Browsing by Author "Sheth, Sameer A."
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Item Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation(Springer Nature, 2023) Fan, Boqiang; Goodman, Wayne; Cho, Raymond Y.; Sheth, Sameer A.; Bouchard, Richard R.; Aazhang, BehnaamThe neuromodulation effect of low-intensity focused ultrasound (LIFU) is highly target-specific. Unintended off-target neuronal excitation can be elicited when the beam focusing accuracy and resolution are limited, whereas the resulted side effect has not been evaluated quantitatively. There is also a lack of methods addressing the minimization of such side effects. Therefore, this work introduces a computational model of unintended neuronal excitation during LIFU neuromodulation, which evaluates the off-target activation area (OTAA) by integrating an ultrasound field model with the neuronal spiking model. In addition, a phased array beam focusing scheme called constrained optimal resolution beamforming (CORB) is proposed to minimize the off-target neuronal excitation area while ensuring effective stimulation in the target brain region. A lower bound of the OTAA is analytically approximated in a simplified homogeneous medium, which could guide the selection of transducer parameters such as aperture size and operating frequency. Simulations in a human head model using three transducer setups show that CORB markedly reduces the OTAA compared with two benchmark beam focusing methods. The high neuromodulation resolution demonstrates the capability of LIFU to effectively limit the side effects during neuromodulation, allowing future clinical applications such as treatment of neuropsychiatric disorders.Item Decoding Depression Severity From Intracranial Neural Activity(Elsevier, 2023) Xiao, Jiayang; Provenza, Nicole R.; Asfouri, Joseph; Myers, John; Mathura, Raissa K.; Metzger, Brian; Adkinson, Joshua A.; Allawala, Anusha B.; Pirtle, Victoria; Oswalt, Denise; Shofty, Ben; Robinson, Meghan E.; Mathew, Sanjay J.; Goodman, Wayne K.; Pouratian, Nader; Schrater, Paul R.; Patel, Ankit B.; Tolias, Andreas S.; Bijanki, Kelly R.; Pitkow, Xaq; Sheth, Sameer A.Background Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. Methods We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. Results Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. Conclusions The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.Item 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, BehnaamObjective. 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.Item Epileptic seizure prediction using spectral width of the covariance matrix(IOP Publishing, 2022) EPMoghaddam, Dorsa; Sheth, Sameer A.; Haneef, Zulfi; Gavvala, Jay; Aazhang, BehnaamObjective. Epilepsy is a common neurological disorder in which patients suffer from sudden and unpredictable seizures. Seizures are caused by excessive and abnormal neuronal activity. Different methods have been employed to investigate electroencephalogram (EEG) data in patients with epilepsy. This paper introduces a simple yet accurate array-based method to study and predict seizures. Approach. We use the CHB-MIT dataset (all 24 cases), which includes scalp EEG recordings. The proposed method is based on the random matrix theory. After applying wavelet decomposition to denoise the data, we analyze the spatial coherence of the epileptic recordings by looking at the width of the covariance matrix eigenvalue distribution at different time and frequency bins. Main results. We train patient-specific support vector machine classifiers to distinguish between interictal and preictal data with high performance and a false prediction rate as low as 0.09 h−1. The proposed technique achieves an average accuracy, specificity, sensitivity, and area under the curve of 99.05%, 93.56%, 99.09%, and 0.99, respectively. Significance. Our proposed method outperforms state-of-the-art works in terms of sensitivity while maintaining a low false prediction rate. Also, in contrast to neural networks, which may achieve high performance, this work provides high sensitivity without compromising interpretability.Item Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology(Elsevier, 2022) Haneef, Zulfi; Yang, Kaiyuan; Sheth, Sameer A.; Aloor, Fuad Z.; Aazhang, Behnaam; Krishnan, Vaishnav; Karakas, CemalSub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEGTM SubQ, NeuroviewTM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.