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Book ; Online: Recent Advances and Challenges on Big Data Analysis in Neuroimaging

Liu, Han / Caffo, Brian / Kang, Jian

2017

Abstract: Big data is revolutionizing our ability to measure and study the human brain. New technology increases the resolution of images that are being study as well as enables researchers to study the brain as it functions. These technological advances are ... ...

Abstract	Big data is revolutionizing our ability to measure and study the human brain. New technology increases the resolution of images that are being study as well as enables researchers to study the brain as it functions. These technological advances are combined with efforts to collect neuroimaging data on large numbers of subjects, in some cases longitudinally. This combination of advances in measurement and scope of studies requires novel development in the statistical analysis. Fast, scalable, robust and accurate models and approaches need to be developed to make headway on these problems. This volume represents a unique collection of researchers providing deep insights on the statistical analysis of big neuroimaging data
Keywords	Science (General) ; Neurosciences. Biological psychiatry. Neuropsychiatry
Size	1 electronic resource (195 p.)
Publisher	Frontiers Media SA
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020095050
ISBN	9782889451289 ; 2889451283
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Book ; Online: Empowering Learning

Moghadas, Babak / Caffo, Brian S.

Standalone, Browser-Only Courses for Seamless Education

2023

Abstract: Massive Open Online Courses (MOOCs) have transformed the educational landscape, offering scalable and flexible learning opportunities, particularly in data-centric fields like data science and artificial intelligence. Incorporating AI and data science ... ...

Abstract	Massive Open Online Courses (MOOCs) have transformed the educational landscape, offering scalable and flexible learning opportunities, particularly in data-centric fields like data science and artificial intelligence. Incorporating AI and data science into MOOCs is a potential means of enhancing the learning experience through adaptive learning approaches. In this context, we introduce PyGlide, a proof-of-concept open-source MOOC delivery system that underscores autonomy, transparency, and collaboration in maintaining course content. We provide a user-friendly, step-by-step guide for PyGlide, emphasizing its distinct advantage of not requiring any local software installation for students. Highlighting its potential to enhance accessibility, inclusivity, and the manageability of course materials, we showcase PyGlide's practical application in a continuous integration pipeline on GitHub. We believe that PyGlide charts a promising course for the future of open-source MOOCs, effectively addressing crucial challenges in online education.
Keywords	Computer Science - Computers and Society
Subject code	370
Publishing date	2023-11-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping Colorectal Distribution of Cell-free and Cell-associated HIV Surrogates Following Simulated Anal Intercourse to Aid Rectal Microbicide Development.

Weld, Ethel D / Ogasawara, Ken / Fuchs, Edward J / Louissaint, Nicolette / Caffo, Brian / Hendrix, Craig W

Journal of acquired immune deficiency syndromes (1999)

2024

Abstract: Background: Anal sex remains the greatest HIV transmission risk for men who have sex with men and carries substantial population attributable risk among women. Despite a growing array of HIV pre-exposure prophylaxis (PrEP) options, rectal microbicides ... ...

Abstract	Background: Anal sex remains the greatest HIV transmission risk for men who have sex with men and carries substantial population attributable risk among women. Despite a growing array of HIV pre-exposure prophylaxis (PrEP) options, rectal microbicides remain desirable as on demand, non-systemic PrEP. Rectal microbicide product development for PrEP requires understanding the spatiotemporal distribution of HIV infectious elements in the rectosigmoid to optimize formulation development. Setting: Outpatient setting with healthy research participants. Methods: Six healthy men underwent simulated receptive anal sex with an artificial phallus fitted with a triple lumen catheter in the urethral position. To simulate ejaculation of HIV-infected semen, autologous seminal plasma laden with autologous blood lymphocytes from apheresis labeled with 111Indium-oxine (cell-associated) and 99mTechnetium-sulfur colloid (cell-free) as HIV surrogates were injected into the rectal lumen through the phallic urethra. Spatiotemporal distribution of each radioisotope was assessed using SPECT/CT over eight hours. Analysis of radiolabel distribution used a flexible principal curve algorithm to quantitatively estimate rectal lumen distribution. Results: Cell-free and cell-associated HIV surrogates distributed to a maximal distance of 15 and 16 cm, respectively, from the anorectal junction (∼19 and ∼20 cm from the anal verge), with a maximal signal intensity located 6 and 7 cm, respectively. There were no significant differences in any distribution parameters between cell-free and cell-associated HIV surrogate. Conclusions: Cell-free and cell-associated HIV surrogate distribution in the rectosigmoid can be quantified with spatiotemporal pharmacokinetic methods. These results describe the ideal luminal target distribution to guide rectal microbicide development.
Language	English
Publishing date	2024-02-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	645053-2
ISSN	1944-7884 ; 1077-9450 ; 0897-5965 ; 0894-9255 ; 1525-4135
ISSN (online)	1944-7884 ; 1077-9450
ISSN	0897-5965 ; 0894-9255 ; 1525-4135
DOI	10.1097/QAI.0000000000003401
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.

