Parkinson’s disease (PD) is a prevalent neurodegenerative disorder characterized primarily by motor dysfunction, manifesting symptoms such as resting tremors, rigidity, bradykinesia, and postural instability (Mazzoni et al., 2012; Cheng and Su, 2020). As the second most common neurodegenerative condition in the elderly, the early diagnosis of PD holds paramount importance for timely intervention and improving patient quality of life (Aarsland et al., 2021). However, the early symptoms of PD can be confounded with other movement disorders such as Multiple System Atrophy, drug-induced Parkinsonism, and vascular Parkinsonism, making accurate early diagnosis a significant challenge (Tolosa et al., 2021). Currently, the diagnosis of PD heavily relies on clinical manifestations and the judgment of experienced clinicians, a method that may lack sensitivity and specificity, particularly in the early stages of the disease (Pahwa and Lyons, 2010; Postuma et al., 2015; Adler et al., 2021). An accurate and early diagnosis is crucial for paving the way for timely interventions, significantly enhancing the patient’s quality of life, and decelerating the progression of the disease (Welte et al., 2015).

With the rapid advancement of neuroimaging technologies, functional imaging has emerged as an essential tool for diagnosing and monitoring neurological disorders (Weiller et al., 2006). Functional near-infrared spectroscopy (fNIRS) stands out as a non-invasive, cost-effective, and user-friendly neuroimaging tool, showing potential in diagnosing and monitoring various neurological conditions (Sun et al., 2018). fNIRS monitors and records changes in cerebral blood oxygenation in real-time, reflecting the activity dynamics of cortical neurons. Its robust resistance to motion artifacts, coupled with superior temporal resolution compared to functional magnetic resonance imaging (fMRI) and better spatial resolution relative to electroencephalogram (EEG), positions fNIRS as a promising tool, particularly in identifying early cognitive impairments in PD patients (Oku and Sato, 2021; Pereira et al., 2023; Su et al., 2023). Current studies utilizing fNIRS have identified differences in frontal cortex activation in PD patients during motor tasks compared to healthy subjects (Feng et al., 2023).

Moreover, the expanding domain of artificial intelligence offers novel opportunities for employing fNIRS in diagnosing clinical disorders (Eastmond et al., 2022). Research indicates that machine learning algorithms can effectively differentiate various brain activities and emotional states based on fNIRS signals, suggesting the potential of this technology for early diagnosis and treatment monitoring of neurological diseases (Qiu et al., 2022a,b). Additionally, scholars have employed machine learning algorithms to unearth latent patterns and features in fNIRS data, developing a novel approach to understanding brain activity (Andreu-Perez et al., 2021; Oku and Sato, 2021; Eastmond et al., 2022). These studies contribute significantly to advancing neuroscience research and lay the groundwork for future clinical applications. However, to date, there has been a paucity of literature on constructing early diagnostic models for PD patients using fNIRS datasets.

This study pioneers the exploration of the feasibility of using fNIRS technology in conjunction with machine learning algorithms for the early diagnosis of Parkinson’s Disease. In this context, our research aims to evaluate the feasibility and effectiveness of integrating fNIRS technology with machine learning algorithms for the early diagnosis of PD. By doing so, we strive to fill a critical gap in the current diagnostic approach, leveraging the strengths of fNIRS in capturing cortical activation patterns and the analytical power of machine learning in deciphering complex data. This synergistic approach is anticipated to enhance the diagnostic accuracy for PD, especially in its early stages, thereby contributing significantly to the field of neurology and offering a beacon of hope for those afflicted by this debilitating condition.

The early diagnosis of PD is paramount for effective patient management and prognosis, presenting a significant challenge within the medical diagnostic realm (Tolosa et al., 2021). Early detection not only significantly enhances disease management and treatment outcomes but also mitigates symptoms and decelerates disease progression (Pahwa and Lyons, 2010). PD is principally characterized by the progressive degeneration of neurons in the substantia nigra pars compacta, manifesting a range of motor and non-motor symptoms (Aarsland et al., 2021). The subtlety and lack of specificity of early symptoms often lead to the failure of traditional clinical diagnostic methods in accurately identifying PD at its onset (Adler et al., 2021). Presently, the diagnosis of early-stage PD heavily depends on medical observation and the assessment of clinical symptoms. However, these conventional approaches are susceptible to subjectivity, potentially culminating in misdiagnoses (Tolosa et al., 2021). The swift advancement of brain imaging technologies, including non-invasive techniques such as Positron Emission Tomography (PET), fMRI, and EEG, has led to their increased integration in detecting PD (Pagano et al., 2016). Concurrently, the evolution of artificial intelligence and pattern recognition technologies has rendered computer-assisted diagnostic tools indispensable in the early diagnosis of PD (Ripic et al., 2023). Despite the promise shown by the integration of advanced ML or DL algorithms with EEG signals, which exhibit marked differences in brain activation and functional connectivity between PD patients and healthy controls, several domains warrant further investigation to overcome the challenges of single-modality data reliance and the opaque nature of decision-making processes in current PD diagnostic models. Notably, most studies gather data with patients at rest, omitting motion or function-related data. This omission is significant as rehabilitation medicine, unlike clinical medicine, prioritizes functional impairments (Hudson, 2020). Moreover, the reliance on single-modality data in previous studies may limit a comprehensive understanding of task-state brain functional characteristics in PD patients (Makarious et al., 2022). Furthermore, the opacity of decision-making processes in current PD diagnostic models—the so-called “black box” effect—is notable. Although numerous ML and DL frameworks show promise in PD detection, a lack of model interpretability impedes understanding the diagnostic rationale, thereby hindering clinical application (Cruz et al., 2023).

