Zi Jing Lin

Comparison of neural correlates of risk decision making between genders: an exploratory fNIRS study of the Balloon Analogue Risk Task (BART).

Functional magnetic resonance imaging (fMRI) research rarely reports gender differences in the neural correlates of risk decision making due to small sample sizes. In this functional near-infrared spectroscopy (fNIRS)-based imaging study of active and passive risk decision making, gender differences in oxygenated hemoglobin (HbO) concentration changes were investigated in the prefrontal cortex (PFC) of healthy adults. Forty adult participants (25-44 years; males=23) completed two sets of 15 balloon trials in active and passive decision making modes of the Balloon Analogue Risk Task (BART). In active mode, participants chose the number of balloon inflations, decided when to collect money, or risked accrued money if balloons exploded. BART is psychometrically well established and has predictive validity to real-world risk taking. The blocked experimental design and modification of BART for fNIRS were guided by a previous fMRI study that examined the neural correlates of risk decision making in young adults [Rao, H., Korczykowski, M., Pluta, J., Hoang, A., Detre, J.A., 2008. Neural correlates of voluntary and involuntary risk taking in the human brain: An fMRI study of the Balloon Analog Risk Task (BART). NeuroImage 42, 902-910]. Our findings were consistent with the previous fMRI study: no or little PFC activation during passive mode but strong PFC activation during active wins and losses among total sample. Active losses in females were associated with more significant bilateral activation in dorsal lateral prefrontal cortex (DLPFC) than males; no significant gender differences were found in DLPFC activation during active wins. Gender differences existed in direction and strength of correlations between BART behavioral and hemodynamic data. This study shows that use of fNIRS is a feasible, accessible, and less costly way to achieve adequate study power and investigate gender differences in neural correlates of risk decision making.

Development of a compensation algorithm for accurate depth localization in diffuse optical tomography

Diffuse optical tomography endures poor depth localization, since its sensitivity decreases severely with increased depth. In this study, we demonstrate a depth compensation algorithm (DCA), which optimally counterbalances the decay nature of light propagation in tissue so as to accurately localize absorbers in deep tissue. The novelty of DCA is to directly modify the sensitivity matrix, rather than the penalty term of regularization. DCA is based on maximum singular values (MSVs) of layered measurement sensitivities; these MSVs are inversely utilized to create a balancing weight matrix for compensating the measurement sensitivity in increased depth. Both computer simulations and laboratory experiments were performed to validate DCA. These results demonstrate that one (or two) 3-cm-deep absorber(s) can be accurately located in both lateral plane and depth within the laboratorial position errors.

Comprehensive investigation of three-dimensional diffuse optical tomography with depth compensation algorithm

A depth compensation algorithm (DCA) can effectively improve the depth localization of diffuse optical tomography (DOT) by compensating the exponentially decreased sensitivity in the deep tissue. In this study, DCA is investigated based on computer simulations, tissue phantom experiments, and human brain imaging. The simulations show that DCA can largely improve the spatial resolution of DOT in addition to the depth localization, and DCA is also effective for multispectral DOT with a wide range of optical properties in the background tissue. The laboratory phantom experiment demonstrates that DCA can effectively differentiate two embedded objects at different depths in the medium. DCA is further validated by human brain imaging using a finger-tapping task. To our knowledge, this is the first demonstration to show that DCA is capable of accurately localizing cortical activations in the human brain in three dimensions.

Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography

Abstract: In diffuse optical tomography (DOT), researchers often face challenges to accurately recover the depth and size of the reconstructed objects. Recent development of the Depth Compensation Algorithm (DCA) solves the depth localization problem, but the reconstructed images commonly exhibit over-smoothed boundaries, leading to fuzzy images with low spatial resolution. While conventional DOT solves a linear inverse model by minimizing least squares errors using L2 norm regularization, L1 regularization promotes sparse solutions. The latter may be used to reduce the over-smoothing effect on reconstructed images. In this study, we combined DCA with L1 regularization, and also with L2 regularization, to examine which combined approach provided us with an improved spatial resolution and depth localization for DOT. Laboratory tissue phantoms were utilized for the measurement with a fiber-based and a camera-based DOT imaging system. The results from both systems showed that L1 regularization clearly outperformed L2 regularization in both spatial resolution and depth localization of DOT. An example of functional brain imaging taken from human in vivo measurements was further obtained to support the conclusion of the study.

Resting-state functional connectivity assessed with two diffuse optical tomographic systems

Functional near-infrared spectroscopy (fNIRS) is recently utilized as a new approach to assess resting-state functional connectivity (RSFC) in the human brain. For any new technique or new methodology, it is necessary to be able to replicate similar experiments using different instruments in order to establish its liability and reproducibility. We apply two different diffuse optical tomographic (DOT) systems (i.e., DYNOT and CW5), with various probe arrangements to evaluate RSFC in the sensorimotor cortex by utilizing a previously published experimental protocol and seed-based correlation analysis. Our results exhibit similar spatial patterns and strengths in RSFC between the bilateral motor cortexes. The consistent observations are obtained from both DYNOT and CW5 systems, and are also in good agreement with the previous fNIRS study. Overall, we demonstrate that the fNIRS-based RSFC is reproducible by various DOT imaging systems among different research groups, enhancing the confidence of neuroscience researchers and clinicians to utilize fNIRS for future applications.

