Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
‘Love and Fear’ as portraited by Affective Neuroscience
1
4
EN
Mohammad
Nami
0000-0003-1410-5340
Neuroscience Center, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge 084301103, Panama
jamsat1@sums.ac.ir
Kosagi-Sharaf
Rao
Neuroscience Center, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge 084301103, Panama
Seithikurippu R
Pandi-Perumal
Somnogen Canada Inc., Toronto, Canada
Babak
Kateb
Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA
babak.kateb@worldbrainmapping.org
10.30476/jamsat.2021.48380
Modern neuroscience is on the verge of exploring new frontiers within various<br />subdisciplines.<br />The question of how our brain with over hundred billion neurons puts together cognition,<br />emotion and behavior has always been captivating. As such, the study of neural processes<br />through which we not only maintain our survival and homeostasis, but also stay<br />productive and functional, has attracted cognitive neuroscientists for decades. With the<br />advent of neurotechnologies and ever-growing research facilities, modern neuroscience<br />has seen a tremendous progress in dealing with such questions. This letter argues the most<br />referenced theories with respect to key concepts in affective neuroscience, i.e. fear, love<br />and related emotions or traits. We hope the present letter is found thought-provoking with<br />regards to further theoretical models and empirical research in affective neuroscience and<br />neuropsychology.
https://jamsat.sums.ac.ir/article_48380.html
https://jamsat.sums.ac.ir/article_48380_81f3a297fac53c1a68aef466cf399d5f.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Computational Aspects and Statistical Models in Sleeping Disorders; an Apriori Algorithm Approach
5
13
EN
Mani
Butwall
0000081788876565
Department of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi1@yahoo.com
Kinshuk Gaurav
Singh
Department of Computer Science, ITM (SLS) Baroda University, Vadodara, India
Raj
Pujara
Department of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi3@yahoo.com
Pranav
Modi
Department of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi4@yahoo.com
Harshvardhan
Sharma
Department of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi5@yahoo.com
Arsh
Vishwakarma
Department of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi6@yahoo.com
Iman
Salehi
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
dbhi7@yahoo.com
Mohammad Javad
Gholamzadeh
Students' Research Committee, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
dbhi8@yahoo.com
Ali-Mohammad
Kamali
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
alikamali321@gmail.com
Milad
Kazemiha
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
kazemiha.sums@gmail.com
Prasun
Chakrabarti
Provost, Techno India JNR, Institute of Technology, Udaipur 313003, Rajasthan, India
drprasun.cse@gmail.com
Mohammad
Nami
0000-0003-1410-5340
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
jamsat1@sums.ac.ir
10.30476/jamsat.2021.48381
Sleep disorders are very common in today’s world as we all are living a relatively competitive life; where we undervalue our mental health. There are some sleep disorders that share almost similar symptoms yet various pathological underpinnings leading to clinical misjudgments; thereby resulting in the inappropriate treatments. The present study has attempted to investigate possible correlation between various types of sleep predicaments. To do so; we used multiple statistical analysis algorithms as well as prediction models on our database and performed manual testing to draw our conclusion. Our analyses revealed that sleep disorders; and namely sleep apnea-hypopnea syndrome; tend to present with related comorbidities
https://jamsat.sums.ac.ir/article_48381.html
https://jamsat.sums.ac.ir/article_48381_77ed71f1acc21b96328773726cd15dcf.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Improved human brain tractographs using multi-shell q-ball diffusion magnetic resonance imaging compared with DTI
14
23
EN
Fatemeh
Haghighi
0000-0002-0447-9720
Medical Physics Department, College of Medical Science, Tarbiat Modares University, 7 Jalal Al Ahmad Street, Tehran, Iran
f.haghighi89@gmail.com
Marzieh
Nezamzadeh
0000-0001-6938-379X
Department of Radiology, Upstate Medical University, State University of New York, 750 E Adams st., Syracuse, NY 13210
m.nezamzadeh@modares.ac.ir
Neda
Mohammadi-Mobarakeh
0000-0002-1804-2378
Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran
nedamohammadi157@gmail.com
10.30476/jamsat.2021.48382
Introduction: Recently, it has been proven that assuming the Gaussian model in DTI<br />method is inappropriate for propagation in a complex substrate such as human brain<br />tissue. High Angular Resolution Diffusion Imaging (HARDI) (or so called q-ball imaging)<br />is known as a model free method that allows to more accurately detect changes in diffusion<br />with different orientations. In this study, after finding the best angle threshold at the Optic<br />Radiation (OR) level, the length and number of reconstructed nerve fibers in this angle<br />were measured using q-ball imaging and were compared with DTI.<br />Materials and Method: Tractographs of q-ball images from the human brains of 10<br />healthy volunteers (30 to 50 years old) were studied using a 3-Tesla scanner. 64 directions<br />of diffusion encoding in two b-values (1000 and 2000 s/mm2), were used for q-ball<br />imaging and in routine b-value of 1000 s/mm2 for DTI. The tractographs were compared<br />at the OR level with the tractography based on q-ball and DTI images. The results were<br />analyzed using t-test. The angle threshold for tractography was selected at 45 degrees by<br />comparing the tractographs in 13 angles.<br />Conclusion: Consequently, the number and length of nerve fibers of OR, measured using<br />the q-ball imaging, were significantly higher than those using the DTI. Finally, the better<br />quality of the tractographs as well as the analyzed quantities, are indicators of larger signalto-<br />noise ratio in q-ball imaging and indicate that q-ball imaging compared to DTI plays an<br />important role in the development of brain nerve mapping.
