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IntroductionEarly detection of depression is a cost-effective way to prevent adverse outcomes on brain physiology, cognition, and health. Here we propose that loneliness and social adaptation are key factors that can anticipate depressive symptoms.MethodsWe analyzed data from two separate samples to evaluate the associations between loneliness, social adaptation, depressive symptoms, and their neural correlates.ResultsFor both samples, hierarchical regression models on self-reported data showed that loneliness and social adaptation have negative and positive effects on depressive symptoms. Moreover, social adaptation reduces the impact of loneliness on depressive symptoms. Structural connectivity analysis showed that depressive symptoms, loneliness, and social adaptation share a common neural substrate. Furthermore, functional connectivity analysis demonstrated that only social adaptation was associated with connectivity in parietal areas.DiscussionAltogether, our results suggest that loneliness is a strong risk factor for depressive symptoms while social adaptation acts as a buffer against the ill effects of loneliness. At the neuroanatomical level, loneliness and depression may affect the integrity of white matter structures known to be associated to emotion dysregulation and cognitive impairment. On the other hand, socio-adaptive processes may protect against the harmful effects of loneliness and depression. Structural and functional correlates of social adaptation could indicate a protective role through long and short-term effects, respectively. These findings may aid approaches to preserve brain health via social participation and adaptive social behavior.

Adverse environments cause well-established detrimental effects on subjects living there. They ultimately increase the likelihood of developing negative cognitive, emotional, and social outcomes. However, these adverse effects are not ubiquitous across all people. While some individuals develop maladaptive behavior and seem unable to cope with these difficulties, others develop strong coping mechanisms to manage the psychosocial effects of an adverse environment. Most research in this field, however, does not include multiple variables or complex models that allow them to control multivariate interactions simultaneously and as such, it is still unclear what interaction of social, emotional, and cognitive traits promote psychosocial adaptation in adverse environments. To address this, we employ an innovative combined methodological approachusing a theoretically driven person-centered approach (Latent Profile Analysis) aided by machine learning classification to model and predict different profiles of psychosocial adaptation within a sample of 254 adults. Our results showed a two-profile solution discriminated in this sample optimally into good and poor configurations for psychosocial adaptations. These results highlight the importance of distinct social, cognitive, and affective resources in developing adequate coping mechanisms in adults living in adverse environments. Importantly, the machine learning procedures allowed us to identify the most important features. High self-esteem and cognitive and behavioral self-regulation, low-stress levels, higher education, and increased social support are crucial factors for differentiating diverse pathways of psychosocial adaptation signaling risk and protective factors with important and straightforward implications for designing psychosocial interventions in these contexts.

Social adaptation arises from the interaction between the individual and the social environment. However, there is little empirical evidence regarding the relationship between social contact and social adaptation. We propose that loneliness and social networks are key factors explaining social adaptation. Here we recruited 64 subjects to analyze the effects of loneliness, social network and their brain functional correlates on social adaptation. A hierarchical regression model confirmed that loneliness and social network have negative and positive effects on social adaptation respectively. Functional connectivity (FC) analysis showed that loneliness was associated with decreased FC between fronto-amygdalar and fronto-parietal regions, while the social network was positively associated with FC between the fronto-temporo-parietal network. Finally, a multidimensional path model examined the combined effects of behavioral and brain predictors of social adaptation. The model revealed that social networks mediated the effects of loneliness on social adaptation. Further, loneliness-related abnormal brain FC (associated with difficulties in cognitive control, emotion regulation, and sociocognitive processes) emerged as the strongest predictor of poor social adaptation. Findings offer insights into the brain indicators of social adaptation and highlight the role of social networks as a buffer against the maladaptive effects of loneliness. These findings may aid social development approaches.

La innovación es el pilar de competitividad de las pequeñas y medianas empresas exportadoras. Esta investigación tiene como objetivo examinar la incidencia de la tecnología, los recursos humanos y la gestión con la innovación en estas empresas exportadoras. El enfoque de la investigación es cuantitativo, el método es deductivo, el tipo de investigación es correlacional, con un diseño no experimental y transversal, utilizando una muestra de 126 empresas exportadoras de la provincia del Guayas-Ecuador. El procesamiento de datos fue a través del análisis factorial y posteriormente se realizó una regresión lineal. Los resultados muestran que la tecnología y la gestión inciden positivamente en la innovación de las pequeñas y medianas empresas exportadoras, donde la gestión es principalmente orientada a los recursos, las capacidades y el conocimiento para lograr la innovación. La investigación aporta a la literatura con evidencia empírica de estas relaciones puesto que estudios previos se han enfocado en el éxito exportador de este tipo de empresas o en examinar la innovación de estas sin enfocarse en las exportadoras. Se concluye en la importancia para los administradores de estas empresas exportadoras, porque a través de incrementar la tecnología y la gestión en estas empresas se aumenta su potencial innovador.