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Towards a Connectivity-Informed Precision Psychiatry Framework for Autism: Proposing Three Evidence-Informed Neurophenotypes
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1
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| Subject:
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Autism and Brain Mapping
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Presenter Author:
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| Name:
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Arada Sarkisian
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Author:
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| Name 1:
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Arada Sarkisian - Presenter
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| Affiliation 1:
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Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
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| Name 2:
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Hamed Shafaroodi - Corresponding
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| Affiliation 2:
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Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Abstract
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Original Research
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introduction :
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Autism Spectrum Disorder (ASD) is characterized by substantial cognitive and neural heterogeneity, complicating the development of targeted interventions. While neuroimaging has identified alterations in intrinsic network connectivity, these findings have not yet been systematically integrated into clinically actionable frameworks. We propose a conceptual, evidence-informed model for neurophenotyping in ASD, based on systematic connectivity patterns across three key brain networks: Default Mode Network (DMN), Dorsal Attention Network (DAN), and Salience Network (SN).
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Methods:
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This theoretical framework is informed by a structured synthesis of over 80 peer-reviewed resting-state fMRI (rs-fMRI) studies in ASD. Key criteria for inclusion were whole-brain connectome analyses, replication across samples (≥30/group when available), standardized preprocessing protocols, and studies reporting clinical or cognitive correlates. We integrate these findings with pilot data from the ABIDE database to derive three candidate neurophenotypes, each with distinct neural signatures tied to cognitive profiles.
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Results:
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We propose the following neurophenotypes:
1. Sensory–Limbic: Increased connectivity within sensory networks and altered SN dynamics, associated with heightened sensory reactivity, anxiety, and emotional instability.
2. Cognitive–HyperSystemizing: Enhanced DAN connectivity and reduced DMN activity, linked to superior systemizing abilities, pattern recognition, and a preference for non-social stimuli.
3. Executive-Dysfunction: Hypoconnectivity within the Frontoparietal Control Network (FPCN), associated with deficits in cognitive flexibility, planning, and working memory.
Each neurophenotype represents a unique neural fingerprint tied to distinct ASD phenotypic characteristics.
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Conclusion:
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This tripartite connectivity-based model serves as a conceptual foundation for precision psychiatry in ASD, offering pathways to phenotype-specific interventions. The framework proposes that targeted neuromodulation or cognitive interventions, aligned with network-specific profiles, may offer more effective, individualized treatment strategies. Further empirical validation using multi-site fMRI data, machine learning classification, and longitudinal studies is needed to substantiate the clinical applicability of this framework.
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| Attachment:
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| Keywords:
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Autism Spectrum Disorder, Magnetic Resonance Imaging, Functional, Brain Connectivity, Neurophenotyping, Resting-State fMRI
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| Key message:
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