Resting-State Functional Connectivity Measurement in the Mouse Brain using a Low Cost Photoacoustic Computed Tomography

Resting-state functional connectivity (RSFC) is an emerging neuroimaging approach that aims to identify low-frequency, spontaneous cerebral hemodynamic fluctuations and their associated functional connections [1, 2]. Recent research suggests that these fluctuations are highly correlated with local neuronal activity [3, 4]. The spontaneous fluctuations relate to activity that is intrinsically generated by the brain, instead of activity attributable to specific tasks or stimuli [2]. A hallmark of functional organization in the cortex is the striking bilateral symmetry of corresponding functional regions in the left and right hemispheres [5]. This symmetry also exists in spontaneous resting-state hemodynamics, where strong correlations are found inter-hemispherically between bilaterally homologous regions, as well as intra-hemispherically within the same functional regions [3]. Clinical studies have demonstrated that RSFC is altered in brain disorders such as Alzheimer’s [6-12]. Alzheimer’s disease disrupts healthy functional network patterns. Due to its task-free nature, RSFC imaging can be performed on patients under anesthesia [13, 14], on patients unable to perform cognitive tasks [15, 16], and even on patients with brain injury [17, 18]. RSFC imaging is also an appealing technique for studying brain diseases in animal models, in particular the mouse, a species that holds the largest variety of neurological disease models [3, 14, 19, 20]. Compared to clinical studies, imaging genetically modified mice allows exploration of molecular pathways underlying the pathogenesis of neurological disorders [21]. The connection between RSFC maps and neurological disorders permits testing and validation of new therapeutic approaches. However, conventional neuroimaging modalities cannot easily be applied to mice. For instance, in functional connectivity magnetic resonance imaging (fcMRI) [22], the resting-state brain activity is determined via the blood oxygen level dependent (BOLD) signal contrast [23]. The correlation analysis central to RSFC requires a high signal-to-noise ratio (SNR). However, achieving a sufficient SNR is made challenging by the high magnetic fields and small voxel size needed for imaging the mouse brain, as well as the complexity of compensating for field inhomogeneities caused by tissue/bone or tissue/air boundaries [24]. Functional connectivity mapping with optical intrinsic signal imaging (fcOIS) was recently introduced as an alternative method to image RSFC in mice [3, 20]. In fcOIS, changes in hemoglobin concentrations are determined based on changes in the reflected light intensity from the surface of the brain [3, 25]. Therefore, neuronal activity can be measured through the neurovascular response, similar to the method used in fcMRI. However, due to the diffusion of light in tissue, the spatial resolution of fcOIS is limited, and experiments are performed using an exposed skull preparation, which increases the complexity for longitudinal imaging. Here, we utilize optical excitation and acoustic detection, using photoacoustic technology, to noninvasively image RSFC in the mouse brain, with a high frame rate, large field of view and high spatial resolution at different depths. More importantly this will generate a good stand for development of the human functional connectivity photoacoustic tomography system. We call our technique, functional connectivity photoacoustic tomography (fcPAT). Previously we developed an fcPAT with ND:YAG Quanta Ray laser (Pro 270) and a 512-element ring transducer [26]. The system although was fast and accurate, it was costly and could not easily be afforded by neuroscientists to study the RSFC. In the new design of the fcPAT, we use high performance cylindrically focused single element transducers instead of full ring transducer array. The signals obtained from the transducers are amplified with low noise amplifiers. The cylindrical structure where the transducers are placed on, is rotated smoothly to collect photoacoustic signal at different views while the frame rate of the reconstructed image is below one second. We use a solid state laser that has been taken out from a low cost tattoo removal laser machine. FPGA-based National Instrument system is used for data acquisition and processing. Seed-based method is used for extracting RSFC correlation maps. Data acquisition, the reconstruction algorithm and data analysis algorithm are implemented in Labview. The schematic of the setup is shown in Fig.1. An Agar phantom in which eight pencil leads are embedded is imaged, the image of which is shown in Fig.2. The animal protocol was approved last week. We are generating the mouse brain vasculature and functional images from which we obtain