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Numerous neuroimaging studies have investigated the neural basis of interindividual differences but the replicability of brain-phenotype associations remains largely unknown. We used the UK Biobank neuroimaging dataset (N = 37,447) to examine associations with six variables related to physical and mental health: age, body mass index, intelligence, memory, neuroticism and alcohol consumption, and assessed the improvement of replicability for brain-phenotype associations with increasing sampling sizes. Age may require only 300 individuals to provide highly replicable associations but other phenotypes required 1,500 to 3,900 individuals. The required sample size showed a negative power law relation with the estimated effect size. When only comparing the upper and lower quarters, the minimally required sample sizes for imaging decreased by 15-75%. Our findings demonstrate that large-scale neuroimaging data are required for replicable brain-phenotype associations, that this can be mitigated by preselection of individuals and that small-scale studies may have reported false positive findings.
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http://dx.doi.org/10.1038/s41562-023-01642-5 | DOI Listing |
J Neurosci
August 2025
Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts 02118
Cognitive, social behavior, speech, and motor skills are known challenges for people with trisomy 21/Down syndrome (DS), but the precise mechanisms that lead to these impactful changes have not yet been described. Data from human and mouse model fetal brains indicate that alterations in prenatal neurogenesis might account for the neurological phenotypes that manifest after birth. Here, we evaluated key features of cortical neurogenesis in the humanized mouse model of DS (TcMAC21 of undetermined sex) to test whether and how the presence of the human HSA21q transchromosome impacts cortical development.
View Article and Find Full Text PDFFractional anisotropy (FA) derived from diffusion MRI is a widely used marker of white matter (WM) integrity. However, conventional FA-based genetic studies focus on phenotypes representing tract- or atlas-defined averages, which may oversimplify spatial patterns of WM integrity and thus limit the genetic discovery. Here, we proposed a deep learning-based framework, termed unsupervised deep representation of WM (UDR-WM), it adopted the voxel-wise FA maps as the input, and to extract brain-wide FA features-referred to as UDIP-FA-that capture distributed microstructural variation without prior anatomical assumptions.
View Article and Find Full Text PDFMol Ther Methods Clin Dev
June 2025
Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory, Georgia Institute of Technology, 315 Ferst Dr., Atlanta, GA 30332, USA.
Mucolipidosis IV (MLIV) is an autosomal-recessive pediatric disease that leads to motor and cognitive deficits and loss of vision. It is caused by loss of function of the lysosomal channel transient receptor potential mucolipin-1, TRPML1, and is associated with an early brain phenotype consisting of glial reactivity, hypomyelination, and lysosomal abnormalities. Although the field is approaching the first translationally relevant therapy, we currently lack a molecular signature of disease that can be used to detect therapeutic efficacy.
View Article and Find Full Text PDFGenet Med Open
March 2025
Department of Pediatric Neurology, Children's National Hospital, Washington, DC.
Introduction: Many rare disorders, particularly neurodevelopmental conditions, manifest structural brain malformations. Just as dysmorphologists rely on facial gestalt recognition to identify syndromes, radiologists and neurologists face similar challenges in identifying the "brain gestalt" of rare disorders-especially when encountering rare conditions or those they have not previously seen. Next-generation phenotyping (NGP) has been proven capable of supporting clinicians in recognizing facial dysmorphic patterns associated with the underlying syndrome through training on thousands of patient photographs.
View Article and Find Full Text PDFFront Physiol
January 2025
Phoenix Children's Research Institute, Department of Child Health, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.
Introduction: Marfan Syndrome (MFS) is a connective tissue disorder due to mutations in fibrillin-1 (), where a missense mutation ( ) can result in systemic increases in the bioavailability and signaling of transforming growth factor-β (TGF-β). In a well-established mouse model of MFS ( ), pre-mature aging of the aortic wall and the progression of aortic root aneurysm are observed by 6-month-of-age. TGF-β signaling has been implicated in cerebrovascular dysfunction, loss of blood-brain barrier (BBB) integrity, and age-related neuroinflammation.
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