Identification of Common Neural Circuit Disruptions in Emotional Processing Across Psychiatric Disorders

McTeague, Lisa M.; Rosenberg, Benjamin M.; Lopez, James W; Carreon, David; Huemer, Julia; Jiang, Ying; Chick, Christina F.; Eickhoff, Simon B.; Etkin, Amit

doi:10.1176/appi.ajp.2019.18111271

Cited by 287 publications

(189 citation statements)

References 65 publications

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“…In line with within-subject models, a set of distributed brain regions exhibited significant model weights ( Fig. 3E) and structure coefficients ( 30,45 . Negative associations with fear were most consistently identified in medial prefrontal and sensorimotor regions.…”

Section: Subjective Fear Is Associated With and Predicted By Distribusupporting

confidence: 62%

Beyond fear centers - a distributed fMRI-based neuromarker for the subjective experience of fear

Zhou

Zhao

Zhang

et al. 2020

Preprint

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The specific neural systems underlying the subjective feeling of fear remain vigorously debated in affective neuroscience. Here, we combined functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n=67), validation (n=20) and generalization (n=31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated ‘fear centers’. This signature provides a neuromarker for monitoring fear-related neuropathology and evaluating novel treatments targeting pathological fear.

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Section: Subjective Fear Is Associated With and Predicted By Distribusupporting

confidence: 62%

Beyond fear centers - a distributed fMRI-based neuromarker for the subjective experience of fear

Zhou

Zhao

Zhang

et al. 2020

Preprint

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“…Previous meta-analyses of emotion processing (McTeague, et al, 2020) and cognitive control tasks (McTeague, et al, 2017) found a transdiagnostic pattern of aberrant activation in the limbic and frontoparietal regions across various psychiatric disorders. Considering these parallel regional disturbances in reappraisal related processes, it is tempting to expect a common pattern of neural disruption underlying reappraisal.…”

Section: Introductionmentioning

confidence: 96%

In search of convergent regional brain abnormality in cognitive emotion regulation: a transdiagnostic neuroimaging meta-analysis

khodadadifar¹,

Soltaninejad²,

Ebneabbasi³

et al. 2020

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Cognitive emotion regulation (i.e., reappraisal) impairment is a key feature in a wide variety of mental disorders, suggesting common nature of disruption across psychiatric diagnoses. However, the extent of potential shared neurobiological disturbances related to reappraisalimpairment is incompletely understood. This study, therefore, aimed to identify neurobiological substrates of disturbed reappraisal using a transdiagnostic approach by performing a quantitative coordinate-based meta-analysis (CBMA) on reappraisal tasks across various mental disorders. Following the best-practice guidelines for neuroimaging meta-analysis, we systematicallysearched PubMed, ScienceDirect and Web of Science databases for whole-brain neuroimaging studies published through February 2020 that compared brain activation in patients with mental disorders and matched healthy controls during a reappraisal task. Out of 1608 publications, we retrieved 32 publications with 1240 unique individuals, providing sufficient power for conducting CBMA using activation likelihood estimation (ALE) method. The reported peak coordinates for the patient/control difference in selected articles were extracted and several ALE analyses were performed to identify spatial convergence. Surprisingly, neither the pooled ALE analysis noradditional analyses restricting to in-/decreased contrasts and more homogeneous grouping of coordinates (i.e., regulation direction, stimulus valence and disorder category) provided significant convergent findings. This CBMA indicates a lack of convergent regional abnormality related to reappraisal across various mental disorders. This might be due to the complex nature of reappraisal, heterogeneous clinical populations or methodological flexibility including preprocessing and analytical methods.

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“…Vergelijkbare analyse van functionele MRI data is op dit moment nog niet beschikbaar, maar meta-analyse van gepubliceerde resultaten laat consistente hypoactivatie van de prefrontale cortex zien en hyperresponsiviteit van de amygdala en hippocampus op emotionele stimuli. 4,5 Voor zulke studies kunnen echter geen effectgroottes worden berekend, maar ook die zullen vermoedelijk niet groot genoeg zijn voor klinische toepassing. Dit type MRI onderzoek is erop gericht om specifieke hersenafwijkingen te lokaliseren.…”

Section: Beeldvorming In De Psychiatrieunclassified

Kunstmatige intelligentie voor klinische toepassing van beeldvorming in de psychiatrie

Wingen¹

2020

Preprint

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The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological assessment of brain scans usually does not explain the psychological symptoms. However, that does not mean that psychological symptoms have no neurobiological basis. Hope has therefore been placed on functional MRI, which measures the activity of the brain. However, this has not yet resulted in clinical applications. A multivariate approach using machine learning analysis now appears to be changing this. Machine learning algorithms can already automate various tasks in radiology. Recent studies show that machine learning analysis of MRI images can also provide diagnostic, prognostic and predictive biomarkers for psychiatry. Larger studies are needed to develop clinical applications, such as clinical decision support systems to support personalized treatment choices.

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Identification of Common Neural Circuit Disruptions in Emotional Processing Across Psychiatric Disorders

Cited by 287 publications

References 65 publications

Beyond fear centers - a distributed fMRI-based neuromarker for the subjective experience of fear

Beyond fear centers - a distributed fMRI-based neuromarker for the subjective experience of fear

In search of convergent regional brain abnormality in cognitive emotion regulation: a transdiagnostic neuroimaging meta-analysis

Kunstmatige intelligentie voor klinische toepassing van beeldvorming in de psychiatrie

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