Modern Views of Machine Learning for Precision Psychiatry

Chen, Zhe; Kulkarni, Prathamesh M.; Galatzer‐Levy, Isaac R.; Bigio, Benedetta; Nasca, Carla

doi:10.36227/techrxiv.19502131

Cited by 4 publications

(2 citation statements)

References 171 publications

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“…( 9) Re-referencing EEG signals to the common average. (10) The filtering of EEG signals to four canonical frequency ranges: theta (4-7 Hz), alpha (8-12 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and low gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50).…”

Section: Eeg Preprocessingmentioning

confidence: 99%

“…First, with an autoencoder-based reconstruction model, we developed a healthy norm of rsEEG connectivity which quantitatively measures the individual deviations of MDD patients in the psychopathological dimensions. Second, to tailor the individual deviations to antidepressant responses, we employed a predictive modeling framework that captures treatment response-correlated individual deviations under supervision, taking advantage of machine learning techniques as they have shown promising capability in precision psychiatry (35). Predictive modeling with the quantified FC deviations was implemented to achieve individual-level prediction of antidepressant responses.…”

Section: Introductionmentioning

confidence: 99%

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Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response

Tong

Xie

et al. 2023

Preprint

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Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient response to treatment. The approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.

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Section: Eeg Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response

Tong

Xie

et al. 2023

Preprint

Self Cite

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Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

Feng,

2024

Brain Sciences

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Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5–10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.

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Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

et al. 2023

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This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.

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Modern Views of Machine Learning for Precision Psychiatry

Cited by 4 publications

References 171 publications

Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response

Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

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