Clinical Applications of Neuroimaging in Psychiatric Disorders

First, Michael B.; Drevets, Wayne C.; Carter, Cameron; Dickstein, Daniel P.; Kasoff, Lauren I.; Kim, Kerri L.; McConathy, Jonathan; Rauch, Scott L.; Saad, Ziad S.; Savitz, Jonathan; Seymour, Karen E.; Sheline, Yvette I.; Zubieta, Jon Kar

doi:10.1176/appi.ajp.2018.1750701

Cited by 51 publications

(47 citation statements)

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“…Despite the important contribution of neuroimaging research to identifying altered amygdala-prefrontal interaction and its normalization as a potential pathological and treatment-sensitive neural marker for neuropsychiatric disorders characterized by emotional dysregulations, it has yet to directly have a therapeutic impact [23]. Given that the currently available therapeutic interventions for anxiety reduction are generally characterized by moderate response rates and potential negative side effects [24-26], innovative treatments that directly target the identified brain markers are needed [27].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Zhao

Yao

et al. 2019

Psychother Psychosom

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Background: Deficient emotion regulation and exaggerated anxiety represent a major transdiagnostic psychopathological marker. On the neural level these deficits have been closely linked to impaired, yet treatment-sensitive, prefrontal regulatory control over the amygdala. Gaining direct control over these pathways could therefore provide an innovative and promising intervention to regulate exaggerated anxiety. To this end the current proof-of-concept study evaluated the feasibility, functional relevance and maintenance of a novel connectivity-informed real-time fMRI neurofeedback training. Methods: In a randomized crossover sham-controlled design, 26 healthy subjects with high anxiety underwent real-time fMRI-guided neurofeedback training to enhance connectivity between the ventrolateral prefrontal cortex (vlPFC) and the amygdala (target pathway) during threat exposure. Maintenance of regulatory control was assessed after 3 days and in the absence of feedback. Training-induced changes in functional connectivity of the target pathway and anxiety ratings served as primary outcomes. Results: Training of the target, yet not the sham control, pathway significantly increased amygdala-vlPFC connectivity and decreased levels of anxiety. Stronger connectivity increases were significantly associated with higher anxiety reduction on the group level. At the follow-up, volitional control over the target pathway was maintained in the absence of feedback. Conclusions: The present results demonstrate for the first time that successful self-regulation of amygdala-prefrontal top-down regulatory circuits may represent a novel intervention to control anxiety. As such, the present findings underscore both the critical contribution of amygdala-prefrontal circuits to emotion regulation and the therapeutic potential of connectivity-informed real-time neurofeedback.

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Section: Introductionmentioning

confidence: 99%

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Zhao

Yao

et al. 2019

Psychother Psychosom

View full text Add to dashboard Cite

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“…Ironically, in this age of heightened awareness of the importance of the brain and its central role in the generation of the symptoms, we associate with psychiatric disorders, the American Psychiatric Association (APA) has taken the position that neuroimaging has no role in Psychiatry. In 2018, the APA published a position paper stating "neuroimaging has yet to have a significant impact on the diagnosis or treatment of individual patients in clinical settings" (37).…”

Section: Neuroimaging Could Guide the Diagnostic Process-if Only…mentioning

confidence: 99%

“…Perhaps most shocking is the fact that this official declaration by the APA (37) was not open to debate. There were no hearings of interested parties.…”

Section: Neuroimaging Could Guide the Diagnostic Process-if Only…mentioning

confidence: 99%

“…By contrast, MRI and functional MRI do not expose the patient to radiation. Unfortunately, these non-invasive MRI modalities have failed to yield clinically useful diagnostic approaches (15,37). A brain FDG PET scan on average carries a 700 mRems exposure (132), while a head CT scan can lead to an exposure of 800 to 900 mRems (132), depending on the imaging protocol.…”

Section: Limitations Of Spect and Pet Imagingmentioning

confidence: 99%

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Functional Neuroimaging in Psychiatry—Aiding in Diagnosis and Guiding Treatment. What the American Psychiatric Association Does Not Know

et al. 2020

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“…However, recently the EU's High‐Level Expert Group on Artificial Intelligence has published a report on “Trustworthy AI” which includes an assessment list for evaluating AI systems (High‐Level Expert Group on Artificial Intelligence, ). Under the heading “Transparency” explainability is listed as a key requirement (First et al, , p. 29). In research on machine learning for medical diagnosis and prediction explainable AI does not currently play a significant role.…”

Section: Explainable Aimentioning

confidence: 99%

Your evidence? Machine learning algorithms for medical diagnosis and prediction

Heinrichs

Eickhoff

2019

Human Brain Mapping

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Computer systems for medical diagnosis based on machine learning are not mere science fiction. Despite undisputed potential benefits, such systems may also raise problems. Two (interconnected) issues are particularly significant from an ethical point of view: The first issue is that epistemic opacity is at odds with a common desire for understanding and potentially undermines information rights. The second (related) issue concerns the assignment of responsibility in cases of failure. The core of the two issues seems to be that understanding and responsibility are concepts that are intrinsically tied to the discursive practice of giving and asking for reasons. The challenge is to find ways to make the outcomes of machine learning algorithms compatible with our discursive practice. This comes down to the claim that we should try to integrate discursive elements into machine learning algorithms. Under the title of “explainable AI” initiatives heading in this direction are already under way. Extensive research in this field is needed for finding adequate solutions.

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Clinical Applications of Neuroimaging in Psychiatric Disorders

Cited by 51 publications

References 0 publications

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Functional Neuroimaging in Psychiatry—Aiding in Diagnosis and Guiding Treatment. What the American Psychiatric Association Does Not Know

Your evidence? Machine learning algorithms for medical diagnosis and prediction

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