In recent decades, the use of fNIRS in cognitive neuroscience has surged, benefiting from its advantages over other neuroimaging modalities like fMRI and EEG/MEG (Su et al., 2023). Notably, fNIRS is harmless, highly tolerant to physical movement, and extremely portable, making it suitable for all potential participant groups and experimental settings, both in and out of the laboratory (Grama et al., 2023). Exploring the combination of fNIRS with machine learning algorithms for early PD diagnosis represents a novel approach in the field of neurodegenerative disease diagnostics (Oku and Sato, 2021). In our study, the SVM model demonstrated excellent performance in differentiating PD patients from control group, with an accuracy of 85% and an F1 score of 0.85, highlighting its diagnostic accuracy. These results are consistent with previous studies, emphasizing the need for innovative non-invasive diagnostic methods for early PD detection (Krokidis et al., 2022). Moreover, SVM models have emerged as potent instruments in biomedical research, especially in classification and regression tasks involving high-dimensional data. The models’ capacity to identify the optimal hyperplane that maximizes the margin between classes in the feature space is crucial for precise prediction and classification in complex biomedical datasets (Cortes and Vapnik, 1995; Lundberg and Lee, 2017). Such a fundamental characteristic of SVMs facilitates the handling of the nuances in neuroimaging data, where distinguishing between healthy individuals and patients with neurological disorders, like PD, is often subtle and embedded within extensive datasets. The deployment of SVM models in neuroimaging data analysis has significantly propelled the field of disease diagnosis forward, offering a non-invasive and efficient means to early detect and differentiate neurological conditions. For example, neuroimaging techniques such as functional MRI and structural MRI produce voluminous data that encapsulates the functional and structural aspects of the brain. Analyzing this data with SVM enables the identification of patterns and biomarkers associated with diseases like PD, Alzheimer’s Disease, and schizophrenia, among others (Orrù et al., 2012; Steardo et al., 2020).

Within the PD context, SVM models have played a pivotal role in differentiating between PD patients and control group by analyzing fNIRS data for subtle changes imperceptible to the human eye. This capability is essential for the early diagnosis of PD, where timely intervention can significantly influence disease management and progression. Studies employing SVM models in conjunction with neuroimaging data have demonstrated high diagnostic accuracy and specificity, highlighting the models’ efficacy in biomedical applications (Buchlak et al., 2019). The SVM model’s high accuracy and F1 score further validate the effectiveness of machine learning methods in managing complex biomedical data.

One significant hurdle in utilizing machine learning models is their inherent “black box” nature, which obscures the decision-making process (Rudin, 2019). Addressing this, the implementation of interpretability techniques emerges as essential. Technologies such as SHAP play a pivotal role in demystifying the logic behind model predictions. Rooted in cooperative game theory, SHAP offers a comprehensive framework to elucidate any machine learning model’s output by assigning an importance value to each feature for a given prediction. This approach not only clarifies how predictive models function but also facilitates the discovery of biomarkers and critical attributes relevant to conditions like PD (Noble, 2006). Applying SHAP to SVM models, particularly in neuroimaging data analysis, represents a significant advancement towards unraveling the intricate biological and pathological phenomena underlying diseases. For instance, in PD diagnostics, SHAP values can identify brain regions and signals crucial for distinguishing PD patients from healthy controls, offering insights that not only improve model transparency but also guide further research and targeted therapeutic strategies (Molnar, 2020).

