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Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post‐operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre‐ and intra‐operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non‐invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre‐existing deficits. Connectome fingerprinting (CF) is a machine‐learning approach that learns associations between resting‐state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting‐state data. Here we utilized CF to train models on high‐quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting‐state fMRI (rs‐fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%–100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task‐related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out‐of‐sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. Practitioner Points: Precision motor network prediction using connectome fingerprinting.Carefully trained models' performance limited by stability of task‐fMRI data.Successful cross‐scanner predictions and motor network mapping in patients with tumor. [ABSTRACT FROM AUTHOR]

Purpose - International trade has always been a medium of interaction of economies to satiate their demand in terms of goods and services. As none of the economies are self sufficient in every product and services required for sustainable growth and development. So their reliance on exports and imports has increased considerably. Due to which economies are exposed to external risks prevalent, May it be financial crisis or pandemics. This research article focuses upon unprecedented effect of COVID-19 pandemic on current account of balance of payments of Indian economy and the impact it had on various constituents of current account. Design/methodology/approach - To respond to the global COVID-19 outbreak, the social economic implications of COVID-19 on specific dimensions of the global economy are analyzed in this study. The situations of COVID-19 has certainly impacted global trade and Indian economy was not an exception to it. Moreover, insights about the effects of the pandemic on trade and transaction during the span in which impact of pandemic was severe has been addressed through this research article. These insights may support strategic decision making and policy framing activities for the top level management in private and government sectors. Findings - With insecurities of a new recession and economic crisis, key moments such as these call for strong and powerful governance in health, business, government, and large society. Instant support measures have to be initiated and adapted for those who can drop through the cracks. Mid and long-term strategies are required to stabilize and motivate the economy during this recession. Originality/value - A comprehensive export and import analysis that consists of report taken from various sources such as, Ministry of commerce, UNCTAD, RBI, IMF. In order to analyze the impact COVID-19 had on a selective parameters of current account of balance of payments of Indian economy. [ABSTRACT FROM AUTHOR]

Context: Respiration is known to modulate neuronal oscillations in the brain and is measured by electroencephalogram (EEG). Sudarshan Kriya Yoga (SKY) is a popular breathing process and is established for its significant effects on the various aspects of physiology and psychology. Aims: This study aimed to observe neuronal oscillations in multifrequency bands and interhemispheric synchronization following SKY. Settings and Design: This study employed before- and after-study design. Subjects and Methods: Forty healthy volunteers (average age 25.45 ± 5.75, 23 males and 17 females) participated in the study. Nineteen-channel EEG was recorded and analyzed for 5 min each: before and after SKY. Spectral power for delta, theta, alpha, beta, and gamma frequency band was calculated using Multi-taper Fast Fourier Transform (Chronux toolbox). The Asymmetry Index was calculated by subtracting the natural log of powers of left (L) hemisphere from the right® to show interhemispheric synchronization. Statistical Analysis: Paired t-test was used for statistical analysis. Results: Spectral power increased significantly in all frequency bands bilaterally in frontal, central, parietal, temporal, and occipital regions of the brain after long SKY. Electrical activity shifted from lower to higher frequency range with a significant rise in the gamma and beta powers following SKY. Asymmetry Index values tended toward 0 following SKY. Conclusions: A single session of SKY generates global brain rhythm dominantly with high-frequency cerebral activation and initiates appropriate interhemispheric synchronization in brain rhythms as state effects. This suggests that SKY leads to better attention, memory, and emotional and autonomic control along with enhanced cognitive functions, which finally improves physical and mental well-being. [ABSTRACT FROM AUTHOR]