Underlying sources of cognitive-anatomical variation in multi-modal neuroimaging and cognitive testing
| Autor: | Kevin Horecka, Nathan Ward, Neal J. Cohen, Patrick Watson, Erick J. Paul, Gillian E. Cooke, Jim M. Monti, Courtney Allen, Arthur F. Kramer, Charles H. Hillman, Aron K. Barbey |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Adult
Male Adolescent Cognitive Neuroscience Individuality Neuroimaging Neuropsychological Tests Article 050105 experimental psychology White matter Young Adult 03 medical and health sciences Cognition 0302 clinical medicine Image Processing Computer-Assisted medicine Humans 0501 psychology and cognitive sciences 05 social sciences Brain Variance (accounting) Magnetic Resonance Imaging Subcortical gray matter Cognitive test Phenotype medicine.anatomical_structure Variation (linguistics) Neurology Female Psychology Neuroscience 030217 neurology & neurosurgery Cognitive psychology Neuroanatomy |
| Zdroj: | NeuroImage. 129:439-449 |
| ISSN: | 1053-8119 |
| Popis: | Healthy adults have robust individual differences in neuroanatomy and cognitive ability not captured by demographics or gross morphology (Luders, Narr, Thompson, & Toga, 2009). We used a hierarchical independent component analysis (hICA) to create novel characterizations of individual differences in our participants (N = 190). These components fused data across multiple cognitive tests and neuroanatomical variables. The first level contained four independent, underlying sources of phenotypic variance that predominately modeled broad relationships within types of data (e.g., “white matter,” or “subcortical gray matter”), but were not reflective of traditional individual difference measures such as sex, age, or intracranial volume. After accounting for the novel individual difference measures, a second level analysis identified two underlying sources of phenotypic variation. One of these made strong, joint contributions to both the anatomical structures associated with the core fronto-parietal “rich club” network (van den Heuvel & Sporns, 2011), and to cognitive factors. These findings suggest that a hierarchical, data-driven approach is able to identify underlying sources of individual difference that contribute to cognitive-anatomical variation in healthy young adults. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect