Risk profiles for heavy drinking in adolescence: differential effects of gender.

Abnormalities across different domains of neuropsychological functioning may constitute a risk factor for heavy drinking during adolescence and for developing alcohol use disorders later in life. However, the exact nature of such multi-domain risk profiles is unclear, and it is further unclear whether these risk profiles differ between genders. We combined longitudinal and cross-sectional analyses on the large IMAGEN sample (N ≈ 1000) to predict heavy drinking at age 19 from gray matter volume as well as from psychosocial data at age 14 and 19-for males and females separately.

Predicting the future relapse of alcohol-dependent patients from structural and functional brain images.

In alcohol dependence, individual prediction of treatment outcome based on neuroimaging endophenotypes can help to tailor individual therapeutic offers to patients depending on their relapse risk. We built a prediction model for prospective relapse of alcohol-dependent patients that combines structural and functional brain images derived from an experiment in which 46 subjects were exposed to alcohol-related cues. The patient group had been subdivided post hoc regarding relapse behavior defined as a consumption of more than 60 g alcohol for male or more than 40 g alcohol for female patients on one occasion during the 3-month assessment period (16 abstainers and 30 relapsers).

Self-organizing maps and clustering methods for matrix data.

In this contribution we present extensions of the Self Organizing Map and clustering methods for the categorization and visualization of data which are described by matrices rather than feature vectors. Rows and Columns of these matrices correspond to objects which may or may not belong to the same set, and the entries in the matrix describe the relationships between them. The clustering task is formulated as an optimization problem: Model complexity is minimized under the constraint, that the error one makes when reconstructing objects from class information is fixed, usually to a small value.

Soft learning vector quantization.

Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent.