Regional Gray Matter Volume Links Rest-Activity Rhythm Fragmentation With Past Cognitive Decline

Smagula, Stephen F.; Chahine, Lana M.; Metti, Andrea L.; Rangarajan, Anusha; Aizenstein, Howard; Tian, Qu; Rosano, Caterina

doi:10.1016/j.jagp.2019.07.009

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Yet, sleep and 24-h activity rhythm disturbances are not clearly associated with NfL in the current study which is embedded in the same cohort, suggesting that poor sleep does not affect neuronal damage as indicated by NfL. Our finding partly contradicts findings from other studies implementing non-invasive structural neuroimaging which do suggest that poor sleep and 24-h activity rhythms are related to global or regional loss of tissue or integrity 5 , 16 , 17 . Together, these and our findings suggest a role for non-neuronal, i.e.…”

Section: Discussioncontrasting

confidence: 94%

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Sleep, 24-h activity rhythms, and plasma markers of neurodegenerative disease

Lysen

Ikram

Ghanbari

et al. 2020

Sci Rep

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Sleep and 24-h activity rhythm disturbances are associated with development of neurodegenerative diseases and related pathophysiological processes in the brain. We determined the cross-sectional relation of sleep and 24-h activity rhythm disturbances with plasma-based biomarkers that might signal neurodegenerative disease, in 4712 middle-aged and elderly non-demented persons. Sleep and activity rhythms were measured using the Pittsburgh Sleep Quality Index and actigraphy. Simoa assays were used to measure plasma levels of neurofilament light chain, and additionally β-amyloid 40, β-amyloid 42, and total-tau. We used linear regression, adjusting for relevant confounders, and corrected for multiple testing. We found no associations of self-rated sleep, actigraphy-estimated sleep and 24-h activity rhythms with neurofilament light chain after confounder adjustment and correction for multiple testing, except for a non-linear association of self-rated time in bed with neurofilament light chain (P = 2.5*10−4). Similarly, we observed no significant associations with β-amyloid 40, β-amyloid 42, and total-tau after multiple testing correction. We conclude that sleep and 24-h activity rhythm disturbances were not consistently associated with neuronal damage as indicated by plasma neurofilament light chain in this population-based sample middle-aged and elderly non-demented persons. Further studies are needed to determine the associations of sleep and 24-h activity rhythm disturbances with NfL-related neuronal damage.

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Section: Discussioncontrasting

confidence: 94%

“…Additionally, at a cellular level, NfL release may signal neuro-axonal damage that does not necessarily lead to neuronal loss 14 , 15 . This suggest that NfL may be of added value to existing studies using non-invasive neuroimaging markers as it may detect more subtle damage to the brain 5 , 16 , 17 .…”

Section: Introductionmentioning

confidence: 96%

Sleep, 24-h activity rhythms, and plasma markers of neurodegenerative disease

Lysen

Ikram

Ghanbari

et al. 2020

Sci Rep

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“…Activity detection at different levels of activity abstraction is described in Stockings et al's systematic review of numerous works [13] that focus on the use of mobile phone sensors to detect human behavior characteristics, and characterizes health-related activities, such as physical activity and sleep. In addition to being used in applications, these devices also have several embedded sensors that have been applied in a variety of fields [14,15], such as activity recognition [16] and, in particular, an activity that helps detect mental health issues [17]. To demonstrate how inertial sensors and GPS traces can be used as measurement instruments in mental diagnosis, Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, results from high-quality analyses of critical variables should be taken seriously, at least when overfitting prevention strategies have been used [16]. The Random Forest algorithm's ensemble learning technique is resistant to overfitting, and it can be seen as a woodland of decision trees, where different trees each focus on a different stochastic aspect of the data [17]. Decisions in decision trees are transparent, and lines of reasoning may be understood [18].…”

Section: Introductionmentioning

confidence: 99%

Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods

Zakariah

Alotaibi

2023

Diagnostics

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Modern technology frequently uses wearable sensors to monitor many aspects of human behavior. Since continuous records of heart rate and activity levels are typically gathered, the data generated by these devices have a lot of promise beyond counting the number of daily steps or calories expended. Due to the patient’s inability to obtain the necessary information to understand their conditions and detect illness, such as depression, objectively, methods for evaluating various mental disorders, such as the Montgomery–Asberg depression rating scale (MADRS) and observations, currently require a significant amount of effort on the part of specialists. In this study, a novel dataset was provided, comprising sensor data gathered from depressed patients. The dataset included 32 healthy controls and 23 unipolar and bipolar depressive patients with motor activity recordings. Along with the sensor data collected over several days of continuous measurement for each patient, some demographic information was also offered. The result of the experiment showed that less than 70 of the 100 epochs of the model’s training were completed. The Cohen Kappa score did not even pass 0.1 in the validation set, due to an imbalance in the class distribution, whereas in the second experiment, the majority of scores peaked in about 20 epochs, but because training continued during each epoch, it took much longer for the loss to decline before it fell below 0.1. In the second experiment, the model soon reached an accuracy of 0.991, which is as expected given the outcome of the UMAP dimensionality reduction. In the last experiment, UMAP and neural networks worked together to produce the best outcomes. They used a variety of machine learning classification algorithms, including the nearest neighbors, linear kernel SVM, Gaussian process, and random forest. This paper used the UMAP unsupervised machine learning dimensionality reduction without the neural network and showed a slightly lower score (QDA). By considering the ratings of the patient’s depressive symptoms that were completed by medical specialists, it is possible to better understand the relationship between depression and motor activity.

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“…Using brain network connectivity maps from 21 patients with schizophrenia and 25 patients with MDD, and a support vector machine classifier, they accomplished an accuracy classification of 82.6% [ 23 ]. Other MRI approaches included rest–activity rhythm data to correlate the anatomy of brain patients and their physical behavior in daily activities [ 24 , 25 ]. The circadian rhythm in psychiatric patients is crucial in diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal

et al. 2022

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Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, most current global metrics hover between 0.33% and 0.75%. Machine-learning scientists use data from diverse sources to analyze, classify, or predict to improve the psychiatric attention, diagnosis, and treatment of MDD, schizophrenia, and other psychiatric conditions. Motor activity data are gaining popularity in mental illness diagnosis assistance because they are a cost-effective and noninvasive method. In the knowledge discovery in databases (KDD) framework, a model to classify depressive and schizophrenic patients from healthy controls is constructed using accelerometer data. Taking advantage of the multiple sleep disorders caused by mental disorders, the main objective is to increase the model’s accuracy by employing only data from night-time activity. To compare the classification between the stages of the day and improve the accuracy of the classification, the total activity signal was cut into hourly time lapses and then grouped into subdatasets depending on the phases of the day: morning (06:00–11:59), afternoon (12:00–17:59), evening (18:00–23:59), and night (00:00–05:59). Random forest classifier (RFC) is the algorithm proposed for multiclass classification, and it uses accuracy, recall, precision, the Matthews correlation coefficient, and F1 score to measure its efficiency. The best model was night-featured data and RFC, with 98% accuracy for the classification of three classes. The effectiveness of this experiment leads to less monitoring time for patients, reducing stress and anxiety, producing more efficient models, using wearables, and increasing the amount of data.

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Regional Gray Matter Volume Links Rest-Activity Rhythm Fragmentation With Past Cognitive Decline

Cited by 7 publications

References 6 publications

Sleep, 24-h activity rhythms, and plasma markers of neurodegenerative disease

Sleep, 24-h activity rhythms, and plasma markers of neurodegenerative disease

Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods

Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal

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