Psychopharmacology Unit, School of Medical Sciences, University of Bristol, UK.British Medical Bulletin (Impact Factor: 3.66). 02/2003; 65:209-22. DOI: 10.1093/bmb/65.1.209
Alcohol and psycho-active substance misuse has far-reaching social, psychological and physical consequences. Advances in neuroimaging technology have allowed neurobiological theories of addiction to become better characterized. We describe the neurobiology of dependence, withdrawal, abstinence and craving states in alcohol, stimulant and opiate misuse. Structural neuroimaging techniques such as CT and MRI with new analytical approaches such as voxel-based morphometry have shown wide-spread changes in stimulant and opiate abuse and atrophy, particularly in the frontal lobes, in alcoholism. Functional neuroimaging techniques such as PET, SPECT and fMRI reveal altered regional cerebral activity by all drugs of abuse. The neurochemistry of addiction, particularly involving dopamine, serotonin, opiate and GABA, has been studied with PET and SPECT and similarities between all drugs of abuse have been found such as reduced dopaminergic markers. The evidence derived from these advances in neuroimaging is likely to herald the emergence of new biological treatments in this important field.
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ABSTRACT: The personality of alcohol dependant patients as a factor influencing the intensity of the alcohol withdrawal syndrome has been seldom examined. Cloninger's biosocial model of personality describes four temperaments (novelty seeking, harm avoidance, reward dependence, persistence) which, except for persistence, are admittedly linked to specific central neurotransmitters, and three characters. Novelty seeking is linked with low levels of mesencephalic dopamine, harm avoidance with high levels of serotonin in the septo-hippocampic system and reward dependence with low levels of noradrenaline in the ascending pathways from the locus coeruleus to the limbic system. The same neurotransmitters pathways are known to be involved in alcohol withdrawal, with a decrease of dopaminergic activity in the mesolimbic system, a decrease of serotonergic activity in the nucleus accumbens and an increase of the noradrenergic system. In view of the similarities between the neurobiological systems involved in Cloninger's model and in the neurobiological changes occurring during the withdrawal period, one would expect to observe severe withdrawal symptoms more frequently for patients with high novelty seeking, low harm avoidance and low reward dependence. To test this hypothesis, alcohol dependent patients according to DSM IV classification criteria who have drunk in the last twenty four hours were included in the study and received a standardized withdrawal treatment. The withdrawal syndrome intensity was examined with repeated measures of CIWA-Ar, the scores of which were correlated with TCI-R. Twenty eight patients, between 30 et 65 years old and drinking 22,2 +/- 12 standard drinks per day were included. Antidepressant drugs, benzodiazepines and neuroleptics treatment introduced before hospitalisation were stopped or decreased as much as possible. A correlation matrix was carried out between all the variables which could influence withdrawal intensity (age at the hospitalisation, age at the begining of the dependance, ratio between the time of the dependance and the patients' age, the number of alcohol withdrawals carried out and the number of standard drinks per day), and showed a positive correlation between the number of standard drinks per day and withdrawal intensity at day 3 (r=0.7, p<0.000), at day 4 (r=0.52, p<0.005), at day 7 (r=0.41, p<0.036) and at day 8 (r=0.44, p<0.02); as between the ratio between the time of the dependance and the patients' age and withdrawal intensity at day 2 (r=0.43, p<0.03) and at day 5 (r=0.5, p<0.01). Therefore, partial correlations were calculated between the dimensions of personality and withdrawal intensity. The study showed a positive correlation between withdrawal intensity and harm avoidance from day 5 onwards (r=0.6 and P<0.003 at day 5, r=0.59 and P<0.004 at day 6, r=0.56 and P<0.006 at day 7, r=0.66 and P<0.001 at day 8), a negative correlation between withdrawal intensity and reward dependence at day 7 and 8 (r=- 0.45 and P<0.037 at day 7, r=- 0.49 and P<0.02 at day 8) and a negative correlation between withdrawal intensity and persistence from day 6 onwards (r=- 0.5 and P<0.017 at day 6, r=- 0.5 and P<0.019 at day 7, r=- 0.51 and P<0.014 at day 8). No correlation was found between withdrawal intensity and novelty seeking. The same relevant results were found again with the 22 patients without anti-depressant drugs' population. Personality dimensions seem to influence alcohol withdrawal intensity once the severe symptomatology is over, while high doses of anti withdrawal treatment in the first days of abstinence may decrease the influence of personality on withdrawal symptoms. The positive correlation between harm avoidance and withdrawal intensity seems to invalidate our neurobiological hypotheses, but can be explained by clinical observations and corroborate studies assessing the influence of personality in benzodiazepine withdrawal intensity and in pain perception. This result encourages the introduction of support therapy during withdrawal and a cognitive-behavioural therapy before withdrawal in order to decrease patients' sensitivity to anxiety. The negative correlation between reward dependence and withdrawal intensity confirms the neurobiological hypotheses, but the weak correlation demands to be cautious in the interpretation of the results. The negative correlation between persistence and withdrawal intensity was expected. The characteristics associated with persistence seem to act as protective factors during alcohol withdrawal, whereas those associated with harm avoidance appear to increase the symptoms of alcohol withdrawal. In contrast, the neurobiological hypotheses are only partially confirmed.L Encéphale 33(3 Pt 1):264-9. · 0.70 Impact Factor
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ABSTRACT: Neurochemical imaging studies can identify molecular targets of abused drugs and link them to the underlying pathology associated with behaviors such as drug dependence, addiction and withdrawal. positron emission tomography (PET) is opening new avenues for the investigation of the neurochemical disturbances underlying drug abuse and addiction and the in vivo mechanisms by which medications might ameliorate these conditions. PET can identify vulnerable human populations, treatment strategies and monitor treatment efficacy. Thus, with this tool and the knowledge it provides, the potential for developing novel drugs and treatment strategies for drug addiction is now close at hand.Drug Discovery Today 05/2005; 10(8):547-62. DOI:10.1016/S1359-6446(05)03412-4 · 6.69 Impact Factor
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ABSTRACT: The segmentation of T1-weighted images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is a fundamental processing step in neuroimaging, the results of which affect many other structural imaging analyses. Variability in the segmentation process can decrease the power of a study to detect anatomical differences, and minimizing such variability can lead to more robust results. This paper outlines a straightforward strategy that can be used (1) to select more optimal data acquisition and processing protocols and (2) to quantify the impact of such optimization. Using this approach with multiple scans of a single subject, we found that the choice of a segmentation algorithm had the largest impact on variability, while the choice of a pulse sequence had the second largest impact. The data indicate that the classification of GM is the most variable, and that the optimal protocol may differ across tissue types. Therefore, the intended use of segmentation data should play a role in optimization. Examples are provided to demonstrate that the minimization of variability is not sufficient for optimization; the overall accuracy of the approach must also be considered. Simple volumetric computations are included to illustrate the potential gain of optimization; these results show that volume estimates from optimal pathways were on average three times less variable than estimates from suboptimal pathways. Therefore, the simple strategy illustrated here can be applied to many studies to optimize tissue segmentation, which should lead to a net increase in the power of structural neuroimaging studies.NeuroImage 02/2006; 29(1):185-202. DOI:10.1016/j.neuroimage.2005.07.035 · 6.36 Impact Factor
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