Vol 5 n° 3 - Anxiety II
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2 4 6 Posters & images in neuroscience fMRI in anxiety Anxiety disorders are highly prevalent.They induce indi- vidual distress and impairment, and are responsible for sig- nificant social costs. Owing to this, the quest for more effi- cient and safer treatments with lesser addictive potential is a major challenge for the pharmaceutical industry. In order to improve the development of anxiolytic drugs, the devel- opment of anxiety models in healthy volunteers is useful. The available models for induction in healthy individuals of anxious states comparable to states observed in anxiety disorders basically fall into the two categories of behavioral and pharmacological. Both categories are investigated in this study; the pharmacological model was used to study panic attack underlying neuronal mechanisms, and behav- ioral models to study anticipatory anxiety. Methods The pharmacological model uses the attenuated panic-like symptoms induced by cholecystokinin-4 (CCK-4) admin- istration and is composed of three functional magnetic res- onance imaging (fMRI) scans. During scan 1, the healthy male  subjects  are  informed  that  they  are  going  to  be injected with placebo.This scan is a control to assess the effects of a simple injection on the brain activity. During scan 2, the subjects are injected with CCK-4.A 0.9% saline solution for placebo or 50 µg of CCK-4 is injected in a bolus fashion in less than 10 s via an intravenous (IV) catheter placed into the vein of the forearm (the side of injection depends on subject vein quality and this was determined by the medical staff ; however, the right side has prevailed). Scan 3 is a behavioral classical condition- ing model, in which the panic attack experienced on CCK- 4 acts as the unconditional stimulus and a red square cou- pled with a clock acts as the conditional stimulus. • The first and the second scans last 10 min: 3 min of baseline, before placebo (first) or CCK-4 (second) injection, and 7 min after the IV injection. • The third scan lasts 13 min: 7 blue and 6 red squares are alternately presented, blue squares are presented for 68 s (17 images) and the red for 52 s (13 images). The blue square presentation is the rest period and the red one is the threat condition period. A timer is used during  the  threat  condition  period,  the  subject  is instructed that he could be administered CCK-4 within the last 10 s. The behavioral model is based on classical aversive con- ditioning: the conditioned stimulus is a visual presenta- tion and the unconditioned stimulus is a somatosensory stimulation. The task is composed of one fMRI session. The acquisition lasts 11 min: 12 blue and 12 red circles are alternately presented for about 27 s (8 images); the blue circle presentation is the rest period and the red the threat condition period.The subject is instructed that he could receive none, one, or two transcutaneous electri- cal nerve stimulations (TENS) of the sural nerve within the threat condition period. Image processing Image processing and statistical analysis were performed with freeware software Medimax (Institut de Physique Biologique, GITIM, Louis Pasteur University, Strasbourg, France).All functional images were registered to the first functional image in the series using an automated registra- tion algorithm (rigid registration). A Gaussian filtering (FWHM [full width at half maximum] =8 mm) and a tem- poral filtering were applied on each EPI (echo-planar imag- ing) image.A correlation coefficient between the observed response  function  and  a  waveform  representing  the expected response was computed for each voxel. For each subject, the activation map was obtained from the corre- lation image using a cluster pixels analysis procedure.Pixels with a correlation coefficient >0.6 were considered as seed points of the clusters identified with a high-connectivity algorithm and a correlation coefficient >0.4. For anatomic registration, a mean image was created with the realigned functional images and was coregistered to the anatomical image with an affine transformation. So the statistical maps could be superimposed on the anatomical image using the transform maps obtained after the affine registration. Copyright © 2003 LLS SAS. All rights reserved