Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics

Marmolejo‐Ramos, Fernando; Tejo, Mauricio; Brabec, Marek; Kužílek, Jakub; Joksimovíc, Srécko; Kovanović, Vitomir; González, Jorge; Kneib, Thomas; Bühlmann, Peter; Kook, Lucas; Briseño‐Sánchez, Guillermo; Ospina, Raydonal

doi:10.1002/widm.1479

Cited by 7 publications

(4 citation statements)

References 134 publications

(177 reference statements)

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“…Given the exploratory nature of the investigation, our statistical analyses consisted of GAMLSS which is a flexible distributional regression approach and is considered as an improvement and extension to the generalized linear models (GLM) and the generalized additive models (GAM) 25 . A first GAMLSS, with SOL as the dependent variable, yielded a significant negative main effect of the MTsat value of the bmGM (p = 2.2 × 10 −5 , p corr = 1.2 × 10 −4 ) and of age (p = 1.2 × 10 −4 , p corr = 7.2 × 10 −4 ) while controlling for body mass index (BMI), total sleep time (TST), and total intracranial volume (TIV), as well as MRI MPM sequence and scanner (see “ Methods ”) (Table 2 , Fig.…”

Section: Resultsmentioning

confidence: 99%

In vivo marker of brainstem myelin is associated to quantitative sleep parameters in healthy young men

Talwar,

Deantoni,

Van Egroo

et al. 2023

Sci Rep

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The regional integrity of brain subcortical structures has been implicated in sleep–wake regulation, however, their associations with sleep parameters remain largely unexplored. Here, we assessed association between quantitative Magnetic Resonance Imaging (qMRI)-derived marker of the myelin content of the brainstem and the variability in the sleep electrophysiology in a large sample of 18-to-31 years healthy young men (N = 321; ~ 22 years). Separate Generalized Additive Model for Location, Scale and Shape (GAMLSS) revealed that sleep onset latency and slow wave energy were significantly associated with MTsat estimates in the brainstem (pcorrected ≤ 0.03), with overall higher MTsat value associated with values reflecting better sleep quality. The association changed with age, however (MTsat-by-age interaction—pcorrected ≤ 0.03), with higher MTsat value linked to better values in the two sleep metrics in the younger individuals of our sample aged ~ 18 to 20 years. Similar associations were detected across different parts of the brainstem (pcorrected ≤ 0.03), suggesting that the overall maturation and integrity of the brainstem was associated with both sleep metrics. Our results suggest that myelination of the brainstem nuclei essential to regulation of sleep is associated with inter-individual differences in sleep characteristics during early adulthood. They may have implications for sleep disorders or neurological diseases related to myelin.

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Section: Resultsmentioning

confidence: 99%

In vivo marker of brainstem myelin is associated to quantitative sleep parameters in healthy young men

Talwar,

Deantoni,

Van Egroo

et al. 2023

Sci Rep

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show abstract

“…Convolution is the method of getting input data and it selects the features of the matrix. The insertion from various courses is combined to get a 2-D array and the outcome is moved to a convolutional layer to generate a new feature (Marmolejo-Ramos et al, 2023). The hidden presentation is formed by applying pooling methods on new features and the final predictions are made by fully connected final layers and the final classification is done by using SoftMax activation function.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

Sudhamathy,

Valliammal

2023

Int. J. Appl. Sci. Eng.

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Learning analytics (LA) is a research domain that leverages the analysis of data from the learning process to gain a deeper understanding and enhance learning outcomes. To classify learner performance, a model has been proposed that combines various deep learning techniques, including convolutional neural network (CNN), Long Short-Term Memory (LSTM), and Bayesian models. The integration of these approaches aims to improve the accuracy and effectiveness of performance classification. CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. The effective classification of learners' performance is achieved by combining the strengths of CNN and LSTM, along with the integration of a Bayesian deep learning model. The performance of the proposed model is estimated using the metrics like Accuracy, Precision, Recall and F1-Score. The model showed improvements in Accuracy, Precision, Recall and F1-Score are 98.18%, 97.09%, 96.38% and 95.35% respectively. The proposed model is compared with another existing model such as LSTM and collaborative machine learning (ML) models in terms of performance metrics. The proposed method attained accuracy of 98.18% which is higher than other existing models.

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“…Given the exploratory nature of our investigation our statistical analyses consisted of GAMLSS which is a flexible distributional regression approach considered as an improvement and extension to the generalized linear models (GLM) and the generalized additive models (GAM) (Marmolejo-Ramos et al, 2023). A first GAMLSS, with SOL as the dependent variable, yielded a significant negative main effect of the MTsat value of the bmGM (p= 2.2x10 -5 , pcorr=1.2x10 -4 ) and of age (p=3.8x10 -6 , pcorr=2.3x10 -5 ) while controlling for body mass index (BMI), total sleep time (TST), and total intracranial volume (TIV), as well as MRI MPM sequence and scanner (see methods) (Table 2, Fig.…”

Section: Latency To Sleep Sws Intensity and Sleep Efficiency Are Asso...mentioning

confidence: 99%

In vivomarker of brainstem myelin is associated to quantitative sleep parameters in healthy young men

Talwar

Deantoni

Egroo

et al. 2023

Preprint

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Brain structural integrity has been suggested to contribute to the variability in human sleep quality and composition. The associations between sleep parameters and the regional integrity of subcortical structures implicated in sleep-wake regulation remain, however, largely unexplored. The present study aimed at assessing association between quantitative Magnetic Resonance Imaging (qMRI)-derived marker of the myelin content of the brainstem with the variability in the sleep electrophysiology in a large sample of healthy young men (N=321; ~22y). Generalized Additive Model for Location, Scale and Shape (GAMLSS) was used to seek associations between sleep metrics and Magnetisation Transfer saturation (MTsat) qMRI values, proxy for myelin content. Separate GAMLSS revealed that sleep onset latency and slow wave sleep intensity were significantly associated with MTsat-derived myelin estimates in the brainstem (pcorrected≤.03), with overall higher MTsat value associated with values reflecting better sleep quality. The association changed with age, however (MTsat-by-age interaction - pcorrected≤.03), with higher MTsat value linked to better values in the two sleep metrics in the individuals of our sample aged ~18 to 20y. Similar associations were detected across different parts of the brainstem (pcorrected≤.03), suggesting that the overall maturation and integrity of the brainstem was associated with both sleep metrics. Our results suggest that myelination of the many reticular nuclei of the brainstem essential to regulation of sleep is associated with inter-individual differences in sleep characteristics during early adulthood. They may have implications for sleep disorders or neurological diseases related to myelin.

show abstract

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics

Cited by 7 publications

References 134 publications

In vivo marker of brainstem myelin is associated to quantitative sleep parameters in healthy young men

In vivo marker of brainstem myelin is associated to quantitative sleep parameters in healthy young men

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

In vivomarker of brainstem myelin is associated to quantitative sleep parameters in healthy young men

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