Functional connectome fingerprint of sleep quality in insomnia patients: Individualized out-of-sample prediction using machine learning

Ma, Xiaofen; Wu, Dalei; Mai, Yuanqi; Xu, Guang; Jian-guo, Tian; Jiang, Guihua

doi:10.1016/j.nicl.2020.102439

Cited by 9 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have also used MRI signals for the identification of insomnia (Li et al 2019, Dai et al 2020, Ma et al 2020. Out of all these studies, Monroe (1967) have the biggest data set containing 96 patients out of which 48are insomniacs and 48 are used as control subjects.…”

Section: Detailed Analysis Of Insomnia Detection Using MLmentioning

confidence: 99%

A systematic review on automatic identification of insomnia

Ingle,

Sharma,

Kumar

et al. 2024

Physiol. Meas.

View full text Add to dashboard Cite

Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual’s sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection.Methods: We followed the recommendations given in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a se- lection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various meth- ods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intel- ligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this de- tection task.Results: Result: The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential com- ponents under investigation in the automated technique include the data input source, objective, machine learning (ML) and deep learning (DL) network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types.Conclusion: Based on our review of the studies featured in this paper, we have identi- fied a notable research gap in the current methods for identifying insomnia and oppor- tunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.

show abstract

Section: Detailed Analysis Of Insomnia Detection Using MLmentioning

confidence: 99%

A systematic review on automatic identification of insomnia

Ingle,

Sharma,

Kumar

et al. 2024

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…Shahid et al ( 22 ) employed CNN segmentation of the pharynx of patients with OSA by analyzing the self-produced MRI data of 50 participants, and the Jaccard coefficient for the pharynx being segmented was approximately 86%. Ma et al ( 23 ) employed the multivariate relevance vector regression method to detect short-term/acute and chronic subtypes by analyzing self-produced MRI data of 73 participants. They found that FC predicted sleep quality in both short-term/acute and chronic insomnia and that FC patterns changed during the transition from short-term/acute to chronic insomnia.…”

Section: Introductionmentioning

confidence: 99%

Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder

Wang,

Ren,

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.

show abstract

“…Individuals who smoke are at greater risk of experiencing deficient sleep ( Brook et al, 2015 ; Cohrs et al, 2014 ; Kashyap et al, 2001 ), depression, anxiety, fatigue, and concentration difficulties ( Hahad et al, 2022 ; Patten et al, 2000 ). Numerous studies have investigated the neural correlates of deficient sleep ( Bai et al, 2022 ; Fjell et al, 2021 ; Liu et al, 2019 ; Ma et al, 2020 ). For instance, poorer sleep quality was related to faster thinning of the right lateral temporal cortex in a longitudinal study of aging ( Fjell et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Functional connectome fingerprint of sleep quality in insomnia patients: Individualized out-of-sample prediction using machine learning

Abstract: Highlight Short-term and chronic insomnia are two subtypes of insomnia. Functional connectome predicts individual sleep quality for both two subtypes. Shared and distinct neural basis underlying poor sleep quality between two subtypes.

Cited by 9 publications

References 56 publications

A systematic review on automatic identification of insomnia

A systematic review on automatic identification of insomnia

Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder

Deficient sleep, altered hypothalamic functional connectivity, depression and anxiety in cigarette smokers

Contact Info

Product

Resources

About