Limbic/paralimbic connection weakening in preschool autism‐spectrum disorder based on diffusion basis spectrum imaging DOI Open Access
Ting Yi, Weikai Li,

Weian Wei

и другие.

European Journal of Neuroscience, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

Abstract This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD normal controls using diffusion basis spectrum imaging (DBSI). DBSI data from 31 patients 30 NC were collected Hunan Children's Hospital. All imported into post‐processing server. The most discriminative features extracted connection, global nodal metrics separately two‐sample t ‐test. To effectively integrate multimodal information, we employed multi‐kernel learning support vector machine (MKL‐SVM). In group, efficiency, local clustering coefficient synchronization lower than while modularity score, hierarchy, normalized coefficient, characteristic path length, small‐world, length assortativity higher. Significant weaker connections are mainly distributed networks. model combining consensus graph can achieve best performance patients, an accuracy 96.72%.The specific brain regions connection weakening associated predominantly located networks, suggesting their involvement abnormal development processes. effective combination information by MKL‐SVM distinguish ASD.

Язык: Английский

Endophenotype trait domains for advancing gene discovery in autism spectrum disorder DOI Creative Commons
Matthew W. Mosconi,

Cassandra J. Stevens,

Kathryn E. Unruh

и другие.

Journal of Neurodevelopmental Disorders, Год журнала: 2023, Номер 15(1)

Опубликована: Ноя. 22, 2023

Abstract Autism spectrum disorder (ASD) is associated with a diverse range of etiological processes, including both genetic and non-genetic causes. For plurality individuals ASD, it likely that the primary causes involve multiple common inherited variants individually account for only small levels variation in phenotypic outcomes. This landscape creates major challenge detecting but important pathogenic effects ASD. To address similar challenges, separate fields medicine have identified endophenotypes, or discrete, quantitative traits reflect likelihood particular clinical condition leveraged study these to map polygenic mechanisms advance more personalized therapeutic strategies complex diseases. Endophenotypes represent distinct class biomarkers useful understanding contributions psychiatric developmental disorders because they are embedded within causal chain between genotype phenotype, proximal action gene(s) than behavioral traits. Despite their demonstrated power guiding new structures conditions, few endophenotypes ASD been integrated into family studies. In this review, we argue advancing knowledge processes contribute can be accelerated by refocusing attention toward identifying endophenotypic reflective mechanisms. pivot requires renewed emphasis on designs measurement familial co-variation infant sibling studies, trio quad designs, analysis monozygotic dizygotic twin concordance select trait dimensions. We also emphasize clarification necessarily will integration transdiagnostic approaches as candidate liability conditions often agnostic diagnostic boundaries. Multiple described, propose focus “endophenotype domains” (ETDs), measured across (e.g., molecular, cellular, neural system, neuropsychological) along pathway from genes behavior. inform our central argument research efforts ETD discovery, first provide brief review concept application psychiatry. Next, highlight key criteria determining value unique considerations Descriptions different assessing then offered, how patterns results may help prioritize future research. present ETDs collectively cover breadth phenomena social, language/communication, cognitive control, sensorimotor processes. These described promising targets gene discovery related autistic traits, serve models domains well overlapping neurodevelopmental disorders.

Язык: Английский

Процитировано

11
Systematic Review and Meta-Analysis of the Effect of Motor Intervention on Cognition, Communication, and Social Interaction in Children with Autism Spectrum Disorder DOI Creative Commons
Marcelo R. Rosales, Christiana Butera,

Rujuta B. Wilson

и другие.

Physical & Occupational Therapy In Pediatrics, Год журнала: 2025, Номер unknown, С. 1 - 23

Опубликована: Май 4, 2025

Conduct a systematic review and meta-analysis on the effects of motor intervention social, communication, cognitive skills in individuals (0-21 years) with autism spectrum disorder (ASD). Seven databases were used to search for randomized control trials (RCT) implementing children ASD; measured outcomes. Twenty-three RCTs selected 66 outcomes 636 participants (range mean age: 4.3 - 12.3 years). Motor interventions had significant, positive effect (1) all combined (i.e. cognitive) (SSMD: 0.41, p = .01), (2) social 0.46, .012) (3) social/communication 0.47, .01) domains, but not domain 0.45, .25) or alone 0.22, .18). In above age nine, 1-year increase corresponded 0.29 decrease SSMD (less effective). have impact should be considered when planning ASD.

Язык: Английский

Процитировано

0
Aggression; Angelman syndrome; NLGN3 DOI

Jill Adams

The Transmitter, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Процитировано

0
Limbic/paralimbic connection weakening in preschool autism‐spectrum disorder based on diffusion basis spectrum imaging DOI Open Access
Ting Yi, Weikai Li,

Weian Wei

и другие.

European Journal of Neuroscience, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

Abstract This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD normal controls using diffusion basis spectrum imaging (DBSI). DBSI data from 31 patients 30 NC were collected Hunan Children's Hospital. All imported into post‐processing server. The most discriminative features extracted connection, global nodal metrics separately two‐sample t ‐test. To effectively integrate multimodal information, we employed multi‐kernel learning support vector machine (MKL‐SVM). In group, efficiency, local clustering coefficient synchronization lower than while modularity score, hierarchy, normalized coefficient, characteristic path length, small‐world, length assortativity higher. Significant weaker connections are mainly distributed networks. model combining consensus graph can achieve best performance patients, an accuracy 96.72%.The specific brain regions connection weakening associated predominantly located networks, suggesting their involvement abnormal development processes. effective combination information by MKL‐SVM distinguish ASD.

Язык: Английский

Процитировано

0