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Article ; Online: Spectra in low‐rank localized layers (SpeLLL) for interpretable time–frequency analysis

Tuft, Marie / Hall, Martica H. / Krafty, Robert T.

Biometrics. 2023 Mar., v. 79, no. 1 p.304-318

2023

Abstract: The time‐varying frequency characteristics of many biomedical time series contain important scientific information. However, the high‐dimensional nature of the time‐varying power spectrum as a surface in time and frequency limits its direct use by ... ...

Abstract	The time‐varying frequency characteristics of many biomedical time series contain important scientific information. However, the high‐dimensional nature of the time‐varying power spectrum as a surface in time and frequency limits its direct use by applied researchers and clinicians for elucidating complex mechanisms. In this article, we introduce a new approach to time–frequency analysis that decomposes the time‐varying power spectrum in to orthogonal rank‐one layers in time and frequency to provide a parsimonious representation that illustrates relationships between power at different times and frequencies. The approach can be used in fully nonparametric analyses or in semiparametric analyses that account for exogenous information and time‐varying covariates. An estimation procedure is formulated within a penalized reduced‐rank regression framework that provides estimates of layers that are interpretable as power localized within time blocks and frequency bands. Empirical properties of the procedure are illustrated in simulation studies and its practical use is demonstrated through an analysis of heart rate variability during sleep.
Keywords	heart rate ; sleep ; time series analysis
Language	English
Dates of publication	2023-03
Size	p. 304-318.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	213543-7
ISSN	0099-4987 ; 0006-341X
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13577
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Article: Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model.

Zhai, Xuetong / Santosa, Hendrik / Krafty, Robert T / Huppert, Theodore J

Neurophotonics

2023 Volume 10, Issue 2, Page(s) 23516

Abstract: Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technology that uses low levels of nonionizing light in the range of red and near-infrared to record changes in the optical absorption and scattering of the underlying tissue ... ...

Abstract	Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technology that uses low levels of nonionizing light in the range of red and near-infrared to record changes in the optical absorption and scattering of the underlying tissue that can be used to infer blood flow and oxygen changes during brain activity. The challenges and difficulties of reconstructing spatial images of hemoglobin changes from fNIRS data are mainly caused by the illposed nature of the optical inverse model. Aim: We describe a Bayesian approach combining several lasso-based regularizations to apply anatomy-prior information to solving the inverse model. Approach: We built a Bayesian hierarchical model to solve the Bayesian adaptive fused sparse overlapping group lasso (Ba-FSOGL) model. The method is evaluated and validated using simulation and experimental datasets. Results: We apply this approach to the simulation and experimental datasets to reconstruct a known brain activity. The reconstructed images and statistical plots are shown. Conclusion: We discuss the adaptation of this method to fNIRS data and demonstrate that this approach provides accurate image reconstruction with a low false-positive rate, through numerical simulations and application to experimental data collected during motor and sensory tasks.
Language	English
Publishing date	2023-02-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2781943-7
ISSN	2329-4248 ; 2329-423X
ISSN (online)	2329-4248
ISSN	2329-423X
DOI	10.1117/1.NPh.10.2.023516
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ordinal regression increases statistical power to predict epilepsy surgical outcomes.

Dickey, Adam S / Krafty, Robert T / Pedersen, Nigel P

Epilepsia open

2022 Volume 7, Issue 2, Page(s) 344–349

Abstract: Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing ... ...

Abstract	Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing epilepsy surgery outcomes. We reviewed 10 manuscripts included in a recent meta-analysis which found that mesial temporal sclerosis (MTS) predicted better surgical outcomes after a stereotactic laser amygdalohippocampectomy (SLAH). We extracted data from 239 patients from eight studies that reported four discrete Engel surgical outcomes after SLAH, stratified by the presence or absence of MTS. The rate of freedom from disabling seizures (Engel I) was 64.3% (110/171) for patients with MTS compared to 44.1% (30/68) without MTS. The statistical power to detect MTS as a predictor for better surgical outcome after a SLAH was 29% using ordinal regression, which was significantly more than the 13% power using binary logistic regression (paired t-test, P < .001). Only 120 patients are needed for this example to achieve 80% power to detect MTS as a predictor using ordinal regression, compared to 210 patients that are needed to achieve 80% power using binary logistic regression. Ordinal regression should be considered when analyzing ordinal outcomes (such as Engel surgical outcomes), especially for datasets with small sample sizes.
MeSH term(s)	Epilepsy, Temporal Lobe/surgery ; Humans ; Seizures ; Treatment Outcome
Language	English
Publishing date	2022-02-23
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2470-9239
ISSN (online)	2470-9239
DOI	10.1002/epi4.12585
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A competing risks regression model for the association between time-varying opioid exposure and risk of overdose.

