1
|
Santhanam P, Nath T, Peng C, Bai H, Zhang H, Ahima RS, Chellappa R. Artificial intelligence and body composition. Diabetes Metab Syndr 2023; 17:102732. [PMID: 36867973 DOI: 10.1016/j.dsx.2023.102732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023]
Abstract
AIMS Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends. METHODS We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review. RESULTS AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis. CONCLUSIONS AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.
Collapse
Affiliation(s)
- Prasanna Santhanam
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| | - Tanmay Nath
- Department Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Cheng Peng
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Harrison Bai
- Department of Radiology and Radiology Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Helen Zhang
- The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Rexford S Ahima
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Rama Chellappa
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| |
Collapse
|
2
|
Nath T, Caffo B, Wager T, Lindquist MA. A machine learning based approach towards high-dimensional mediation analysis. Neuroimage 2023; 268:119843. [PMID: 36586543 PMCID: PMC10332048 DOI: 10.1016/j.neuroimage.2022.119843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/02/2022] [Accepted: 12/27/2022] [Indexed: 12/30/2022] Open
Abstract
Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome.
Collapse
Affiliation(s)
- Tanmay Nath
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
| | - Brian Caffo
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Tor Wager
- The Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Martin A Lindquist
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| |
Collapse
|
3
|
Santhanam P, Nath T, Lindquist MA, Cooper DS. Relationship Between TSH Levels and Cognition in the Young Adult: An Analysis of the Human Connectome Project Data. J Clin Endocrinol Metab 2022; 107:1897-1905. [PMID: 35389477 DOI: 10.1210/clinem/dgac189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT The nature of the relationship between serum thyrotropin (TSH) levels and higher cognitive abilities is unclear, especially within the normal reference range and in the younger population. OBJECTIVE To assess the relationship between serum TSH levels and mental health and sleep quality parameters (fluid intelligence [Gf], MMSE (Mini-Mental State Examination), depression scores, and, finally, Pittsburgh Sleep Quality Index (PSQI) scores (working memory, processing speed, and executive function) in young adults. METHODS This was a retrospective analysis of the data from the Human Connectome Project (HCP). The HCP consortium is seeking to map human brain circuits systematically and identify their relationship to behavior in healthy adults. Included were 391 female and 412 male healthy participants aged 22-35 years at the time of the screening interview. We excluded persons with serum TSH levels outside the reference range (0.4-4.5 mU/L). TSH was transformed logarithmically (log TSH). All the key variables were normalized and then linear regression analysis was performed to assess the relationship between log TSH as a cofactor and Gf as the dependent variable. Finally, a machine learning method, random forest regression, predicted Gf from the dependent variables (including alcohol and tobacco use). The main outcome was normalized Gf (nGf) and Gf scores. RESULTS Log TSH was a significant co-predictor of nGF in females (β = 0.31(±0.1), P < .01) but not in males. Random forest analysis showed that the model(s) had a better predictive value for females (r = 0.39, mean absolute error [MAE] = 0.81) than males (r = 0.24, MAE = 0.77). CONCLUSION Higher serum TSH levels might be associated with higher Gf scores in young women.
Collapse
Affiliation(s)
- Prasanna Santhanam
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tanmay Nath
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David S Cooper
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
4
|
Lauer J, Zhou M, Ye S, Menegas W, Schneider S, Nath T, Rahman MM, Di Santo V, Soberanes D, Feng G, Murthy VN, Lauder G, Dulac C, Mathis MW, Mathis A. Multi-animal pose estimation, identification and tracking with DeepLabCut. Nat Methods 2022; 19:496-504. [PMID: 35414125 PMCID: PMC9007739 DOI: 10.1038/s41592-022-01443-0] [Citation(s) in RCA: 95] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 03/04/2022] [Indexed: 11/23/2022]
Abstract
Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking-features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal's identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.
Collapse
Affiliation(s)
- Jessy Lauer
- Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA
| | - Mu Zhou
- Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Shaokai Ye
- Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - William Menegas
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steffen Schneider
- Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Tanmay Nath
- Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA
| | - Mohammed Mostafizur Rahman
- Department for Molecular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute (HHMI), Chevy Chase, MD, USA
| | - Valentina Di Santo
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Daniel Soberanes
- Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA
| | - Guoping Feng
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Venkatesh N Murthy
- Department for Molecular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - George Lauder
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Catherine Dulac
- Department for Molecular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute (HHMI), Chevy Chase, MD, USA
| | - Mackenzie Weygandt Mathis
- Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
- Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA.
| | - Alexander Mathis
- Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
- Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA.
