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1936

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Afni offers customer call center, digital engagement, and back office solutions for companies so they can develop meaningful and profitable relationships with customers. The company is located in Bloomington, Illinois.

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404 Brock Drive

Bloomington, Illinois, 61702,

United States

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Altered Functional Connectivity and Topological Organization of Brain Networks Correlate to Cognitive Impairments After Sleep Deprivation

Jul 15, 2022

Anatomical Data Preprocessing The T1 images were converted into the BIDS dataset. They were then corrected for intensity non-uniformity using N4BiasFieldCorrection 34 which was provided by ANTs 2.3.3. The derived images were skull-stripped using OASIS30ANTs as the target template. The remaining brain tissues were segmented into the cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) using BET (FSL 5.0.9). A classic method, which reconciles ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle, 35 was applied to refine the brain mask estimated previously. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with antsRegistration (ANTs 2.3.3), using brain-extracted versions of both T1 reference and the T1 template. Meanwhile, ICBM 152 Nonlinear Asymmetrical template version 2009c was used for spatial normalization. Functional Data Preprocessing First, the custom methodology of fMRIPrep 36 was used to generate the reference volume and its skull-stripped version. Susceptibility distortion correction (SDC) was omitted. Bbregister (FreeSurfer), which implements boundary-based registration, was applied for co-registering the fMRI reference and T1 reference. Moreover, slice-time was corrected using 3dTshift from AFNI and spatiotemporal filtering was conducted using mcflirt (FSL). The BOLD time series were resampled into standard space and a preprocessed BOLD run was generated in the MNI 152 NLin2009c Asym space. Framewise displacement (FD), DVARS, and three region-wise global signals were calculated using the preprocessed BOLD. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction (CompCor). The above components were dropped from the BOLD and frames that exceeded a threshold of 0.5 mm FD or 1.5 standardized DVARS were annotated as motion outliers. Gridded (volumetric) resampling was performed using ants Apply Transforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels. Large-Scale Network Calculation The Dosenbach atlas, 37 which contains 142 regions of interests (ROIs) (except 18 ROIs in the cerebellum), was selected to extract the BOLD signals which were averaged across all voxels in the ROIs. Each node of the atlas was a sphere with a radius of 5 mm. Pearson’s correlation coefficient of the BOLD signals was calculated to define the functional connectivity (FC) for a pair of ROIs. The values of FC were transformed to z-scores using Fisher’s r‐to‐z formula. To explore the relationship of each large-scale network, we also classified suprathreshold edges by their membership in the networks defined by Yeo et al and Yan et al. 33 , 38 The seven networks were the visual network (VN, 22 ROIs located in the occipital lobe and posterior fusiform gyrus), somatosensory‐motor network (SMN, 29 ROIs located in the precentral and postcentral gyrus and auditory cortex), DAN (14 ROIs located in the temporo‐occipital cortex, angular gyrus, superior parietal lobule, and premotor cortex), ventral attention network (VAN, 16 ROIs located in the supramarginal gyrus, insula, middle frontal gyrus, and supplementary motor area), subcortical network (SCN, seven ROIs located in the putamen and thalamus), FPN (21 ROIs located in the superior parietal lobule, precuneus, lateral frontal cortex, and dorsal cingulate cortex), and DMN (33 ROIs located in the inferior parietal lobule, posterior cingulate cortex, lateral temporal cortex, and ventral and medial prefrontal cortex). The location of the seven large-scale networks is shown in Figure 1 . The average of the FC z-scores of all the involved edges was used to evaluate the FC among the seven networks. The paired t-test was used to compare the FC within and between groups (p < 0.05, False Discovery Rate Correction). Figure 1 The seven large-scale networks screened in the brain map. Abbreviations: DAN, dorsal attention network; DMN, default mode network; FPN, frontoparietal network; L, left; R, right; SCN, subcortical network; SMN, somatosensory network; VAN, ventral attention network; VN, visual network. Small-World Network Analysis In this