MRI,Diffusion tensor imaging,Multi-shell Q-ball Imaging,Tractography
https://jamsat.sums.ac.ir/article_48382.html
https://jamsat.sums.ac.ir/article_48382_50fdce0ad22a74cefed5708d6a48d9da.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Translation and cross-cultural adaptation of the traumatic injuries distress scale to persian
24
32
EN
Shirin
Modarresi
0000-0002-2652-0480
School of Physical Therapy, Western University, London, ON, Canad
smodarre@uwo.ca
Golale
Modarresi
Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
golale.modarresi@gmail.com
Maryam
Farzad
0000-0002-5470-5319
Department of Health and Rehabilitation Sciences, Western University, London, ON, Canada
mfarzad@uwo.ca
Erfan
Shafiee
0000-0002-5449-2878
Department of Health and Rehabilitation Sciences, Western University, London, ON, Canada
eshafiee@uwo.ca
Mahshad
Maleki
University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
malekimahshadmm@gmail.com
Joy C.
MacDermid
School of Physical Therapy, Western University, London, ON, Canada
David M.
Walton
School of Physical Therapy, Western University, London, ON, Canada
10.30476/jamsat.2021.48383
Objective: Psychological factors have been consistent predictors of recovery following<br />musculoskeletal injuries. The Traumatic Injuries Distress Scale (TIDS) is a risk-based<br />prognostic screening tool that has been developed for predicting recovery from acute<br />musculoskeletal trauma. The purpose of this study was to translate and cross-culturally<br />adapt the TIDS to Persian.<br />Methods: The forward-backward translation technique was used to translate the TIDS<br />from English to Persian. The final version was obtained by consensus with the translation<br />committee. Cognitive interviews were used to evaluate lingual accuracy and cultural or<br />contextual appropriateness. 13 participants completed cognitive interviews based on the<br />talk-aloud and probing approach to explore individual items.<br />Results: Participants (age range 22-58) had no problems in questions two, six, eight, and<br />11. Participants identified potential issues in 4/6 areas of a cognitive interview coding<br />system: comprehension/clarity, inadequate response definition, perspective modification,<br />reference point, and calibration across items. These issues informed changes made to<br />arrive at the final version of the P-TIDS.<br />Conclusions: The TIDS, which is a tool to assess psychological distress following<br />musculoskeletal trauma was translated and culturally adapted to Persian. Through<br />cognitive interviewing, some issues were identified that were related to cross-cultural<br />interpretation and application of the items that were resolved through rewording and<br />recalibration of the tool. The TIDS-Pcan be a significant addition to the toolbox of Persian<br />healthcare providers for assessing the risk of developing chronic pain post-musculoskeletal<br />trauma. Psychometric studies are now underway to further evaluate the properties of the<br />translated tool.
