Biomedical subjects

Andreas Konrad

Publications and source records attributed to Andreas Konrad.

3 recordsLinked to original sources

PubMed2006

Smoking impacts on prefrontal attentional network function in young adult brains.

RATIONALE: There is abundant evidence from clinical and preclinical studies that acute administration of nicotine has beneficial effects on attentional network function in the brain. In contrast, little is known about potentially neurotoxic effects on the attentional network during neurodevelopmentally critical periods, such as during adolescence and early adulthood. METHODS: Using event-related functional MRI (fMRI), we investigated prefrontal attentional network function in young adults (n=15 regular smokers and n=12 never-smokers; age: 22.6+/-1.5 years). Duration of smoking was 6.9+/-2.3 years (range of 2-10). Smokers were allowed to smoke ad libitum before the fMRI scanning was conducted. RESULTS: As expected from literature, prefrontal attentional network activity was significantly reduced in smokers compared to nonsmokers (Z=2.1; P=0.036). In smokers, we found that the history of smoking duration (years) is directly related to the extent of diminished attentional network activity (R=-0.67; P=0.012). CONCLUSIONS: To our best knowledge, the relationship between the duration of smoking history and prefrontal attentional network function has not yet been reported. This finding might suggest that several years of chronic nicotine abuse may be sufficient to exert long-lasting effects on the brain function of adolescents and young adults.

Adolescent↗

PubMed2006

COMT genotype predicts BOLD signal and noise characteristics in prefrontal circuits.

OBJECTIVE: Prefrontal dopamine (DA) is catabolized by the COMT (catechol-O-methyltransferase) enzyme. Literature suggests that the Val/Met single nucleotide polymorphism (SNP) in the COMT gene predicts executive cognition in humans with Val carriers showing poorer performance due to less available synaptic DA. Recent fMRI studies are thought to agree with these studies having demonstrated prefrontal hyperactivation during n-back and attention-requiring tasks. This was interpreted as "less efficient" processing due to impaired signal-to-noise ratio (SNR) of neuronal activity. However, electrophysiological studies of neuronal SNR in primates and humans imply that prefrontal cortex should show a diminished prefrontal BOLD response in Val carriers. In the present study, we addressed the question of whether the prefrontal SNR of the BOLD response is decreased in Val carriers using a visual oddball task and an approach to analysis of fMRI data that maximizes noise characterization. METHODS: We investigated N=17 homozygous Met carriers compared with N=24 Val carriers matched for age, sex, education, IQ, reaction time (variability) and head motion. Event-related fMRI was conducted presenting 160 visual stimuli (40 targets, checkerboard reversal). Subjects had to respond as quickly as possible to targets by button press. In the fMRI GLM [y(t)=beta*x(t)+c+e(t)] analysis, voxel-by-voxel 'activation' [y(t)] as well as residual noise variance [e(t)=sigma2] were calculated using a conservative full-width half maximum (FWHM=6 mm). RESULTS: As compared to Val carriers, we observed a stronger and more extended BOLD responses in homozygous Met carriers in left supplementary motor area (SMA) extending to ACC and dorsolateral prefrontal cortex. Vice versa, increased levels of noise were seen in Val carriers surrounding the peak activation maximum. DISCUSSION: In line with our expectations from prior electrophysiological studies, we observed a diminished BOLD response and increased noise in Val carriers. This suggests that the DA stabilizes cortical microcircuits by sharpening the signal and suppressing surrounding noise.

Adult↗

PubMed2006

Distributed BOLD-response in association cortex vector state space predicts reaction time during selective attention.

Human cortical information processing is thought to be dominated by distributed activity in vector state space (Churchland, P.S., Sejnowski, T.J., 1992. The Computational Brain. MIT Press, Cambridge.). In principle, it should be possible to quantify distributed brain activation with independent component analysis (ICA) through vector-based decomposition, i.e., through a separation of a mixture of sources. Using event-related functional magnetic resonance imaging (fMRI) during a selective attention-requiring task (visual oddball), we explored how the number of independent components within activated cortical areas is related to reaction time. Prior to ICA, the activated cortical areas were determined on the basis of a General linear model (GLM) voxel-by-voxel analysis of the target stimuli (checkerboard reversal). Two activated cortical areas (temporoparietal cortex, medial prefrontal cortex) were further investigated as these cortical regions are known to be the sites of simultaneously active electromagnetic generators which give rise to the compound event-related potential P300 during oddball task conditions. We found that the number of independent components more strongly predicted reaction time than the overall level of "activation" (GLM BOLD-response) in the left temporoparietal area whereas in the medial prefrontal cortex both ICA and GLM predicted reaction time equally well. Comparable correlations were not seen when principle components were used instead of independent components. These results indicate that the number of independently activated components, i.e., a high level of cortical activation complexity in cortical vector state space, may index particularly efficient information processing during selective attention-requiring tasks. To our best knowledge, this is the first report describing a potential relationship between neuronal generators of cognitive processes, the associated electrophysiological evidence for the existence of distributed networks and BOLD fMRI signals using information from model order selection techniques.

Adult↗