Fear-induced brain activations distinguish anxious and trauma-exposed brains

Wen, Zhenfu; Marin, Marie‐France; Blackford, Jennifer Urbano; Chen, Zhe; Milad, Mohammed R.

doi:10.1038/s41398-020-01193-7

Cited by 8 publications

(7 citation statements)

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“…Multimodal data fusion techniques can reveal how data modalities interact (49) and improve SUDEP prediction (50). Greater sample size would greatly improve clinical prognosis and decision (51)(52)(53).…”

Section: Discussionmentioning

confidence: 99%

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

et al. 2022

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ObjectiveSudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls.MethodsThis multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve.ResultsThe logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction.ConclusionsOur results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

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Section: Discussionmentioning

confidence: 99%

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

et al. 2022

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“…Using a neuroimaging cohort study (n = 304 adults, 92 patients with anxiety, 74 trauma-exposed individuals, 138 matched controls), Wen et al. 158 examined how the fMRI activations of 10 brain regions that were commonly activated during fear conditioning and extinction ( Figure 4 A) might distinguish anxious or trauma-exposed brains from controls. They proposed a CNN classifier ( Figure 4 B) to map fear-induced fMRI activities in space and time to a prediction probability score indicating that the subject belongs to the anxious group.…”

Section: How Can ML Help Psychiatry?mentioning

confidence: 99%

“… (E) Distribution of AUCs based on brain activations within the 10-node fear randomly selected brain regions (figures were adapted from Wen et al. 158 with permission). …”

Section: How Can ML Help Psychiatry?mentioning

confidence: 99%

Modern views of machine learning for precision psychiatry

Chen¹,

Kulkarni²,

Galatzer‐Levy³

et al. 2022

Patterns

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“…Case Study 3: Classification of anxious vs. non-anxious brains from fear extinction learning task-based fMRI. Using a neuroimaging cohort study (n = 304 adults, 92 anxiety patients, 74 trauma-exposed individuals, 138 matched controls), Wen et al [153] examined how the fMRI activations of 10 brain regions that were commonly activated during fear conditioning and extinction (Fig. 4A) might distinguish anxious or trauma-exposed brains from controls.…”

Section: Case Studiesmentioning

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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Fear-induced brain activations distinguish anxious and trauma-exposed brains

Cited by 8 publications

References 68 publications

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Modern views of machine learning for precision psychiatry

Modern Views of Machine Learning for Precision Psychiatry

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