Traumatic Injuries Distress Scale,Musculoskeletal injuries,Prognosis,Cross cultural adaptation,cognitive interview
https://jamsat.sums.ac.ir/article_48383.html
https://jamsat.sums.ac.ir/article_48383_9cf0a473e0a74702919c9ccc4a47a325.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Artificial intelligence and stochastic processbased analysis of human psychiatric disorders
33
53
EN
Yashvi
Bhavsar
Department of Computer Science and Engineering, Institute of Technology & Management Universe, Dhanora Tank Road, Near Jarod, Vadodara - 391510,
Gujarat, India
dbhi31@yahoo.com
Khyati
Mistry
Department of Computer Science and Engineering, Institute of Technology & Management Universe, Dhanora Tank Road, Near Jarod, Vadodara - 391510,
Gujarat, India
dbhi32@yahoo.com
Nishchay
Parikh
Department of Computer Science and Engineering, Institute of Technology & Management Universe, Dhanora Tank Road, Near Jarod, Vadodara - 391510,
Gujarat, India
dbhi33@yahoo.com
Himani
Shah
Department of Computer Science and Engineering, Institute of Technology & Management Universe, Dhanora Tank Road, Near Jarod, Vadodara - 391510,
Gujarat, India
dbhi34@yahoo.com
Adarsh
Saraswat
Department of Computer Science and Engineering, Institute of Technology & Management Universe, Dhanora Tank Road, Near Jarod, Vadodara - 391510,
Gujarat, India
dbhi35@yahoo.com
Helia
Givian
Preclinical Core Facility, Tehran University of Medical Sciences, Tehran, Iran
givian.he@gmail.com
Mojataba
Barzegar
Intelligent quantitative biomedical imaging (iqbmi), Tehran, 1955748171, Iran
dbhi38@yahoo.com
Maryam
Hosseini
Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 71348-14336, Iran
dbhi39@yahoo.com
Khojaste
Rahimi Jaberi
Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 71348-14336, Iran
dbhi40@yahoo.com
Archana
Magare
Department of Computer Science and Engineering, Institute of Technology & Management Universe, Dhanora Tank Road, Near Jarod, Vadodara - 391510,
Gujarat, India
dbhi42@yahoo.com
Mohammad Javad
Gholamzadeh
Students' Research Committee, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
dbhi8@yahoo.com
Hadi
Aligholi
0000-0002-2241-5296
Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 71348-14336, Iran
hadialigholi@yahoo.com
Ali-Mohammad
Kamali
Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 71348-14336, Iran
alikamali321@gmail.com
Prasun
Chakrabarti
Provost, Techno India JNR, Institute of Technology, Udaipur 313003, Rajasthan, India
drprasun.cse@gmail.com
Mohammad
Nami
0000-0003-1410-5340
Society for Brain Mapping and Therapeutics (SBMT), Brain Mapping Foundation (BMF), Middle East Brain + Initiative, Los Angeles, CA 90272, CA, USA
jamsat1@sums.ac.ir
10.30476/jamsat.2021.48384
This paper contains an analysis and comparison of different classifiers on different datasets<br />of Psychiatric Disorders- Personality Disorder, Depression, Anxiety, Schizophrenia<br />and Alzheimer's disease. Psychiatric disorders are also referred to as mental disorders,<br />abnormalities of the mind that result in persistent behavior which can seriously cause day<br />to day function and life. Stochastic in AI refers to if there is any uncertainty or randomness<br />involved in results and are used during optimization; Using this process also helps to<br />provide precise results. The study of stochastic process in AI uses mathematical knowledge<br />and techniques from probability, set theory, calculus, linear algebra and mathematical<br />analysis like Fourier analysis, real analysis, and functional analysis. this technique is used<br />to construct neural network for making artificial intelligent mode for processing and<br />minimizing human effort. This paper contains classifiers like SVM, MLP, LR, KNN, DT,<br />and RF. Several types of attributes are used and have been trained by Weka tool, MATLAB,<br />and Python. The results show that the SVM classifier showed the best performance for all<br />the attributes and disorders researched in this paper.
