Modal Analysis of Cerebrovascular Effects for Digital Health Integration of Neurostimulation Therapies-A Review of Technology Concepts.

Transcranial electrical stimulation (tES) is increasingly recognized for its potential to modulate cerebral blood flow (CBF) and evoke cerebrovascular reactivity (CVR), which are crucial in conditions like mild cognitive impairment (MCI) and dementia. This study explores the impact of tES on the neurovascular unit (NVU), employing a physiological modeling approach to simulate the vascular response to electric fields generated by tES. Utilizing the FitzHugh-Nagumo model for neuroelectrical activity, we demonstrate how tES can initiate vascular responses such as vasoconstriction followed by delayed vasodilation in cerebral arterioles, potentially modulated by a combination of local metabolic demands and autonomic regulation (pivotal locus coeruleus). Here, four distinct pathways within the NVU were modeled to reflect the complex interplay between synaptic activity, astrocytic influences, perivascular potassium dynamics, and smooth muscle cell responses. Modal analysis revealed characteristic dynamics of these pathways, suggesting that oscillatory tES may finely tune the vascular tone by modulating the stiffness and elasticity of blood vessel walls, possibly by also impacting endothelial glycocalyx function. The findings underscore the therapeutic potential vis-à-vis blood-brain barrier safety of tES in modulating neurovascular coupling and cognitive function needing the precise modulation of NVU dynamics. This technology review supports the human-in-the-loop integration of tES leveraging digital health technologies for the personalized management of cerebral blood flow, offering new avenues for treating vascular cognitive disorders. Future studies should aim to optimize tES parameters using computational modeling and validate these models in clinical settings, enhancing the understanding of tES in neurovascular health.

Cerebral and muscle near-infrared spectroscopy during lower-limb muscle activity - volitional and neuromuscular electrical stimulation.

Chronic venous insufficiency (CVI) can lead to blood clotting in the deep veins of the legs, a disease known as deep vein thrombosis. An estimated 40 percent of people in the United States have venous insufficiency that may be ameliorated with neuromuscular electrical stimulation (NMES). Near-infrared spectroscopy (NIRS) is a non-invasive optical imaging method for monitoring hemodynamics. NIRS, being an optical technique has no stimulation artefact, can be combined with NMES for theranostics application. In this study, we combined muscle NIRS (mNIRS) with electromyogram (EMG) of the calf muscles to detect blood volume changes (based on total hemoglobin concentration) in the muscle during volitional tiptoe movements at different frequencies. Also, blood volume changes were measured during NMES (using the geko™ device) at different device settings. In the mNIRS+NMES study, we also measured the cerebral hemodynamics using functional NIRS (fNIRS). The mNIRS was conducted using a frequency domain (FD) method (called FDNIRS) that used a multi-distance method to isolate muscle hemodynamics. FDNIRS-EMG study in ten healthy humans found a statistically significant (p<0.05) effect of the tiptoe frequencies on the EMG magnitude (and power) that increased with tiptoe frequency. Also, the muscle blood volume (standing/rest) decreased (p<0.01) with increasing tiptoe frequency and increasing NMES intensity that was statistically significantly (p<0.05) different between males and females. Moreover, increasing NMES intensity led to a statistically significant (p<0.01) increase in the cerebral blood volume - measured with fNIRS. Therefore, combined mNIRS and fNIRS with NMES can provide a theranostics application for brain+muscle in CVI.

A deep learning model for detection and tracking in high-throughput images of organoid.

Organoid, an in vitro 3D culture, has extremely high similarity with its source organ or tissue, which creates a model in vitro that simulates the in vivo environment. Organoids have been extensively studied in cell biology, precision medicine, drug toxicity, efficacy tests, etc., which have been proven to have high research value. Periodic observation of organoids in microscopic images to obtain morphological or growth characteristics is essential for organoid research. It is difficult and time-consuming to perform manual screens for organoids, but there is no better solution in the prior art. In this paper, we established the first high-throughput organoid image dataset for organoids detection and tracking, which experienced experts annotate in detail. Moreover, we propose a novel deep neural network (DNN) that effectively detects organoids and dynamically tracks them throughout the entire culture. We divided our solution into two steps: First, the high-throughput sequential images are processed frame by frame to detect all organoids; Second, the similarities of the organoids in the adjacent frames are computed, and the organoids on the adjacent frames are matched in pairs. With the help of our proposed dataset, our model achieves organoids detection and tracking with fast speed and high accuracy, effectively reducing the burden on researchers. To our knowledge, this is the first exploration of applying deep learning to organoid tracking tasks. Experiments have demonstrated that our proposed method achieved satisfactory results on organoid detection and tracking, verifying the great potential of deep learning technology in this field.

A proof of concept 'phase zero' study of neurodevelopment using brain organoid models with Vis/near-infrared spectroscopy and electrophysiology.

Homeostatic control of neuronal excitability by modulation of synaptic inhibition (I) and excitation (E) of the principal neurons is important during brain maturation. The fundamental features of in-utero brain development, including local synaptic E-I ratio and bioenergetics, can be modeled by cerebral organoids (CO) that have exhibited highly regular nested oscillatory network events. Therefore, we evaluated a 'Phase Zero' clinical study platform combining broadband Vis/near-infrared(NIR) spectroscopy and electrophysiology with studying E-I ratio based on the spectral exponent of local field potentials and bioenergetics based on the activity of mitochondrial Cytochrome-C Oxidase (CCO). We found a significant effect of the age of the healthy controls iPSC CO from 23 days to 3 months on the CCO activity (chi-square (2, N = 10) = 20, p = 4.5400e-05), and spectral exponent between 30-50 Hz (chi-square (2, N = 16) = 13.88, p = 0.001). Also, a significant effect of drugs, choline (CHO), idebenone (IDB), R-alpha-lipoic acid plus acetyl-L-carnitine (LCLA), was found on the CCO activity (chi-square (3, N = 10) = 25.44, p = 1.2492e-05), spectral exponent between 1 and 20 Hz (chi-square (3, N = 16) = 43.5, p = 1.9273e-09) and 30-50 Hz (chi-square (3, N = 16) = 23.47, p = 3.2148e-05) in 34 days old CO from schizophrenia (SCZ) patients iPSC. We present the feasibility of a multimodal approach, combining electrophysiology and broadband Vis-NIR spectroscopy, to monitor neurodevelopment in brain organoid models that can complement traditional drug design approaches to test clinically meaningful hypotheses.