Disrupted Development of Neural Connections by Alcohol Initiation in Adolescence
The primary aims of this ongoing longitudinal study are: to conduct a comprehensive investigation of brain development during adolescence and early adulthood; and to determine how brain development is altered when individuals begin to use alcohol (as well as other drugs, such as marijuana) during this period. The researchers employ an extensive two-day data collection protocol at each study time point, consisting of behavioral assessments (interviews, questionnaires, computerized testing), brain magnetic resonance imaging (MRI; high resolution anatomical scans, several types of diffusion scans, spectroscopy, resting functional scans), and electroencephalography (EEG). They also have a one-time collection of genetic data (single-nucleotide polymorphisms). Data collection waves occur at two-year intervals and currently the researchers are completing their fifth assessment. In their analyses within and across these types of data, the researchers investigate the refinement of brain network connectivity during normal adolescent development and identify alterations due to alcohol and drug use. MSI resources are heavily used to achieve this “connectivity” aspect of this brain-behavior research, which relates directly to the goals of the Human Connectome Project. For example, using high-resolution anatomical MRI scans, the researchers extract complete representations of the cortical surface in both brain hemispheres; using diffusion MRI scans they compute measures of the microstructural organization of neural fibers that connect brain regions, and then conduct a “virtual dissection” of these fibers using probabilistic tractography; using resting functional MRI scans they measure neurophysiological activity across multiple overlapping brain networks; using EEG recordings they identify the coordinated synchronization of electrophysiological activity within brain networks in response to external stimuli; and so on. MSI resources are used in all of these analyses, for both data preprocessing and permutation-based statistics.
A bibliography of this group’s publications is attached.
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