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Mellema CJ, Montillo AA. Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI. J Neural Eng 2023; 20:066023. [PMID: 37963396 DOI: 10.1088/1741-2552/ad0c5f] [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: 07/06/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms ofreproducibilityand theability to predict individual traitsin order to demonstrate these measures' internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits.Main results.The proposed new FC measure ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.
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Affiliation(s)
- Cooper J Mellema
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- Advanced Imaging Research Center, Dallas, TX, United States of America
- Radiology Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
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Chen S, Redline S, Eden UT, Prerau MJ. Dynamic models of obstructive sleep apnea provide robust prediction of respiratory event timing and a statistical framework for phenotype exploration. Sleep 2022; 45:6657760. [PMID: 35932480 PMCID: PMC9742895 DOI: 10.1093/sleep/zsac189] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/25/2022] [Indexed: 12/15/2022] Open
Abstract
Obstructive sleep apnea (OSA), in which breathing is reduced or ceased during sleep, affects at least 10% of the population and is associated with numerous comorbidities. Current clinical diagnostic approaches characterize severity and treatment eligibility using the average respiratory event rate over total sleep time (apnea-hypopnea index). This approach, however, does not characterize the time-varying and dynamic properties of respiratory events that can change as a function of body position, sleep stage, and previous respiratory event activity. Here, we develop a statistical model framework based on point process theory that characterizes the relative influences of all these factors on the moment-to-moment rate of event occurrence. Our results provide new insights into the temporal dynamics of respiratory events, suggesting that most adults have a characteristic event pattern that involves a period of normal breathing followed by a period of increased probability of respiratory event occurrence, while significant differences in event patterns are observed among gender, age, and race/ethnicity groups. Statistical goodness-of-fit analysis suggests consistent and substantial improvements in our ability to capture the timing of individual respiratory events using our modeling framework. Overall, we demonstrate a more statistically robust approach to characterizing sleep disordered breathing that can also serve as a basis for identifying future patient-specific respiratory phenotypes, providing an improved pathway towards developing individualized treatments.
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Affiliation(s)
- Shuqiang Chen
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Michael J Prerau
- Corresponding author. Michael J. Prerau, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, 221 Longwood Avenue, Boston, MA 02115, USA.
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Ye G, Zhang J, Bi Z, Zhang W, Zhang M, Zhang Q, Wang M, Chen J. Dominant factors of the phosphorus regulatory network differ under various dietary phosphate loads in healthy individuals. Ren Fail 2021; 43:1076-1086. [PMID: 34193019 PMCID: PMC8253199 DOI: 10.1080/0886022x.2021.1945463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The purpose of this study was to explore the contribution of each factor of the phosphorus metabolism network following phosphorus diet intervention via Granger causality analysis. METHODS In this study, a total of six healthy male volunteers were enrolled. All participants sequentially received regular, low-, and high-phosphorus diets. Consumption of each diet lasted for five days, with a 5-day washout period between different diets. Blood and urinary samples were collected on the fifth day of consumption of each diet at 9 time points (00:00, 04:00, 08:00, 10:00, 12:00, 14:00, 16:00, 20:00, 24:00) for measurements of serum levels of phosphate, calcium, PTH, FGF23, BALP, α-Klotho, and 1,25 D and urinary phosphorus excretion. Granger causality and the centrality of the above variables in the phosphorus network were analyzed by pairwise panel Granger causality analysis using the time-series data. RESULTS The mean age of the participants was 28.5 ± 2.1 years. By using Granger causality analysis, we found that the α-Klotho level had the strongest connection with and played a key role in influencing the other variables. In addition, urinary phosphorus excretion was frequently regulated by other variables in the network of phosphorus metabolism following a regular phosphorus diet. After low-phosphorus diet intervention, serum phosphate affected the other factors the most, and the 1,25 D level was the main outcome factor, while urinary phosphorus excretion was the most strongly associated variable in the network of phosphorus metabolism. After high-phosphorus diet intervention, FGF23 and 1,25 D played a more critical role in active regulation and passive regulation in the Granger causality analysis. CONCLUSIONS Variations in dietary phosphorus intake led to changes in the central factors involved in phosphorus metabolism.
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Affiliation(s)
- Guoxin Ye
- Nephrology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Zhang
- Division of Nutrition, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhaori Bi
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Weichen Zhang
- Nephrology, Huashan Hospital, Fudan University, Shanghai, China
| | - Minmin Zhang
- Nephrology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qian Zhang
- Nephrology, Huashan Hospital, Fudan University, Shanghai, China
| | - Mengjing Wang
- Nephrology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Chen
- Nephrology, Huashan Hospital, Fudan University, Shanghai, China
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Hosseinzadeh Kassani P, Xiao L, Zhang G, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3290-3299. [PMID: 32340941 PMCID: PMC7735538 DOI: 10.1109/tmi.2020.2990371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Human brain development is a complex and dynamic process caused by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies.
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Abstract
In epilepsy research, the analysis of rodent electroencephalogram (EEG) has been performed by many laboratories with a variety of techniques. However, the acquisition and basic analysis of rodent EEG have only recently been standardized. Since a number of software platforms and increased computational power have become widely available, advanced rodent EEG analysis is now more accessible to investigators working with rodent models of epilepsy. In this review, the approach to the analysis of rodent EEG will be examined, including the evaluation of both epileptiform and background activity. Major caveats when employing these analyses, cellular and circuit-level correlates of EEG changes, and important differences between rodent and human EEG are also reviewed. The currently available techniques show great promise in gaining a deeper understanding of the complexities hidden within the EEG in rodent models of epilepsy.
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Affiliation(s)
- Atul Maheshwari
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA.,Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
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Stacey W, Kramer M, Gunnarsdottir K, Gonzalez-Martinez J, Zaghloul K, Inati S, Sarma S, Stiso J, Khambhati AN, Bassett DS, Smith RJ, Liu VB, Lopour BA, Staba R. Emerging roles of network analysis for epilepsy. Epilepsy Res 2020; 159:106255. [PMID: 31855828 PMCID: PMC6990460 DOI: 10.1016/j.eplepsyres.2019.106255] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 12/08/2019] [Indexed: 11/29/2022]
Abstract
In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.
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Affiliation(s)
- William Stacey
- Department of Neurology, Department of Biomedical Engineering, University of Michigan, United States.
| | - Mark Kramer
- Department of Mathematics and Statistics, Center of Systems Neuroscience, Boston University, United States
| | | | | | - Kareem Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, NIH, United States
| | - Sara Inati
- Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, NIH, United States
| | - Sridevi Sarma
- Department of Neurology, Department of Biomedical Engineering, University of Michigan, United States
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, United States
| | - Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, United States
| | | | - Rachel J Smith
- Department of Biomedical Engineering, University of California, Irvine, United States
| | - Virginia B Liu
- Department of Pediatrics, University of California, Irvine, United States; Department of Child Neurology, Children's Hospital of Orange County, CA, United States
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, United States
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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