scholarly journals Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room

2021 ◽  
Vol 11 (4) ◽  
pp. 468
Author(s):  
Somayeh B. Shafiei ◽  
Zhe Jing ◽  
Kristopher Attwood ◽  
Umar Iqbal ◽  
Sena Arman ◽  
...  
Keyword(s):  
Operating Room ◽  
Pearson Correlation ◽  
Brain Network ◽  
P Value ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Metrics ◽  
Electroencephalogram Eeg ◽  
Surgeon Performance ◽  
Eeg Features

Objective: The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon’s (RAS) performance and distraction level in the operating room. Materials and Methods: Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were utilized to select EEG features that have a significant relationship with surgeon performance and distraction while carrying out a RAS surgical task in the OR. Results: RAS surgeon performance and distraction had a relationship with the surgeon’s functional brain network metrics as recorded throughout OR surgery. We also found a significant negative Pearson correlation between performance and the distraction level (−0.37, p-value < 0.0001). Conclusions: The method proposed in this study has potential for evaluating RAS surgeon performance and the level of distraction. This has possible applications in improving patient safety, surgical mentorship, and training.

scholarly journals Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Yanshuang Ren ◽  
Wensheng Zhang
Keyword(s):  
Pearson Correlation ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Low Rank ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

Age-related changes in functional connectivity between young adulthood and late adulthood

2015 ◽  
Vol 7 (10) ◽  
pp. 4111-4122 ◽  
Author(s):  
Xin Xu ◽  
Qifan Kuang ◽  
Yongqing Zhang ◽  
Huijun Wang ◽  
Zhining Wen ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Young Adulthood ◽  
Brain Network ◽  
Late Adulthood ◽  
Functional Brain ◽  
Age Related ◽  
Functional Brain Network ◽  
Significant Difference ◽  
Network Metrics ◽  
Age Related Changes

The functional brain network in late adulthood has been found to show a significant difference from that in young adulthood using a variety of network metrics.

Research on Functional Brain Network Metrics for Depression Patients Automatic Identification

2013 ◽  
Vol 427-429 ◽  
pp. 1440-1446
Author(s):  
Chen Cheng ◽  
Wen Zhao Liu ◽  
Jun Jie Chen
Keyword(s):  
Network Topology ◽  
Neurological Diseases ◽  
Brain Network ◽  
Classification Model ◽  
Support Vector ◽  
Automatic Identification ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Metrics ◽  
Abnormal Brain

Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.

scholarly journals Fatigue and resting-state functional brain networks in breast cancer patients treated with chemotherapy

2021 ◽  
Author(s):  
Biniam Melese Bekele ◽  
Maryse Luijendijk ◽  
Sanne B. Schagen ◽  
Michiel de Ruiter ◽  
Linda Douw
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Resting State ◽  
Pearson Correlation ◽  
Brain Network ◽  
Breast Cancer Patients ◽  
Functional Brain ◽  
Local Efficiency ◽  
Functional Brain Network ◽  
The Impact

Abstract Purpose This longitudinal study aimed to disentangle the impact of chemotherapy on fatigue and hypothetically associated functional brain network alterations. Methods In total, 34 breast cancer patients treated with chemotherapy (BCC +), 32 patients not treated with chemotherapy (BCC −), and 35 non-cancer controls (NC) were included. Fatigue was assessed using the EORTC QLQ-C30 fatigue subscale at two time points: baseline (T1) and six months after completion of chemotherapy or matched intervals (T2). Participants also underwent resting-state functional magnetic resonance imaging (rsfMRI). An atlas spanning 90 cortical and subcortical brain regions was used to extract time series, after which Pearson correlation coefficients were calculated to construct a brain network per participant per timepoint. Network measures of local segregation and global integration were compared between groups and timepoints and correlated with fatigue. Results As expected, fatigue increased over time in the BCC + group (p = 0.025) leading to higher fatigue compared to NC at T2 (p = 0.023). Meanwhile, fatigue decreased from T1 to T2 in the BCC − group (p = 0.013). The BCC + group had significantly lower local efficiency than NC at T2 (p = 0.033), while a negative correlation was seen between fatigue and local efficiency across timepoints and all participants (T1 rho = − 0.274, p = 0.006; T2 rho = − 0.207, p = 0.039). Conclusion Although greater fatigue and lower local functional network segregation co-occur in breast cancer patients after chemotherapy, the relationship between the two generalized across participant subgroups, suggesting that local efficiency is a general neural correlate of fatigue.

Functional brain network reorganization in HIV infection

2021 ◽  
Author(s):  
Silvia Minosse ◽  
Eliseo Picchi ◽  
Francesca Di Giuliano ◽  
Loredana Sarmati ◽  
Elisabetta Teti ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Brain Network Reorganization
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