- Department for Molecular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
| |
Collapse
|
5
|
Khitan Z, Nath T, Santhanam P. Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort. J Clin Hypertens (Greenwich) 2021; 23:2137-2145. [PMID: 34847294 PMCID: PMC8696217 DOI: 10.1111/jch.14397] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 10/23/2021] [Accepted: 11/08/2021] [Indexed: 12/01/2022]
Abstract
Albuminuria and estimated glomerular filtration rate (e-GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we have used machine learning methods to assess the risk of albuminuria in persons with diabetes using body composition and other determinants of metabolic health. This study is a comparative analysis of the different methods to predict albuminuria in persons with diabetes mellitus who are older than 40 years of age, using the LOOK AHEAD study cohort-baseline characteristics. Age, different metrics of body composition, duration of diabetes, hemoglobin A1c, serum creatinine, serum triglycerides, serum cholesterol, serum HDL, serum LDL, maximum exercise capacity, systolic blood pressure, diastolic blood pressure, and the ankle-brachial index are used as predictors of albuminuria. We used Area under the curve (AUC) as a metric to compare the classification results of different algorithms, and we show that AUC for the different models are as follows: Random forest classifier-0.65, gradient boost classifier-0.61, logistic regression-0.66, support vector classifier -0.61, multilayer perceptron -0.67, and stacking classifier-0.62. We used the Random forest model to show that the duration of diabetes, A1C, serum triglycerides, SBP, Maximum exercise Capacity, serum creatinine, subtotal lean mass, DBP, and subtotal fat mass are important features for the classification of albuminuria. In summary, when applied to metabolic imaging (using DXA), machine learning techniques offer unique insights into the risk factors that determine the development of albuminuria in diabetes.
Collapse
Affiliation(s)
- Zeid Khitan
- Division of NephrologyDepartment of MedicineJoan C Edwards School of MedicineMarshall UniversityHuntingtonWest VirginiaUSA
| | - Tanmay Nath
- Department of BiostatisticsBloomberg School of Public HealthJohns Hopkins University, BaltimoreMarylandUSA
| | - Prasanna Santhanam
- Division of EndocrinologyDiabetes, & MetabolismDepartment of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| |
Collapse
|
6
|
Santhanam P, Solnes L, Nath T, Roussin JP, Gray D, Frey E, Sgouros G, Ladenson PW. Real-time quantitation of thyroidal radioiodine uptake in thyroid disease with monitoring by a collar detection device. Sci Rep 2021; 11:18479. [PMID: 34531443 PMCID: PMC8446004 DOI: 10.1038/s41598-021-97408-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/25/2021] [Indexed: 11/30/2022] Open
Abstract
Radioactive iodine (RAI) is safe and effective in most patients with hyperthyroidism but not all individuals are cured by the first dose, and most develop post-RAI hypothyroidism. Postoperative RAI therapy for remnant ablation is successful in 80–90% of thyroid cancer patients and sometimes induces remission of nonresectable cervical and/or distant metastatic disease but the effective tumor dose is usually not precisely known and must be moderated to avoid short- and long-term adverse effects on other tissues. The Collar Therapy Indicator (COTI) is a radiation detection device embedded in a cloth collar secured around the patient’s neck and connected to a recording and data transmission box. In previously published experience, the data can be collected at multiple time points, reflecting local cervical RAI exposure and correlating well with conventional methods. We evaluated the real-time uptake of RAI in patients with hyperthyroid Graves’ disease and thyroid cancer. We performed a pilot feasibility prospective study. Data were analyzed using R© (version 4.0.3, The R Foundation for Statistical Computing, 2020), and Python (version 3.6, Matplotlib version 3.0.3). The COTI was able to provide a quantitative temporal pattern of uptake within the thyroid in persons with Graves’ disease and lateralized the remnant tissue in persons with thyroid cancer. The study has demonstrated that the portable collar radiation detection device outside of a healthcare facility is accurate and feasible for use after administration of RAI for diagnostic studies and therapy to provide a complete collection of fractional target radioactivity data compared to that traditionally acquired with clinic-based measurements at one or two time-points. Clinical Trials Registration NCT03517579, DOR 5/7/2018.
