In Vivo Insights: Near-Infrared Photon Sampling of Reflectance Spectra from Cranial and Extracranial Sites in Healthy Individuals and Patients with Essential Tremor

• Published in: Photonics Vol. 11; no. 11; p. 1025
• Main Authors: Currà, Antonio, Gasbarrone, Riccardo, Gattabria, Davide, Bonifazi, Giuseppe, Serranti, Silvia, Greco, Daniela, Missori, Paolo, Fattapposta, Francesco, Picciano, Alessandra, Maffucci, Andrea, Trompetto, Carlo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Algorithms; Analysis; Artificial intelligence; Biomarkers; Brain stem; Calibration; Cerebral cortex; chemometric analysis; Chemometrics; Data mining; Diagnosis; Discriminant analysis; Dystonia; essential tremor (ET); Human motion; Human tissues; in vivo analysis; In vivo methods and tests; Infrared analysis; Infrared signatures; Infrared spectra; Infrared spectroscopy; Least squares; Machine learning; Medical history; Medical imaging; Movement disorders; Near infrared radiation; near-infrared spectroscopy (NIRS); Neuroimaging; Neurophysiology; Optical properties; Patients; Quality of life; Reflectance; reflectance spectra; Regression analysis; Regression models; Sensors; Skin; Skull; Spectral signatures; Spectroscopic analysis; Spectroscopy; Spectrum analysis; Tremor; Tremor (Muscular contraction); Tremors; Italy; United States > US; Massachusetts
• ISSN: 2304-6732; 2304-6732
• DOI: 10.3390/photonics11111025

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients.