1. Migration and human capital development in Africa : the brain drain to brain gain philosophyUche Abamba Osakede, Joel Tobiloba Adeyemo, Opeyemi Eunice Olagunju, Olayinka Omolara Adenikinju, Johnson Olaosebikan, Akanni Olayinka Lawanson, Olanrewaju Olaniyan, 2026, original scientific article Abstract: Evidence on the effect of remittances on human capital development in Africa remains mixed, with limited evidence on the educational attainment of most African migrants. Understanding these patterns is crucial to assessing whether migration leads to brain drain or brain gain. This study examines labour migration from Africa and the role of remittances in shaping school enrolment rates. Using the panel-corrected standard error (PCSE) and feasible generalized least squares (FGLS) methods, the analysis accounts for heteroscedasticity, autocorrelation, and cross-sectional dependence, providing reliable estimates. The results indicate that most African migrants have attained secondary level of education, while remittances significantly improve primary and secondary enrolment, with no significant effect on tertiary education. Findings also showed that real GDP, government expenditure, and access to electricity enhance enrolment outcomes. These findings suggest that while remittances build foundational human capital, policy interventions are needed to channel resources toward educational outcomes, particularly higher education, to ensure sustainable growth in Africa.
Keywords: migration, brain drain, brain gain
Published in RUP: 24.08.2026; Views: 489; Downloads: 22
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2. Baseline resting-state network integration modulates task performance and aftereffectRok Požar, Tim Martin, Mary Katherine Kerlin, Aidan McColligan, Bruno Giordani, Voyko Kavcic, 2026, original scientific article Abstract: Understanding how intrinsic brain networks adapt to cognitive demands is central to neuroscience. The aim of this study was to examine how eyes-open and eyes-closed resting-state network integration, derived from electroencephalography before and after a visual oddball task, relates to task performance in young adults. Task engagement reduced global integration in theta, lower alpha, and beta bands, independent of eye condition, indicating a transient shift toward a less demanding post-task configuration. Eyes-open resting states consistently exhibited higher integration than eyes-closed in the upper alpha band, both before and after the task, reflecting enhanced inter-regional communication and sensory readiness. Importantly, higher pre-task beta-band integration during eyes-open resting state predicted faster reaction times and larger post-task decreases in integration, highlighting baseline network organization as a determinant of cognitive efficiency and neural flexibility. These findings support the concept of neural reserve, where intrinsic network efficiency and adaptability underpin both performance readiness and dynamic reorganization. Overall, the results demonstrate that resting-state network integration— modulated by both eye condition and task engagement—captures fundamental aspects of the brain’s capacity for efficient and flexible cognitive function.
Keywords: electroencephalography, brain network integration, cognitive task, neuropsychology
Published in RUP: 29.01.2026; Views: 956; Downloads: 4
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3. Role of music therapy in the development of language skills in children with autism spectrum disorder : a systematic literature reviewLucija Mlakar, Vesna Posavčević, 2026, review article Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that typically emerges in early childhood, marked by difficulties in communication, social interaction, behaviour, and emotional regulation. Despite these challenges, many children with ASD demonstrate exceptional musical abilities, making music a powerful medium for enhancing self-expression, fostering social bonds, and supporting neurological development crucial for speech and social skills. Historically, minimally verbal children with ASD were often excluded from research due to the difficulty of assessment using standardised tools; however, recent advancements have enabled more inclusive studies. Over the past decade, naturalistic approaches have gained prominence, with music therapy emerging as a particularly promising intervention. A systematic literature review, based on original research sourced from PubMed, Sage, and ScienceDirect, examined six studies involving children aged two to twelve years with minimal verbal abilities and a clinical diagnosis of autism. These studies consistently found that music therapy significantly supports the development of language and social communication skills, while also enhancing fronto-temporal brain connectivity. The review contributes valuable insights into the current state of research, underscores the importance of early intervention and parental involvement, and lays the groundwork for further exploration into the role of music therapy in language development for children with ASD.
Keywords: autism spectrum disorder, children, minimal language abilities, social communication, fronto-temporal brain connectivity, music therapy, non-music therapy
Published in RUP: 28.01.2026; Views: 1255; Downloads: 32
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4. Deep learning for brain MRI tissue and structure segmentation : a comprehensive reviewNedim Šišić, Peter Rogelj, 2025, review article Abstract: Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field.
Keywords: magnetic resonance imaging, brain, image segmentation, deep learning
Published in RUP: 10.10.2025; Views: 1717; Downloads: 25
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10. Transkranialna magnetna stimulacija (TMS) za terapevtske namene : zaključna nalogaKristjan Bajec, 2018, undergraduate thesis Keywords: TMS, rTMS, depresija, parametri stimulacije, efektivnost terapije, možganska aktivnost, TMS, rTMS, depression, stimulation parameters, therapy effectiveness, brain activity
Published in RUP: 17.04.2018; Views: 10425; Downloads: 119
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