Collapse
Affiliation(s)
- Prasanna Santhanam
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, 1830 E. Monument St./Ste. 333, Baltimore, MD, 21287, USA.
| | - Lilja Solnes
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Tanmay Nath
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21287, USA
| | | | | | - Eric Frey
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - George Sgouros
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Paul W Ladenson
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, 1830 E. Monument St./Ste. 333, Baltimore, MD, 21287, USA
| |
Collapse
|
7
|
Nath T, Ahima RS, Santhanam P. Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach. PLoS One 2021; 16:e0248039. [PMID: 33788855 PMCID: PMC8011752 DOI: 10.1371/journal.pone.0248039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/18/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetes mellitus is associated with increased cardiovascular disease (CVD) related morbidity, mortality and death. Exercise capacity in persons with type 2 diabetes has been shown to be predictive of cardiovascular events. In this study, we used the data from the prospective randomized LOOK AHEAD study and used machine learning algorithms to help predict exercise capacity (measured in Mets) from the baseline data that included cardiovascular history, medications, blood pressure, demographic information, anthropometric and Dual-energy X-Ray Absorptiometry (DXA) measured body composition metrics. We excluded variables with high collinearity and included DXA obtained Subtotal (total minus head) fat percentage and Subtotal lean mass (gms). Thereafter, we used different machine learning methods to predict maximum exercise capacity. The different machine learning models showed a strong predictive performance for both females and males. Our study shows that using baseline data from a large prospective cohort, we can predict maximum exercise capacity in persons with diabetes mellitus. We show that subtotal fat percentage is the most important feature for predicting the exercise capacity for males and females after accounting for other important variables. Until now, BMI and waist circumference were commonly used surrogates for adiposity and there was a relative under-appreciation of body composition metrics for understanding the pathophysiology of CVD. The recognition of body fat percentage as an important marker in determining CVD risk has prognostic implications with respect to cardiovascular morbidity and mortality.
Collapse
Affiliation(s)
- Tanmay Nath
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Rexford S. Ahima
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Prasanna Santhanam
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| |
Collapse
|
8
|
Nath T, Ahima RS, Santhanam P. DXA measured body composition predicts blood pressure using machine learning methods. J Clin Hypertens (Greenwich) 2020; 22:1098-1100. [PMID: 32497407 DOI: 10.1111/jch.13914] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/02/2020] [Accepted: 05/06/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Tanmay Nath
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Rexford S Ahima
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Prasanna Santhanam
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
9
|
Nath T, Mathis A, Chen AC, Patel A, Bethge M, Mathis MW. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat Protoc 2019; 14:2152-2176. [PMID: 31227823 DOI: 10.1038/s41596-019-0176-0] [Citation(s) in RCA: 506] [Impact Index Per Article: 101.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 04/09/2019] [Indexed: 12/13/2022]
Abstract
Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open-source toolbox called DeepLabCut that builds on a state-of-the-art human pose-estimation algorithm to allow a user to train a deep neural network with limited training data to precisely track user-defined features that match human labeling accuracy. Here, we provide an updated toolbox, developed as a Python package, that includes new features such as graphical user interfaces (GUIs), performance improvements, and active-learning-based network refinement. We provide a step-by-step procedure for using DeepLabCut that guides the user in creating a tailored, reusable analysis pipeline with a graphical processing unit (GPU) in 1-12 h (depending on frame size). Additionally, we provide Docker environments and Jupyter Notebooks that can be run on cloud resources such as Google Colaboratory.