Li, Xingyuan / Chang, Chung-Chou H / Donohue, Julie M / Krafty, Robert T

Statistical methods in medical research

2022 Volume 31, Issue 6, Page(s) 1013–1030

Abstract: In the opioid research, predicting the risk of overdose or other adverse outcomes from opioid prescription patterns can help health professionals identify high-risk individuals. Challenges may arise in modeling the exposure-time-response association if ... ...

Abstract	In the opioid research, predicting the risk of overdose or other adverse outcomes from opioid prescription patterns can help health professionals identify high-risk individuals. Challenges may arise in modeling the exposure-time-response association if the intensity, duration, and timing of exposure vary among subjects, and if exposures have a cumulative or latency effect on the risk. Further challenges may arise when the data involve competing risks, where subjects may fail from one of multiple events and failure from one precludes the risk of experiencing others. In this study, we proposed a competing risks regression model via subdistribution hazards to directly estimate the association between longitudinal patterns of opioid exposure and cumulative incidence of opioid overdose. The model incorporated weighted cumulative effects of the exposure and used penalized splines in the partial likelihood equation to estimate the weights flexibly. The proposed model is able to distinguish different opioid prescription patterns even though these patterns have the same overall intensity during the study period. Performance of the model was evaluated through simulation.
MeSH term(s)	Analgesics, Opioid/adverse effects ; Data Interpretation, Statistical ; Humans ; Incidence ; Probability ; Proportional Hazards Models
Chemical Substances	Analgesics, Opioid
Language	English
Publishing date	2022-02-09
Publishing country	England
Document type	Journal Article
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802221075933
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Article: Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.

Krafty, Robert T

Journal of time series analysis

2015 Volume 37, Issue 4, Page(s) 435–450

Abstract: Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same ... ...

Abstract	Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.
Language	English
Publishing date	2015-10-14
Publishing country	England
Document type	Journal Article
ZDB-ID	1473831-4
ISSN	1467-9892 ; 0143-9782
ISSN (online)	1467-9892
ISSN	0143-9782
DOI	10.1111/jtsa.12166
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Article ; Online: Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

Fu, Haoyi / Tang, Lu / Rosen, Ori / Hipwell, Alison E / Huppert, Theodore J / Krafty, Robert T

Biostatistics (Oxford, England)

2023

Abstract: With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually ... ...

Abstract	With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.
Language	English
Publishing date	2023-12-23
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/kxad034
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Article ; Online: Evaluating the timing of differences in activity related to depression symptoms across adulthood in the United States.

Smagula, Stephen F / Capps, Chandler S / Krafty, Robert T

Journal of affective disorders

2021 Volume 284, Page(s) 64–68

Abstract: Background: Relative activity deficits found in people with (verses without) depression symptoms/disorders may accumulate uniformly throughout the day, or they may tend to be expressed at specific times. Evidence for the latter would suggest times when ... ...

