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Psychiatric research has been hampered by an explanatory gap betweenpsychiatric symptoms and their neural underpinnings, which has resulted in poortreatment outcomes. This situation has prompted us to shift from symptom-baseddiagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders asdisorders of neural circuitry. Promising candidates for data-driven diagnosisinclude resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers.Although biomarkers have been developed with the aim of diagnosing patients andpredicting the efficacy of therapy, the focus has shifted to the identificationof biomarkers that represent therapeutic targets, which would allow for morepersonalized treatment approaches. This type of biomarker (i.e., theranosticbiomarker) is expected to elucidate the disease mechanism of psychiatricconditions and to offer an individualized neural circuit-based therapeutictarget based on the neural cause of a condition. To this end, researchers havedeveloped rs-fcMRI-based biomarkers and investigated a causal relationshipbetween potential biomarkers and disease-specific behavior using functional MRI(fMRI)-based neurofeedback on functional connectivity. In this review, weintroduce recent approach for creating a theranostic biomarker, which consistsmainly of two parts: (i) developing an rs-fcMRI-based biomarker that canpredict diagnosis and/or symptoms with high accuracy, and (ii) the introductionof a proof-of-concept study investigating the relationship between normalizingthe biomarker and symptom changes using fMRI-based neurofeedback. In parallelwith the introduction of recent studies, we review rs-fcMRI-based biomarker andfMRI-based neurofeedback, focusing on the technological improvements andlimitations associated with clinical use.

Resting-state functional connectivity-based biomarkers and functional  MRI-based neurofeedback for psychiatric disorders: a challenge for developing  theranostic biomarkers