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Review
. 2024 Nov;50(1):196-200.
doi: 10.1038/s41386-024-01888-1. Epub 2024 May 22.

Resting state functional brain connectivity in child and adolescent psychiatry: where are we now?

Affiliations
Review

Resting state functional brain connectivity in child and adolescent psychiatry: where are we now?

Lucina Q Uddin et al. Neuropsychopharmacology. 2024 Nov.

Erratum in

Abstract

Approaching the 30th anniversary of the discovery of resting state functional magnetic resonance imaging (rsfMRI) functional connectivity, we reflect on the impact of this neuroimaging breakthrough on the field of child and adolescent psychiatry. The study of intrinsic functional brain architecture that rsfMRI affords across a wide range of ages and abilities has yielded numerous key insights. For example, we now know that many neurodevelopmental conditions are associated with more widespread circuit alterations across multiple large-scale brain networks than previously suspected. The emergence of population neuroscience and effective data-sharing initiatives have made large rsfMRI datasets publicly available, providing sufficient power to begin to identify brain-based subtypes within heterogeneous clinical conditions. Nevertheless, several methodological and theoretical challenges must still be addressed to fulfill the promises of personalized child and adolescent psychiatry. In particular, incomplete understanding of the physiological mechanisms driving developmental changes in intrinsic functional connectivity remains an obstacle to further progress. Future directions include cross-species and multimodal neuroimaging investigations to illuminate such mechanisms. Data collection and harmonization efforts that span multiple countries and diverse cohorts are urgently needed. Finally, incorporating naturalistic fMRI paradigms such as movie watching should be a priority for future research efforts.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Unsupervised machine learning can be used to parse heterogeneity and identify subtypes within heterogeneous neurodevelopmental disorders based on rsfMRI functional connectivity metrics.

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