Background The aim of this study was to noninvasively measure regional contributions of vasculature in the human body using magnetohydrodynamic voltages (V MHD ) obtained from electrocardiogram (ECG) recordings performed inside MRI’s static magnetic field (B 0 ). Integrating the regional V MHD over the S wave -T wave segment of the cardiac cycle (V segment ) provides a noninvasive method for measuring regional blood volumes, which can be rapidly obtained during MRI without incurring additional cost. Methods V MHD was extracted from 12-lead ECG traces acquired during gradual introduction into a 3T MRI. Regional contributions were computed utilizing weights based on B 0 ’s strength at specified distances from isocenter. V segment mapping was performed in six subjects and validated against MR angiograms (MRA). Results Fluctuations in V segment , which presented as positive trace deflections, were found to be associated with aortic-arch flow in the thoracic cavity, the main branches of the abdominal aorta, and the bifurcation of the common iliac artery. The largest fluctuation corresponded to the location where the aortic arch was approximately orthogonal to B 0 . The smallest fluctuations corresponded to areas of vasculature that were parallel to B 0 . Significant correlations (specifically, Spearman’s ranked correlation coefficients of 0.96 and 0.97 for abdominal and thoracic cavities, respectively) were found between the MRA and V segment maps (p < 0.001). Conclusions A novel non-invasive method to extract regional blood volumes from ECGs was developed and shown to be a rapid means to quantify peripheral and abdominal blood volumes.
by
Mai-Anh T. Vu;
Tülay Adali;
Demba Ba;
Gyorgy Buzsaki;
David Carlson;
Katherine Heller;
Conor Liston;
Cynthia Rudin;
Vikaas S. Sohal;
Alik S. Widge;
Helen S Mayberg;
Guillermo Sapiro;
Kafui Dzirasa
With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and eperimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health’s Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still eists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.
Objective Uncertain biological consequences of titanium-magnet (Ti-mag) tongue implants constrain application of the Tongue Drive System (TDS), a brain-tongue-computer interface for individuals with severe physical impairment. Here we describe oromotor function and tongue tissue response following Ti-Mag implantation and explantation in the miniature pig, an animal model with a tongue similar in size to humans. Design A 1.8 × 6.2 mm Ti-mag tracer was implanted into the anterior tongue in five Yucatan minipigs. X-rays were taken immediately and >six days after implantation to evaluate tracer migration. In three minipigs, the tracer was explanted?>16 days after implantation. Twenty-five days post-explantation, tongue tissue was harvested and processed for histological and immunohistochemical (IHC) markers of healing. In two minipigs tissue markers of healing were evaluated post-mortem following >12 days implantation. Drink cycle rate (DCR) was characterized to determine the impact of procedures on oromotor function. Results Neither implantation (N = 5) nor explantation (N = 3) changed DCR. X-rays revealed minimal tracer migration (N = 4, 0–4 mm). By histology and IHC a robust capsule was present two weeks post-implantation with limited fibrosis. Explantation produced localized fibrosis and limited muscle remodeling. Conclusions These findings suggest the safety of Ti-mag anterior tongue implants for assistive technologies in humans.
The BOLD signal reflects hemodynamic events within the brain, which in turn are driven by metabolic changes and neural activity. However, the link between BOLD changes and neural activity is indirect and can be influenced by a number of non-neuronal processes. Motion and physiological cycles have long been known to affect the BOLD signal and are present in both humans and animal models. Differences in physiological baseline can also contribute to intra- and inter-subject variability. The use of anesthesia, common in animal studies, alters neural activity, vascular tone, and neurovascular coupling. Most intriguing, perhaps, are the contributions from other processes that do not appear to be neural in origin but which may provide information about other aspects of neurophysiology. This review discusses different types of noise and non-neuronal contributors to the BOLD signal, sources of variability for animal studies, and insights to be gained from animal models.
A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, in the absence of a "gold standard" for comparison, evaluating the performance of the SWC in typical resting-state data is challenging. This study uses simulated networks (SNs) with known transitions to examine the effects of parameters such as window length, window offset, window type, noise, filtering, and sampling rate on the SWC performance. The SWC time course was calculated for all node pairs of each SN and then clustered using the k-means algorithm to determine how resulting brain states match known configurations and transitions in the SNs. The outcomes show that the detection of state transitions and durations in the SWC is most strongly influenced by the window length and offset, followed by noise and filtering parameters. The effect of the image sampling rate was relatively insignificant. Tapered windows provide less sensitivity to state transitions than rectangular windows, which could be the result of the sharp transitions in the SNs. Overall, the SWC gave poor estimates of correlation for each brain state. Clustering based on the SWC time course did not reliably reflect the underlying state transitions unless the window length was comparable to the state duration, highlighting the need for new adaptive window analysis techniques.
OBJECTIVE: We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value. METHODS AND PROCEDURES: To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG. RESULTS: The validation demonstrates a correlation value of 0.85-0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8-0.9 (p < 0.05), and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer). CONCLUSION: The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique. CLINICAL IMPACT: Using this type of ECG digitization tool to carry out retrospective studies on large databases, which rely on paper ECG records, studies of emerging ECG features can be performed. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient's electronic medical record.
