What aspects of mindfulness and emotion regulation underpin self-harm in individuals with borderline personality disorder?

Natividad, Ana; Huxley, Elizabeth; Townsend, Michelle L.; Grenyer, Brin F. S.; Pickard, Judy A.

doi:10.1080/09638237.2023.2182425

Cited by 4 publications

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mental health is undeniably an important issue in today's world, as in 2019, the WHO found that over 970 million people were living with a mental disorder 1 . Mental disorders have devastating impacts on individuals, severely impairing important functions of the brain, such as emotion regulation [62][63][64] , behavior 52 , and general cognition 52 . Poor mental health has serious consequences, the worst of them being suicide.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for Mental Disorder Prediction

Greiner,

Zhang

2024

Preprint

View full text Add to dashboard Cite

Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting inconditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual’s mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5% to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by nearly 10%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self-reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for Mental Disorder Prediction

Greiner,

Zhang

2024

Preprint

View full text Add to dashboard Cite

show abstract

Mental health in adolescence: the role of schools-based social emotional teaching

Kuyken

Blakemore

Byford

et al. 2023

Journal of Mental Health

View full text Add to dashboard Cite

Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction

Greiner,

Zhang

2024

Brain Inf.

View full text Add to dashboard Cite

Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting in conditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual’s mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5 to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by 4%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self- reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

show abstract

What aspects of mindfulness and emotion regulation underpin self-harm in individuals with borderline personality disorder?

Cited by 4 publications

References 55 publications

Multi-Modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for Mental Disorder Prediction

Multi-Modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for Mental Disorder Prediction

Mental health in adolescence: the role of schools-based social emotional teaching

Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction

Contact Info

Product

Resources

About