Browsing by Author "Ahsan, Fatima"

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    EMvelop stimulation: minimally invasive deep brain stimulation using temporally interfering electromagnetic waves
    (IOP Publishing, 2022) Ahsan, Fatima; Chi, Taiyun; Cho, Raymond; Sheth, Sameer A.; Goodman, Wayne; Aazhang, Behnaam
    Objective. Recently, the temporal interference stimulation (TIS) technique for focal noninvasive deep brain stimulation (DBS) was reported. However, subsequent computational modeling studies on the human brain have shown that while TIS achieves higher focality of electric fields than state-of-the-art methods, further work is needed to improve the stimulation strength. Here, we investigate the idea of EMvelop stimulation, a minimally invasive DBS setup using temporally interfering gigahertz (GHz) electromagnetic (EM) waves. At GHz frequencies, we can create antenna arrays at the scale of a few centimeters or less that can be endocranially implanted to enable longitudinal stimulation and circumvent signal attenuation due to the scalp and skull. Furthermore, owing to the small wavelength of GHz EM waves, we can optimize both amplitudes and phases of the EM waves to achieve high intensity and focal stimulation at targeted regions within the safety limit for exposure to EM waves. Approach. We develop a simulation framework investigating the propagation of GHz EM waves generated by line current antenna elements and the corresponding heat generated in the brain tissue. We propose two optimization flows to identify antenna current amplitudes and phases for either maximal intensity or maximal focality transmission of the interfering electric fields with EM waves safety constraint. Main results. A representative result of our study is that with two endocranially implanted arrays of size × each, we can achieve an intensity of 12 V m−1 with a focality of at a target deep in the brain tissue. Significance. In this proof-of-principle study, we show that the idea of EMvelop stimulation merits further investigation as it can be a minimally invasive way of stimulating deep brain targets and offers benefits not shared by prior methodologies of electrical or magnetic stimulation.
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    EMvelop Stimulation: Minimally Invasive Deep Brain Stimulation using Temporally Interfering Electromagnetic Waves
    (2024-04-18) Ahsan, Fatima; Aazhang, Behnaam; Robinson, Jacob T; Xie, Chong; Szablowski, Jerzy O
    This thesis focuses on developing a novel brain stimulation methodology by using temporally interfering gigahertz (GHz) electromagnetic (EM) waves, termed EMvelop stimulation. Our work on EMvelop stimulation addresses two key aspects of developing this novel methodology: obtaining high electric field intensity and focality at target regions deep inside the brain tissue and fast and robust data-driven electric field estimation. First, we validate the idea of EMvelop stimulation using multi-physics modeling and algorithmic optimization simulations. We show that at GHz frequencies, we can create antenna arrays at the scale of a few centimeters or less that can be endocranially implanted to enable longitudinal stimulation and circumvent signal attenuation due to the scalp and skull. Furthermore, owing to the small wavelength of GHz EM waves, we can optimize both amplitudes and phases of the EM waves to achieve high intensity and focal stimulation at targeted regions. We develop a simulation framework investigating the propagation of GHz EM waves and the corresponding heat generated in the brain tissue. We propose two optimization flows to identify antenna current amplitudes and phases for either maximal intensity or maximal focality transmission of the interfering electric fields with EM waves safety constraint. A representative result of our study is that with two endocranially implanted arrays of size 4.2 cm x 4.7 cm each, we can achieve an intensity of 12 V/m with a focality of 3.6 cm at a target deep in the brain tissue. To the best of our knowledge, this is the first time the idea of EMvelop stimulation was proposed and investigated, and we demonstrated its benefits over prior methodologies of electrical stimulation. Second, a common factor across electromagnetic methodologies of brain stimulation is the optimization of essential dosimetry parameters, like amplitude, phase, and the location of one or more transducers, which controls the stimulation strength and targeting precision. Since obtaining in-vivo measurements for the electric field distribution inside the biological tissue is challenging, physics-based simulators are used. However, these physics-based simulators are computationally expensive and time-consuming, making computing the electric field repeatedly for optimization purposes computationally prohibitive. To overcome this issue, we trained a U-Net model using 14 segmented human magnetic resonance images (MRIs). Once trained, the model inputs a segmented human MRI and the antenna location and outputs the corresponding electric field. At 1.5 GHz, on the validation dataset consisting of 6 patients, we can estimate the electric field with the magnitude of complex correlation coefficient of 0.978. Additionally, we could calculate the electric field with a mean time of 4.4 ms for a potential antenna location. On average, this is at least 1200 times faster than the time required by state-of-the-art physics-based simulator COMSOL. The significance of this work is that it shows the possibility of real-time calculation of the electric field from the patient MRI and coordinates for the antenna, making it possible to optimize the amplitude, phase, and location of several different transducers with stochastic gradient descent since our model is a continuous function. Our work shows the potential of EMvelop stimulation to be portable, discreet, and continuously operable brain stimulation technology while being minimally invasive. The aim of our work is to expand the therapeutic options available to an even larger number of patients with neurological and psychiatric disorders.
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    Leveraging Massive MIMO Spatial Diversity in Random Access
    (2018-11-30) Ahsan, Fatima; Sabharwal, Ashutosh
    Random access is a crucial building block for nearly all wireless networks, and impacts both the overall spectral efficiency and latency in communication. In next-generation networks, it is expected that the diverse new services will be served by cellular networks, e.g. connections to Unmanned-Air-Vehicles (UAVs) and Internet-of-Things(IoT) devices, potentially increasing the node density served per base-station. Higher node density implies increased latency in random access operation, due to increased packet collision events. In this thesis, we show via analytical evaluation and monte-carlo simulations that the large spatial degrees of freedom available in massive MIMO systems can potentially be leveraged to reduce random access latency. We show that with large arrays, the spatial channel “codes” of each user are also potentially separable, providing another avenue for the receiver to distinguish overlapping users in the Angle-of-Arrival space. First, using one-ring propagation model, we evaluate how the random access collision probability depends on the aperture size of the array and the spread of user’s signal Angle-of-Arrivals at the base-station, as a function of the user-density and the number of random access codes. Then, in order to practically achieve the analytical performance bounds, we present a simple clustering algorithm inspired by the channel parameters obtained from experimental studies on UAV’s air to base-station channel and on LTE’s 3GPP channel model for ground to base-station traffic. Our numerical evaluations show that depending on the scattering environment and antenna array size, we can attain 2.5x to 6.5x reduction in collision probability. The result of evaluating our algorithm on UAV’s air to base-station channel shows that as a function of node density 1.6x to 3.7x reduction in collision probability is possible with practical array sizes. Moreover, we also show that with parameters from LTE’s 3GPP channel model, nearly 1.7x to 2.5x reduction in collision probability is achievable using our proposed algorithm.