“Papers of School of Cognitive Sciences”

 

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  71. M. Parsa, H. Yousefi Rad, H. Vaezi, G. Hossein-Zadeh, S. Setarehdan, R. Rostami , H. Rostami and A. Vahabie,
EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions,
Computer Methods and Programs in Biomedicine 240(2023),   [abstract]

DOI: https://doi.org/10.1016/j.cmpb.2023.107683

   72. J. Lee, M. Beirami, R. Ebrahimpour, C. Puyana, M. Tsoukas and K. Avanaki,
Optical coherence tomography confirms non-malignant pigmented lesions in phacomatosis pigmentokeratotica using a support vector machine learning algorithm,
Skin Res Technol 29(2023),   [abstract]
DOI: https://doi.org/10.1111/srt.13377

   73. S. Mazaheri, M. Zendehdel and A. Haghparast,
Restraint stress potentiates sensitivity to the antinociceptive effect of morphine through orexin receptors in the ventral tegmental area,
Neuropeptides 101(2023),   [abstract]
DOI: https://doi.org/10.1016/j.npep.2023.102353

   74. M. Mohammadi, K. Eskandari, R. Azizbeigi and A. Haghparast,
The inhibitory effect of cannabidiol on the rewarding properties of methamphetamine in part mediates by interacting with the hippocampal D1-like dopamine receptors,
Prog Neuropsychopharmacol Biol Psychiatry 126(2023),   [abstract]
DOI: https://doi.org/10.1016/j.pnpbp.2023.110778

   75. P. Navidi, S. Saeedpour , S. Ershadmanesh , M. Miandari Hossein and B. Bahrami ,
Prosocial learning: Model-based or model-free?,
Plos One 18(2023),   [abstract]
DOI: https://doi.org/10.1371/journal.pone.0287563

   76. S. Jamali, M. Aliyari Shoorehdeli, M. Daliri and A. Haghparast,
Differential Aspects of Natural and Morphine Reward-related Behaviors in Conditioned Place Preference Paradigm,
Basic and Clinical Neuroscience (2023),   [abstract]
DOI: https://doi.org/10.32598/bcn.2021.3071.1

   77. M. Mokari-Mahallati, R. Ebrahimpour, N. Bagheri and H. Karimi-Rouzbahani,
Deeper neural network models better reflect how humans cope with contrast variation in object recognition,
Neuroscience Research 192(2023),   [abstract]
DOI: https://doi.org/10.1016/j.neures.2023.01.007

   78. F. Keyvanfard, A. Rahimi Nasab and A. Nasiraei-Moghaddam,
Brain subnetworks most sensitive to alterations of functional connectivity in Schizophrenia: a data-driven approach,
Front Neuroinform 17(2023),   [abstract]
DOI: https://doi.org/10.3389/fninf.2023.1175886

   79. S. Karimi-Haghighi, M. Mahmoudi , F. Sayehmiri, R. Mozafari and A. Haghparast,
Endocannabinoid system as a therapeutic target for psychostimulants relapse: A systematic review of preclinical studies,
European Journal of Pharmacology 951(2023),   [abstract]
DOI: https://doi.org/10.1016/j.ejphar.2023.175669

   80. P. Sadeghi Shabestari, S. Zendehrouh, A. Ahmadi, S. Jafari, N. Parvaresh and M. Eslami,
Analyzing the network of parent-rated ADHD symptoms before and 2 weeks after the onset of pharmaceutical treatment,
J Child Adolesc Psychiatr Nurs (2023),   [abstract]
DOI: https://doi.org/10.1111/jcap.12421

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