Role of medial cortical, hippocampal and striatal interactions during cognitive set-shifting.

To date, few studies have examined the functional connectivity of brain regions involved in complex executive function tasks, such as cognitive set-shifting. In this study, eighteen healthy volunteers performed a cognitive set-shifting task modified from the Wisconsin card sort test while undergoing functional magnetic resonance imaging. These modifications allowed better disambiguation between cognitive processes and revealed several novel findings: 1) peak activation in the caudate nuclei in the first instance of negative feedback signaling a shift in rule, 2) lowest caudate activation once the rule had been identified, 3) peak hippocampal activation once the identity of the rule had been established, and 4) decreased hippocampal activation during the generation of new rule candidates. This pattern of activation across cognitive set-shifting events suggests that the caudate nuclei play a role in response generation when the identity of the new rule is unknown. In contrast, the reciprocal pattern of hippocampal activation suggests that the hippocampi help consolidate knowledge about the correct stimulus-stimulus associations, associations that become inappropriate once the rule has changed. Functional connectivity analysis using Granger Causality Mapping revealed that caudate and hippocampal regions interacted indirectly via a circuit involving the medial orbitofrontal and posterior cingulate regions, which are known to bias attention towards stimuli based on expectations built up from task-related feedback. Taken together, the evidence suggests that these medial regions may mediate striato-hippocampal interactions and hence affect goal-directed attentional transitions from a response strategy based on stimulus-reward heuristics (caudate-dependent) to one based on stimulus-stimulus associations (hippocampus-dependent).

Growth inhibitory effect of the ternary complex factor Net on human pancreatic carcinoma cell lines.

Pancreatic carcinoma is one of the most aggressive malignancies and carries the most dismal prognoses of all cancers. A better understanding of the genes involving in tumor development may allow us to tackle this rapidly progressive disease. The Net gene belongs to the ternary complex transcription factor (TCF) family and is regulated by the Ras/mitogen-activated protein kinase-signaling pathway. Under basal conditions, Net shows strong repressing function on transcription of proto-oncogene gene c-fos. Moreover, the lower expression of Net has been noted in some carcinoma cells, such as cervical cancer. To study the effect of Net on c-fos expression and its potential role in the growth of pancreatic carcinoma, we developed a recombinant plasmid, a pEGFP-N1-Net, which codes for Net-EGFP fusion proteins, and stably transfected it into BxPC-3 human pancreatic carcinoma cells. Using stable transformants, we were able to show that overexpression of Net decreased the expression of c-fos and inhibited pancreatic cancer cell proliferation. Cell cycle analysis demonstrated that Net overexpression inhibited cell cycle progression. These findings suggested that loss of Net repression could augment c-fos expression and further trigger neoplastic cell proliferation, which was involved in the pathogenesis of pancreatic cancer. Therefore, Net might be a potential target for the treatment of c-fos-positive pancreatic cancer.

Reproducibility of brain tissue volumes in longitudinal studies: effects of changes in signal-to-noise ratio and scanner software.

It is imperative that users of voxel-based morphometry (VBM) be aware of its reproducibility and the factors which influence results. We assessed the reproducibility of a VBM software package (SPM5) in measuring gray matter (GM) and white matter (WM) volumes from at least two consecutive 3D T1-weighted studies in 64 subjects. Factors investigated were the inter-study interval (ISI: 2.2 h to 124 days), signal-to-noise ratio (SNR: number of image averages (NA)=1 or 2), scanner software version and idle time. SNR was measured by direct estimation of tissue noise (SNR(TN)) and mean intensity in noise-only voxels (SNR(NO)). After the scanner software upgrade, voxel intensity increased 5-fold and WM mean SNR(TN) by 24% (p<0.001). Mean WM and GM volume changes in consecutive studies were near 0% (absolute SD of 7 ml and 10 ml respectively). Studies acquired with original scanner software showed a small (1.6%) mean GM volume increase attributed to SNR(TN) increases in five subjects due to scanner maintenance. GM volumes increased with SNR(TN) across the software upgrade (up to 4.3%; p<0.01) and NA=2 acquisitions (up to 4.1%; p<0.001). GM and WM volumes were independent of ISI when ISI did not encompass the software change. Scanner idle times of >6 h decreased SNR by 7% (p<0.001). SPM5 failed to include visible peripheral GM in only 2 subjects. SNR(TN) increases were greater than SNR(NO) increases across the software upgrade. It was concluded that SNR changes significantly influence SPM5-derived GM volumes. SNR may be influenced by scanner software upgrades and hardware condition and should be routinely assessed in studies of brain volume.