Mapping distributed brain function and networks with diffuse optical tomography

Functional Imaging of the Developing Brain at the Bedside Using Diffuse Optical Tomography

While histological studies and conventional magnetic resonance imaging (MRI) investigations have elucidated the trajectory of structural changes in the developing brain, less is known regarding early functional cerebral development. Recent investigations have demonstrated that resting-state functional connectivity MRI (fcMRI) can identify networks of functional cerebral connections in infants. However, technical and logistical challenges frequently limit the ability to perform MRI scans early or repeatedly in neonates, particularly in those at greatest risk for adverse neurodevelopmental outcomes. High-density diffuse optical tomography (HD-DOT), a portable imaging modality, potentially enables early continuous and quantitative monitoring of brain function in infants. We introduce an HD-DOT imaging system that combines advancements in cap design, ergonomics, and data analysis methods to allow bedside mapping of functional brain development in infants. In a cohort of healthy, full-term neonates scanned within the first days of life, HD-DOT results demonstrate strong congruence with those obtained using co-registered, subject-matched fcMRI and reflect patterns of typical brain development. These findings represent a transformative advance in functional neuroimaging in infants, and introduce HD-DOT as a powerful and practical method for quantitative mapping of early functional brain development in normal and high-risk neonates.

Comparison of procedures for co-registering scalp-recording locations to anatomical magnetic resonance images

Functional brain imaging techniques require accurate co-registration to anatomical images to precisely identify the areas being activated. Many of them, including diffuse optical imaging, rely on scalp-placed recording sensors. Fiducial alignment is an effective and rapid method for co-registering scalp sensors onto anatomy, but is quite sensitive to placement errors. Surface Euclidean distance minimization using the Levenberq-Marquart algorithm (LMA) has been shown to be very accurate when based on good initial guesses, such as precise fiducial alignment, but its accuracy drops substantially with fiducial placement errors. Here we compared fiducial and LMA co-registration methods to a new procedure, the iterative closest point-to-plane (ICP2P) method, using simulated and real data. An advantage of ICP2P is that it eliminates the need to identify fiducials and is, therefore, entirely automatic. We show that, typically, ICP2P is as accurate as fiducial-based LMA, but is less sensitive to initial placement errors. However, ICP2P is more sensitive to spatially correlated noise in the description of the head surface. Hence, the best technique for co-registration depends on the type of data available to describe the scalp and the surface defined by the recording sensors. Under optimal conditions, co-registration error using surface-fitting procedures can be reduced to ~ 3 mm.

Spatial resolution in depth for time-resolved diffuse optical tomography using short source-detector separations

Diffuse optical tomography for medical applications can require probes with small dimensions involving short source-detector separations. Even though this configuration is seen at first as a constraint due to the challenge of depth sensitivity, we show here that it can potentially be an asset for spatial resolution in depth. By comparing two fiber optic probes on a test object, we first show with simulations that short source-detector separations improve the spatial resolution down to a limit depth. We then confirm these results in an experimental study with a state-of-the-art setup involving a fast-gated single-photon avalanche diode allowing maximum depth sensitivity. We conclude that short source-detector separations are an option to consider for the design of probes so as to improve image quality for diffuse optical tomography in reflectance.

Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes

It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level-dependent signals was analyzed. A three-way analysis of variance ([Formula: see text]) indicated that the main effect of fNIRS signal depth on the correlation is significant [[Formula: see text], [Formula: see text]]. This result indicates that the MD-ICA method successfully separates fNIRS signals into spatially deep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method.

Towards real-time detection of seizures in awake rats with GPU-accelerated diffuse optical tomography

BACKGROUND

Advancement in clinically relevant studies like seizure interruption using functional neuro imaging tools has shown that specific changes in hemodynamics precede and accompany seizure onset and propagation. However, preclinical seizure experiments need to be conducted in awake animals with images reconstructed and displayed in real-time.

METHODS

This article describes an approach that can be utilized to tackle these challenges. A subject specific head interface and restraining method was designed to allow for DOT to imaging of hemodynamic changes in unanesthetized rats during evoked acute seizures. Using CUDA programming model, the finite-element based nonlinear iterative algorithm for image reconstruction was parallelized.

RESULTS

Early hemodynamic changes were monitored in real time and observed tens of seconds prior to seizure onset. Utilizing the massive parallelization offered by graphic processing units (GPU), DOT was extended to online image reconstruction within 1s.

COMPARISON WITH EXISTING METHODS

Pre-seizure state related hemodynamic changes were detected in awake rats. 3D monitoring of hemodynamic changes was performed in real time with our parallelized image reconstruction procedure.

CONCLUSION

Diffuse optical tomography (DOT) is a promising neuroimaging tool for the investigation of seizures in awake animals.

Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex

Image recovery in diffuse optical tomography (DOT) of the human brain often relies on accurate models of light propagation within the head. In the absence of subject specific models for image reconstruction, the use of atlas based models are showing strong promise. Although there exists some understanding in the use of some limited rigid model registrations in DOT, there has been a lack of a detailed analysis between errors in geometrical accuracy, light propagation in tissue and subsequent errors in dynamic imaging of recovered focal activations in the brain. In this work 11 different rigid registration algorithms, across 24 simulated subjects, are evaluated for DOT studies in the visual cortex. Although there exists a strong correlation (R(2) = 0.97) between geometrical surface error and internal light propagation errors, the overall variation is minimal when analysing recovered focal activations in the visual cortex. While a subject specific mesh gives the best results with a 1.2 mm average location error, no single algorithm provides errors greater than 4.5 mm. This work demonstrates that the use of rigid algorithms for atlas based imaging is a promising route when subject specific models are not available.

Broadband fiber-optical parametric amplification for ultrafast time-stretch imaging at 1.0 μm

We demonstrate a broadband all-fiber-optical parametric amplifier for ultrafast time-stretch imaging at 1.0 μm, featured by its compact design, alignment-free, high efficiency, and flexible gain spectrum through fiber nonlinearity- and dispersion-engineering: specifically on a dispersion-stabilized photonic-crystal fiber (PCF) to achieve a net gain over 20 THz (75 nm) and a highest gain of ~6000 (37.5 dB). Another unique feature of the parametric amplifier, over other optical amplifiers, is the coherent generation of a synchronized signal replica (called idler) that can be exploited to offer an extra 3-dB gain by optically superposing the signal and idler. It further enhances signal contrast and temporal stability. For proof-of-concept purpose, ultrahigh speed and diffraction-limited time-stretch microscopy is demonstrated with a single-shot line-scan rate of 13 MHz based on the dual-band (signal and idler) detection. Our scheme can be extended to other established bioimaging modalities, such as optical coherence tomography, near infrared fluorescence, and photoacoustic imaging, where weak signal detection at high speed is required.

Neurophotonics: non-invasive optical techniques for monitoring brain functions

The aim of this review is to present the state of the art of neurophotonics, a recently founded discipline lying at the interface between optics and neuroscience. While neurophotonics also includes invasive techniques for animal studies, in this review we focus only on the non-invasive methods that use near infrared light to probe functional activity in the brain, namely the fast optical signal, diffuse correlation spectroscopy, and functional near infrared spectroscopy methods. We also present an overview of the physical principles of light propagation in biological tissues, and of the main physiological sources of signal. Finally, we discuss the open issues in models, instrumentation, data analysis and clinical approaches.