Electromagnetic Field Stimulation Therapy for Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative dementia worldwide. AD is a multifactorial disease that causes a progressive decline in memory and function precipitated by toxic beta-amyloid (Aß) proteins, a key player in AD pathology. In 2022, 6.5 million Americans lived with AD, costing the nation $321billion. The standard of care for AD treatment includes acetylcholinesterase inhibitors (AchEIs), NMDA receptor antagonists, and monoclonal antibodies (mAbs). However, these methods are either: 1) ineffective in improving cognition, 2) unable to change disease progression, 3) limited in the number of therapeutic targets, 4) prone to cause severe side effects (brain swelling, microhemorrhages with mAb, and bradycardia and syncope with AchEIs), 5) unable to effectively cross the blood-brain barrier, and 6) lack of understanding of the aging process on the disease. mAbs are available to lower Aß, but the difficulties of reducing the levels of the toxic Aß proteins in the brain without triggering brain swelling or microhemorrhages associated with mAbs make the risk-benefit profile of mAbs unclear. A novel multitarget, effective, and safe non-invasive approach utilizing Repeated Electromagnetic Field Stimulation (REMFS) lowers Aß levels in human neurons and memory areas, prevents neuronal death, stops disease progression, and improves memory without causing brain edema or bleeds in AD mice. This REMFS treatment has not been developed for humans because current EMF devices have poor penetration depth and inhomogeneous E-field distribution in the brain. Here, we discussed the biology of these effects in neurons and the design of optimal devices to treat AD.

Numerical Modeling and Computer Simulation of a Meander Line Antenna for Alzheimer's Disease Treatment, a Feasibility Study.

Alzheimer's disease (AD) is a brain disorder that eventually causes memory loss and the ability to perform simple cognitive functions; research efforts within pharmaceuticals and other medical treatments have minimal impact on the disease. Our preliminary biological studies showed that Repeated Electromagnetic Field Stimulation (REFMS) applying an EM frequency of 64 MHz and a specific absorption rate (SAR) of 0.4 - 0.9 W/kg decrease the level of amyloid-ß peptides (Aß), which is the most likely etiology of AD. This study emphasizes uniform E/H field and SAR distribution with adequate penetration depth penetration through multiple human head layers driven with low input power for safety treatments. In this work, we performed numerical modeling and computer simulations of a portable Meander Line antenna (MLA) to achieve the required EMF parameters to treat AD. The MLA device features a low cost, small size, wide bandwidth, and the ability to integrate into a portable system. This study utilized a High-Frequency Simulation System (HFSS) in the design of the MLA with the desired characteristics suited for AD treatment in humans. The team designed a 24-turn antenna with a 60 cm length and 25 cm width and achieved the required resonant frequency of 64 MHz. Here we used two numerical human head phantoms to test the antenna, the MIDA and spherical head phantom with six and seven tissue layers, respectively. The antenna was fed from a 50-Watt input source to obtain the SAR of 0.6 W/kg requirement in the center of the simulated brain tissue layer. We found that the E/H field and SAR distribution produced was not homogeneous; there were areas of high SAR values close to the antenna transmitter, also areas of low SAR value far away from the antenna. This paper details the antenna parameters, the scattering parameters response, the efficiency response, and the E and H field distribution; we presented the computer simulation results and discussed future work for a practical model.

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images.

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

Numerical Analysis and Design of an EMF Birdcage Wearable Device for the Treatment of Alzheimer's Disease: A Feasibility Study.

In this study, we performed a numerical analysis of a novel EMF Birdcage wearable device for the treatment of Alzheimer's disease (AD). We designed the new device to generate and radiate a frequency of 64 MHz and a specific absorption rate (SAR) of 0.6 W/kg to a simulated human brain tissue. We determined these parameters from our experimental studies on primary human brain cultures at the Indiana University School of Medicine (IUSM). We found that this frequency and SAR decreased the toxic Aß levels in the cell cultures. The frequency of 64 MHZ has good skin depth penetration, which will easily pass through the various head layers, including hair, skin, fat, dura, the cerebrospinal (CSF), and grey matter, and reach deeply into the brain tissues. The SAR of 0.6 W/kg was achieved with lower power input and energy, decreasing the probability of thermal injury. Therefore, these parameters enhance the safety of these potential treatments. This Birdcage device emulates a small-scale MRI machine, producing the same 64 MHz frequency at much lower operating input power. In this work, we utilized a high-frequency simulation system (HFSS/EMPro) software to produce the birdcage structure for the required EMF parameters. The 64 MHz radiating frequency produced the scattering S11 parameter of -15 dbs. We obtained a SAR of 0.6 W/kg when an input power of 100 W was applied. The coil dimensions were found to be near 15 cm in height and 22 cm in diameter, which fits in wearable systems. We found that the distribution of the electric field and SAR radiate homogeneously over the simulated human head with good penetration into the brain, which proves to be an appropriate potential therapeutic strategy for Alzheimer's disease.

