Human brain tissue identification using coherent anti-Stokes Raman scattering spectroscopy and diffuse reflectance spectroscopy for deep brain stimulation surgery.

Significance: We assess the feasibility of using diffuse reflectance spectroscopy (DRS) and coherent anti-Stokes Raman scattering spectroscopy (CARS) as optical tools for human brain tissue identification during deep brain stimulation (DBS) lead insertion, thereby providing a promising avenue for additional real-time neurosurgical guidance. Aim: We developed a system that can acquire CARS and DRS spectra during the DBS surgery procedure to identify the tissue composition along the lead trajectory. Approach: DRS and CARS spectra were acquired using a custom-built optical probe integrated in a commercial DBS lead. The lead was inserted to target three specific regions in each of the brain hemispheres of a human cadaver. Spectra were acquired during the lead insertion at constant position increments. Spectra were analyzed to classify each spectrum as being from white matter (WM) or gray matter (GM). The results were compared with tissue classification performed on histological brain sections. Results: DRS and CARS spectra obtained using the optical probe can identify WM and GM during DBS lead insertion. The tissue composition along the trajectory toward a specific target is unique and can be differentiated by the optical probe. Moreover, the results obtained with principal component analysis suggest that DRS might be able to detect the presence of blood due to the strong optical absorption of hemoglobin. Conclusions: It is possible to use optical measurements from the DBS lead during surgery to identify WM and GM and possibly the presence of blood in human brain tissue. The proposed optical tool could inform the surgeon during the lead placement if the lead has reached the target as planned. Our tool could eventually replace microelectrode recordings, which would streamline the process and reduce surgery time. Further developments are required to fully integrate these tools into standard clinical procedures.

Wavelength-swept spontaneous Raman spectroscopy system improves fiber-based collection efficiency for whole brain tissue classification.

Significance: Raman spectroscopy is a valuable technique for tissue identification, but its conventional implementation is hindered by low efficiency due to scattering. Addressing this limitation, we are further developing the wavelength-swept Raman spectroscopy approach. Aim: We aim to enhance Raman signal detection by employing a laser capable of sweeping over a wide wavelength range to sequentially excite tissue with different wavelengths, paired with a photodetector featuring a fixed narrow-bandpass filter for collecting the Raman signal at a specific wavelength. Approach: We experimentally validate our technique using a fiber-based swept-source Raman spectroscopy setup. In addition, simulations are conducted to assess the efficacy of our approach in comparison with conventional spectrometer-based Raman spectroscopy. Results: Our simulations reveal that the wavelength-swept configuration leads to a significantly stronger signal compared with conventional spectrometer-based Raman spectroscopy. Experimentally, our setup demonstrates an improvement of at least 200× in photon detection compared with the spectrometer-based setup. Furthermore, data acquired from different regions of a fixed monkey brain using our technique achieves 99% accuracy in classification via k -nearest neighbor analysis. Conclusions: Our study showcases the potential of wavelength-swept Raman spectroscopy for tissue identification, particularly in highly scattering media, such as the brain. The developed technique offers enhanced signal detection capabilities, paving the way for future in vivo applications in tissue characterization.

Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data.

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 restingstate 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.

Tools and tutorial on practical ray tracing for microscopy.

Significance: An advanced understanding of optical design is necessary to create optimal systems but this is rarely taught as part of general curriculum. Compounded by the fact that professional optical design software tools have a prohibitive learning curve, this means that neither knowledge nor tools are easily accessible. Aim: In this tutorial, we introduce a raytracing module for Python, originally developed for teaching optics with ray matrices, to simplify the design and optimization of optical systems. Approach: This module is developed for ray matrix calculations in Python. Many important concepts of optical design that are often poorly understood such as apertures, aperture stops, and field stops are illustrated. Results: The module is explained with examples in real systems with collection efficiency, vignetting, and intensity profiles. Also, the optical invariant, an important benchmark property for optical systems, is used to characterize an optical system. Conclusions: This raytracing Python module will help improve the reader's understanding of optics and also help them design optimal systems.