Alzheimer’s disease,Anxiety,Artificial Intelligence,depression,DT,KNN,Logistic regression,MLP,Personality Disorder,RF,Schizophrenia,SVM
https://jamsat.sums.ac.ir/article_48384.html
https://jamsat.sums.ac.ir/article_48384_afe61c2902c6060ed4a26f42dae236bc.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Classification of schizophrenia from feature-model analysis of bilaterally correlated diagnosis, symptoms, and imaging findings pyramid
54
63
EN
YTanvi
Patel
Deptartment of Computer Science, ITM (SLS) Baroda University, Vadodara, India
Shreyansh
Dalwadi
Deptartment of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi21@yahoo.com
Nen
Bakraniya
Deptartment of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi22@yahoo.com
Apurva
Desai
Deptartment of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi23@yahoo.com
Nirmal
Kachhiya
Deptartment of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi24@yahoo.com
Het
Parikh
Deptartment of Computer Science, ITM (SLS) Baroda University, Vadodara, India
dbhi26@yahoo.com
Mohammad Javad
Gholamzadeh
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
dbhi8@yahoo.com
Ali- Mohammad
Kamali
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
Milad
Kazemiha
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
kazemiha.sums@gmail.com
Prasun
Chakrabarti
Provost, Techno India JNR, Institute of Technology, Udaipur 313003, Rajasthan, India
drprasun.cse@gmail.com
Mohammad
Nami
0000-0003-1410-5340
DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
jamsat1@sums.ac.ir
10.30476/jamsat.2021.48385
Schizophrenia (SZ) is a mental illness that impairs a person's mental capacity, emotional<br />dispositions, and personal and social quality of life. Manual SZ patient screening is timeconsuming,<br />expensive, and prone to human mistakes. As a result, a autonomous, relatively<br />accurate, and reasonably economical system for diagnosing schizophrenia patients is<br />required. Machine learning methods are capable of learning subtle hidden patterns from<br />high dimensional imaging data and achieve significant correlations for the classification<br />of Schizophrenia. In this study, the diverse types of symptoms of the affected person are<br />selected which have the weights assigned by cross-correlations and the model classifies<br />the probability of schizophrenia in the person based on the highest weighted symptoms<br />present in the report of the patient using machine learning classifiers. The classification<br />is made by various classifiers in which the Support Vector Machine (SVM) gives the<br />best result. In the neuroscience domain, it has been one of the most popular machinelearning<br />tools. SVM with Radial Basis Function kernel helps to distinguish between<br />patients and healthy controls with significant accuracy of 76% without normalization and<br />Principal Component Analysis (PCA). The K nearest neighbor’s algorithm also with no<br />normalization and PCA showed an accuracy of 73% in predicting SZ which is remarkably<br />close to the SVM given the small size dataset.
Schizophrenia (SZ) Classification,Healthy Controls (HC),Support Vector Machine (SVM),Magnetic Resonance images (MRI),Principal Component Analysis (PCA),Functional MRI (fMRI),Structural MRI (sMRI),Independent Component Analysis (ICA)
https://jamsat.sums.ac.ir/article_48385.html
https://jamsat.sums.ac.ir/article_48385_44e50008f555a46cd4c4ccd1f5951aa4.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Evaluation of nootropic activity of standardized Epipremnum aureum extract against scopolamine-induced amnesia in experimental animals
64
71
EN
Sreemoy
Kanti das
Lecturer, Faculty of Pharmacy, Lincoln University College, Selangor, Malaysia
G. S
Chakraborthy
Principal and Professor, Parul Institute of Pharmacy & Research, Gujarat, India
Tulika
Chakrabarti
Department of Chemistry, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India
tulika.chakrabarti20@gmail.com
Prasun
Chakrabarti
Provost and Institute Endowed Distinguished Senior Chair Professor, Techno India NJR Institute of Technology, Udaipur 313003, Rajasthan, India
drprasun.cse@gmail.com
Mohammad Javad
Gholamzadeh
Students’ Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
dbhi8@yahoo.com
Mohammad
Nami
0000-0003-1410-5340
Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
jamsat1@sums.ac.ir
10.30476/jamsat.2021.48386
Introduction: Various plant species of genus Epipremnum have already been reported<br />to have different types of pharmacological activities. However, another plant of the same<br />genus Epipremnum aureum has not been scientifically exposed to a significant extent to<br />date. Although it contains many bioactives, it has only been studied for antidepressant<br />activity. The present study aims to evaluate the nootropic potential of standardized extract<br />of Epipremnum aureum against scopolamine-induced amnesia in experimental animals.<br />Method: The nootropic potential of Epipremnum aureum was evaluated using an elevated<br />plus maze and Morris water maze apparatus. A dose of 400mg/kg and 600mg/kg was used<br />to access the nootropic activity. Scopolamine (0.4 mg/kg) was used to induce amnesia in<br />mice. Additionally, the anti-acetylcholinesterase activity of the extract was evaluated by<br />measuring the level of acetylcholinesterase in the mice brain.<br />Result: Epipremnum aureum was found to increase memory and reverse the amnesic<br />action of scopolamine in a dose-dependent manner. In elevated plus maze and Morris<br />water maze, Epipremnum aureum decreased the transfer latency as compared to the control<br />group. Further biochemical investigation revealed an increased level of acetylcholine and<br />decreased level of TBARS resulting in reversing the effect of scopolamine in amnesic mice.<br />Conclusion: Epipremnum aureum showed positive results in reversing the amnesia<br />action of scopolamine which may be the probable mechanism for its memory retention<br />activity. Based on the experimental outcome, the present study provides a piece of scientific<br />evidence for the nootropic potential of Epipremnum aureum in experimental animals.
