ASD vs DD and HC subjects. The tract where FA impairment in ASD and DD subjects was the most similar was the left ILF.
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Affiliation(s)
- Michal Hrdlicka
- Department of Child Psychiatry, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Jan Sanda
- Department of Radiology, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Tomas Urbanek
- Institute of Psychology, Academy of Sciences, Brno, Czech Republic
| | - Martin Kudr
- Department of Pediatric Neurology, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Iva Dudova
- Department of Child Psychiatry, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Stepanka Kickova
- Department of Child Psychiatry, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Lenka Pospisilova
- Department of Child Psychiatry, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic.,Charles University First Faculty of Medicine, Prague, Czech Republic
| | - Marketa Mohaplova
- Department of Child Psychiatry, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Alice Maulisova
- Department of Pediatric Neurology, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Pavel Krsek
- Department of Pediatric Neurology, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Martin Kyncl
- Department of Radiology, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Marek Blatny
- Department of Child Psychiatry, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic.,Department of Psychology, Faculty of Arts, Masaryk University, Brno, Czech Republic
| | - Vladimir Komarek
- Department of Pediatric Neurology, Charles University Second Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
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Wu Y, Zhang F, Makris N, Ning Y, Norton I, She S, Peng H, Rathi Y, Feng Y, Wu H, O'Donnell LJ. Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder. Neuroimage 2018; 181:16-29. [PMID: 29890329 PMCID: PMC6415925 DOI: 10.1016/j.neuroimage.2018.06.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 06/02/2018] [Accepted: 06/05/2018] [Indexed: 01/17/2023] Open
Abstract
This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.
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Affiliation(s)
- Ye Wu
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nikos Makris
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuping Ning
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Isaiah Norton
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shenglin She
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Hongjun Peng
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuanjing Feng
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Huawang Wu
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China.
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Dadalko OI, Travers BG. Evidence for Brainstem Contributions to Autism Spectrum Disorders. Front Integr Neurosci 2018; 12:47. [PMID: 30337860 PMCID: PMC6180283 DOI: 10.3389/fnint.2018.00047] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 09/18/2018] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects one in 59 children in the United States. Although there is a mounting body of knowledge of cortical and cerebellar contributions to ASD, our knowledge about the early developing brainstem in ASD is only beginning to accumulate. Understanding how brainstem neurotransmission is implicated in ASD is important because many of this condition’s sensory and motor symptoms are consistent with brainstem pathology. Therefore, the purpose of this review was to integrate epidemiological, behavioral, histological, neuroimaging, and animal evidence of brainstem contributions to ASD. Because ASD is a neurodevelopmental condition, we examined the available data through a lens of hierarchical brain development. The review of the literature suggests that developmental alterations of the brainstem could have potential cascading effects on cortical and cerebellar formation, ultimately leading to ASD symptoms. This view is supported by human epidemiology findings and data from animal models of ASD, showing that perturbed development of the brainstem substructures, particularly during the peak formation of the brainstem’s monoaminergic centers, may relate to ASD or ASD-like behaviors. Furthermore, we review evidence from human histology, psychophysiology, and neuroimaging suggesting that brainstem development and maturation may be atypical in ASD and may be related to key ASD symptoms, such as atypical sensorimotor features and social responsiveness. From this review there emerges the need of future research to validate early detection of the brainstem-based somatosensory and psychophysiological behaviors that emerge in infancy, and to examine the brainstem across the life span, while accounting for age. In all, there is preliminary evidence for brainstem involvement in ASD, but a better understanding of the brainstem’s role would likely pave the way for earlier diagnosis and treatment of ASD.
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Affiliation(s)
- Olga I Dadalko
- Motor and Brain Development Lab, Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
| | - Brittany G Travers
- Motor and Brain Development Lab, Occupational Therapy Program in the Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States
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Zhang F, Wu Y, Norton I, Rigolo L, Rathi Y, Makris N, O'Donnell LJ. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 2018; 179:429-447. [PMID: 29920375 PMCID: PMC6080311 DOI: 10.1016/j.neuroimage.2018.06.027] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/01/2018] [Accepted: 06/08/2018] [Indexed: 12/15/2022] Open
Abstract
This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.
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Affiliation(s)
- Fan Zhang
- Harvard Medical School, Boston, USA.
| | - Ye Wu
- Harvard Medical School, Boston, USA
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Sydnor VJ, Rivas-Grajales AM, Lyall AE, Zhang F, Bouix S, Karmacharya S, Shenton ME, Westin CF, Makris N, Wassermann D, O'Donnell LJ, Kubicki M. A comparison of three fiber tract delineation methods and their impact on white matter analysis. Neuroimage 2018; 178:318-331. [PMID: 29787865 DOI: 10.1016/j.neuroimage.2018.05.044] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 04/09/2018] [Accepted: 05/18/2018] [Indexed: 12/20/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.
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Affiliation(s)
- Valerie J Sydnor
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana María Rivas-Grajales
- 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, Charlestown, MA, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 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, Charlestown, MA, USA
| | - Fan Zhang
- Laboratory for Mathematics in Imaging, 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
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sarina Karmacharya
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's 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, Charlestown, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - Carl-Fredrik Westin
- Laboratory for Mathematics in Imaging, 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
| | - 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, Charlestown, MA, USA
| | - Demian Wassermann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Athena, Université Cote d'Azur, Inria, France; Parietal, CEA, Université Paris-Saclay, INRIA Saclay Île-de-France, France
| | - Lauren J O'Donnell
- Laboratory for Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- 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, Charlestown, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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The Original Social Network: White Matter and Social Cognition. Trends Cogn Sci 2018; 22:504-516. [PMID: 29628441 DOI: 10.1016/j.tics.2018.03.005] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Revised: 03/06/2018] [Accepted: 03/12/2018] [Indexed: 01/24/2023]
Abstract
Social neuroscience has traditionally focused on the functionality of gray matter regions, ignoring the critical role played by axonal fiber pathways in supporting complex social processes. In this paper, we argue that research on white matter is essential for understanding a range of topics in social neuroscience, such as face processing, theory of mind, empathy, and imitation, as well as clinical disorders defined by aberrant social behavior, such as prosopagnosia, autism, and schizophrenia. We provide practical advice on how best to carry out these studies, which ultimately will substantially deepen our understanding of the neurobiological basis of social behavior.
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Zhang F, Wu W, Ning L, McAnulty G, Waber D, Gagoski B, Sarill K, Hamoda HM, Song Y, Cai W, Rathi Y, O'Donnell LJ. Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis. Neuroimage 2018; 171:341-354. [PMID: 29337279 DOI: 10.1016/j.neuroimage.2018.01.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/13/2022] Open
Abstract
This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Weining Wu
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China; Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Gloria McAnulty
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Deborah Waber
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Borjan Gagoski
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Kiera Sarill
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Hesham M Hamoda
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Yang Song
- School of Information Technologies, The University of Sydney, Sydney, Australia
| | - Weidong Cai
- School of Information Technologies, The University of Sydney, Sydney, Australia
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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