Specifically, during cognitive tasks, the notable influence of channels CH01, CH04, CH05, and CH08 in FPC indicates a deviation in brain activation patterns in PD patients relative to healthy individuals. This observation is instrumental in dissecting the neural mechanisms of PD, potentially shaping the development of precise therapies or interventions. The FPC’s integral role in high-level cognitive functions, such as decision-making, problem-solving, and social cognition, underscores its significance in complex cognitive processes, rendering it a vital focus for neurodegenerative disease research. Its strategic relevance is amplified by its connectivity with diverse brain networks, facilitating the integration of cognitive and emotional data to influence behavior and decision-making (Gilbert et al., 2006; Burgess et al., 2007). Concentrating on the FPC might shed light on the early cognitive and neural alterations linked to PD, extending the focus beyond conventional motor symptoms. Prior studies have emphasized the FPC’s role in cognitive functionalities and its potential alterations due to PD pathology (Goldman et al., 2018; Aarsland et al., 2021). Investigating the FPC’s role in executive functions could reveal how PD impacts brain regions tasked with high-order cognitive processes, significantly enriching our understanding of the disease’s progression and its impact on patient quality of life (Daffner, 2010). The emphasis on the FPC in future research is warranted not merely due to its pivotal role in cognitive functions and decision-making but also for its potential to deepen our understanding of PD. Such targeted research promises to broaden diagnostic, therapeutic, and rehabilitative approaches, significantly refining PD management strategies. By exploring the FPC’s involvement more thoroughly, we can discover new avenues for early diagnosis, personalized medicine, and targeted interventions, ultimately enhancing PD patients’ prognosis and quality of life. Moreover, network analyses employing resting-state functional MRI (rs-fMRI) are increasingly utilized in PD patient studies to identify and substantiate neurodegenerative disease associations (Albano et al., 2022). These networks serve not only as markers for disease processes but also as supplementary tools for clinical diagnosis and therapeutic trial screenings (Filippi et al., 2019). A study that applied SHAP in interpreting SVM-based neuroimaging analysis for PD underscored the FPC’s substantial role, offering a profound insight into the disease’s neuroanatomical foundations. Such interpretability is vital for bridging the gap between machine learning predictions and clinical decision-making, enabling a more informed and nuanced approach to disease diagnosis and management (Rodriguez-Perez and Bajorath, 2020).

Despite its advantages, the application of SHAP in enhancing the interpretability of SVM models in biomedical research is not devoid of challenges. The computational complexity of calculating SHAP values, especially for large datasets common in neuroimaging studies, poses a significant hurdle. Additionally, while SHAP provides a more intuitive understanding of model predictions, translating these insights into actionable clinical strategies requires careful consideration and further validation. Future research should focus on developing more efficient algorithms for computing SHAP values and exploring methods to integrate these interpretations into clinical workflows seamlessly. Moreover, the potential of SHAP to uncover novel biomarkers and therapeutic targets warrants further exploration, with interdisciplinary collaboration between computer scientists, biologists, and clinicians being pivotal for leveraging these insights to improve patient care.

Despite encouraging results, early PD diagnosis remains a complex and evolving field. The non-specific nature of early PD symptoms and the lack of reliable biomarkers contribute to this complexity (Ma et al., 2023). This study highlights fNIRS’s potential in identifying distinct cerebral blood flow patterns between PD patients and control group, underlining its promise as an objective indicator for early PD diagnosis. Yet, it’s crucial to clarify that our findings primarily suggest the potential utility of fNIRS, rather than definitively establishing its diagnostic capability. Further research is necessary to validate fNIRS as a reliable diagnostic tool for PD, emphasizing the need for integrating it with other diagnostic modalities and exploring larger, more diverse datasets. We must also acknowledge limitations in our study, such as potential biases in sample selection and unconsidered variables like lifestyle and genetic factors that may affect fNIRS data and diagnostic accuracy. Future research should focus on refining these diagnostic procedures, considering a wider range of machine learning models and larger datasets to improve the accuracy and reliability of early PD diagnosis.

In conclusion, this study paves the way for future research to explore more comprehensive machine learning models and integrate larger, more diverse datasets. Advances in neuroimaging and machine learning hold great promise for improving early PD diagnosis, potentially leading to better patient outcomes and more effective management strategies. Future research must also address the potential biases and unconsidered variables identified in this study to develop more robust diagnostic models.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by Ethics Committee of Beijing Rehabilitation Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any identifiable images or data included in this article.

PH: Data curation, Formal analysis, Methodology, Project administration, Resources, Software, Visualization, Writing – original draft. YJ: Data curation, Investigation, Project administration, Resources, Software, Writing – original draft, Writing – review & editing. JW: Writing – original draft, Data curation. CW: Writing – original draft, Project administration, Methodology, Formal analysis, Data curation. YL: Writing – review & editing, Software, Methodology, Formal analysis. BF: Data curation, Formal analysis, Methodology, Resources, Supervision, Writing – original draft. HW: Formal analysis, Investigation, Methodology, Writing – original draft. YW: Writing – review & editing, Writing – original draft, Validation, Supervision, Software, Investigation, Conceptualization. SQ: Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Formal analysis, Data curation, Conceptualization.