1. Baseline resting-state network integration modulates task performance and aftereffectRok Požar, Tim Martin, Mary Katherine Kerlin, Aidan McColligan, Bruno Giordani, Voyko Kavcic, 2026, izvirni znanstveni članek Opis: 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.
Ključne besede: electroencephalography, brain network integration, cognitive task, neuropsychology
Objavljeno v RUP: 29.01.2026; Ogledov: 207; Prenosov: 2
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2. Role of music therapy in the development of language skills in children with autism spectrum disorder : a systematic literature reviewLucija Mlakar, Vesna Posavčević, 2026, pregledni znanstveni članek Opis: 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.
Ključne besede: autism spectrum disorder, children, minimal language abilities, social communication, fronto-temporal brain connectivity, music therapy, non-music therapy
Objavljeno v RUP: 28.01.2026; Ogledov: 227; Prenosov: 7
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3. Deep learning for brain MRI tissue and structure segmentation : a comprehensive reviewNedim Šišić, Peter Rogelj, 2025, pregledni znanstveni članek Opis: 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.
Ključne besede: magnetic resonance imaging, brain, image segmentation, deep learning
Objavljeno v RUP: 10.10.2025; Ogledov: 630; Prenosov: 7
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9. Transkranialna magnetna stimulacija (TMS) za terapevtske namene : zaključna nalogaKristjan Bajec, 2018, diplomsko delo Ključne besede: TMS, rTMS, depresija, parametri stimulacije, efektivnost terapije, možganska aktivnost, TMS, rTMS, depression, stimulation parameters, therapy effectiveness, brain activity
Objavljeno v RUP: 17.04.2018; Ogledov: 8857; Prenosov: 98
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10. Vpliv stimulansov (kokaina) na delovanje živčevja ter posledično na funkcioniranje v vsakdanjem življenju : zaključna nalogaLara Gregorčič Vidović, 2016, diplomsko delo Ključne besede: stimulants, cocaine, heroin, brain, addiction, reward system, functioning in everyday life, nervous system, final assignment
Objavljeno v RUP: 22.11.2017; Ogledov: 3780; Prenosov: 58
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