RSFC results. The results will be presented in WMIC 2016. Resting-state functional connectivity (RSFC) is an emerging neuroimaging approach that aims to identify low-frequency, spontaneous cerebral hemodynamic fluctuations and their associated functional connections [1, 2]. Recent research suggests that these fluctuations are highly correlated with local neuronal activity [3, 4]. The spontaneous fluctuations relate to activity that is intrinsically generated by the brain, instead of activity attributable to specific tasks or stimuli [2]. A hallmark of functional organization in the cortex is the striking bilateral symmetry of corresponding functional regions in the left and right hemispheres [5]. This symmetry also exists in spontaneous resting-state hemodynamics, where strong correlations are found inter-hemispherically between bilaterally homologous regions, as well as intra-hemispherically within the same functional regions [3]. Clinical studies have demonstrated that RSFC is altered in brain disorders such as Alzheimer’s [6-12]. Alzheimer’s disease disrupts healthy functional network patterns. Due to its task-free nature, RSFC imaging can be performed on patients under anesthesia [13, 14], on patients unable to perform cognitive tasks [15, 16], and even on patients with brain injury [17, 18]. RSFC imaging is also an appealing technique for studying brain diseases in animal models, in particular the mouse, a species that holds the largest variety of neurological disease models [3, 14, 19, 20]. Compared to clinical studies, imaging genetically modified mice allows exploration of molecular pathways underlying the pathogenesis of neurological disorders [21]. The connection between RSFC maps and neurological disorders permits testing and validation of new therapeutic approaches. However, conventional neuroimaging modalities cannot easily be applied to mice. For instance, in functional connectivity magnetic resonance imaging (fcMRI) [22], the resting-state brain activity is determined via the blood oxygen level dependent (BOLD) signal contrast [23]. The correlation analysis central to RSFC requires a high signal-to-noise ratio (SNR). However, achieving a sufficient SNR is made challenging by the high magnetic fields and small voxel size needed for imaging the mouse brain, as well as the complexity of compensating for field inhomogeneities caused by tissue/bone or tissue/air boundaries [24]. Functional connectivity mapping with optical intrinsic signal imaging (fcOIS) was recently introduced as an alternative method to image RSFC in mice [3, 20]. In fcOIS, changes in hemoglobin concentrations are determined based on changes in the reflected light intensity from the surface of the brain [3, 25]. Therefore, neuronal activity can be measured through the neurovascular response, similar to the method used in fcMRI. However, due to the diffusion of light in tissue, the spatial resolution of fcOIS is limited, and experiments are performed using an exposed skull preparation, which increases the complexity for longitudinal imaging. Here, we utilize optical excitation and acoustic detection, using photoacoustic technology, to noninvasively image RSFC in the mouse brain, with a high frame rate, large field of view and high spatial resolution at different depths. More importantly this will generate a good stand for development of the human functional connectivity photoacoustic tomography system. We call our technique, functional connectivity photoacoustic tomography (fcPAT). Previously we developed an fcPAT with ND:YAG Quanta Ray laser (Pro 270) and a 512-element ring transducer [26]. The system although was fast and accurate, it was costly and could not easily be afforded by neuroscientists to study the RSFC. In the new design of the fcPAT, we use high performance cylindrically focused single element transducers instead of full ring transducer array. The signals obtained from the transducers are amplified with low noise amplifiers. The cylindrical structure where the transducers are placed on, is rotated smoothly to collect photoacoustic signal at different views while the frame rate of the reconstructed image is below one second. We use a solid state laser that has been taken out from a low cost tattoo removal laser machine. FPGA-based National Instrument system is used for data acquisition and processing. Seed-based method is used for extracting RSFC correlation maps. Data acquisition, the reconstruction algorithm and data analysis algorithm are implemented in Labview. The schematic of the setup is shown in Fig.1. An Agar phantom in which eight pencil leads are embedded is imaged, the image of which is shown in Fig.2. The animal protocol was approved last week. We are generating the mouse brain vasculature and functional images from which we obtain RSFC results. The results will be presented in WMIC 2016.