ArticleFrontiers in psychiatry2012
Connectomic intermediate phenotypes for psychiatric disorders.
Article in Frontiers in psychiatry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it, 119 citations in OpenAlex.
- Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review.Schizophrenia bulletin · 2024Pooled it
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- Associations among structural brain alterations, respiratory symptoms and cognitive impairment in patients with chronic obstructive pulmonary disease-related subclinical cognitive vulnerability: a multimodal neuroimaging study.Frontiers in aging neuroscience · 2026Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Translational network neuroscience: Nine roadblocks and possible solutions.Network neuroscience (Cambridge, Mass.) · 2025Article
- Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.PLoS computational biology · 2024Article
- Causal Relationship between Aging and Anorexia Nervosa: A White-Matter-Microstructure-Mediated Mendelian Randomization Analysis.Biomedicines · 2024Article
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
- Alteration in Asymmetry of White Matter Network of Parkinson's Disease.Contrast media & molecular imaging · 2022Article
- Uncovering Cortical Units of Processing From Multi-Layered Connectomes.Frontiers in neuroscience · 2022Article
- Grey and white matter microstructure is associated with polygenic risk for schizophrenia.Molecular psychiatry · 2021Article
- Towards the Idea of Molecular Brains.International journal of molecular sciences · 2021Review
- Altered resting-state functional connectivity of the default mode and central executive networks following cognitive processing therapy for PTSD.Behavioural brain research · 2021Article
- Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners.Journal of Intelligence · 2021Article
- Functional connectome-wide associations of schizophrenia polygenic risk.Molecular psychiatry · 2021Article
- Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia.Brain sciences · 2021Article
- Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder.Human brain mapping · 2021Article
- Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2020Article
- Node centrality measures are a poor substitute for causal inference.Scientific reports · 2019Article
- Elaboration Benefits Source Memory Encoding Through Centrality Change.Scientific reports · 2019Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psychiatric disorders are phenotypically heterogeneous entities with a complex genetic basis. To mitigate this complexity, many investigators study so-called intermediate phenotypes (IPs) that putatively provide a more direct index of the physiological effects of candidate genetic risk variants than overt psychiatric syndromes. Magnetic resonance imaging (MRI) is a particularly popular technique for measuring such phenotypes because it allows interrogation of diverse aspects of brain structure and function in vivo. Much of this work however, has focused on relatively simple measures that quantify variations in the physiology or tissue integrity of specific brain regions in isolation, contradicting an emerging consensus that most major psychiatric disorders do not arise from isolated dysfunction in one or a few brain regions, but rather from disturbed interactions within and between distributed neural circuits; i.e., they are disorders of brain connectivity. The recent proliferation of new MRI techniques for comprehensively mapping the entire connectivity architecture of the brain, termed the human connectome, has provided a rich repertoire of tools for understanding how genetic variants implicated in mental disorder impact distinct neural circuits. In this article, we review research using these connectomic techniques to understand how genetic variation influences the connectivity and topology of human brain networks. We highlight recent evidence from twin and imaging genetics studies suggesting that the penetrance of candidate risk variants for mental illness, such as those in SLC6A4, MAOA, ZNF804A, and APOE, may be higher for IPs characterized at the level of distributed neural systems than at the level of spatially localized brain regions. The findings indicate that imaging connectomics provides a powerful framework for understanding how genetic risk for psychiatric disease is expressed through altered structure and function of the human connectome.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.