1
|
Theis N, Rubin J, Cape J, Iyengar S, Prasad KM. Threshold Selection for Brain Connectomes. Brain Connect 2023; 13:383-393. [PMID: 37166374 PMCID: PMC10517318 DOI: 10.1089/brain.2022.0082] [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] [Indexed: 05/12/2023] Open
Abstract
Introduction: Structural and functional brain connectomes represent macroscale data collected through techniques such as magnetic resonance imaging (MRI). Connectomes may contain noise that contributes to false-positive edges, thereby obscuring structure-function relationships and data interpretation. Thresholding procedures can be applied to reduce network density by removing low-signal edges, but there is limited consensus on appropriate selection of thresholds. This article compares existing thresholding methods and introduces a novel alternative "objective function" thresholding method. Methods: The performance of thresholding approaches, based on percolation and objective functions, is assessed by (1) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and by (2) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. An application to empirical data is provided. Results: Our proposed objective function-based threshold exhibits the best performance in terms of resulting in high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks. Discussion: Existing network thresholding methods yield widely different results when graph metrics are subsequently computed. Thresholding based on the objective function maintains a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides a proof of principle that objective function thresholding could offer a useful approach to reducing the network density of functional connectivity data.
Collapse
Affiliation(s)
- Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jonathan Rubin
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Joshua Cape
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Konasale M. Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA
- Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, Pennsylvania, USA
| |
Collapse
|
2
|
Lv Q, Zeljic K, Zhao S, Zhang J, Zhang J, Wang Z. Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning. Neurosci Bull 2023; 39:1309-1326. [PMID: 37093448 PMCID: PMC10387015 DOI: 10.1007/s12264-023-01057-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 02/17/2023] [Indexed: 04/25/2023] Open
Abstract
Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
Collapse
Affiliation(s)
- Qian Lv
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Kristina Zeljic
- School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK
| | - Shaoling Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Jiangtao Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Jianmin Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
| |
Collapse
|
3
|
Reed JD, Blackwell KT. Prediction of Neural Diameter From Morphology to Enable Accurate Simulation. Front Neuroinform 2021; 15:666695. [PMID: 34149388 PMCID: PMC8209307 DOI: 10.3389/fninf.2021.666695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/10/2021] [Indexed: 11/29/2022] Open
Abstract
Accurate neuron morphologies are paramount for computational model simulations of realistic neural responses. Over the last decade, the online repository NeuroMorpho.Org has collected over 140,000 available neuron morphologies to understand brain function and promote interaction between experimental and computational research. Neuron morphologies describe spatial aspects of neural structure; however, many of the available morphologies do not contain accurate diameters that are essential for computational simulations of electrical activity. To best utilize available neuron morphologies, we present a set of equations that predict dendritic diameter from other morphological features. To derive the equations, we used a set of NeuroMorpho.org archives with realistic neuron diameters, representing hippocampal pyramidal, cerebellar Purkinje, and striatal spiny projection neurons. Each morphology is separated into initial, branching children, and continuing nodes. Our analysis reveals that the diameter of preceding nodes, Parent Diameter, is correlated to diameter of subsequent nodes for all cell types. Branching children and initial nodes each required additional morphological features to predict diameter, such as path length to soma, total dendritic length, and longest path to terminal end. Model simulations reveal that membrane potential response with predicted diameters is similar to the original response for several tested morphologies. We provide our open source software to extend the utility of available NeuroMorpho.org morphologies, and suggest predictive equations may supplement morphologies that lack dendritic diameter and improve model simulations with realistic dendritic diameter.
Collapse
Affiliation(s)
- Jonathan D Reed
- Krasnow Institute of Advanced Study, George Mason University, Fairfax, VA, United States.,Department of Biology, George Mason University, Fairfax, VA, United States
| | - Kim T Blackwell
- Krasnow Institute of Advanced Study, George Mason University, Fairfax, VA, United States.,Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA, United States
| |
Collapse
|
4
|
Wang Z, Xin J, Wang Z, Yao Y, Zhao Y, Qian W. Brain functional network modeling and analysis based on fMRI: a systematic review. Cogn Neurodyn 2021; 15:389-403. [PMID: 34040667 PMCID: PMC8131458 DOI: 10.1007/s11571-020-09630-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 08/05/2020] [Accepted: 08/20/2020] [Indexed: 12/12/2022] Open
Abstract
In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.
Collapse
Affiliation(s)
- Zhongyang Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Junchang Xin
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, Shenyang, China
| | - Zhiqiong Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ USA
| | - Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Wei Qian
- College of Engineering, The University of Texas at El Paso, El Paso, TX USA
| |
Collapse
|
6
|
Zhan Y, Wei J, Liang J, Xu X, He R, Robbins TW, Wang Z. Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model. Am J Psychiatry 2021; 178:65-76. [PMID: 32539526 DOI: 10.1176/appi.ajp.2020.19101091] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. METHODS Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. RESULTS Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. CONCLUSIONS The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.
Collapse
Affiliation(s)
- Yafeng Zhan
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Jianze Wei
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Jian Liang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Xiu Xu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Ran He
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Trevor W Robbins
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Zheng Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| |
Collapse
|