Yao, Jianing / Yu, Jinglun / Caffo, Brian / Page, Stephanie C / Martinowich, Keri / Hicks, Stephanie C

bioRxiv : the preprint server for biology

2024

Abstract: Recent advances in spatially-resolved single-omics and multi-omics technologies have led to the emergence of computational tools to detect or predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins and ... ...

Abstract	Recent advances in spatially-resolved single-omics and multi-omics technologies have led to the emergence of computational tools to detect or predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins and cell types provide multiple perspectives and a more complete understanding of tissue architecture. Here, we introduce Proust, a scalable tool to predict discrete domains using spatial multi-omics data by combining the low-dimensional representation of biological profiles based on graph-based contrastive self-supervised learning. Our scalable method integrates multiple data modalities, such as RNA, protein, and H&E images, and predicts spatial domains within tissue samples. Through the integration of multiple modalities, Proust consistently demonstrates enhanced accuracy in detecting spatial domains, as evidenced across various benchmark datasets and technological platforms.
Language	English
Publishing date	2024-02-04
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.02.02.578662
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Regression models for partially localized fMRI connectivity analyses.

Smith, Bonnie B / Zhao, Yi / Lindquist, Martin A / Caffo, Brian

bioRxiv : the preprint server for biology

2023

Abstract: Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. This can come in the form of one-edge- ... ...

Abstract	Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. This can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is the assumption of complete localization (or spatial alignment) of brain regions across subjects. Alternative approaches completely eschew localization assumptions by treating connections as statistically exchangeable (for example, using the density of connectivity between nodes). Yet other approaches, such as hyperalignment, attempt to align subjects on function as well as structure, thereby achieving a different sort of template-based localization. In this paper, we propose the use of simple regression models to characterize connectivity. To that end, we build regression models on subject-level Fisher transformed regional connection matrices using geographic distance, homotopic distance, network labels, and region indicators as covariates to explain variation in connections. While we perform our analysis in template-space in this paper, we envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. A byproduct of this style of analysis is the ability to characterize the fraction of variation in subject-level connections explained by each type of covariate. Using Human Connectome Project data, we found that network labels and regional characteristics contribute far more than geographic or homotopic relationships (considered non-parametrically). In addition, visual regions had the highest explanatory power (i.e., largest regression coefficients). We also considered subject repeatability and found that the degree of repeatability seen in fully localized models is largely recovered using our proposed subject-level regression models. Further, even fully exchangeable models retain a sizeable amount of repeatability information, despite discarding all localization information. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.
Language	English
Publishing date	2023-04-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.20.537694
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Regression models for partially localized fMRI connectivity analyses.

Smith, Bonnie B / Zhao, Yi / Lindquist, Martin A / Caffo, Brian

Frontiers in neuroimaging

2023 Volume 2, Page(s) 1178359

Abstract: Background: Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come ... ...

Abstract	Background: Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated. Methods: In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches. Results: We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model-which uses only partial location information about pairs of ROI's-is comparably as high as the repeatability obtained using full location information. Discussion: While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.
Language	English
Publishing date	2023-11-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	3123824-5
ISSN	2813-1193 ; 2813-1193
ISSN (online)	2813-1193
ISSN	2813-1193
DOI	10.3389/fnimg.2023.1178359
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Corrigendum: Editorial: Explainable artificial intelligence models and methods in finance and healthcare.

Caffo, Brian S / D'Asaro, Fabio A / Garcez, Artur / Raffinetti, Emanuela

Frontiers in artificial intelligence

2023 Volume 6, Page(s) 1157762

Abstract: This corrects the article DOI: 10.3389/frai.2022.970246.]. ...