Collapse
Affiliation(s)
- Tanmay Nath
- Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA
| | - Alexander Mathis
- Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA.,Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, USA
| | - An Chi Chen
- Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa
| | - Amir Patel
- Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa
| | - Matthias Bethge
- Tübingen AI Center & Centre for Integrative Neuroscience, Eberhard Karls Universität Tübingen, Tübingen, Germany
| | | |
Collapse
|
10
|
Abstract
Background The male predominance in the prevalence of autism spectrum disorder (ASD) has motivated research on sex differentiation in ASD. Multiple sources of evidence have suggested a neurophenotypic convergence of ASD-related characteristics and typical sex differences. Two existing, albeit competing, models provide predictions on such neurophenotypic convergence. These two models are testable with neuroimaging. Specifically, the Extreme Male Brain (EMB) model predicts that ASD is associated with enhanced brain maleness in both males and females with ASD (i.e., a shift-towards-maleness). In contrast, the Gender Incoherence (GI) model predicts a shift-towards-maleness in females, yet a shift-towards-femaleness in males with ASD. Methods To clarify whether either model applies to the intrinsic functional properties of the brain in males with ASD, we measured the statistical overlap between typical sex differences and ASD-related atypicalities in resting-state fMRI (R-fMRI) datasets largely available in males. Main analyses focused on two large-scale R-fMRI samples: 357 neurotypical (NT) males and 471 NT females from the 1000 Functional Connectome Project and 360 males with ASD and 403 NT males from the Autism Brain Imaging Data Exchange. Results Across all R-fMRI metrics, results revealed coexisting, but network-specific, shift-towards-maleness and shift-towards-femaleness in males with ASD. A shift-towards-maleness mostly involved the default network, while a shift-towards-femaleness mostly occurred in the somatomotor network. Explorations of the associated cognitive processes using available cognitive ontology maps indicated that higher-order social cognitive functions corresponded to the shift-towards-maleness, while lower-order sensory motor processes corresponded to the shift-towards-femaleness. Conclusions The present findings suggest that atypical intrinsic brain properties in males with ASD partly reflect mechanisms involved in sexual differentiation. A model based on network-dependent atypical sex mosaicism can synthesize prior competing theories on factors involved in sex differentiation in ASD. Electronic supplementary material The online version of this article (10.1186/s13229-018-0192-x) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Dorothea L Floris
- 1Hassenfeld Children's Hospital at NYU Langone Health, Department of Child and Adolescent Psychiatry, Child Study Center, 1 Park Avenue, New York City, NY 10016 USA
| | - Meng-Chuan Lai
- 2Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, ON M6J 1H4 Canada.,3Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH UK
| | - Tanmay Nath
- 1Hassenfeld Children's Hospital at NYU Langone Health, Department of Child and Adolescent Psychiatry, Child Study Center, 1 Park Avenue, New York City, NY 10016 USA
| | - Michael P Milham
- 4Center for the Developing Brain, Child Mind Institute, New York, NY 10022 USA.,5Nathan S Kline Institute for Psychiatric Research, Orangeburg, NY 10962 USA
| | - Adriana Di Martino
- 1Hassenfeld Children's Hospital at NYU Langone Health, Department of Child and Adolescent Psychiatry, Child Study Center, 1 Park Avenue, New York City, NY 10016 USA
| |
Collapse
|
11
|
Aoki Y, Yoncheva YN, Chen B, Nath T, Sharp D, Lazar M, Velasco P, Milham MP, Di Martino A. Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. JAMA Psychiatry 2017; 74:1120-1128. [PMID: 28877317 PMCID: PMC5710226 DOI: 10.1001/jamapsychiatry.2017.2573] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
IMPORTANCE Clinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasingly appreciated, but the underlying brain mechanisms remain unknown to date. OBJECTIVE To examine associations between white matter organization and 2 commonly co-occurring neurodevelopmental conditions, ASD and ADHD, through both categorical and dimensional approaches. DESIGN, SETTING, AND PARTICIPANTS This investigation was a cross-sectional diffusion tensor imaging (DTI) study at an outpatient academic clinical and research center, the Department of Child and Adolescent Psychiatry at New York University Langone Medical Center. Participants were children with ASD, children with ADHD, or typically developing children. Data collection was ongoing from December 2008 to October 2015. MAIN OUTCOMES AND MEASURES The primary measure was voxelwise fractional anisotropy (FA) analyzed via tract-based spatial statistics. Additional voxelwise DTI metrics included radial diffusivity (RD), mean diffusivity (MD), axial diffusivity (AD), and mode of anisotropy (MA). RESULTS This cross-sectional DTI study analyzed data from 174 children (age range, 6.0-12.9 years), selected from a larger sample after quality assurance to be group matched on age and sex. After quality control, the study analyzed data from 69 children with ASD (mean [SD] age, 8.9 [1.7] years; 62 male), 55 children with ADHD (mean [SD] age, 9.5 [1.5] years; 41 male), and 50 typically developing children (mean [SD] age, 9.4 [1.5] years; 38 male). Categorical analyses revealed a significant influence of ASD diagnosis on several DTI metrics (FA, MD, RD, and AD), primarily in the corpus callosum. For example, FA analyses identified a cluster of 4179 voxels (TFCE FEW corrected P < .05) in posterior portions of the corpus callosum. Dimensional analyses revealed associations between ASD severity and FA, RD, and MD in more extended portions of the corpus callosum and beyond (eg, corona radiata and inferior longitudinal fasciculus) across all individuals, regardless of diagnosis. For example, FA analyses revealed clusters overall encompassing 12121 voxels (TFCE FWE corrected P < .05) with a significant association with parent ratings in the social responsiveness scale. Similar results were evident using an independent measure of ASD traits (ie, children communication checklist, second edition). Total severity of ADHD-traits was not significantly related to DTI metrics but inattention scores were related to AD in corpus callosum in a cluster sized 716 voxels. All these findings were robust to algorithmic correction of motion artifacts with the DTIPrep software. CONCLUSIONS AND RELEVANCE Dimensional analyses provided a more complete picture of associations between ASD traits and inattention and indexes of white matter organization, particularly in the corpus callosum. This transdiagnostic approach can reveal dimensional relationships linking white matter structure to neurodevelopmental symptoms.
