Using generative models to make probabilistic statements about hippocampal engagement in MEG.
ABSTRACT: Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports neuronal current flow on both the cortical and hippocampal surface. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration error and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is <3 mm and the sensor-level signal-to-noise ratio (SNR) is >-20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts.
Project description:Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1mm. Estimates of relative co-registration error were <1.5mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.
Project description:Molecular sensors based on intramolecular Förster resonance energy transfer (FRET) have become versatile tools to monitor regulatory molecules in living tissue. However, their use is often compromised by low signal strength and excessive noise. We analyzed signal/noise (SNR) aspects of spectral FRET analysis methods, with the following conclusions: The most commonly used method (measurement of the emission ratio after a single short wavelength excitation) is optimal in terms of signal/noise, if only relative changes of this uncalibrated ratio are of interest. In the case that quantitative data on FRET efficiencies are required, these can be calculated from the emission ratio and some calibration parameters, but at reduced SNR. Lux-FRET, a recently described method for spectral analysis of FRET data, allows one to do so in three different ways, each based on a ratio of two out of three measured fluorescence signals (the donor and acceptor signal during a short-wavelength excitation and the acceptor signal during long wavelength excitation). Lux-FRET also allows for calculation of the total abundance of donor and acceptor fluorophores. The SNR for all these quantities is lower than that of the plain emission ratio due to unfavorable error propagation. However, if ligand concentration is calculated either from lux-FRET values or else, after its calibration, from the emission ratio, SNR for both analysis modes is very similar. Likewise, SNR values are similar, if the noise of these quantities is related to the expected dynamic range. We demonstrate these relationships based on data from an Epac-based cAMP sensor and discuss how the SNR changes with the FRET efficiency and the number of photons collected.
Project description:To estimate the neural generators of magnetoencephalographic (MEG) signals, MEG data have to be co-registered with an anatomical image, typically an MR image. Optically-pumped magnetometers (OPMs) enable the construction of on-scalp MEG systems providing higher sensitivity and spatial resolution than conventional SQUID-based MEG systems. We present a co-registration method that can be applied to on-scalp MEG systems, regardless of the number of sensors. We apply a structured-light scanner to create a surface mesh of the subject's head and the sensor array, which we fit to the MR image. We quantified the reproducibility of the mesh and localised current dipoles with a phantom. Additionally, we measured somatosensory evoked fields (SEFs) to median nerve stimulation and compared the dipole positions between on-scalp and SQUID-based systems. The scanner reproduced the head surface with <1 mm error. Phantom dipoles were localised with 2.1 mm mean error. SEF dipoles corresponding to the P35m response for OPMs were well localised to the somatosensory cortex, while SQUID dipoles for two subjects were erroneously localised to the motor cortex. The developed co-registration method is inexpensive, fast and can easily be applied to on-scalp MEG. It is more convenient than traditional co-registration methods while also being more accurate.
Project description:The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind's direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate's accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.
Project description:Diseases involving the medial temporal lobes (MTL) such as Alzheimer's disease and mesial temporal sclerosis pose an ongoing diagnostic challenge because of the difficulty in identifying conclusive imaging features, particularly in pre-clinical states. Abnormal neuronal connectivity may be present in the circuitry of the MTL, but current techniques cannot reliably detect those abnormalities. Diffusion tensor imaging (DTI) has shown promise in defining putative abnormalities in connectivity, but DTI studies of the MTL performed to date have shown neither dramatic nor consistent differences across patient populations. Conventional DTI methodology provides an inadequate depiction of the complex microanatomy present in the medial temporal lobe because of a typically employed low isotropic resolution of 2.0-2.5 mm, a low signal-to-noise ratio (SNR), and echo-planar imaging (EPI) geometric distortions that are exacerbated by the inhomogeneous magnetic environment at the skull base. In this study, we pushed the resolving power of DTI to near-mm isotropic voxel size to achieve a detailed depiction of mesial temporal microstructure at 3 T. High image fidelity and SNR at this resolution are achieved through several mechanisms: (1) acquiring multiple repetitions of the minimum field of view required for hippocampal coverage to boost SNR; (2) utilizing a single-refocused diffusion preparation to enhance SNR further; (3) performing a phase correction to reduce Rician noise; (4) minimizing distortion and maintaining left-right distortion symmetry with axial-plane parallel imaging; and (5) retaining anatomical and quantitative accuracy through the use of motion correction coupled with a higher-order eddy-current correction scheme. We combined this high-resolution methodology with a detailed segmentation of the MTL to identify tracks in all subjects that may represent the major pathways of the MTL, including the perforant pathway. Tractography performed on a subset of the data identified similar tracks, although they were lesser in number. This detailed analysis of MTL substructure may have applications to clinical populations.
