Annals of Neurosciences最新文献

Background: World Health Organisation (WHO) in 2024 identified that approximately one in 100 children globally has autism spectrum disorder (ASD). ASD is a collection of neurodevelopmental disorders that impact a person's ability to socially interact and communicate, which can typically be noticed in early childhood. While 'autism' as a term was initially used for schizophrenic patients, later psychiatrists Dr. Kanner and paediatrician Dr. Asperger introduced it as a syndrome in children with behavioural differences in social interaction and communication with restrictive and repetitive interests. In today's time, the umbrella term 'ASDs' is used to describe a clinically heterogeneous group of neurodevelopmental disorders (NDDs).

Purpose: To examine the role of traditional approaches and the potential effectiveness of artificial intelligence (AI) methods in dealing with ASDs for improving the accuracy in its diagnosis and treatment.

Methodology: The study adopts a narrative review approach to understand the application of AI in ASD. For this purpose, around a hundred research articles were selected from the years 2010-2024. Inclusion and exclusion criteria were identified. The review is organised and grounded on the medical treatment, occupational remedy, vocational remedy, psychology, family remedy and recuperation engineering.

Results and conclusion: The results show the undisputed role of AI and its ability to identify early indicators of autism, in accordance with the UN Sustainable Development Goal 3 (Good Health and Well-being) and Goal 16 (Peace, Justice and Strong Institutions). Further, healthcare sectors which are using a variety of AI analyses on data sources, genetics, neuroimaging, behavioural patterns and electronic medical records are able to early detect for individualised evaluation of ASD. The significance of timely interventions with the help of machine learning (ML) algorithms demonstrates high accuracy in differentiating ASD from neurotypical development and other developmental disorders.AI-driven therapeutic interventions expand social interactions and communication skills in people with ASD in the form of virtual reality-based training, augmentative communication systems and robot-assisted therapies. Thus, the future of AI in ASD holds promise for improving diagnostic accuracy, implementing telehealth platforms and customising treatment plans, despite obstacles such as data privacy and interpretability.

Background: Modern integrative frameworks such as the self-awareness, self-regulation and self-transcendence (S-ART) and LIBRE/EMC² highlight the neurocognitive-affective mechanisms underpinning mindfulness in young learners.

Purpose: This study explored how trait-level psychological dispositions and academic factors relate to electrophysiological and behavioural markers of state mindfulness in novice student meditators.

Methods: Ninety-seven university students (mean age = 24.59 years) participated in the study, with 89 consenting to electroencephalography (EEG) recording during a tristage Ānāpānasati-inspired meditation. EEG spectral powers were analysed across five neural oscillatory bands in the prefrontal, occipital, and default mode regions to assess state mindfulness, complemented by behavioural evaluation using the Amsterdam Resting-State Questionnaire. Additionally, trait-level data encompassing personality, values, mindfulness and academic background were gathered through validated self-report measures.

Results: The mindfulness trait of Acting with Awareness was associated with reduced Discontinuity of Mind and Theory of Mind, as well as heightened theta power during meditation, indicating enhanced breath-focused attention. Discontinuity of Mind also correlated with elevated posterior alpha and prefrontal beta, consistent with fragmented or effortful mentation. Planning-related thinking was consistently associated with self-reported early STEM subjects' proficiency, and corresponded with reduced delta and alpha power-suggesting unnecessary mentation during meditation. While value traits showed negligible robust behavioural correlations, EEG findings revealed that individuals scoring higher on Stimulation values exhibited lower prefrontal alpha power, possibly reflecting heightened alertness while meditating. Modest associations also emerged between Extraversion, Emotional Stability and state mindfulness dimensions across neurobehavioural indices.

Conclusion: These findings offer preliminary support for trait-to-state continuity in early meditative experience, highlighting how attentional traits and academic conditioning may shape neural engagement in novices. Although predictive value was weak, the results underscore the importance of dispositional context in shaping novice state mindfulness, warranting further replications and investigations to clarify the directionality and stability of the observed trait-state associations.

Background: Creativity involves the generation of novel ideas that are original and unique. It is a subjective process, and few studies are available in support of objective measures. Available tests of creativity are limited to questions related to an individual's trait and subjective responses. Though creativity is a divergent construct, an objective approach to computing and marking one as creative is required. This is so because creativity is an important factor for success, and a subjective approach would bring bias.

