Healthy individuals show robust functional connectivity during rest, which is stronger in adults than in children. Connectivity occurs between the posterior and anterior portions of the default network, a group of structures active in the absence of a task, including the posterior cingulate cortex and the superior frontal gyrus. Previous studies found weaker posterior-anterior connectivity in the default network in adults and adolescents with autism spectrum disorders (ASD). However, these studies used small a priori regions of interest ("seeds") to calculate connectivity. Since seed location for all participants was chosen based on controls' brains, these studies' analyses are more tailored to controls than individuals with ASD. An alternative is to use a data-driven approach, such as self-organizing maps (SOM), to create a reference for each participant to calculate connectivity. We used individualized resting-state clusters identified by an SOM algorithm to corroborate previous findings of weaker posterior-anterior connectivity in the ASD group and examine age-related changes in the ASD and control groups. Thirty-nine adolescents with ASD and 41 controls underwent a 10-minute, eyes-open, resting-state functional MRI scan. The SOM analysis revealed that adolescents with ASD versus controls have weaker connectivity between the posterior hub of the default network and the right superior frontal gyrus. Additionally, controls have larger increases in connectivity with age compared to the ASD group. These findings indicate that SOM is a complementary method for calculating connectivity in a clinical population. Additionally, adolescents with ASD have a different developmental trajectory of the default network compared to controls.
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http://dx.doi.org/10.1016/j.brainres.2010.10.102 | DOI Listing |
J Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
View Article and Find Full Text PDFBrain Topogr
January 2025
Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, No 152, Ai Guo Road, Dong Hu District, Nanchang, Jiangxi, 330006, China.
Stroke is a condition characterized by damage to the cerebral vasculature from various causes, resulting in focal or widespread brain tissue damage. Prior neuroimaging research has demonstrated that individuals with stroke present structural and functional brain abnormalities, evident through disruptions in motor, cognitive, and other vital functions. Nevertheless, there is a lack of studies on alterations in static and dynamic functional network connectivity in the brains of stroke patients.
View Article and Find Full Text PDFBrain Dev
January 2025
Department of Pediatric Neurology, Okayama University Hospital and Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
Introduction: Epileptic encephalopathy (EE) is a serious clinical issue that manifests as part of developmental and epileptic encephalopathy (DEE), particularly in childhood epilepsy. In EE, neurocognitive functions and behavior are impaired by intense epileptiform electroencephalogram (EEG) activity. Hypotheses of pathophysiological mechanisms behind EE are reviewed to contribute to an effective solution for EE.
View Article and Find Full Text PDFPsychiatry Res
December 2024
Department of Psychiatry, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, PR China. Electronic address:
Background: Auditory verbal hallucinations (AVHs) in schizophrenia (SCZ) are linked to brain network abnormalities. Resting-state fMRI studies often assume stable networks during scans, yet dynamic changes related to AVHs are not well understood.
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Background: The Amyloid-Tau-Neurodegeneration (ATN) biomarker framework for Alzheimer's disease (AD) indicates binary (presence/absence) designations for each type of pathology, without regard for anatomical distribution. Neurodegeneration is designated as positive if atrophy or hypometabolism are found on imaging. However, Clifford Jack et al.
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