Neural correlates of emotional processing in psychosis risk and onset – A systematic review and meta-analysis of fMRI studies

Lukow, Paulina; Kiemes, Amanda; Kempton, Matthew J.; Turkheimer, Federico; McGuire, Philip; Modinos, Gemma

doi:10.1016/j.neubiorev.2021.03.010

Cited by 19 publications

(19 citation statements)

References 94 publications

(204 reference statements)

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“…Under specifically designed cognitive paradigms, the collected neuroimaging data enable us to examine brain activities associated with certain experimental tasks and study their relationship with cognitive dysfunctions. Typical measures of taskrelated brain activities include event-related potential and event-related spectral perturbation, and reward or emotional processing-related functional activation [39], [40].…”

Section: Neuroimaging Analysismentioning

confidence: 99%

Modern Views of Machine Learning for Precision Psychiatry

Chen¹,

Prathamesh²,

Kulkarni³

et al. 2022

Preprint

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In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

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Section: Neuroimaging Analysismentioning

confidence: 99%

Modern Views of Machine Learning for Precision Psychiatry

Chen¹,

Prathamesh²,

Kulkarni³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Most of the literature on emotional processing in schizophrenia focuses on the visual modality, including emotional images and facial expressions ( 4 ). For example, patients with schizophrenia (PwS) have been found to misattribute emotions to neutral facial expressions, as well as misinterpret emotional facial expressions ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, deficits in selective-attention and inhibition are typical of schizophrenia. They have been identified in the visual domain (e.g., color-word Stroop) ( 17 – 19 ), with cross-modal visual-auditory stimuli [for a review, see ( 4 )], and in the auditory domain ( 11 , 13 ). In other daily situations, speech processing may involve the integration of the two channels to generate a coherent spoken message.…”

Section: Introductionmentioning

confidence: 99%

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Processing of Spoken Emotions in Schizophrenia: Forensic and Non-forensic Patients Differ in Emotional Identification and Integration but Not in Selective Attention

2022

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Patients with schizophrenia (PwS) typically demonstrate deficits in visual processing of emotions. Less is known about auditory processing of spoken-emotions, as conveyed by the prosodic (tone) and semantics (words) channels. In a previous study, forensic PwS (who committed violent offenses) identified spoken-emotions and integrated the emotional information from both channels similarly to controls. However, their performance indicated larger failures of selective-attention, and lower discrimination between spoken-emotions, than controls. Given that forensic schizophrenia represents a special subgroup, the current study compared forensic and non-forensic PwS. Forty-five PwS listened to sentences conveying four basic emotions presented in semantic or prosodic channels, in different combinations. They were asked to rate how much they agreed that the sentences conveyed a predefined emotion, focusing on one channel or on the sentence as a whole. Their performance was compared to that of 21 forensic PwS (previous study). The two groups did not differ in selective-attention. However, better emotional identification and discrimination, as well as better channel integration were found for the forensic PwS. Results have several clinical implications: difficulties in spoken-emotions processing might not necessarily relate to schizophrenia; attentional deficits might not be a risk factor for aggression in schizophrenia; and forensic schizophrenia might have unique characteristics as related to spoken-emotions processing (motivation, stimulation).

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“…However, there are several concurrent reports of hyperactivation of the amygdala (30,31), or of null results (32)(33)(34). Meta-analyses are ideally suited for disambiguating such confusion, and two of them have actually been performed in an attempt to quantify brain abnormalities during the processing of emotional stimuli in healthy first-degree relatives (35) and CHR individuals (36). While no significant differences were found in the latter group (36), widespread clusters of hyperactivation were found in healthy first-degree relatives (35).…”

Section: Introductionmentioning

confidence: 99%

Are Brain Responses to Emotion a Reliable Endophenotype of Schizophrenia? An Image-based fMRI Meta-analysis

Fiorito

Alemán

Blasi

et al. 2022

Preprint

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Background: Impaired emotion processing constitutes a key dimension of schizophrenia and a possible endophenotype of this illness. Empirical studies consistently report poorer emotion recognition performance in patients with schizophrenia as well as in individuals at enhanced risk of schizophrenia ("at risk"). fMRI studies also report consistent patterns of abnormal brain activation in response to emotional stimuli in patients, in particular decreased amygdala activation. In contrast, brain-level abnormalities in at-risk individuals are more elusive. We address this gap using an image-based meta-analysis of the fMRI literature. Methods: fMRI studies investigating brain responses to negative emotional stimuli and reporting a comparison between at-risk individuals and healthy controls were identified. Frequentist and Bayesian voxel-wise meta-analyses were performed separately, by implementing a random effect model with unthresholded group-level T-maps from individual studies as input. Results: Seventeen studies with a cumulative total of 677 at-risk individuals and 805 healthy controls were included. Frequentist analyses did not reveal significant differences between at-risk individuals and healthy controls. These results were replicated with Bayesian analyses, providing strong evidence for the absence of meaningful brain activation differences across the entire brain. Region of interest analyses specifically focusing on the amygdala confirmed the lack of group differences in this region. Conclusions: These results suggest that brain activation patterns in response to emotional stimuli are unlikely to constitute a reliable endophenotype of schizophrenia. We suggest that future studies rather focus on impaired functional connectivity as an alternative and promising endophenotype.

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Neural correlates of emotional processing in psychosis risk and onset – A systematic review and meta-analysis of fMRI studies

Cited by 19 publications

References 94 publications

Modern Views of Machine Learning for Precision Psychiatry

Modern Views of Machine Learning for Precision Psychiatry

Processing of Spoken Emotions in Schizophrenia: Forensic and Non-forensic Patients Differ in Emotional Identification and Integration but Not in Selective Attention

Are Brain Responses to Emotion a Reliable Endophenotype of Schizophrenia? An Image-based fMRI Meta-analysis

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