Abstract	Background: Relative activity deficits found in people with (verses without) depression symptoms/disorders may accumulate uniformly throughout the day, or they may tend to be expressed at specific times. Evidence for the latter would suggest times when behavioral approaches are most needed to reduce depression and its health consequences. Methods: We performed a secondary-data analysis of participants who contributed valid accelerometer data at the 2005-2006 National Health and Nutrition Examination Survey (n=4390). Participants were categorized according to the Patient Health Questionnaire-9 standard cut-point of ≥10 (i.e., people with versus without clinically significant depression symptoms). Average levels of accelerometer-measured activity in two-hour bins were the dependent variable in mixed models testing if the relationship between depression status and activity level differed by time of day; and if any such relations varied by age group (18-29 years, 30-44 years, 45-59 years, and 60+ years). Results: In adults over the age of 30, people with depression symptoms had generally lower levels of activity across the day, but these effects were most markedly pronounced in the morning hours. We found no differences in activity levels associated with prevalent depression symptoms among people 18-30 years of age. Limitations: Core aspects of depression pathophysiology that produce these different activity patterns and confer their effects on mood were not measured. Conclusions: In adults 30 years and older, efforts to ameliorate relative activity deficits associated with depression may benefit from considering the apparently outsized role of inactivity that occurs in the morning.
MeSH term(s)	Adolescent ; Adult ; Affect ; Depression/epidemiology ; Humans ; Nutrition Surveys ; Patient Health Questionnaire ; United States/epidemiology ; Young Adult
Language	English
Publishing date	2021-02-02
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2021.01.069
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Article ; Online: Spectra in low-rank localized layers (SpeLLL) for interpretable time-frequency analysis.

Tuft, Marie / Hall, Martica H / Krafty, Robert T

Biometrics

2021 Volume 79, Issue 1, Page(s) 304–318

Abstract: The time-varying frequency characteristics of many biomedical time series contain important scientific information. However, the high-dimensional nature of the time-varying power spectrum as a surface in time and frequency limits its direct use by ... ...

Abstract	The time-varying frequency characteristics of many biomedical time series contain important scientific information. However, the high-dimensional nature of the time-varying power spectrum as a surface in time and frequency limits its direct use by applied researchers and clinicians for elucidating complex mechanisms. In this article, we introduce a new approach to time-frequency analysis that decomposes the time-varying power spectrum in to orthogonal rank-one layers in time and frequency to provide a parsimonious representation that illustrates relationships between power at different times and frequencies. The approach can be used in fully nonparametric analyses or in semiparametric analyses that account for exogenous information and time-varying covariates. An estimation procedure is formulated within a penalized reduced-rank regression framework that provides estimates of layers that are interpretable as power localized within time blocks and frequency bands. Empirical properties of the procedure are illustrated in simulation studies and its practical use is demonstrated through an analysis of heart rate variability during sleep.
MeSH term(s)	Computer Simulation ; Sleep ; Time Factors ; Heart Rate/physiology
Language	English
Publishing date	2021-10-28
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13577
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Article: Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series

Li, Zeda / Krafty, Robert T

Journal of the American Statistical Association. 2019 Jan. 2, v. 114, no. 525

2019

Abstract: This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may ... ...

Abstract	This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood-based penalized spline models of modified Cholesky components, which provide flexible nonparametric estimates that preserve positive definite structures of spectral matrices. The approach is formulated in a Bayesian framework, in which the number and location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. By averaging over the distribution of partitions, the approach can approximate both abrupt and slowly varying changes in spectral matrices. Empirical performance is evaluated in simulation studies and illustrated through analyses of electroencephalography during sleep and of the El Niño-Southern Oscillation. Supplementary materials for this article are available online.
Keywords	Bayesian theory ; El Nino ; Markov chain ; Monte Carlo method ; electroencephalography ; equations ; models ; sleep ; time series analysis
Language	English
Dates of publication	2019-0102
Size	p. 453-465.
Publishing place	Taylor & Francis
Document type	Article
ZDB-ID	2064981-2
ISSN	1537-274X ; 0003-1291 ; 0162-1459
ISSN (online)	1537-274X
ISSN	0003-1291 ; 0162-1459
DOI	10.1080/01621459.2017.1415908
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Article: Adaptive Bayesian Spectral Analysis of High-dimensional Nonstationary Time Series.

Li, Zeda / Rosen, Ori / Ferrarelli, Fabio / Krafty, Robert T

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

2021 Volume 30, Issue 3, Page(s) 794–807

Abstract: This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation ... ...

Abstract	This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk towards zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo (SAMC) techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography.
Language	English
Publishing date	2021-03-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2014382-5
ISSN	1537-2715 ; 1061-8600
ISSN (online)	1537-2715
ISSN	1061-8600
DOI	10.1080/10618600.2020.1868305
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