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{
    "pk": 24539,
    "title": "Modulation of rhythmic brain circuitry alters the pattern of experience-based decision processing",
    "subtitle": null,
    "abstract": "Understanding and modulating cognitive aspects of decision-making and reinforcement learning are crucial for addressing neuropsychiatric problems like substance use disorders (SUD). We developed a non-invasive stimulation method to modulate theta phase synchronization between the medial prefrontal cortex and right lateral prefrontal cortex. Our EEG-informed modulation led to bidirectional changes in learning-based decision-making, including error-related components and brain signatures. In fact, by combining HD-tACS with mathematical modeling, we revealed that in-phase/antiphase HD-tACS over the mPFC and rPFC significantly altered (synchronized/desynchronized) theta phase coupling between these regions, influencing decision accuracy (improved/impaired), and neurocomputational parameters of learning-based decision-making. Additionally, this modulation rescued/disrupted the causal link between brain error monitoring and cognitive control systems in healthy/SUD participants, and reshaped punishment-guided decision and learning components. We concluded theta rhythms in the mPFC and mPFC-rPFC coupling play a unifying causal role in regulating choice, learning, and behavioral adaptation in both healthy and patient populations.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Cognitive Neuroscience; Learning; Brain Stimulation; Computational Modeling; Electroencephalography (EEG)"
        }
    ],
    "section": "Abstracts",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/1b78f317",
    "frozenauthors": [
        {
            "first_name": "sadegh",
            "middle_name": "",
            "last_name": "Ghaderi",
            "name_suffix": "",
            "institution": "Institute of Cognitive and Brain Sciences Cognitive modeling",
            "department": ""
        },
        {
            "first_name": "Mohammad",
            "middle_name": "",
            "last_name": "Hemami",
            "name_suffix": "",
            "institution": "Institute of Cognitive and Brain Sciences",
            "department": ""
        },
        {
            "first_name": "Jamal",
            "middle_name": "",
            "last_name": "Amani Rad",
            "name_suffix": "",
            "institution": "Shahid Beheshti University",
            "department": ""
        },
        {
            "first_name": "Reza",
            "middle_name": "",
            "last_name": "khosrowabadi",
            "name_suffix": "",
            "institution": "Institute of Cognitive and Brain Sciences",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2024-01-01T18:00:00Z",
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}