Elahe Parham

Parkinson’s disease (PD) is characterized by the progressive loss of dopamine (DA) neurons of the substantia nigra pars compacta (SNc), leading to the appearance of debilitating motor symptoms, in addition to nonmotor symptoms.^1,2 This neurodegenerative disorder is incurable, and although some motor symptoms can be alleviated by the administration of the DA metabolic precursor levodopa, such pharmacotherapy eventually leads to adverse side effects in the form of dyskinesias, leaving very limited options for alternative treatments.^{3 – 6} As the second most common neurodegenerative disease next to Alzheimer’s disease (AD), it is a burden on healthcare services worldwide, especially with the rapidly increasing aging population.⁷ For this reason, research efforts are being made to find alternative ways to detect PD, alleviate its symptoms, slow its progression, and find a cure.

We introduce a wavelength-swept Raman spectroscopy technique that enhances signal detection compared to conventional Raman spectrometers using a sweeping laser and wide-area detector. Validated experimentally, it enables rapid, fiber-based data acquisition for brain tissue differentiation.

SignificanceWe introduce a fast Raman spectroscopy (SSRS) system that reduces acquisition time and enhances data quality, providing a breakthrough in SSRS for real-time applications. We demonstrate its utility in differentiating brain tissue regions based on lipid and protein content.AimOur primary goal was to develop a fast SSRS system that enables rapid data acquisition for in vivo applications. We aimed to investigate its effectiveness in differentiating brain tissue types by analyzing lipid and protein content, ultimately enhancing classification accuracy and supporting advancements in medical diagnostics.ApproachWe implemented an optimized circuit and signal processing technique to reduce high-frequency noise and improve signal-to-noise ratio. Brain tissue measurements were validated against staining models, and classification accuracy was tested with principal component analysis (PCA) and support vector machine (SVM).ResultsOur SSRS system captures spectra in 1 s which is significantly faster than similar systems. This rapid method enables real-time monitoring and accurate classification of brain regions based on lipid–protein content, confirmed by neurofilament and Nissl staining correlations (R2=0.75 and 0.55, respectively). Tissue classification showed 80.20% accuracy using spectral intensity at the wavenumbers associated with C–H, CH3, and CH2 vibrations and 81.23% accuracy using PCA-derived features (PC1, PC2, and PC3).ConclusionsThe fast-SSRS system marks a significant advance in Raman spectroscopy, improving speed and data quality. Our setup captures finer spectral details, facilitating reliable differentiation of tissue types, as verified by staining methods and PCA. This method shows promise for real-time tissue analysis and medical diagnostics, outperforming traditional Raman techniques in speed and data throughput.

Diffuse reflectance spectroscopy (DRS) is a promising technique for non-invasive monitoring of tissue oxygen saturation (StO₂). However, the interpretation of DRS data can be complicated by the presence of confounding factors such as the volume fraction of blood, tissue scattering, and lipid content which both absorb and scatter. Principal component analysis (PCA) is a multivariate statistical method that can help overcome these challenges by extracting relevant information from complex datasets and providing new dimensions used to estimate parameters such as concentrations. In this study, we present a PCA-based algorithm for estimating retinal StO₂ from DRS measurements. We evaluated the performance of our algorithm using simulated data and experimental measurements on a retinal tissue phantom model. Our results show that the PCA-based algorithm can estimate the value of StO₂ with a root-mean-square error of 6.38% in the presence of confounding factors. Our study demonstrates the potential of PCA as a powerful tool for extracting the concentration of components from complex DRS.

Raman scattering is a phenomenon in which light is inelastically scattered by a material in a way that shifts the frequency of the light to different wavelengths. This shift in frequency can be used to determine the vibrational modes of the material and is therefore a powerful tool for chemical analysis and material characterization.^1,2 In Raman spectroscopy, a laser beam is used to excite the sample molecules to a virtual higher energy state. The excited molecules then re-emit light with a different energy when they return to a vibrational excited state, and the difference in frequency between the excitation and scattered light is used to identify chemical bonds and the structure of the sample.^{3 – 5} In Raman spectroscopy, the basic setup typically includes a monochromatic light source, usually in the visible or near-infrared range; a sample holder; and a spectrometer as the detector.³

Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease.¹ The primary clinical signs of PD are motor symptoms that include resting tremors, muscular rigidity, and slowness of movements.² The initial treatment of PD usually starts by administering the dopamine precursor l-dopa. However, over time, this pharmacological treatment becomes less effective, and most patients will eventually suffer from l-dopa-induced dyskinesia.^3,4 For these patients, deep brain stimulation (DBS) surgery is often the recommended treatment option.⁵

Engineers and scientists operating in fields such as neurophotonics, remote sensing, medicine, or even industrial manufacturing are often tasked with building or modifying imaging systems for customized applications. They must often resort to expert advice from optical designers to obtain clear and quantitative answers to their specific optics problems. Optical designers, with their knowledge and specialized software tools such as Zemax, Oslo, or CODE V, can indeed provide these answers. However, third-party expert advice can be prohibitively expensive, difficult to interpret without basic knowledge, and can lead to sub-optimal solutions if the requirements are miscommunicated to the external consultant. For the general engineer, scientist, and especially for trainees, learning these professional software tools is often not an option because of the abundance of features targeted to advanced optical designers that end up confusing trainees and non-experts alike. Yet, there should be a solution, between simplified analytical solutions and expert tools that can help non-experts tackle moderately complicated problems by themselves to obtain quantitative answers. It is therefore the purpose of the present tutorial to provide both the knowledge and the tools for this audience.

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman’s general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and resting-state fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.