EMF Antenna Exposure on a Multilayer Human Head Simulation for Alzheimer Disease Treatments.

In this paper, we follow up with our preliminary biological studies that showed that Repeated electromagnetic field stimulation (REMFS) decreased the toxic amyloid-beta (Aß) levels, which is considered to be the cause of Alzheimer's disease (AD). The REMFS parameters of these exposures were a frequency of 64 MHz and a Specific absorption rate (SAR) of 0.4 to 0.9 W/Kg in primary human neuronal cultures. In this work, an electromagnetic field (EMF) model was simulated using high-frequency simulation system (HFSS/EMPro) software. Our goal was to achieve the EM parameters (EMF Frequency and SAR) required to decrease the toxic Aß levels in our biological studies in a simulated human head. The simulations performed here will potentially lead to the successful development of an exposure system to treat Alzheimer's disease patients. A popular VFH (very high frequency) patch microstrip antenna system was considered in the study. The selection was based on simple and easy construction and appropriateness to the VHF applications. The evaluation of the SAR and temperature distribution on the various head layers, including skin, fat, dura, the cerebrospinal (CSF), and grey matter, brain tissues, were determined for efficacy SAR and safety temperature increase on a simulated human head. Based on a current pulse of 1 A peak current fed to the antenna feeder, a maximum SAR of 0.6 W/Kg was achieved. A range of 0.4 to 0.6 SAR was observed over the various layers of the simulated human head. The initial design of the antenna indicated an antenna size in the order of 1 m in length and width, suggesting a stationary practical model for AD therapy. Future direction is given for wearable antenna and exposure system, featuring high efficiency and patient comfort.

Multidimensional insights into the repeated electromagnetic field stimulation and biosystems interaction in aging and age-related diseases.

We provide a multidimensional sequence of events that describe the electromagnetic field (EMF) stimulation and biological system interaction. We describe this process from the quantum to the molecular, cellular, and organismal levels. We hypothesized that the sequence of events of these interactions starts with the oscillatory effect of the repeated electromagnetic stimulation (REMFS). These oscillations affect the interfacial water of an RNA causing changes at the quantum and molecular levels that release protons by quantum tunneling. Then protonation of RNA produces conformational changes that allow it to bind and activate Heat Shock Transcription Factor 1 (HSF1). Activated HSF1 binds to the DNA expressing chaperones that help regulate autophagy and degradation of abnormal proteins. This action helps to prevent and treat diseases such as Alzheimer's and Parkinson's disease (PD) by increasing clearance of pathologic proteins. This framework is based on multiple mathematical models, computer simulations, biophysical experiments, and cellular and animal studies. Results of the literature review and our research point towards the capacity of REMFS to manipulate various networks altered in aging (Reale et al. PloS one 9, e104973, 2014), including delay of cellular senescence (Perez et al. 2008, Exp Gerontol 43, 307-316) and reduction in levels of amyloid-ß peptides (Aß) (Perez et al. 2021, Sci Rep 11, 621). Results of these experiments using REMFS at low frequencies can be applied to the treatment of patients with age-related diseases. The use of EMF as a non-invasive therapeutic modality for Alzheimer's disease, specifically, holds promise. It is also necessary to consider the complicated and interconnected genetic and epigenetic effects of the REMFS-biological system's interaction while avoiding any possible adverse effects.

Eletromagnetic Detection of Mild Brain Injury: A Novel Imaging Approach to Post Concussive Syndrome.

