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1.
Cereb Cortex ; 32(12): 2611-2620, 2022 06 07.
Article in English | MEDLINE | ID: mdl-34729592

ABSTRACT

The age- and time-dependent effects of binge drinking on adolescent brain development have not been well characterized even though binge drinking is a health crisis among adolescents. The impact of binge drinking on gray matter volume (GMV) development was examined using 5 waves of longitudinal data from the National Consortium on Alcohol and NeuroDevelopment in Adolescence study. Binge drinkers (n = 166) were compared with non-binge drinkers (n = 82 after matching on potential confounders). Number of binge drinking episodes in the past year was linked to decreased GMVs in bilateral Desikan-Killiany cortical parcellations (26 of 34 with P < 0.05/34) with the strongest effects observed in frontal regions. Interactions of binge drinking episodes and baseline age demonstrated stronger effects in younger participants. Statistical models sensitive to number of binge episodes and their temporal proximity to brain volumes provided the best fits. Consistent with prior research, results of this study highlight the negative effects of binge drinking on the developing brain. Our results present novel findings that cortical GMV decreases were greater in closer proximity to binge drinking episodes in a dose-response manner. This relation suggests a causal effect and raises the possibility that normal growth trajectories may be reinstated with alcohol abstinence.


Subject(s)
Binge Drinking , Gray Matter , Adolescent , Alcohol Drinking , Brain/diagnostic imaging , Ethanol/pharmacology , Gray Matter/diagnostic imaging , Humans , Magnetic Resonance Imaging/methods
2.
Proc IEEE Int Symp Biomed Imaging ; 2008: 812-815, 2008 May.
Article in English | MEDLINE | ID: mdl-28593030

ABSTRACT

Change detection is a critical task in the diagnosis of many slowly evolving pathologies. This paper describes an approach that semi-automatically performs this task using longitudinal medical images. We are specifically interested in meningiomas, which experts often find difficult to monitor as the tumor evolution can be obscured by image artifacts. We test the method on synthetic data with known tumor growth as well as ten clinical data sets. We show that the results of our approach highly correlate with expert findings but seem to be less impacted by inter- and intra-rater variability.

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