Spread spectrum time-resolved diffuse optical measurement system for enhanced sensitivity in detecting human brain activity

Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches

Background

Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established.

Methods

Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD. Supervised classifiers (e.g., support vector machine (SVM), k-nearest neighbors (KNN)) cooperated with cross-validation were implemented to evaluate the predictive capability of selected features in a training set. Another test set that was not involved in developing the algorithms enabled the independent assessment of the model's generalization.

Findings

For the classification with the optimal fusion features, the SVM classifier achieved the highest accuracy of 75.6% ± 4.7% in the nested cross-validation, and the correct prediction rate of 78.0% with a sensitivity of 75.0% and a specificity of 81.4% in the test set. Moreover, the multiway ANOVA test on clinical and demographic factors confirmed that twenty out of 39 optimal features were significantly correlated with the MDD-distinctive consequence.

Interpretation

The abnormal prefrontal activity of MDD may be quantified as diminished relative intensity and inappropriate activation timing of hemodynamic response, resulting in an objectively measurable biomarker for assessing cognitive deficits and screening MDD at the early stage.

Funding

This study was funded by NUS iHeathtech Other Operating Expenses (R-722-000-004-731).

Image reconstruction with the chaotic fiber laser in scattering media

The reconstruction of the size, position, optical properties, and structure of the object in scattering media was realized with a chaotic fiber laser. The light from the chaotic fiber laser was split into two parts. One part was used as the detection signal to detect the object, and the other was used as the reference signal; then, the two signals were cross correlated. The attenuation of light in scattering media was attributed to scattering and absorption. The theoretical model of the peak value of cross correlation of the chaotic signals as projection data were established by the attenuation law, and the filtered back-projection algorithms were used to realize the image reconstruction. The mean squared error, the normalized mean squared error, the peak signal-to-noise ratio, and the structural similarity index of the reconstructed image were analyzed. The results show that the high resolution of the reconstructed image benefits from the high signal-to-noise ratio with the chaotic fiber laser based on a delta-like cross-correlation function.

Dual wavelength spread-spectrum time-resolved diffuse optical instrument for the measurement of human brain functional responses

Near-infrared spectroscopy has proven to be a valuable method to monitor tissue oxygenation and haemodynamics non-invasively and in real-time. Quantification of such parameters requires measurements of the time-of-flight of light through tissue, typically achieved using picosecond pulsed lasers, with their associated cost, complexity, and size. In this work, we present an alternative approach that employs spread-spectrum excitation to enable the development of a small, low-cost, dual-wavelength system using vertical-cavity surface-emitting lasers. Since the optimal wavelengths and drive parameters for optical spectroscopy are not served by commercially available modules as used in our previous single-wavelength demonstration platform, we detail the design of a custom instrument and demonstrate its performance in resolving haemodynamic changes in human subjects during apnoea and cognitive task experiments.

Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss-Scientific and clinical challenges

There has been a spurt in structural neuroimaging studies of the effect of hearing loss on the brain. Specifically, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) technologies provide an opportunity to quantify changes in gray and white matter structures at the macroscopic scale. To date, there have been 32 MRI and 23 DTI studies that have analyzed structural differences accruing from pre- or peri-lingual pediatric hearing loss with congenital or early onset etiology and postlingual hearing loss in pre-to-late adolescence. Additionally, there have been 15 prospective clinical structural neuroimaging studies of children and adolescents being evaluated for cochlear implants. The results of the 70 studies are summarized in two figures and three tables. Plastic changes in the brain are seen to be multifocal rather than diffuse, that is, differences are consistent across regions implicated in the hearing, speech and language networks regardless of modes of communication and amplification. Structures in that play an important role in cognition are affected to a lesser extent. A limitation of these studies is the emphasis on volumetric measures and on homogeneous groups of subjects with hearing loss. It is suggested that additional measures of morphometry and connectivity could contribute to a greater understanding of the effect of hearing loss on the brain. Then an interpretation of the observed macroscopic structural differences is given. This is followed by discussion of how structural imaging can be combined with functional imaging to provide biomarkers for longitudinal tracking of amplification. This article is categorized under: Developmental Biology > Developmental Processes in Health and Disease Translational, Genomic, and Systems Medicine > Translational Medicine Laboratory Methods and Technologies > Imaging.

Laplace-domain diffuse optical measurement

Time-domain diffuse optical measurement systems determine depth-resolved absorption changes by using the time of flight distribution of the detected photons. It is well known that certain feature data, such as the Laplace transform of the temporal point spread function, is sufficient for image reconstruction and diffuse optical sensing. Conventional time-domain systems require the acquisition of full temporal profiles of diffusive photons and then numerically compute the feature dataset, for example, Laplace transformed intensities for imaging applications. We have proposed a novel method for directly obtaining the Laplace transform data. Our approach can significantly improve the data acquisition speed for time-domain diffuse optical imaging. We also demonstrated that the use of negative Laplace parameters can provide enhanced sensitivity to perturbations located in deep regions.

A spread spectrum approach to time-domain near-infrared diffuse optical imaging using inexpensive optical transceiver modules

We introduce a compact time-domain system for near-infrared spectroscopy using a spread spectrum technique. The proof-of-concept single channel instrument utilises a low-cost commercially available optical transceiver module as a light source, controlled by a Kintex 7 field programmable gate array (FPGA). The FPGA modulates the optical transceiver with maximum-length sequences at line rates up to 10Gb/s, allowing us to achieve an instrument response function with full width at half maximum under 600ps. The instrument is characterised through a set of detailed phantom measurements as well as proof-of-concept in vivo measurements, demonstrating performance comparable with conventional pulsed time-domain near-infrared spectroscopy systems.