Attenuated post-movement beta rebound reflects psychomotor alterations in major depressive disorder during a simple visuomotor task: a MEG study

Abstract Background Psychomotor alterations are a common symptom in patients with major depressive disorder (MDD). The primary motor cortex (M1) plays a vital role in the mechanism of psychomotor alterations. Post-movement beta rebound (PMBR) in the sensorimotor cortex is abnormal in patients with motor abnormalities. However, the changes in M1 beta rebound in patients with MDD remain unclear. This study aimed to primarily explore the relationship between psychomotor alterations and PMBR in MDD. Methods One hundred thirty-two subjects were enrolled in the study, comprising 65 healthy controls (HCs) and 67 MDD patients. All participants performed a simple right-hand visuomotor task during MEG scanning. PMBR was measured in the left M1 at the source reconstruction level with the time–frequency analysis method. Retardation factor scores and neurocognitive test performance, including the Digit Symbol Substitution Test (DSST), the Making Test Part A (TMT-A), and the Verbal Fluency Test (VFT), were used to measure psychomotor functions. Pearson correlation analyses were used to assess relationships between PMBR and psychomotor alterations in MDD. Results The MDD group showed worse neurocognitive performance than the HC group in all three neurocognitive tests. The PMBR was diminished in patients with MDD compared to HCs. In a group of MDD patients, the reduced PMBR was negatively correlated with retardation factor scores. Further, there was a positive correlation between the PMBR and DSST scores. PMBR is negatively associated with the TMT-A scores. Conclusion Our findings suggested that the attenuated PMBR in M1 could illustrate the psychomotor disturbance in MDD, possibly contributing to clinical psychomotor symptoms and deficits of cognitive functions

Altered resting-state brain activity in major depressive disorder comorbid with subclinical hypothyroidism: A regional homogeneity analysis

Background: Major depressive disorder (MDD), a common mental disorder worldwide, frequently coexists with various physical illnesses, and recent studies have shown an increased prevalence of subclinical hypothyroidism (SHypo) among MDD patients. However, the neural mechanisms shared and unique to these disorders and the associated alterations in brain function remain largely unknown. This study investigated the potential brain function mechanisms underlying comorbid MDD and SHypo. Method: Thirty MDD patients (non-comorbid group), 30 MDD patients comorbid with SHypo (comorbid group), 26 patients with SHypo, and 30 healthy controls were recruited for resting-state functional magnetic resonance imaging (rs-fMRI). We used regional homogeneity (ReHo) to examine differences in internal cerebral activity across the four groups. Results: Compared with the non-comorbid group, the comorbid group exhibited significantly higher ReHo values in the right orbital part of the middle frontal gyrus (ORBmid) and bilateral middle frontal gyrus; decreased ReHo values in the right middle temporal gyrus, right thalamus, and right superior temporal gyrus, and right insula. Within the comorbid group, serum TSH levels were negatively associated with the ReHo values of the right insula; the ReHo values of the right Insula were negatively associated with the retardation factor score; the ReHo values of the right ORBmid were positively correlated with the anxiety/somatization factor scores. Conclusions: These findings provide valuable clues for exploring the shared neural mechanisms between MDD and SHypo and have important implications for understanding the pathophysiological mechanisms of the comorbidity of the two disorders

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018