scholarly journals Correction to ‘Brain serotonin deficiency leads to social communication deficits in mice’

2016 ◽  
Vol 12 (1) ◽  
pp. 20160002 ◽  
Author(s):  
D. Beis ◽  
K. Holzwarth ◽  
M. Flinders ◽  
V. Mosienko ◽  
M. Bader ◽  
...  
Keyword(s):  
Social Communication ◽  
Brain Serotonin ◽  
Communication Deficits ◽  
Serotonin Deficiency

scholarly journals Brain serotonin deficiency leads to social communication deficits in mice

2015 ◽  
Vol 11 (3) ◽  
pp. 20150057 ◽  
Author(s):  
D. Beis ◽  
K. Holzwarth ◽  
M. Flinders ◽  
M. Bader ◽  
M. Wöhr ◽  
...  
Keyword(s):  
Tryptophan Hydroxylase ◽  
Sexual Preference ◽  
Brain Serotonin ◽  
Adult Males ◽  
Social Situations ◽  
Tryptophan Hydroxylase 2 ◽  
Social Contacts ◽  
Interaction Partners ◽  
Female Urine ◽  
Serotonin Deficiency

A deficit in brain serotonin is thought to be associated with deteriorated stress coping behaviour, affective disorders and exaggerated violence. We challenged this hypothesis in mice with a brain-specific serotonin depletion caused by a tryptophan hydroxylase 2 (TPH2) deficiency. We tested TPH2-deficient ( Tph2 −/– ) animals in two social situations. As juveniles, Tph2 −/− mice displayed reduced social contacts, whereas ultrasonic vocalizations (USVs) were unchanged within same-sex same-genotype pairings. Interestingly, juvenile females vocalized more than males across genotypes. Sexually naive adult males were exposed to fresh male or female urine, followed by an interaction with a conspecific, and re-exposed to urine. Although Tph2 −/− mice showed normal sexual preference, they were hyper-aggressive towards their interaction partners and did not vocalize in response to sexual cues. These results highlight that central serotonin is essential for prosocial behaviour, especially USV production in adulthood, but not for sexual preference.

scholarly journals Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms

2021 ◽  
Vol 12 ◽  
Author(s):  
Ping-I Lin ◽  
Mohammad Ali Moni ◽  
Susan Shur-Fen Gau ◽  
Valsamma Eapen
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Microarray Data ◽  
Social Communication ◽  
Language Impairment ◽  
Cholesterol Biosynthesis ◽  
Learning Algorithms ◽  
Fold Change ◽  
Machine Learning Algorithms ◽  
Communication Deficits

Objectives: The identification of subgroups of autism spectrum disorder (ASD) may partially remedy the problems of clinical heterogeneity to facilitate the improvement of clinical management. The current study aims to use machine learning algorithms to analyze microarray data to identify clusters with relatively homogeneous clinical features.Methods: The whole-genome gene expression microarray data were used to predict communication quotient (SCQ) scores against all probes to select differential expression regions (DERs). Gene set enrichment analysis was performed for DERs with a fold-change >2 to identify hub pathways that play a role in the severity of social communication deficits inherent to ASD. We then used two machine learning methods, random forest classification (RF) and support vector machine (SVM), to identify two clusters using DERs. Finally, we evaluated how accurately the clusters predicted language impairment.Results: A total of 191 DERs were initially identified, and 54 of them with a fold-change >2 were selected for the pathway analysis. Cholesterol biosynthesis and metabolisms pathways appear to act as hubs that connect other trait-associated pathways to influence the severity of social communication deficits inherent to ASD. Both RF and SVM algorithms can yield a classification accuracy level >90% when all 191 DERs were analyzed. The ASD subtypes defined by the presence of language impairment, a strong indicator for prognosis, can be predicted by transcriptomic profiles associated with social communication deficits and cholesterol biosynthesis and metabolism.Conclusion: The results suggest that both RF and SVM are acceptable options for machine learning algorithms to identify AD subgroups characterized by clinical homogeneity related to prognosis.

Brain Serotonin Deficiency

2016 ◽  
Author(s):  
Angels García-Cazorla ◽  
Rafael Artuch
Keyword(s):  
Environmental Factors ◽  
Sleep Disturbance ◽  
Strong Evidence ◽  
Cognitive Disorders ◽  
Neurological Symptoms ◽  
Brain Serotonin ◽  
Heterogeneous Condition ◽  
Serotonin Deficiency ◽  
Genetic And Environmental Factors

Brain serotonin deficiency is a heterogeneous condition whose etiology remains unknown in the majority of cases. Strong evidence supports a major role for brain serotonin deficiency in common conditions such as depression and other psychiatric and cognitive disorders, which are probably due to interactions between genetic and environmental factors. Mendelian monogenic conditions leading to brain serotonin deficiency have also been identified, but they are rare. These diseases are associated with defects in other neurotransmitters (primarily dopamine), and it is difficult to link serotonin deficiency with specific neurological syndromes. Secondary serotonin deficiency is also common. In adults, when serotonin deficiency is thought to contribute to neurological symptoms such as sleep disturbance and alterations in behavior, treatment with serotonin precursors may be useful.

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