Collapse
Affiliation(s)
- Yuta Aoki
- Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, New York
| | - Yuliya N. Yoncheva
- Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, New York
| | - Bosi Chen
- Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, New York
| | - Tanmay Nath
- Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, New York
| | - Dillon Sharp
- Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, New York
| | - Mariana Lazar
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York
| | - Pablo Velasco
- Center for Brain Imaging, New York University, New York
| | - Michael P. Milham
- The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York,Child Mind Institute, New York, New York
| | - Adriana Di Martino
- Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, New York
| |
Collapse
|
12
|
Hartzell JF, Davis B, Melcher D, Miceli G, Jovicich J, Nath T, Singh NC, Hasson U. Brains of verbal memory specialists show anatomical differences in language, memory and visual systems. Neuroimage 2015; 131:181-92. [PMID: 26188261 DOI: 10.1016/j.neuroimage.2015.07.027] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 06/19/2015] [Accepted: 07/08/2015] [Indexed: 12/14/2022] Open
Abstract
We studied a group of verbal memory specialists to determine whether intensive oral text memory is associated with structural features of hippocampal and lateral-temporal regions implicated in language processing. Professional Vedic Sanskrit Pandits in India train from childhood for around 10years in an ancient, formalized tradition of oral Sanskrit text memorization and recitation, mastering the exact pronunciation and invariant content of multiple 40,000-100,000 word oral texts. We conducted structural analysis of gray matter density, cortical thickness, local gyrification, and white matter structure, relative to matched controls. We found massive gray matter density and cortical thickness increases in Pandit brains in language, memory and visual systems, including i) bilateral lateral temporal cortices and ii) the anterior cingulate cortex and the hippocampus, regions associated with long and short-term memory. Differences in hippocampal morphometry matched those previously documented for expert spatial navigators and individuals with good verbal working memory. The findings provide unique insight into the brain organization implementing formalized oral knowledge systems.
Collapse
Affiliation(s)
- James F Hartzell
- Center for Mind/Brain Sciences (CIMeC), University of Trento, 38060, Italy.
