Neural evidence for long-term marriage shaping the functional brain network organization between couples

Comparative study of neonatal brain injury fetuses using machine learning methods for perinatal data

OBJECTIVE

CTG is used to record the fetus's fetal heart rate and uterine contraction signal during pregnancy. The prenatal fetal intrauterine monitoring level can be used to evaluate the fetal intrauterine safety status and reduce the morbidity and mortality of the perinatal fetus. Perinatal asphyxia is the leading cause of neonatal hypoxic-ischemic encephalopathy and one of the leading causes of neonatal death and disability. Severe asphyxia can cause brain and permanent nervous system damage and leave different degrees of nervous system sequelae.

METHODS

This paper evaluates the classification performance of several machine learning methods on CTG and provides the auxiliary ability of clinical judgment of doctors. This paper uses the data set on the public database UCI, with 2126 samples.

RESULTS

The accuracy of each model exceeds 80%, of which XGBoost has the highest accuracy of 91%. Other models are Random tree (90%), light (90%), Decision tree (83%), and KNN (81%). The performance of the model in other indicators is XGBoost (precision: 90%, recall: 93%, F1 score: 90%), Random tree (precision: 88%, recall: 91%, F1 score: 89%), lightGBM (precision: 87%, recall: 93%, F1 score: 90%), Decision tree (precision: 83%, recall: 86%, F1 score: 84%), KNN (precision: 77%, recall: 85%, F1 score: 81%).

CONCLUSION

The performance of XGBoost is the best of all models. This result also shows that using the machine learning method to evaluate the fetus's health status in CTG data is feasible. This will also provide and assist doctors with an objective assessment to assist in clinical diagnosis.

Dual-targeted repetitive transcranial magnetic stimulation modulates brain functional network connectivity to improve cognition in mild cognitive impairment patients

Background: Mild cognitive impairment (MCI) is a condition between normal aging and dementia; nearly 10-15% of MCI patients develop dementia annually. There are no effective interventions for MCI progression. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique that has attempted to improve the overall cognitive function of MCI patients. However, it does not affect episodic memory improvement. Methods: In this study, we engaged 15 clinically diagnosed MCI patients and normal controls to explore the effect of dual-targeted rTMS on progressing cognitive function, particularly episodic memory in MCI patients. Resting-state EEG recordings and neuropsychological assessments were conducted before and after the intervention. EEG features were extracted using an adaptive algorithm to calculate functional connectivity alterations in relevant brain regions and the mechanisms of altered brain functional networks in response to dual-target rTMS. Results: The study revealed that the functional brain connectivity between the right posterior cingulate gyrus (PCC) and the right dorsal caudate nucleus (DC) was significantly reduced in MCI patients compared to normal controls (p < 0.001). Dual-target rTMS increased the strength of the reduced functional connectivity (p < 0.001), which was related to cognitive enhancement (p < 0.05). Conclusion: This study provides a new stimulation protocol for rTMS intervention. Improving the functional connectivity of the right PCC to the right DC is a possible mechanism by which rTMS improves overall cognitive and memory function in MCI patients.

Altered functional network activities for behavioral adjustments and Bayesian learning in young men with Internet gaming disorder

BACKGROUND AND AIMS

Deficits in cognitive control represent a core feature of addiction. Internet Gaming Disorder (IGD) offers an ideal model to study the mechanisms underlying cognitive control deficits in addiction, eliminating the confounding effects of substance use. Studies have reported behavioral and neural deficits in reactive control in IGD, but it remains unclear whether individuals with IGD are compromised in proactive control or behavioral adjustment by learning from the changing contexts.

METHODS

Here, fMRI data of 21 male young adults with IGD and 21 matched healthy controls (HC) were collected during a stop-signal task. We employed group independent component analysis to investigate group differences in temporally coherent, large-scale functional network activities during post-error slowing, the typical type of behavioral adjustments. We also employed a Bayesian belief model to quantify the trial-by-trial learning of the likelihood of stop signal - P(Stop) - a broader process underlying behavioral adjustment, and identified the alterations in functional network responses to P(Stop).

RESULTS

The results showed diminished engagement of the fronto-parietal network during post-error slowing, and weaker activity in the ventral attention and anterior default mode network in response to P(Stop) in IGD relative to HC.

DISCUSSION AND CONCLUSIONS

These results add to the literatures by suggesting deficits in updating and anticipating conflicts as well as in behavioral adjustment according to contextual information in individuals with IGD.

Klotho, BDNF, NGF, GDNF Levels and Related Factors in Withdrawal Period in Chronic Cannabinoid Users

Klotho and neurotropic factors have recently been shown to be related to some psychiatric disorders and neurocognitive disorders, but there is no study on this issue within substance users. In this study, brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), glial derived neurotrophic factor (GDNF) and klotho serum levels of a patient group consisting of 27 chronic cannabis users according to the DSM-V and 27 healthy volunteers were compared, and their relationships with other the clinical features of other patients were evaluated. Clinical scales, the Buss-Perry Aggression Scale, and the Substance Craving Scale were repeated on the first day of hospitalisation and on the seventh day of withdrawal. BDNF, GDNF, NGF and klotho levels were analysed using the ELISA method. There was no differences between the cannabinoid use disorder group and the control group regarding their klotho and other neurotrophic levels, but initiation age of cannabis use was negatively correlated with these levels. In addition, there was a relationship between verbal aggression scores and BDNF and NGF levels. There was a positive correlation between klotho and neurotrophic factors in all groups (patient group Day 1, patient group Day 7, control group) (p < 0.01). When comparing the difference between the correlations using the cocor (a comprehensive solution for the statistical comparison of correlations), the klotho-GDNF and klotho-NGF correlations for the first day of the patient group and the control group were different. In this study, rather than a difference in klotho levels and neurotropic factors, a significant relationship between these markers and each other and clinical parameters was demonstrated; further studies are needed to understand the exact mechanism.