Acetyl-cholinesterase,Morris water maze,Elevated plus maze,thin-layer chromatography
https://jamsat.sums.ac.ir/article_48386.html
https://jamsat.sums.ac.ir/article_48386_4fcf0380fb8dfe7230f42da1c43f0090.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Profiling cognitive performance and sleep quality measures in patients with age-related macular degeneration
72
80
EN
Mehrdad
Afarid
Department of Ophthalmology, Poostchi Ophthalmology Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
afaridm@sums.ac.ir
Hooman
Rezaie
Students’ Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
mohammad.khebreh@gmail.com
Behzad
Khademi
Department of Ophthalmology, Poostchi Ophthalmology Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
khademi.behzad@gmail.com
Mohammad Javad
Gholamzadeh
Students’ Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
dbhi8@yahoo.com
Mohammad
Nami
0000-0003-1410-5340
Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
jamsat1@sums.ac.ir
10.30476/jamsat.2021.48387
Objective: This study is aimed at profiling cognitive functions in patients with age-related<br />macular degeneration (AMD).<br />Method: This cross-sectional investigation enrolled 45 patients with AMD and 45 age- and<br />sex-matched controls. The overall cognitive performance in AMD sufferers versus control<br />subjects was assessed using the Persian version of Addenbrooke’s Cognitive Examination<br />battery (ACE-R). Subjects’ sleep quality was also evaluated using the Pittsburgh Sleep<br />Quality Index (PSQI). The mean global assessment and subscale scores were statistically<br />compared between groups.<br />Results: The mean global scores for ACE-R in AMD and control groups (80.4±12.3 and<br />86 ± 9.6, respectively) were found to be statistically different (p=0.018). On the other hand,<br />there was no significant difference (p=0.793) between the AMD and control groups in<br />terms of PSQI scores (9.7±2.8 and 9.8±2.8, respectively).<br />Conclusion: AMD patients seem to have cognitively underperformed in memory<br />and verbal fluency domains compared to the control group. Evidence on cognitive<br />impairments in patients with AMD may possibly herald neurocognitive insufficiencies<br />and have common pathological mechanisms with dementias.
Age-Related Macular Degeneration,Cognitive Performance,Sleep quality,Dementia
https://jamsat.sums.ac.ir/article_48387.html
https://jamsat.sums.ac.ir/article_48387_9bba26c25652aa92bace9a5990b54bc1.pdf
Shiraz University of Medical Sciences
Journal of Advanced Medical Sciences and Applied Technologies
2423-5903
2538-4473
6
1
2021
12
01
Association of Sleep Spindles, Sleep Apnea, and Other Polysomnography Parameters; a Single Center Preliminarily Report
81
85
EN
Mohammad Javad
Gholamzadeh
Students’ Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
dbhi8@yahoo.com
Reza
Fereidooni
0000-0001-5131-3291
Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
rezafereidooni@yahoo.com
10.30476/jamsat.2021.48388
Introduction: Obstructive sleep apnea (OSA) is associated with arousals due to the<br />cessation of breathing during sleep. On the other hand, sleep spindles, an EEG wave mainly<br />seen in stage 2 of non-REM sleep (N2), are responsible for many functions including the<br />maintenance of sleep. We aimed to investigate the association between sleep spindles and<br />OSA and compare the additional polysomnography (PSG) metrics in a group of patients<br />with OSA.<br />Materials and Method: Fifty consecutive patients with moderate and severe OSA were<br />recruited. Association of apnea-hypopnea index (AHI) with spindles in N2 and arousals<br />were evaluated. Other PSG metrics were compared in the moderate versus severe group.<br />Results: Body mass and snore indices were significantly more in the severe group (p=0.002<br />and p<0.001, respectively). Arousals were more frequently seen in severe OSA cases<br />(p=0.064). Sleep spindle index did not have any relationship with AHI and the number<br />of arousals. However, arousals were weakly correlated with AHI (Spearman’s rho= 0.293,<br />p=0.039) and snore index (Spearman’s rho= 0.365, p=0.010).<br />Conclusion: Severity of OSA did not show a clear correlation with spindle density in N2.<br />Further studies with larger samples and a control group are needed to prove a relationship<br />between sleep spindles and OSA.
Sleep Spindle,Obstructive Sleep Apnea,Polysomnography
https://jamsat.sums.ac.ir/article_48388.html
https://jamsat.sums.ac.ir/article_48388_d75adddf15e58b4d9fcd4643d807d1b1.pdf