Purpose: The present study aims to develop a creativity score using the Rorschach Inkblot Test (RIBT) and then test it with eye-tracking technology.

Methods: Thirty-four university students were recruited for the study using a purposive sampling technique. RIBT cards were shown on a computer screen with an eye tracker mounted on it. Their responses were recorded and analysed to develop a novel construct of the Creativity measure. The Creativity score is then divided into high, medium and low creativity using the k-means clustering algorithm. Eye parameters of fixations, saccades and pupil diameter were explored for each group.

Results: ANOVA revealed significant differences between the three groups. In the high-creativity group, fixation count, variations in pupil diameter and total saccadic duration were higher than their counterparts. Mean fixation duration was highest for the low-creativity group.

Conclusion: The results indicated that using unstructured blots with Eye-tracking technology helps assess creativity objectively, further broadening avenues to measure creativity.

Background: Children with chronic kidney disease (CKD) are at risk of inferior neurocognitive outcomes. As the brain develops rapidly during the early years of life, we wanted to find out the impact of CKD on neurocognition when it occurs during this time and any disease-associated risk factors.

Methods: A cross-sectional case-control study was conducted in the Paediatric Nephrology Clinic, PGIMER, Chandigarh. After obtaining parental consent, 52 children with CKD Stage 3-5 and 52 age-matched case controls were enrolled in the study. Neurocognitive outcome was assessed by the Developmental Assessment Scales for Indian Infants (DASII). Proper statistical tests were used to analyse the data.

Results: Mean comparison revealed that CKD children performed significantly lower on mental functions, motor functions and developmental quotient of DASII. Neurocognitive functions declined with disease progression, with children of CKD Stage 5 scoring lowest. Further, there was a significant difference in weight, height/length and head circumference between those in Stage 3 and 5. Similarly, there are significant mean differences in Hb, MCH, and MCHC levels between Stage 3 and both Stage 4 and 5, but no significant difference between those in Stage 4 and 5.

Conclusion: CKD is a debilitating condition that can have a significant impact on the health and development of children. Neurocognitive outcome declines as the stage of CKD progresses, which worsens their overall outcomes.

Background: Memory, learning, language and decision-making are just some of the cognitive abilities that may be negatively impacted by neurological illnesses such as dementia, anxiety and depression.

Purpose: This research aims to examine the influence of demographic variables on the prevalence of dementia, anxiety and depression in patients recovering from COVID-19.

Methods: This research looks at those who are at risk of developing dementia, anxiety or depression after being exposed to COVID-19. The hospital EC (Ethics Committee-Unique Hospital, Surat, India) granted consent, as did the Dehradun Institute of Technology University's research ethics committee (DITU/UREC/2022/04/6). Participants actively partook and gave informed consent. Patient data were collected with the assistance of medical personnel, and participants had to fulfil the inclusion and exclusion criteria. A questionnaire was distributed, and data were examined based on participant replies.

Results: The COVID-19 pandemic has had widespread effects on both physical and mental health, leading to increased risks of dementia, anxiety and depression. Elderly individuals are most susceptible to dementia, likely due to pre-existing vulnerabilities and the significant neurological impact of COVID-19 on this age group. Young adults exhibited a notable increase in anxiety, possibly linked to factors such as social isolation, economic uncertainty and disruptions to daily life. Additionally, depression prevalence has significantly risen among younger individuals following the pandemic.

Conclusion: The study provides important insights into the age-related impact of COVID-19 on mental health. Older adults are more prone to dementia and anxiety, while younger individuals show a higher prevalence of depression. These findings underscore the varying mental health effects across age groups, highlighting the need for targeted mental health interventions for both the elderly and young adults post-COVID-19.

Background: Caregivers of children with chronic kidney disease (CKD) often face significant psychological and social challenges. This study aimed to assess the extent of psychological distress, quality of life (QOL), coping strategies and associated risk factors among caregivers and to evaluate whether these issues intensify with advanced stages of CKD.

Methods: A cross-sectional observational study was conducted at a tertiary care centre in North India, enrolling 50 consecutive caregivers of children with CKD between July 2020 and June 2021. Participants were categorised into two groups: Early-stage CKD (Stages 1-2) and advanced-stage CKD (Stages 3-5). Caregivers were assessed using the Paediatric Renal Caregiver Burden Scale (PRCBS), Depression Anxiety Stress Scale (DASS-21), World Health Organisation Quality of Life Scale-Brief (WHOQOL-BREF) and Ways of Coping Checklist. Relevant statistical analyses were performed.