Abstract	[This corrects the article DOI: 10.3389/frai.2022.970246.].
Language	English
Publishing date	2023-02-20
Publishing country	Switzerland
Document type	Published Erratum
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2023.1157762
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Article ; Online: A Unified Framework on Generalizability of Clinical Prediction Models.

Wan, Bohua / Caffo, Brian / Vedula, S Swaroop

Frontiers in artificial intelligence

2022 Volume 5, Page(s) 872720

Abstract: To be useful, clinical prediction models (CPMs) must be generalizable to patients in new settings. Evaluating generalizability of CPMs helps identify spurious relationships in data, provides insights on when they fail, and thus, improves the ... ...

Abstract	To be useful, clinical prediction models (CPMs) must be generalizable to patients in new settings. Evaluating generalizability of CPMs helps identify spurious relationships in data, provides insights on when they fail, and thus, improves the explainability of the CPMs. There are discontinuities in concepts related to generalizability of CPMs in the clinical research and machine learning domains. Specifically, conventional statistical reasons to explain poor generalizability such as inadequate model development for the purposes of generalizability, differences in coding of predictors and outcome between development and external datasets, measurement error, inability to measure some predictors, and missing data, all have differing and often complementary treatments, in the two domains. Much of the current machine learning literature on generalizability of CPMs is in terms of dataset shift of which several types have been described. However, little research exists to synthesize concepts in the two domains. Bridging this conceptual discontinuity in the context of CPMs can facilitate systematic development of CPMs and evaluation of their sensitivity to factors that affect generalizability. We survey generalizability and dataset shift in CPMs from both the clinical research and machine learning perspectives, and describe a unifying framework to analyze generalizability of CPMs and to explain their sensitivity to factors affecting it. Our framework leads to a set of signaling statements that can be used to characterize differences between datasets in terms of factors that affect generalizability of the CPMs.
Language	English
Publishing date	2022-04-29
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2022.872720
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: B-value and empirical equivalence bound: A new procedure of hypothesis testing.

Zhao, Yi / Caffo, Brian S / Ewen, Joshua B

Statistics in medicine

2022 Volume 41, Issue 6, Page(s) 964–980

Abstract: In this study, we propose a two-stage procedure for hypothesis testing, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced empirical equivalence bound (EEB). In 2016, the ... ...

Abstract	In this study, we propose a two-stage procedure for hypothesis testing, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced empirical equivalence bound (EEB). In 2016, the American Statistical Association released a policy statement on P-values to clarify the proper use and interpretation in response to the criticism of reproducibility and replicability in scientific findings. A recent solution to improve reproducibility and transparency in statistical hypothesis testing is to integrate P-values (or confidence intervals) with practical or scientific significance. Similar ideas have been proposed via the equivalence test, where the goal is to infer equality under a presumption (null) of inequality of parameters. However, the definition of scientific significance/equivalence can sometimes be ill-justified and subjective. To circumvent this drawback, we introduce the B-value and the EEB, which are both estimated from the data. Performing a second-stage equivalence test, our procedure offers an opportunity to improve the reproducibility of findings across studies.
MeSH term(s)	Humans ; Reproducibility of Results ; Research Design
Language	English
Publishing date	2022-01-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.9298
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Longitudinal regression of covariance matrix outcomes.

Zhao, Yi / Caffo, Brian S / Luo, Xi

Biostatistics (Oxford, England)

2022 Volume 25, Issue 2, Page(s) 385–401

Abstract: In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously ... ...

Abstract	In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the identity matrix and the sample covariance matrix under the high-dimensional case. Through extensive simulation studies, the proposed approach achieves good performance in identifying the covariate-related components and estimating the model parameters. Applying to a longitudinal resting-state functional magnetic resonance imaging data set from the Alzheimer's Disease (AD) Neuroimaging Initiative, the proposed approach identifies brain networks that demonstrate the difference between males and females at different disease stages. The findings are in line with existing knowledge of AD and the method improves the statistical power over the analysis of cross-sectional data.
MeSH term(s)	Male ; Female ; Humans ; Models, Statistical ; Cross-Sectional Studies ; Likelihood Functions ; Computer Simulation ; Brain/diagnostic imaging
Language	English
Publishing date	2022-09-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2031500-4
ISSN	1468-4357 ; 1465-4644
ISSN (online)	1468-4357
ISSN	1465-4644
DOI	10.1093/biostatistics/kxac045
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