Biology subjects

Medina, L. E.

Publications and source records attributed to Medina, L. E..

5 recordsLinked to original sources

bioRxiv2026

Retinal Electrophysiological Patterns in Alzheimer's Disease: A Multi-Domain Signal Processing Framework for Non-Invasive Biomarker Discovery Using a Portable ERG Device

Alzheimers disease (AD) is a neurodegenerative disorder affecting more than 55 million people worldwide, with a diagnosis that remains predominantly clinical and frequently delayed. The electroretinogram (ERG) offers a non-invasive electrophysiological method for detecting retinal dysfunction associated with neurodegeneration; however, it remains unclear whether robust and reliable candidate biomarkers can be extracted from ERG signals beyond conventional amplitude- and latency-based parameters. Here we present a pilot study of a multi-domain signal processing framework applied to ERGs recorded from 46 participants (20 AD patients, 26 controls) with a handheld device (RETeval, LKC Technologies) using sinusoidal (1-50 Hz) and photopic ISCEV protocols. Five complementary techniques were implemented: (i) multiscale fuzzy entropy (MSFuzzyEn); (ii) FFT harmonic analysis; (iii) stimulus-response wavelet time-frequency coherence (WTC); (iv) a novel inter-cycle lag variant of sample entropy (SampEnT), introduced to isolate cycle-to-cycle retinal response consistency independently of stimulus periodicity; and (v) discrete wavelet transform (DWT) for energetic extraction of oscillatory potentials (OPs). Univariate comparisons (Mann-Whitney, Cliffs{delta} , Benjamini-Hochberg FDR) identified seven significant candidate biomarkers (q < 0.05), five with large effect size: AUCfast (|{delta}| = 0.546, q = 0.009), Slopevery-slow (|{delta}| = 0.554, q = 0.007), R14f (|{delta}| = 0.515, q = 0.031), SampEnT (|{delta}| = 0.504, q = 0.019) and WTCR,mean (|{delta}| = 0.531, q = 0.023); and two with medium effect size (OP_amp_sum, band_snr). A logistic regression classifier combining three candidate biomarkers, validated by leave-one-out cross-validation, achieved ROC-AUC = 0.858, sensitivity = 70.0% and specificity = 88.5% (n = 46). These proof-of-concept results demonstrate that multi-domain ERG analysis captures retinal temporal dysfunction signatures in AD that are inaccessible to standard clinical analysis, supporting further investigation of portable ERG devices as a source of non-invasive candidate biomarkers for early AD detection.

bioengineering↗

bioRxiv2025

XBP1s as a Therapeutic Target to Preserve Retinal Function During Aging and Neurodegeneration

Loss of physiological complexity, characterized by reduced adaptive multiscale coordination among system components, is increasingly recognized as a hallmark of aging and neurodegenerative disease. The retina, a window into the brain, offers a unique, accessible platform to monitor neurodegenerative disorders such as Alzheimers disease (AD). Here, we investigate the therapeutic potential of the unfolded protein response transcription factor XBP1s in preserving retinal function during aging and AD-related pathology in murine models. Using micro-electroretinography with multielectrode arrays, we recorded retinal responses to chirp and white noise stimuli in four mouse models: wild-type (WT), XBP1s-overexpressing (TgXBP1s), AD model (5xFAD), and their crossbreed (TgXBP1s/5xFAD) at approximately 3 and 7 months of age. We assessed retinal signals through entropy-based complexity measures and wavelet coherence between stimulus and response. While WT and 5xFAD mice exhibited age-related decline in retinal complexity, TgXBP1s and TgXBP1s/5xFAD mice maintained higher complexity levels and increased Wcoh in adulthood, indicating functional preservation. These results demonstrate that sus-tained XBP1s expression protects retinal electrophysiological integrity and highlight the retinas value as a scalable, noninvasive biomarker platform to evaluate therapeutic efficacy targeting neurodegenerative mechanisms.

neuroscience↗

bioRxiv2023

Towards a more accurate quasi-static approximation of the electric potential for neurostimulation with kilohertz-frequency sources