INTRODUCTION: Mild traumatic brain injury (mTBI) is a common injury, with nearly 3 - 4 million cases annually in the United States alone. Neuroimaging in patients with mTBI provides little benefit, and is usually not indicated as the diagnosis is primarily clinical. It is theorized that microvascular trauma to the brain may be present in mTBI, that may not be captured by routine MRI and CT scans. Electromagnetic (EM) waves may provide a more sensitive medical imaging modality to provide objective data in the diagnosis of mTBI. METHODS: COMSOL simulation software was utilized to mimic the anatomy of the human skull including skin, cranium, cerebrospinal fluid (CSF), gray-matter tissue of the brain, and microvasculature within the neural tissue. The effects of penetrating EM waves were simulated using the finite element analysis software and results were generated to identify feasibility and efficacy. Frequency ranges from 7 GHz to 15 GHz were considered, with 0.6 and 1 W power applied. RESULTS: Variations between the differing frequency levels generated different energy levels within the neural tissue-particularly when comparing normal microvasculature versus hemorrhage from microvasculature. This difference within the neural tissue was subsequently identified, via simulation, serving as a potential imaging modality for future work. CONCLUSION: The use of electromagnetic imaging of the brain after concussive events may play a role in future mTBI diagnosis. Utilizing the proper depth frequency and wavelength, neural tissue and microvascular trauma may be identified utilizing finite element analysis.

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.

BACKGROUND: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. METHODS: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. RESULTS: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. CONCLUSIONS: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.

Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/.

Electromagnetic simulation for diagnosing damage to femoral neck vasculature: A feasibility study.

BACKGROUND: Femoral neck fractures are common injuries managed by orthopedic surgeons across the world. From pediatrics to geriatrics, disruption of the blood supply to the femoral neck is a well-recognized source of morbidity and mortality, oftentimes resulting in avascular necrosis of the femoral head. This devastating complication occurs in 10-45% of femoral neck fractures. Therefore, it is vital for orthopedic surgeons provide efficient treatment of this injury, in order to optimize the patient's potential outcome and prevent long-term sequelae. METHODS: In this study, the anatomy of the proximal femur, including femoral metaphysis, femoral neck, vasculature, and femoral head, were simulated in COMSOL Finite Element Analysis (FEA) software. Electric fields were generated in a fashion that exploited disruptions within the vasculature of the femoral neck. This study was aimed at developing an alternative imaging modality for narrowing or disrupting the femoral neck's vasculature. The variables used for investigation included: frequency, penetration depth, and magnitude of the electrical energy. These variables, when combined, allowed for enhanced simulated visualization of the vasculature of the femoral neck and theoretically expedited diagnosis of obvious, or occult, femoral neck injury. RESULTS: Simulated blood vessels were developed in two-dimensions: the phi direction (circular), and z-direction. Two different frequencies, 3 GHz, and 5 GHz were considered, with 100-J energy pulses within blood vessels of 2.54 mm in diameter. The fat surrounding the bone to the outside surface body was simulated at 0.25 inch (0.65 cm). An additional model, with layered fat and skin above the vessels, was simulated at 2000J and successfully able to visualize the femoral neck's blood vessels. Results showed a distinguished E field across the blood boundary of nearly 170 V/M. CONCLUSIONS: The electric field simulation data within the Phi and Z directions promises the feasibility of a subsequent practical model.

Electromagnetic simulation of non-invasive approach for the diagnosis of diabetic foot ulcers.

Diabetic foot ulcers are systemic diseases that affect all blood vessels within the human body. From major blood vessels to microvasculature, hardening, thickening, and narrowing of blood vessels ultimately results to diminished blood flow to end organs. The detrimental effects of peripheral vascular disease are well recognized across medicine, particularly with regards to diabetic foot ulcers. Diabetic foot ulcers (DFU) are common across all fields of medicine, including but not limited to: orthopedics, vascular surgery, podiatry, general internal medicine, and infectious disease. As the population of the United States continues to grow in age and obesity, diabetes and DFU are becoming more and more prevalent in our medical society. Current approaches to diagnosing peripheral vascular disease ultimately result in some degree of invasiveness for the patient. Preliminary lab studies, such as the ankle-brachial index and Doppler ultrasound of peripheral arteries, provide efficient safe screening methods. However, these studies lack quantification of the degree of vascular stenosis and are unable to accurately assess the location of narrowing. In current practice, radiologists are called upon to for angiography of the blood vessels using contrast dye. This provides an additional risk for diabetic patients: a population inherently at risk for renal disease. In this study, we proposed utilizing electromagnetic simulation with boundary conditions set at various layers of human tissues. More specifically, the human foot was analyzed using COMSOL multi-physics software in attempt to visualize, analyze, and quantify the degree of peripheral vascular disease, which plays a pivotal role in the development of diabetic foot ulcers. The simulation was conducted for a patient's foot, with bone, blood vessels, and surrounding fat layers to emulate the anatomy of a diabetic foot. A 2-D scan was obtained to assess and visualize the blood vessel's narrowing, widening, vascular turbulence, or occlusion. The analysis was conducted at two frequencies, 2 GHz and 5 GHz, and compared to one another to assess the accuracy of clinical diagnosis. An electric field was generated throughout the 2D model at 20, 50, and 100 Joules, respectively. The simulation was able to adequately predict and stratify varying degrees of occlusion within peripheral vasculature. This study, though a simulation in nature, shows promise for being able to accurately diagnose the peripheral vasculature using electromagnetic parameters. This feasibility study proved successful for possible future implementation using MEMS/NEMS device systems to be designed to detect EM parameters to serve as a diagnostic tool for the early detection of peripheral vascular disease, and ultimately, diabetic foot ulcers.

Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans.

The hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.4 mm in plane) T2-weighted turbo spin-echo sequence that requires 8 min. With acceleration, the protocol can be acquired in 4 min. We performed a comparative study of hippocampal subfield volumes using standard and accelerated protocols on a Siemens Prisma 3T MRI in an independent sample of older adults that included 10 cognitively normal controls, 9 individuals with subjective cognitive decline, 10 with mild cognitive impairment, and 6 with a clinical diagnosis of Alzheimer's disease (AD). The Automatic Segmentation of Hippocampal Subfields (ASHS) software was used to segment 9 primary labeled regions including hippocampal subfields and neighboring cortical regions. Intraclass correlation coefficients were computed for reliability tests between 4 and 8 min scans within and across the four groups. Pairwise group analyses were performed, covaried for age, sex and total intracranial volume, to determine whether the patterns of group differences were similar using 4 vs. 8 min scans. The 4 and 8 min protocols, analyzed by ASHS segmentation, yielded similar volumetric estimates for hippocampal subfields as well as comparable patterns of differences between study groups. The accelerated protocol can provide reliable imaging data for investigation of hippocampal subfields in AD-related MRI studies and the decreased scan time may result in less vulnerability to motion.

Antenna Design and SAR Analysis on Human Head Phantom Simulation for Future Clinical Applications.

BACKGROUND: The rapid development of a variety of devices that emit Radiofrequency Electromagnetic fields (RF-EMF) has sparked growing interest in their interaction with biological systems and the beneficial effects on human health. As a result, investigations have been driven by the potential for therapeutic applications, as well as concern for any possible negative health implications of these EM energies [1-4]. Recent results have indicated specific tuning of experimental and clinical RF exposure may lead to their clinical application toward beneficial health outcomes [5]. METHOD: In the current study, a mathematical and computer simulation model to analyze a specific RF-EMF exposure on a human head model was developed. Impetus for this research was derived from results of our previous experiments which revealed that Repeated Electromagnetic Field Stimulation (REMFS) decreased the toxic levels of beta amyloid (Aß) in neuronal cells, thereby suggesting a new potential therapeutic strategy for the treatment of Alzheimer's disease (AD). Throughout development of the proposed device, experimental variables such as the EM frequency range, specific absorption rate (SAR), penetration depth, and innate properties of different tissues have been carefully considered. RESULTS: RF-EMF exposure to the human head phantom was performed utilizing a Yagi-Uda antenna type possessing high gain (in the order of 10 dbs) at a frequency of 64 MHz and SAR of 0.6 W/Kg. In order to maximize the EM power transmission in one direction, directors were placed in front of the driven element and reflectors were placed behind the driven element. So as to strategically direct the EM field into the center of the brain tissue, while providing field linearity, our analysis considered the field distribution for one versus four antennas. Within the provided dimensions of a typical human brain, results of the Bioheat equation within COMSOL Multiphysics version 5.2a software demonstrated less than a 1 m˚K increase from the absorbed EM power.

Dynamic thermal/acoustic response for human bone materials at different energy levels: A diagnosis approach.

