Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Study
Abstract
Neuropsychiatric disorders, such as Alzheimer's disease (AD), depression, and autism spectrum disorder (ASD), are characterized by linguistic and acoustic abnormalities, offering potential biomarkers for early detection. Despite the promise of multi-modal approaches, challenges like multi-lingual generalization and the absence of a unified evaluation framework persist. To address these gaps, we propose FEND (Foundation model-based Evaluation of Neuropsychiatric Disorders), a comprehensive multi-modal framework integrating speech and text modalities for detecting AD, depression, and ASD across the lifespan. Leveraging 13 multi-lingual datasets spanning English, Chinese, Greek, French, and Dutch, we systematically evaluate multi-modal fusion performance. Our results show that multi-modal fusion excels in AD and depression detection but underperforms in ASD due to dataset heterogeneity. We also identify modality imbalance as a prevalent issue, where multi-modal fusion fails to surpass the best mono-modal models. Cross-corpus experiments reveal robust performance in task- and language-consistent scenarios but noticeable degradation in multi-lingual and task-heterogeneous settings. By providing extensive benchmarks and a detailed analysis of performance-influencing factors, FEND advances the field of automated, lifespan-inclusive, and multi-lingual neuropsychiatric disorder assessment. We encourage researchers to adopt the FEND framework for fair comparisons and reproducible research.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the FEND framework, which systematically evaluates neuropsychiatric disorders using multi-modal and multi-lingual data. This work significantly advances the field by providing a standardized methodology for assessing the effectiveness of foundation models in detecting complex disorders, thereby enhancing reproducibility and comparability in future research.
Methodology
The proposed FEND framework is a significant advancement in the evaluation of neuropsychiatric disorders, integrating multi-modal data (speech and text) across various languages and age groups. The methodology is well-structured, employing both mono-modal and multi-modal analyses with a clear focus on addressing existing gaps in the literature, such as modality imbalance and dataset heterogeneity. The use of foundation models for feature extraction is innovative, and the systematic evaluation across 13 datasets provides a robust basis for comparison. However, the reliance on classical fusion methods could be seen as a limitation, as newer techniques may yield better results.
Results & evaluation
The experimental setup is comprehensive, with detailed benchmarks established for each disorder (AD, depression, ASD). The results indicate that while multi-modal fusion shows promise, it does not consistently outperform the best mono-modal models, particularly in the case of ASD. This highlights the challenges faced in achieving effective multi-modal integration, particularly with heterogeneous datasets. The analysis of performance-influencing factors is thorough, providing valuable insights for future research.
Reproducibility
The paper emphasizes reproducibility through the commitment to releasing code and establishing standardized evaluation metrics. The uniform training protocol across experiments enhances comparability, which is crucial for the integrity of the benchmarks. However, the absence of a publicly accessible demo or project URL limits immediate reproducibility for external researchers.
Limitations
Key limitations include the underperformance of multi-modal fusion in certain contexts, particularly with ASD due to dataset heterogeneity. Additionally, the framework may benefit from exploring more advanced fusion techniques beyond classical methods. The paper could also address the potential biases in datasets and their implications for generalizability across diverse populations.
Broader impact
The FEND framework has significant implications for the early detection and assessment of neuropsychiatric disorders, potentially leading to improved diagnostic tools and interventions. By addressing multi-lingual and multi-modal challenges, the framework could facilitate broader applications in clinical settings and contribute to the development of more inclusive mental health technologies. The main contribution of this paper is the introduction of the FEND framework, which systematically evaluates neuropsychiatric disorders using multi-modal and multi-lingual data. This work significantly advances the field by providing a standardized methodology for assessing the effectiveness of foundation models in detecting complex disorders, thereby enhancing reproducibility and comparability in future research.
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