Atlas-guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision-making responses in young adults

Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)‐based analysis can overcome this limitation. In this study, we combine the atlas‐guided 3D DOT image reconstruction with GLM‐based analysis (i.e., voxel‐wise GLM analysis) to investigate the brain activity that is associated with risk decision‐making processes. Risk decision‐making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk‐taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision‐making from 37 human participants (22 males and 15 females). Voxel‐wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas‐guided DOT images. In this work, we wish to demonstrate the excellence of using voxel‐wise GLM analysis with DOT to image and study cognitive functions in response to risk decision‐making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active‐choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Hum Brain Mapp 35:4249–4266, 2014.

Algorithmic depth compensation improves quantification and noise suppression in functional diffuse optical tomography.

Accurate depth localization and quantitative recovery of a regional activation are the major challenges in functional diffuse optical tomography (DOT). The photon density drops severely with increased depth, for which conventional DOT reconstruction yields poor depth localization and quantitative recovery. Recently we have developed a depth compensation algorithm (DCA) to improve the depth localization in DOT. In this paper, we present an approach based on the depth-compensated reconstruction to improve the quantification in DOT by forming a spatial prior. Simulative experiments are conducted to demonstrate the usefulness of this approach. Moreover, noise suppression is a key to success in DOT which also affects the depth localization and quantification. We present quantitative analysis and comparison on noise suppression in DOT with and without depth compensation. The study reveals that appropriate combination of depth-compensated reconstruction with the spatial prior can provide accurate depth localization and improved quantification at variable noise levels.

EMBOLIC MIDDLE CEREBRAL ARTERY OCCLUSION MODEL USING THROMBIN AND FIBRINOGEN COMPOSED CLOTS IN RAT

Ischemic stroke accounts for over 80% in total human stroke which mostly affect middle cerebral artery (MCA) territory. Embolic stroke models induced by injection of homologous clots into the internal carotid artery and MCA closely mimic human stroke and have been commonly used in stroke research. Studies indicate that the size and composition of clots are critical for the reproducibility of the stroke model. In the present study, we modified the homologous clots formation by addition of thrombin and fibrinogen which produced even distribution of fibrin with tight cross linkage of red blood cells. We optimized the embolic MCA occlusion model in rats using different size of the mixed clots. A precise lodgment of the clots at the MCA bifurcation and highly reproducible ischemic lesion in the MCA territory were demonstrated in the embolic MCA occlusion model induced by injection of 10 pieces of 1-mm long mixed clots made in PE-60 catheter. We further tested the effect of recombinant tissue plasminogen activator (rtPA) in this embolic MCA occlusion model. rtPA induced thrombolysis, improved neurological outcome, and significantly reduced ischemic lesion volume when administered at 1h after embolism as compared with control. In summary, we have established a reproducible embolic MCA occlusion model using clots made of homologous blood, thrombin and fibrinogen. The mixed clots enable precise lodgment at the MCA bifurcation which is responsive to thrombolytic therapy of rtPA.

Tutorial on use of intraclass correlation coefficients for assessing intertest reliability and its application in functional near-infrared spectroscopy–based brain imaging

Abstract. Test-retest reliability of neuroimaging measurements is an important concern in the investigation of cognitive functions in the human brain. To date, intraclass correlation coefficients (ICCs), originally used in inter-rater reliability studies in behavioral sciences, have become commonly used metrics in reliability studies on neuroimaging and functional near-infrared spectroscopy (fNIRS). However, as there are six popular forms of ICC, the adequateness of the comprehensive understanding of ICCs will affect how one may appropriately select, use, and interpret ICCs toward a reliability study. We first offer a brief review and tutorial on the statistical rationale of ICCs, including their underlying analysis of variance models and technical definitions, in the context of assessment on intertest reliability. Second, we provide general guidelines on the selection and interpretation of ICCs. Third, we illustrate the proposed approach by using an actual research study to assess intertest reliability of fNIRS-based, volumetric diffuse optical tomography of brain activities stimulated by a risk decision-making protocol. Last, special issues that may arise in reliability assessment using ICCs are discussed and solutions are suggested.

Interleaved imaging of cerebral hemodynamics and blood flow index to monitor ischemic stroke and treatment in rat by volumetric diffuse optical tomography

Diffuse optical tomography (DOT) has been used by several groups to assess cerebral hemodynamics of cerebral ischemia in humans and animals. In this study, we combined DOT with an indocyanine green (ICG)-tracking method to achieve interleaved images of cerebral hemodynamics and blood flow index (BFI) using two middle cerebral artery occlusion (MCAO) rat models. To achieve volumetric images with high-spatial resolution, we first integrated a depth compensation algorithm (DCA) with a volumetric mesh-based rat head model to generate three-dimensional (3D) DOT on a rat brain atlas. Then, the experimental DOT data from two rat models were collected using interleaved strategy for cerebral hemodynamics and BFI during and after ischemic stroke, with and without a thrombolytic therapy for the embolic MCAO model. The acquired animal data were further analyzed using the integrated rat-atlas-guided DOT method to form time-evolving 3D images of both cerebral hemodynamics and BFI. In particular, we were able to show and identify therapeutic outcomes of a thrombolytic treatment applied to the embolism-induced ischemic model. This paper demonstrates that volumetric DOT is capable of providing high-quality, interleaved images of cerebral hemodynamics and blood perfusion in small animals during and after ischemic stroke, with excellent 3D visualization and quantifications.