تغییرات در شبکه استراحت مغزی در شرایط محرومیت از خواب با استفاده از تصویربرداری تشدید مغناطیسی عملکردی

مقدمه: خواب یک فرآیند ضروری برای حفظ تعادل همه اعضای بدن است. کم خوابی یک پدیده رایج در جوامع مدرن است، اما اثرات طولانی مدت آن بر عملکرد شناختی مغز کمتر مورد مطالعه علمی قرار گرفته است. علی رغم مطالعات انجام شده در زمینه اندازه‌گیری هوشیاری، هوشیاری و توجه، اجماع کمتری در مورد تاثیرات کم خوابی بر بسیاری از عملکردهای شناختی سطح بالا از جمله کارکردهای ادراک، حافظه و عملکردهای اجرایی وجود دارد. روش کار: در این مطالعه ما از داده ‌های تصاویر تشدید مغناطیسی عملکردی حالت استراحت (rs-fmri) جمع ‌آوری شده توسط دانشگاه استکهلم جهت پروژه خواب استفاده نمودیم. این داده‌ها شامل تصاویر گرفته شده از افراد در دو حالت خواب کامل و محرومیت نسبی از خواب بود. جهت تحلیل تصاویر و مطالعه تفاوت‌های بین دو حالت، نرم افزار fsl را به کار بردیم. بدین منظور ابتدا با انجام آنالیز ica بر روی تصاویر rs-fmri، مولفه ‌های مستقل مکانی به صورت گروهی استخراج گردیدند و در ادامه مولفه ‌های به دست آمده به هفت شبکه مغزی معرفی شده در اطلس yeo_7networks تخصیص داده شدند. یافته ‌ها: بر اساس نتایج، امکان مقایسه اتصالات درون شبکه‌ ای برای 5 شبکه حالت استراحت وجود داشت که در شبکه ‌های n1 و n5 تفاوت معناداری مشاهده شد. علاوه بر آن بررسی اتصالات بین شبکه‌ ای نشان داد که ارتباط زوج شبکه ‌های n2-n6 و n3-n7 در حالت کم خوابی و شرایط طبیعی اختلاف معناداری دارند.  نتیجه ‌گیری: وجود تفاوت آماری معنادار در اتصالات درون شبکه ‌ای هر یک از شبکه ‌های بینایی و لیمبیک در حالت کم خوابی و طبیعی را می توان ناشی از تاثیر کم خوابی بر کارکردهای شناختی حافظه و توجه برشمرد. 

alteration in brain resting state networks after sleep deprivation using functional magnetic resonance imaging

introduction sleep deprivation (ad) is a common phenomenon in modern societies, but its long-term effects on cognitive brain function have been less scientifically studied. despite studies measuring alertnessand attention, there are fewer consensuses on the effects of sleep deprivation on many high-level cognitive functions. this study can refer to the functions of perception, memory, and executive functions (1, 2). sleep is an essential process for maintaining the balance of all body organs. although each person spends an average of one-third of their life asleep, accurate information on the sleep mechanism has yet to be available (3, 5).since the studies carried out in this field have not provided detailed information on inter-and-intra network connections, additional detailed information on them, which was not available in preceding studies, can be acquired by conducting studies into the brain’s functional networks using methods including independent elements of data extraction. therefore, this study, by making use of images obtained from magnetic resonance imaging of the brain at rest and in a deep sleep, which stockholm university conducted, reviews and compares the intra and inter networks in the seven major brain networks (n1-n7) in deep sleep and sleep deprivation. accordingly, this study aims to investigate the effects of sleep deprivation on the brain’s involved networks and identify their connections. the purpose of this study is first to compare and evaluate the studies of network communication in sd at rest, which can be an accurate summary of the networks and areas of the brain that change in sd. then, the effects of sdon the involved brain networks and their identification, the relationship of the identified networks with each other, and finally, the cognitive functions affected by the intervened networks on the functional brain imaging resonance data (fmri) the extracted standard of the stockholm university sleepy brain project is addressed by the independent component analysis (ica) method.methodsin the stockholm university sleepy brain project, imaging was done for each person in two sessions: once after normal sleep and once with partial sd. according to the random paradigm, participants should either experience full sleep or sleep only three hours a night (waking up at the usual time every day) (8).brain imaging scans were performed using ge’s three-discoverytesla mri scanner, model 750, with the help of an 8-channel coil. structural images of t1 and t2 were obtained to normalize fmri images, as well as morphological processing. adjustment parameters for anatomical imaging include the following: fov 24, slice thickness 1 mm, sagittal data acquisition, cluttered data acquisition, and full head coverage. resting data were obtained using an epi sequence with a fov of 28.8, slices were 3 mm thick, and no gaps were made between brain sections. covers, taking data in a cluttered way, echo time (te) equal to 30 milliseconds, repetition time (tr) equal to 2.5, seconds and philip angle equal to 75 degrees.in order to analyze the rest state data related to the study whose specifications were stated, fsl software and matlab toolbox were used. the present study also used free-surfer software to preprocess t1 anatomical data.due to the adverse effects of low-frequency displacement and head movement on the decomposition of data components, motion correction, and removal of displacements and other appropriate predicates before the main zinc analysis, data were processed using pre-stats preprocessors from the melodic tool in fsl software. because the rs-fmri data were obtained in a cluttered manner, the slice-timing correction step was performed with the same constraint, and in order to correct the candidates’ head movements, a head movement correction algorithm was applied to the data.ica analysis was used to process rs-fmri images (9). to do this, the multisession temporal concatenation tool in melodic and the preprocessing and steps required for group data analysis in this tool were used. spatial ica analysis was performed using 20 independent component maps (ic maps) to detect resting state networks (rsn) from the control group.the yeo_7 networks atlas was used to extract the matrix of brain connections using the outputs of these analyzes. in order to determine and establish the correspondence between yeo_7networks atlas networks and 20 components of extraction ica for each individual due to the limited number of independent components (20 components), as well as the number of atlas networks (7 networks), this step is inspected. a careful eye was performed. accordingly, one of the yeo atlas networks was assigned to each independent extraction component, according to table 1.resultsthe results of in-network comparisons of networks corresponding to the yeo atlas and, in the next step, the results of standard comparisons and statistical analyses related to cross-network analyzes were evaluated. considering that the yeo standard atlas of seven networks was used to study brain networks, a total of twenty components extracted from ica analysis (16 components after removal of non-brain components) on this subset of seven networks were distributed, the details of which are given in table 1. the relationship between the quantity components was examined by identifying the networks between which in-network analyzes are possible (n1, n2, n5, n6, and n7). calculation of this quantity, i.e., the quantity of intra-network communication was calculated from the communication matrix obtained from time series of 16 independent components arranged in order and accordance with the n1 to n7 networks.table 1. the relationship between the components of the ica analysis and the networks defined in the yeo_7networks atlasicnetwork            ic_01           yeo_7networks_1ic_05           ic_09           ic_02yeo_7networks_2ic_07           ic_06yeo_7networks_3ic_20yeo_7networks_4           ic_14yeo_7networks_5ic_17           ic_13yeo_7networks_6ic_15ic_04yeo_7networks_7ic_08ic_12ic_16ic_19ic_10cerebellum            ic_03white matteric_11csf           ic_18artifactas shown in table 2, considering the threshold of 0.05 as a significant level of difference between the two groups, the inter-network communication for the network pairs n1-n7, n2-n6, n3-n7 and finally, n4-n6 have a significant difference in sd and normal.it should be noted that the results and quantitative studies between the 16 direct components of ica