ObjectiveOur goal was to determine the conditions for which a more precise calculation of the electric potential than the quasi-static approximation may be needed in models of electrical neurostimulation, particularly for signals with kilohertz-frequency components. ApproachWe conducted a comprehensive quantitative study of the differences in nerve fiber activation and conduction block when using the quasi-static and Helmholtz approximations for the electric potential in a model of electrical neurostimulation. Main resultsWe first show that the potentials generated by sources of unbalanced pulses exhibit different transients as compared to those of energy-balanced pulses, and this is disregarded by the quasi-static assumption. Secondly, the relative errors for current-distance curves were below 3%, while for strength-duration curves the variations ranged between 1-17%, but could be improved to less than 3% across the range of pulse duration by providing a corrected quasi-static conductivity. Third, we extended our analysis to trains of pulses and reported a "congruence area" below 700 Hz, where the fidelity of fiber responses is maximal for suprathreshold stimulation. Further examination of waveforms and polarities revealed similar fidelities in the congruence area, but significant differences were observed beyond this area. However, the spike-train distance revealed differences in activation patterns when comparing the response generated by each model. Finally, in simulations of conduction-block, we found that block thresholds exhibited errors above 20% for repetition rates above 10 kHz. Yet, employing a corrected value of the conductivity improved the agreement between models, with errors no greater than 8%. SignificanceOur results emphasize that the quasi-static approximation cannot be naively extended to electrical stimulation with high-frequency components, and notable differences can be observed in activation patterns. As well, we introduce a methodology to obtain more precise model responses using the quasi-static approach, which can be a valuable resource in computational neuroengineering.

neuroscience↗

bioRxiv2022

A partially averaged system to model neuron responses to interferential current stimulation

The interferential current (IFC) therapy is a noninvasive electrical neurostimulation technique intended to activate deep neurons using surface electrodes. In IFC, two independent kilohertz-frequency currents purportedly intersect where an interference field is generated. However, the effects of IFC on neurons within and outside the interference field are not completely understood, and it is unclear whether this technique can reliable activate deep target neurons without side effects. In recent years, realistic computational models of IFC have been introduced to quantify the effects of IFC on brain cells, but they are often complex and computationally costly. Here, we introduce a simplified model of IFC based on the FitzHugh-Nagumo (FHN) model of a neuron. By considering a modified averaging method, we obtain a non-autonomous approximated system, with explicit representation of relevant IFC parameters. For this approximated system we determine conditions under which it reliably approximates the complete FHN system under IFC stimulation, and we mathematically prove its ability to predict nonspiking states. In addition, we perform numerical simulations that show that the interference effect is observed only for a narrow set of IFC parameters and, in particular, for a beat frequency no higher than about 100 [Hz]. Our novel model tailored to the IFC technique contributes to the understanding of neurostimulation modalities using this type of signals, and can have implications in the design of noninvasive electrical stimulation therapies.

neuroscience↗

bioRxiv2019

Creating a neuroprosthesis for active tactile exploration of textures

Intracortical microstimulation (ICMS) of the primary somatosensory cortex (S1) can produce percepts that mimic somatic sensation and thus has potential as an approach to sensorize prosthetic limbs. However, it is not known whether ICMS could recreate active texture exploration--the ability to infer information about object texture by using ones fingertips to scan a surface. Here we show that ICMS of S1 can convey information about the spatial frequencies of invisible virtual gratings through a process of active tactile exploration. Two rhesus monkeys scanned pairs of visually identical screen objects with the fingertip of a hand avatar, controlled via a joystick and later via a brain-machine interface, to find the one with denser virtual gratings. The gratings consisted of evenly spaced ridges that were signaled through ICMS pulses generated when the avatars fingertip crossed each ridge. The monkeys learned to interpret these ICMS patterns evoked by the interplay of their voluntary movements and the virtual textures of each object. Discrimination accuracy across a range of grating densities followed Webers law of just-noticeable differences (JND), a finding that matches normal cutaneous sensation. Moreover, one monkey developed an active scanning strategy where avatar velocity was integrated with the ICMS pulses to interpret the texture information. We propose that this approach could equip upper-limb neuroprostheses with direct access to texture features acquired during active exploration of natural objects.

neuroscience↗