study, the correlation coefficient matrix derived from the Dosenbach atlas was processed into an undirected binary matrix using the sparsity threshold method. The range of sparsity was from 0.05 to 0.5, and the step of sparsity was 0.05. The topological organizational changes in the whole brain functional network were described by analyzing small-world metrics, network efficiency and nodal efficiency. The small-world metrics mainly included the clustering coefficient (Cp) and characteristic path length (Lp), which represented the mean clustering coefficient and characteristic path length of 100 random networks. The network efficiency included global efficiency (Eg) and the local efficiency (Eloc). The Eloc is the mean local efficiency over all nodes in the network, and the Eg is defined as the measure of the global efficiency of parallel information transfer in the network. Small-world metrics and network efficiency between the two states were compared using paired t-tests (p < 0.05). The paired t-test of nodal efficiency between the two states utilized multiple corrections (p < 0.05, False Discovery Rate Correction). Results Demographic and Clinical Information Thirty healthy participants (14 females) were recruited in the current study. Tests for five cognitive abilities from RBANS were performed in SD and RW states and compared using the paired t-test or a non-parametric test. We observed a significant decline in attention (t = −7.79, p < 0.001) after SD. No significant differences were observed between the SD and RW states in immediate memory (t = 1.81, p = 0.08), visuospatial/constructional (z = −1.55, p = 0.12), language (t = 1.02, p = 0.31), and delayed memory (t = 0.65, p = 0.52). Detailed results are shown in Table 1 . Table 1 The Results of RBANS Between SD and RW States Large-Scale Network FC Large-scale within- and between-network FC were calculated between the SD and RW states. The participants showed a significant decrease in between-network FC in SMN-DMN, SMN-FPN, and SMN-VAN, and an increase in between-network FC in DAN-VAN and DAN-SMN. No abnormal within-network FC was observed after SD. This finding suggested extensive abnormal between-network FCs after SD. The details are shown in Table 2 and Figure 2 . Table 2 Large-Scale Between-Network FC Changes Figure 2 Altered large-scale network functional connectivity between the SD and RW states. For T value color bar, blue indicates functional connectivity decrease while red indicates functional connectivity increase. The result was corrected by FDR-corrected p < 0.05 (two-tailed). Abbreviations: DAN, dorsal attention network; DMN, default mode network; FC, functional connectivity; FPN, frontoparietal network; L, left; R, right; SCN, subcortical network; SMN, somatosensory network; VAN, ventral attention network; VN, visual network; RW, rested wakefulness; SD, sleep deprivation. Topological Properties of Brain Networks The sparsity range of 0.05 ≤ sparsity ≤ 0.5 was selected to construct the matrices. We compared the small-world parameters between the SD and RW states. The Cp and Lp of the brain networks showed significant reductions in the SD state compared to the RW state (Cp, 0.05 ≤ sparsity ≤ 0.5; Lp, 0.1 ≤ sparsity ≤ 0.2). Regarding network efficiency, Eloc (t = −2.42, p = 0.0185) significantly decreased after SD. This finding suggested decreased small-world properties after SD. Detailed results are shown in Figure 3 . Figure 3 Altered network topological properties between the SD and RW states. (A) The clustering coefficient (Cp), characteristic path length (Lp) and local efficiency (Eloc) across a sparsity range between 0.05 and 0.5. Asterisks indicate a significant difference at this sparsity threshold. (B) Violin plots illustrating the area under the curve (AUC) parameters of the Cp, Lp and Eloc for SD and RW states. *p < 0.05. Abbreviations: RW, rested wakefulness; SD, sleep deprivation. We also observed decreased nodal efficiency in the SMN (right parietal lobe, right precentral gyrus and left temporal lobe), DMN (left ventral prefrontal cortex, left anterior prefrontal cortex, left precuneus, and right anterior cingulate cortex), and VAN (right post insula) after SD. Detailed results are shown in Figure 4 and Table 3 . Table 3 Brain Regions in Decreased Nodal Efficiency Between SD and RW States Figure 4 Group differences in efficiency at the nodal level. Insignificant nodes are shown as yellow spheres, whereas red (RW > SD) spheres denote significant differences after FDR correction. The size of the significant nodes reflects the effect sizes of group differences. Abbreviations: RW, rested wakefulness; SD, sleep deprivation. Correlation