| | - Ben Davis
- Center for Mind/Brain Sciences (CIMeC), University of Trento, 38060, Italy
| | - David Melcher
- Center for Mind/Brain Sciences (CIMeC), University of Trento, 38060, Italy
| | - Gabriele Miceli
- Center for Mind/Brain Sciences (CIMeC), University of Trento, 38060, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMeC), University of Trento, 38060, Italy
| | - Tanmay Nath
- National Brain Research Centre, Manesar, Gurgaon Dist., Haryana 122 050, India
| | | | - Uri Hasson
- Center for Mind/Brain Sciences (CIMeC), University of Trento, 38060, Italy
| |
Collapse
|
13
|
Abstract
Objectives Metal-on-metal hip resurfacing (MOMHR) is available as an alternative
option for younger, more active patients. There are failure modes
that are unique to MOMHR, which include loosening of the femoral
head and fractures of the femoral neck. Previous studies have speculated
that changes in the vascularity of the femoral head may contribute
to these failure modes. This study compares the survivorship between
the standard posterior approach (SPA) and modified posterior approach
(MPA) in MOMHR. Methods A retrospective clinical outcomes study was performed examining
351 hips (279 male, 72 female) replaced with Birmingham Hip Resurfacing
(BHR, Smith and Nephew, Memphis, Tennessee) in 313 patients with
a pre-operative diagnosis of osteoarthritis. The mean follow-up
period for the SPA group was 2.8 years (0.1 to 6.1) and for the
MPA, 2.2 years (0.03 to 5.2); this difference in follow-up period
was statistically significant (p < 0.01). Survival analysis was
completed using the Kaplan–Meier method. Results At four years, the Kaplan–Meier survival curve for the SPA was
97.2% and 99.4% for the MPA; this was statistically significant
(log-rank; p = 0.036). There were eight failures in the SPA and
two in the MPA. There was a 3.5% incidence of femoral head collapse
or loosening in the SPA and 0.4% in the MPA, which represented a
significant difference (p = 0.041). There was a 1.7% incidence of
fractures of the femoral neck in the SPA and none in the MPA (p
= 0.108). Conclusion This study found a significant difference in survivorship at
four years between the SPA and the MPA (p = 0.036). The clinical
outcomes of this study suggest that preserving the vascularity of
the femoral neck by using the MPA results in fewer vascular-related
failures in MOMHRs. Cite this article: Bone Joint Res 2014;3:150–4
Collapse
Affiliation(s)
- K M Takamura
- UCLA David Geffen School of Medicine, 10833 Le Conte Avenue, Los Angeles, California 90095, USA
| | - P Maher
- Weill Cornell Medical College, 1300 York Ave, New York, New York 10021, USA
| | - T Nath
- Center for Hip Preservation, 535 East 70th Street, New York, New York 10021, USA
| | - E P Su
- Hospital for Special Surgery, 535 East 70th street, New York, New York 10021, USA
| |
Collapse
|
14
|
Kumar A, Gupta N, Nath T, Sharma JB, Sharma S. Thyroid function tests in pregnancy. Indian J Med Sci 2003; 57:252-8. [PMID: 14510343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
The recognition of abnormality in thyroid function tests during pregnancy is important for the welfare of the mother as well as fetus. The values of serum tri-iodothyronine (T3), thyroxine (T4), thyroid-stimulating hormone (TSH) in nonpregnant women are not applicable during pregnancy and also differ in iodine deficient areas. In the present study, one hundred and twenty-four apparently normal, healthy young primigravidas with no known metabolic disorders and normal carbohydrate gestational intolerance test, consecutively attending the antenatal clinic were included in the study. The serum tri-iodothyronine (T3), thyroxine (T4) and thyroid-stimulating hormone (TSH) in these women were estimated. In the first trimester, the mean T3 values were found to be 1.85 nmol/L, which increased to a mean of 2.47 nmol/L in the second trimester and declined in the third trimester to 1.82 nmo/L. Mean T4 levels were also seen to rise from 164.50 nmol/L in the first trimester to 165.80 nmol/L in the second trimester and then decreased in the third trimester to 159.90 nmol/L. Mean TSH levels were seen to rise progressively through the three trimesters of pregnancy from 1.20 microlU/ ml in the first trimester to 2.12 microlU/ml in the second trimester and further to 3.30 microlU/ml in the third trimester of pregnancy. Three asymptomatic pregnant women (2.5%) were found to have abnormal TSH values with normal T3 and T4 levels and good obstetric outcome. This pilot study also indicates the range to T3 as 1.7 - 4.3 nmol/L in second trimester and 0.4 - 3.9 nmol/L in third trimester, T4 as 92.2 - 252.8 nmol/L in second trimester and 108.2 - 219.0 nmol/ L in third trimester, and TSH as 0.1 - 5.5 microlU/ml in second trimester and 0.5- 7.6 microlU/ml in third trimester of pregnancy.
Collapse
Affiliation(s)
- A Kumar
- Department of Obstetrics and Gynaecology, Maulana Azad Medical College and Lok Nayak Hospital, New Delhi.
| | | | | | | | | |
Collapse
|
15
|
|
16
|
Khalil A, Nath T, Srivastava G, Saini L, gupta S. Assessment of ascorbic acid nutritional status in 100 preschool children. Indian Pediatr 1976; 13:21-4. [PMID: 1278941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
17
|
Khurana V, Agarwal KN, Gupta S, Nath T. Estimation of total protein in breast milk of Indian lactating mothers. Indian Pediatr 1970; 7:156-8. [PMID: 5534803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|