This prospective study explored the application of texture features extracted from T2WI and apparent diffusion coefficient (ADC) maps in predicting recurrence of advanced cervical cancer patients treated with concurrent chemoradiotherapy (CCRT). We included 34 patients with advanced cervical cancer who underwent pelvic MR imaging before, during and after CCRT. Radiomic feature extraction was performed by using software at T2WI and ADC maps. The performance of texture parameters in predicting recurrence was evaluated. After a median follow-up of 31 months, eleven patients (32.4%) had recurrence. At four weeks after CCRT initiated, the most textural parameters (four T2 textural parameters and two ADC textural parameters) showed significant difference between the recurrence and nonrecurrence group (P values range, 0.002~0.046). Among them, RunLengthNonuniformity (RLN) from T2 and energy from ADC maps were the best selected predictors and together yield an AUC of 0.885. The support vector machine (SVM) classifier using ADC textural parameters performed best in predicting recurrence, while combining T2 textural parameters may add little value in prognosis. T2 and ADC textural parameters have potential as non-invasive imaging biomarkers in early predicting recurrence in advanced cervical cancer treated with CCRT.
Contrast-enhanced ultrasound imaging utilizing the nonlinear responses of microbubbles is proposed for identifying ventricular shunt malfunction. The developed method suppresses the signal from walls of a shunt catheter and background tissues, and allows accurate measurements of the cerebrospinal fluid flow within the shunt catheter using a relatively small concentration of microbubbles. The flow rates estimated in the linear mode were significantly underestimated (65% at 0.1ml/min and 5.0% microbubble concentration) while estimates using the nonlinear mode were not. Overall, the nonlinear responses of microbubbles improve the estimation of flow rates in a shunt catheter at low concentrations of microbubbles.
Purpose: This study introduces a real-time contrast-enhanced ultrasound imaging method with recently developed laser-activated nanodroplets (LANDs), a new class of phase-change nanometer-scale contrast agents that provides perceptible, sustained high-contrast with ultrasound. Methods: In response to pulsed laser irradiation, the LANDs -, which contain liquid perfluorohexane and optical fuses - blink (vaporize and recondense). That is, they change their state from liquid nanodroplets to gas microbubbles, and then back to liquid nanodroplets. In their gaseous microbubble state, the LANDs provide high-contrast ultrasound, but the microbubbles formed in situ typically recondense in tens of milliseconds. As a result, LAND visualization by standard, real-time ultrasound is limited. However, the periodic optical triggering of LANDs allows us to observe corresponding transient, periodic changes in ultrasound contrast. This study formulates a probability function that measures how ultrasound temporal signals vary in periodicity. Then, the estimated probability is mapped onto a B-scan image to construct a LAND-localized, contrast-enhanced image. We verified our method through phantom and in vivo experiments using an ultrasound system (Vevo 2100, FUJIFILM VisualSonics, Inc., Toronto, ON, Canada) operating with a 40-MHz linear array and interfaced with a 10 Hz Nd:YAG laser (Phocus, Opotek Inc., Carlsbad, CA, USA) operating at the fundamental 1064 nm wavelength. Results: From the phantom study, the results showed improvements in the contrast-to-noise ratio of our approach over conventional ultrasound ranging from 129% to 267%, with corresponding execution times of 0.10 to 0.29 s, meaning that the developed method is computationally efficient while yielding high-contrast ultrasound. Furthermore, in vivo sentinel lymph node (SLN) imaging results demonstrated that our technique could accurately identify the SLN. Conclusions: The results indicate that our approach enables efficient and robust LAND localization in real time with substantially improved contrast, which is essential for the successful translation of this contrast agent platform to clinical settings.
Ternary chalcopyrite CuInS2 quantum dots (QDs) have been extensively studied in recent years as an alternative to conventional QDs for solar energy conversion applications. However, compared with the well-established photophysics in prototypical CdSe QDs, much less is known about the excited properties of CuInS2 QDs. In this work, using ultrafast spectroscopy, we showed that both conduction band (CB) edge electrons and copper vacancy (VCu) localized holes were susceptible to surface trappings in CuInS2 QDs. These trap states could be effectively passivated by forming quasi-type II CuInS2/CdS core/shell QDs, leading to a single-exciton (with electrons delocalized among CuInS2/CdS CB and holes localized in VCu) half lifetime of as long as 450 ns. Because of reduced electron-hole overlap in quasi-type II QDs, Auger recombination of multiple excitons was also suppressed and the bi-exciton lifetime was prolonged to 42 ps in CuInS2/CdS QDs from 10 ps in CuInS2 QDs. These demonstrated advantages, including passivated trap states, long single and multiple exciton lifetimes, suggest that quasi-type II CuInS2/CdS QDs are promising materials for photovoltaic and photocatalytic applications.