Project description:High-resolution wide field-of-view (FOV) microscopic imaging plays an essential role in various fields of biomedicine, engineering, and physical sciences. As an alternative to conventional lens-based scanning techniques, lensfree holography provides a new way to effectively bypass the intrinsical trade-off between the spatial resolution and FOV of conventional microscopes. Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance during image acquisition, and sub-optimum solution to the phase retrieval problem, typical lensfree microscopes only produce compromised imaging quality in terms of lateral resolution and signal-to-noise ratio (SNR). Here, we propose an adaptive pixel-super-resolved lensfree imaging (APLI) method which can solve, or at least partially alleviate these limitations. Our approach addresses the pixel aliasing problem by Z-scanning only, without resorting to subpixel shifting or beam-angle manipulation. Automatic positional error correction algorithm and adaptive relaxation strategy are introduced to enhance the robustness and SNR of reconstruction significantly. Based on APLI, we perform full-FOV reconstruction of a USAF resolution target (~29.85?mm2) and achieve half-pitch lateral resolution of 770?nm, surpassing 2.17 times of the theoretical Nyquist-Shannon sampling resolution limit imposed by the sensor pixel-size (1.67µm). Full-FOV imaging result of a typical dicot root is also provided to demonstrate its promising potential applications in biologic imaging.
Project description:Accurate R2* measurements are critical for many abdominal imaging applications. Conventionally, R2* maps are derived via the monoexponential fitting of signal decay within a series of gradient-echo (GRE) images reconstructed from multichannel datasets combined using a root sum-of-squares (RSS) approach. However, the noise bias at low-SNR TEs from RSS-reconstructed data often causes the underestimation of R2* values. In phantom, ex vivo animal model and normal volunteer studies, we investigated the accuracy of low-SNR R2* measurement when combining truncation and coil combination methods. The accuracy for R2* estimations was shown to be affected by the intrinsic R2* value, SNR level and the chosen reconstruction method. The R2* estimation error was found to decrease with increasing SNR level, decreasing R2* value and the use of the optimal B1-weighted combined (OBC) image reconstruction method. Data truncation based on rigorous voxel-wise SNR estimates can reduce R2* measurement error in the setting of low SNR with fast signal decay. When optimal SNR truncation thresholds are unknown, the OBC method can provide optimal R2* measurements given the minimal truncation requirements.
Project description:Magnetoencephalography (MEG) is a neuroimaging technique that accurately captures the rapid (sub-millisecond) activity of neuronal populations. Interpretation of functional data from MEG relies upon registration to the participant's anatomical MRI. The key remaining step is to transform the participant's MRI into the MEG head coordinate space. Although both automated and manual approaches to co-registration are available, the relative accuracy of two approaches has not been systematically evaluated. The goal of the present study was to compare the accuracy of manual and automated co-registration. Resting MEG and T1-weighted MRI data were collected from 90 participants. Automated and manual co-registration were performed on the same subjects, and the inter-method reliability of the two methods assessed using the intra-class correlation. Median co-registration error for both methods was within acceptable limits. Inter-method reliability was in the "good" range for co-registration error, and the "good" to "excellent" range for translation and rotation. These results suggest that the output of the automated co-registration procedure is comparable to that achieved using manual co-registration.
Project description:This paper proposes a composite channel virtual time reversal mirror (CCVTRM) for vertical sensor array (VSA) processing and applies it to long-range underwater acoustic (UWA) communication in shallow water. Because of weak signal-to-noise ratio (SNR), it is unable to accurately estimate the channel impulse response of each sensor of the VSA, thus the traditional passive time reversal mirror (PTRM) cannot perform well in long-range UWA communication in shallow water. However, CCVTRM only needs to estimate the composite channel of the VSA to accomplish time reversal mirror (TRM), which can effectively mitigate the inter-symbol interference (ISI) and reduce the bit error rate (BER). In addition, the calculation of CCVTRM is simpler than traditional PTRM. An UWA communication experiment using a VSA of 12 sensors was conducted in the South China Sea. The experiment achieves a very low BER communication at communication rate of 66.7 bit/s over an 80 km range. The results of the sea trial demonstrate that CCVTRM is feasible and can be applied to long-range UWA communication in shallow water.