BACKGROUND: The non-invasive diagnostic approaches have gained high attention in recent years, utilizing high technology sensor systems, including infrared, microwave devices, acoustic transducers, etc. The patient safety, high resolution images, and reliability are among the driving forces toward high technology approaches. The thermal and acoustic responses of the materials may reflect the important research parameters such as penetration depth, power consumption, and temperature change used for the practical models of the system. This paper emphasizes the approach for orthopedic application where the bone densities were considered in simulation to designate the type of human bones. METHODS: Thermal energy pulses were applied in order to study the penetration depth, the maximum temperature change; spatially and dynamically, and the acoustic pressure distribution over the bone thickness. The study was performed to optimize the amount of energy introduced into the materials that generate the temperature value for high resolution beyond the noise level. RESULTS: Three different energy pulses were used; 1 J, 3 J and 5 J. The thermal energy applied to the four bone materials, cancellous bone, cortical bone, red bone marrow, and yellow bone marrow were producing relative changes in temperature. The maximum change ranges from 0.5 K to 2 K for the applied pulses. The acoustic pressure also ranges from 210 to 220 dB among the various types of bones. CONCLUSION: The results obtained from simulation suggest that a practical model utilizing infra-red scanning probe and piezoelectric devices may serve for the orthopedic diagnostic approach. The simulations for multiple layers such as skin interfaced with bone will be reserved for future considerations.

Electromagnetic and Thermal Simulations of Human Neurons for SAR Applications.

The impact of the electromagnetic waves (EM) on human neurons (HN) has been under investigation for decades, in efforts to understand the impact of cell phones (radiation) on human health, or radiation absorption by HN for medical diagnosis and treatment. Research issues including the wave frequency, power intensity, reflections and scattering, and penetration depths are of important considerations to be incorporated into the research study. In this study, computer simulation for the EM exposure to HN was studied for the purpose of determining the upper limits of the electric and magnetic field intensities, power consumption, reflections and transmissions, and the change in temperature resulting from the power absorption by human neurons. Both high frequency structural simulators (HFSS) from ANSYS software, and COMSOL multi-physics were used for the simulation of the EM transmissions and reflections, and the temperature profile within the cells, respectively. For the temperature profile estimation, the study considers an electrical source of 0.5 watt input power, 64 MHz. The EM simulation was looking into the uniformity of the fields within the sample cells. The size of the waveguide was set to be appropriate for a small animal model to be conducted in the future. The incident power was fully transmitted throughout the waveguide, and less than 1% reflections were observed from the simulation. The minimum reflected power near the sample under investigation was found to be with negligible reflected field strengths. The temperature profile resulting from the COMSOL simulation was found to be near 0.25 m°K, indicating no change in temperature on the neuro cells under the EM exposure. The paper details the simulation results for the EM response determined by HFSS, and temperature profile simulated by COMSOL.

Non-invasive photo acoustic approach for human bone diagnosis.

The existing modalities of bone diagnosis including X-ray and ultrasound may cite drawback in some cases related to health issues and penetration depth, while the ultrasound modality may lack image quality. Photo acoustic approach however, provides light energy to the acoustic wave, enabling it to activate and respond according to the propagating media (which is type of bones in this case). At the same time, a differential temperature change may result in the bio heat response, resulting from the heat absorbed across the multiple materials under study. In this work, we have demonstrated the features of using photo acoustic modality in order to non-invasively diagnose the type of human bones based on their electrical, thermal, and acoustic properties that differentiate the output response of each type. COMSOL software was utilized to combine both acoustic equations and bio heat equations, in order to study both the thermal and acoustic responses through which the differential diagnosis can be obtained. In this study, we solved both the acoustic equation and bio heat equations for four types of bones, bone (cancellous), bone (cortical), bone marrow (red), and bone marrow (yellow). 1 MHz acoustic source frequency was chosen and 10(5) W/m(2) power source was used in the simulation. The simulation tested the dynamic response of the wave over a distance of 5 cm from each side for the source. Near 2.4 cm was detected from simulation from each side of the source with a temperature change of within 0.5 K for various types of bones, citing a promising technique for a practical model to detect the type of bones via the differential temperature as well as the acoustic was response via the multiple materials associated with the human bones (skin and blood). The simulation results suggest that the PA technique may be applied to non-invasive diagnosis for the different types of bones, including cancerous bones. A practical model for detecting both the temperature change via IR sensors, and acoustic wave signals may be detected via sensitive pressure transducer, which is reserved for future realization.

A New Statistical Image Analysis Approach and Its Application to Hippocampal Morphometry.

In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer's disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.

Building a Surface Atlas of Hippocampal Subfields From High Resolution T2-weighted MRI Scans Using Landmark-free Surface Registration.

The hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools. But this method had some limitations, including the analysis of T1-weighted MRI scans without detailed subfield information and hippocampal registration without using important subfield information. To bridge these gaps, in this work, we propose a new framework for building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans by integrating state-of-the-art methods for automated segmentation of hippocampal subfields and landmark-free, subfield-aware registration of hippocampal surfaces. Our experimental results have shown the promise of the new framework.