Results: Caregivers of children with advanced CKD reported higher levels of psychological distress, with 88.5% experiencing moderate to severe depression, 38% anxiety and 35% stress. In contrast, caregivers of early-stage CKD children reported depression in 21%, with no cases of moderate to severe anxiety or stress. QOL scores were significantly lower and burden scores significantly higher, in the advanced CKD group. Lower socioeconomic status, reduced maternal education and low household income were linked to higher distress and poorer QOL. Problem-focused coping strategies were commonly used across both groups.

Conclusion: Psychosocial difficulties and caregiver burden increase with CKD severity and adversely affect QOL. Early identification and psychological support for caregivers may enhance overall outcomes for both the child and family.

Background: The rising prevalence of social media use among youth has prompted growing concern about its psychological implications. Platforms such as Instagram, which prioritises visual content and lifestyle displays, has been associated with mental health issues such as anxiety, depression and stress. However, limited research in the Indian context has specifically addressed the mental health outcomes associated with content-specific engagement, particularly with food and restaurant-related imagery.

Objective: This study aimed to investigate the association between Instagram usage frequency and mental health, measured through depression, anxiety and stress among youth aged 16-25 years in Pune, India. It further examined whether higher engagement with restaurant and food-related content correlates with increased psychological distress.

Methods: A cross-sectional survey was conducted among 300 participants using the Depression, Anxiety and Stress Scale-21 (DASS-21) and a structured Instagram usage questionnaire. Data were analysed using descriptive statistics, Pearson correlations, analysis of variance (ANOVA) and regression analysis to evaluate associations between usage patterns and psychological outcomes.

Results: The majority of participants (93%) reported daily Instagram use, with 32% categorised as heavy users (>3 hours/day). Heavy users showed significantly higher DASS-21 scores across all subscales. Depression (p = .003), anxiety (p = .019) and stress (p < .001) scores increased in a stepwise manner from light to heavy users. Participants who frequently followed restaurant-related content and acted upon such recommendations, reported significantly elevated stress and anxiety levels. Regression models confirmed that both total usage and content-specific engagement independently predicted psychological distress.

Conclusion: Frequent Instagram use and high engagement with food and restaurants content is associated with elevated levels of depression, anxiety and stress in young adults. These findings emphasise the need for targeted digital literacy and mental health interventions tailored to content consumption behaviours in emerging adults.

Background: Artificial intelligence (AI) is increasingly shaping daily decision-making by enhancing efficiency and consistency. However, prolonged AI use may impose cognitive strain, attention depletion, information overload, and decision fatigue.

Aim: To investigate the relationships among AI anxiety, attitudes toward AI, cognitive performance, trust in AI, and decision fatigue, particularly emphasising long-term AI interaction.

Methods: A structured survey was administered both online and offline to a sample of 500 adults (290 males, 210 females) in the Delhi-NCR region, with a mean age of 24.2 ± 3.4 years. The survey assessed participant's AI anxiety, AI attitude, cognitive skills, decision fatigue, and trust (encompassing reliability, productivity, and user control). Descriptive statistics and Pearson correlation analyses were conducted to explore the relationships between these variables.

Result: Participants reported moderately high AI anxiety (mean = 4.62, SD = 1.14) and generally positive attitudes toward AI (mean = 5.01, SD = 1.06). A strong but marginally non-significant correlation (r = 0.81, p = .053) was found between favourable attitudes and technology usage frequency. High trust in AI-measured via reliability (r = 0.597), productivity (r = 0.985), and control (r = 0.829)-correlated with prior positive AI experience. Long-term AI use was significantly associated with mental exhaustion, attention strain, and information overload (r = 0.905), and inversely associated with decision-making self-confidence (r = -0.360).

Conclusion: The integration of AI in task performance resulted in improved efficiency and user confidence; however, prolonged utilisation may precipitate cognitive fatigue, diminished focus, and attenuated user agency. To mitigate these adverse effects, strategic design approaches prioritising user empowerment, transparency, and cognitive facilitation are essential for maximising benefits while upholding mental health and well-being.