Analysis To explore the relationship between the altered FC and topological features of brain networks and attention, we first conducted a correlation analysis between changes in FC (ΔX = XSD-XRW) and changes in attention score. The change in FC in DAN-SMN change was negatively correlated with the change in attention score (R = −0.39, p = 0.032). We then conducted the correlation analysis between changes in the attention score and changes in the topological measure. The changes in Cp, Lp and Elocal s were positively correlated with the change in attention score (Cp, R = 0.42, p = 0.02; Lp, R = 0.48, p = 0.0069; Eloc, R = 0.40, p = 0.027). Detailed results are shown in Figure 5 . Figure 5 Correlation analysis between the altered FC and topological features of brain networks and attention. Abbreviations: ΔX=XSD-XRW; Cp, clustering coefficient; Eloc, local efficiency; Lp, characteristic path length; RW, rested wakefulness; SD, sleep deprivation. Discussion To the best of our knowledge, this is the first study to utilize both large-scale network FC and topological properties based on surface to explore the mechanisms underlying cognitive impairments after 24 h of SD. We observed a significant decline in attention after SD. Compared to the RW state, the large-scale brain network results showed decreased between-network FC in SMN-DMN, SMN-FPN, and SMN-VAN and increased between-network FC in DAN-VAN and DAN-SMN after 24 h of SD. The Cp, Lp and Elocal decreased after SD. Moreover, the decreased attention score was positively correlated with the decreased topological measures, and negatively correlated with the increased FC of DAN-SMN. Our findings demonstrated that SD altered the FC in extensive brain networks and decreased small-world properties of resting-state networks. Abnormal Large-Scale Network FC After SD In the large-scale brain network, we found decreased between-network FC in SMN-DMN, SMN-FPN, and SMN-VAN and increased between-network FC in DAN -VAN, DAN-SMN after SD. In line with our results, the MEG study revealed large-scale rearrangements in the functional network after 24 h of SD. 29 Previous fMRI studies also demonstrated that abnormalities in the functional brain networks in SD involve SMN, DMN, Salience Network (SN), DAN, and FPN. 11 , 12 , 39 DMN is an internally directed network, which was reported to show a decrease in FC after SD. 11 DMN can impact the rest–stimulus interactions in the corresponding sensory cortices, 40 which may explain the decreased FC in DMN-SMN after SD. It was also shown that SD affected DAN which is associated with the top-down deployment of attention. 41 , 42 Several fMRI studies showed that SD changed the intrinsic connectivity within the DAN and the related anti-correlated network (ie DMN). 11 , 19 FPN, which mainly supports the control of information processing, contributes to verbal expression, memory, and cognitive control. 43 SD lead to a decrease in FC in FPN-DMN, which is associated with working-memory performance. 12 The SMN was found to be more affected (more pairs of large-scale networks) than other networks in the current study. Brain regions of the SMN control motor, somatosensory and auditory processing, and are responsible for external stimuli and internally generated movement. It was demonstrated that the brain networks related to sleep and wakefulness are modulated by sensory inputs, and both sensory information and deprivation may induce changes in the brain networks related to sleep and wakefulness. 44 One previous study also illustrated that the altered FC in SMN is associated with the vulnerability of objective vigilance after SD. 45 Thus, our results may support the notion that the SMN is the core network of altered large-scale networks due to sleep loss. The DAN controls goal-oriented top-down deployment of attention, 46 and the VAN, partly overlapping with SN mediates stimulus-driven bottom-up attentional reorienting. 47 The interaction between the two networks is competitive among multiple stimuli in the visual cortex and mediated the selection of behaviorally relevant information. 48 A previous study on healthy participants demonstrated that granger causal influences from VAN to DAN are negatively associated with attention. 49 Regions of the SMN are spatially adjacent to regions of the DAN and VAN in the brain, and cooperate with DAN and VAN during the external tasks. 38 Furthermore, it was reported that SD affects only the top-down processing of attention. 