Introduction: Many studies have revealed that drug addicted individuals exhibit impaired performance on executive function tests but a few studies have been conducted on executive functions of drug addicts in Iran. To contribute to this understanding, the present study was designed to assess some domains related to executive functions including cognitive flexibility, attention and speed of mental processing in a sample of drug addicts in comparison with a sample of non-drug addicts.

Methods:

Introduction: A fixed hemodynamic response function (HRF) is commonly used for functional magnetic resonance imaging (fMRI) analysis. However, HRF may vary from region to region and subject to subject. We investigated the effect of locally estimated HRF (in functionally homogenous parcels) on activation detection sensitivity in a heroin cue reactivity study.
Methods: We proposed a novel exploratory method for brain parcellation based on a probabilistic model to segregate the brain into spatially connected and functionally homogeneous components. Then, we estimated HRF and detected activated regions in response to an experimental task in each parcel using a joint detection estimation (JDE) method. We compared the proposed JDE method with the general linear model (GLM) that uses a fixed HRF and is implemented in FEAT (as a part of FMRIB Software Library, version 4.1).
Results: 1) Regions detected by JDE are larger than those detected by fixed HRF, 2) In group analysis, JDE found areas of activation not detected by fixed HRF. It detected drug craving a priori “regions-of-interest” in the limbic lobe (anterior cingulate cortex [ACC], posterior cingulate cortex [PCC] and cingulate gyrus), basal ganglia, especially striatum (putamen and
head of caudate), and cerebellum in addition to the areas detected by the fixed HRF method, 3) JDE obtained higher Z-values of local maxima compared to those obtained by fixed HRF.
Conclusion: In our study of heroin cue reactivity, our proposed method (that estimates HRF locally) outperformed the conventional GLM that uses a fixed HRF.

Introduction: Genes often have multiple polymorphisms that interact with each other and the environment in different individuals. Variability in the opioid receptors can influence opiate withdrawal and dependence. In humans, A118G Single Nucleotide Polymorphisms (SNP) on μ-Opioid Receptor (MOR), 36 G>T in κ-Opioid Receptor (KOR), and T921C in the δ-Opioid Receptor (DOR) have been found to associate with substance dependence. 
Methods: To investigate the association between opioid receptors gene polymorphism and heroin addiction, 100 control subjects with no history of opioid use, and 100 heroin addicts (50% males and 50% females) in Tehran (capital of Iran), were evaluated. A118G, 36 G>T, and T921C SNPs on the MOR, KOR, DOR genes, respectively, were genotyped by sequencing. 
Results: We found no differences in either allele or genotype frequency for MOR, KOR and DOR genes SNPs between controls and subjects addicted to heroin. 
Conclusion: The relationships among polymorphisms may be important in determining the risk profile for complex diseases such as addiction, but opioid addiction is a multifactorial syndrome which is partially hereditary and partially affected by the environment.