Pioneering studies have shown that individual correlation measures from resting-state functional magnetic resonance imaging studies can identify another scan from that same individual. This method is known as "connectotyping" or functional connectome "fingerprinting." We analyzed a unique dataset of 12-30 years old (N = 140) individuals who had two distinct resting state scans on the same day and again 12-18 months later to assess the sensitivity and specificity of fingerprinting accuracy across different time scales (same day, ~1.5 years apart) and developmental periods (youths, adults). Sensitivity and specificity to identify one's own scan was high (average AUC = 0.94), although it was significantly higher in the same day (average AUC = 0.97) than 1.5-years later (average AUC = 0.91). Accuracy in youths (average AUC = 0.93) was not significantly different from adults (average AUC = 0.96). Multiple statistical methods revealed select connections from the Frontoparietal, Default, and Dorsal Attention networks enhanced the ability to identify an individual. Identification of these features generalized across datasets and improved fingerprinting accuracy in a longitudinal replication data set (N = 208). These results provide a framework for understanding the sensitivity and specificity of fingerprinting accuracy in adolescents and adults at multiple time scales. Importantly, distinct features of one's "fingerprint" contribute to one's uniqueness, suggesting that cognitive and default networks play a primary role in the individualization of one's connectome.
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http://dx.doi.org/10.1002/hbm.25118 | DOI Listing |
Insights Imaging
January 2025
Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Introduction: A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.
Materials And Methods: Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals.
Cardiovasc Diagn Ther
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Operational Research Center in Healthcare, Near East University, Nicosia, Turkey.
Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years.
View Article and Find Full Text PDFClin Transl Allergy
January 2025
Division of ENT Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
Background: Seasonal allergic rhinitis (AR) impacts public health by affecting work productivity and quality of life. The Swedish tree pollen season starts in February with alder and hazel pollination, followed by birch and ends with oak in May. Systemic corticosteroids are often prescribed when topical treatments fail, despite limited evidence supporting their efficacy.
View Article and Find Full Text PDFAnn Diagn Pathol
January 2025
Akdeniz University, Faculty of Medicine, Department of Pathology, Konyaaltı, 07070 Antalya, Turkey.
POLE status determination is necessary for the molecular classification of endometrial carcinomas (EC). However, this determination is only achievable by molecular techniques, which are not available in many practice settings. A previously published study reported elevated AMF/GPI and AMFR/gp78 levels in POLE-mutant EC.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Accounting & Finance, Feliciano School of Business, Montclair State University, Montclair, New Jersey, United States of America.
This study uses the Oracle SQL certification exam questions to explore the design of automatic classifiers for exam questions containing code snippets. SQL's question classification assigns a class label in the exam topics to a question. With this classification, questions can be selected from the test bank according to the testing scope to assemble a more suitable test paper.
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