50 In the current study, we observed that the increased FC in DAN-SMN negatively correlated with the decreased attention score. Hence, we speculated that the increased FC in DAN-SMN might be served as a biomarker for the abnormal top-down processing of attention after SD. In short, the abnormal between-network FC after SD might explain the SD‑induced impairments in cognition. Decreased Small-World Properties After SD We conducted the graph theory analysis following the construction of the resting-state networks, and observed that the Cp, Lp and Elocal decreased after SD. The topological result revealed that functional brain networks underwent rearrangements at the global level. In line with our results, the decreased topological features in functional and structural brain networks were reported in numerous neurological and psychiatric disorders, such as major depression, primary insomnia, and shift work disorder. 51–53 A recent EEG study showed a significant decrease of small-worldness in delta and theta bands after 24 h of SD. 28 Nevertheless, a published graph theory-based study on the whole brain networks revealed that the small-world property of resting-state networks is significantly enhanced after 34 h of SD. 30 We speculated that this contradictory result was due to the difference in the duration of SD and circadian influences. Cognitive performances, including vigilant attention, during the morning hours following a sleepless night, are partially restored until the afternoon despite continuing SD. 54 Moreover, our results revealed that the decreased attention scores positively correlated with the decreased topological measures. Hence, we speculated that the worse attention performance after 24 h of SD than 34 h of SD led to the decreased small-world property of brain networks. To demonstrate the strength of analysis method in our study, we used the surface-based preprocessing method, which was better than the volume-based method for registration, reproducibility of algorithms and surface reconstructions. 55 Moreover, we utilized a different approach for large-scale brain network analysis from the previous approach to explore SD-related abnormalities in intrinsic FC in all pairs of brain networks. Limitations and Future Directions However, there were certain limitations. First, the recruited participants were not monitored in the lab and simply reported their sleep duration the night before the RW scan, which probably had some effect on the results. Secondly, several studies have suggested that longer resting-state scans improve reliability and replicability. 56 , 57 In the current study, we collected 490-s resting-state data. Future studies with longer resting-state scans are needed to validate our results. Thirdly, our results showed higher scores for immediate and delayed memory and language after SD, which could not be used to interpret the mechanisms underlying memory decline. We think that certain RBANS tasks have practice effects, especially when the test is repeated within 24 h. It may be necessary to set up a separate rested control group to compare performance changes in future studies. Finally, only young participants were recruited, and other factors (eg body composition and physical fitness) were not controlled in the current study. Therefore, the results could not be extrapolated to individuals in other age groups. Participants from a broader age range should be recruited in the future. Conclusion Our results revealed the abnormal FC in extensive brain networks and decreased small-world property of resting-state networks after 24 h of SD. Furthermore, our results suggested that the increased FC of DAN-SMN and decreased topological features of brain networks may act as neural indicators for attention decline after SD. Data Sharing Statement The data that support the findings of this study are available from the corresponding author (Hongxiao Jia) upon reasonable request. Additionally, the individual deidentified participant data are available after contacting the corresponding author via email ( [email protected] ). The data will be available immediately following publication without an end date. Acknowledgments This study is supported by the National Natural Science Foundation (Grant no.81904120, 82004437), Beijing Hospitals Authority Youth Program (Grant no. QML20201901), Beijing Natural Science Foundation (Grant no. 7212050), Beijing Hospitals Authority Clinical Medicine Development of Special Funding (Grant no. ZYLX202129), and Beijing Hospitals Authority’s Ascent Plan (Grant no. DFL20191901). Disclosure

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  • When was Afni founded?

    Afni was founded in 1936.

  • Where is Afni's headquarters?

    Afni's headquarters is located at 404 Brock Drive, Bloomington.

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