Biomedical subjects

Dimitris Samaras

Publications and source records attributed to Dimitris Samaras.

3 recordsLinked to original sources

PubMed2007

Is decreased prefrontal cortical sensitivity to monetary reward associated with impaired motivation and self-control in cocaine addiction?

OBJECTIVE: This study attempted to examine the brain's sensitivity to monetary rewards of different magnitudes in cocaine abusers and to study its association with motivation and self-control. METHOD: Sixteen cocaine abusers and 13 matched healthy comparison subjects performed a forced-choice task under three monetary value conditions while brain activation was measured with functional magnetic resonance imaging. Objective measures of state motivation were assessed by reaction time and accuracy, and subjective measures were assessed by self-reports of task engagement. Measures of trait motivation and self-control were assessed with the Multidimensional Personality Questionnaire. RESULTS: The cocaine abusers demonstrated an overall reduced regional brain responsivity to differences between the monetary value conditions. Also, in comparison subjects but not in cocaine abusers, reward-induced improvements in performance were associated with self-reports of task engagement, and money-induced activations in the lateral prefrontal cortex were associated with parallel activations in the orbitofrontal cortex. For cocaine abusers, prefrontal cortex sensitivity to money was instead associated with motivation and self-control. CONCLUSIONS: These findings suggest that in cocaine addiction 1) activation of the corticolimbic reward circuit to gradations of money is altered; 2) the lack of a correlation between objective and subjective measures of state motivation may be indicative of disrupted perception of motivational drive, which could contribute to impairments in self-control; and 3) the lateral prefrontal cortex modulates trait motivation and deficits in self-control, and a possible underlying mechanism may encompass a breakdown in prefrontal-orbitofrontal cortical communication.

Adult↗

PubMed2006

Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics.

In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the recent result which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. We provide two methods to estimate the spherical harmonic basis images spanning this space from just one image. Our first method builds the statistical model based on a collection of 2D basis images. We demonstrate that, by using the learned statistics, we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation. Compared to the first method, the second method builds the statistical models directly in 3D spaces by combining the spherical harmonic illumination representation and a 3D morphable model of human faces to recover basis images from images across both poses and illuminations. After estimating the basis images, we use the same recognition scheme for both methods: we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements. Comparison of the two methods is also provided.

Algorithms↗

PubMed2005

Exploiting temporal information in functional magnetic resonance imaging brain data.

Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data analysis methods. In this paper, we contribute a comprehensive framework for spatial and temporal exploration of fMRI data, and apply it to a challenging case study: separating drug addicted subjects from healthy non-drug-using controls. To our knowledge, this is the first time that learning on fMRI data is performed explicitly on temporal information for classification in such applications. Experimental results demonstrate that, by selecting discriminative features, group classification can be successfully performed on our case study although training data are exceptionally high dimensional, sparse and noisy fMRI sequences. The classification performance can be significantly improved by incorporating temporal information into machine learning. Both statistical and neuroscientific validation of the method's generalization ability are provided. We demonstrate that incorporation of computer science principles into functional neuroimaging clinical studies, facilitates deduction about the behavioral probes from the brain activation data, thus providing a valid tool that incorporates objective brain imaging data into clinical classification of psychopathologies and identification of genetic vulnerabilities.

Algorithms↗