Biology subjects

Palacios, A. G.

Publications and source records attributed to Palacios, A. G..

10 recordsLinked to original sources

bioRxiv2026

Optimized AAV to express the unfolded protein response transcription factor XBP1s ameliorates Alzheimer's disease features in mouse models

Proteostasis impairment at the level of the endoplasmic reticulum (ER) is a salient feature of Alzheimers disease (AD). The unfolded protein response (UPR) is the main pathway to cope with ER stress, where the expression of the transcription factor X-Box binding protein 1 (XBP1) is central to establish repair programs. To artificially enforce the adaptive capacity of the UPR in the AD brain, we recently reported the protective effects of overexpressing active XBP1 in the brain using adeno-associated vectors (AAVs) of AD mice, in addition to aged animals. Here we have generated a next generation vector suitable for clinical testing by (i) expressing codon-optimized human XBP1s without artificial tags, (ii) the use of the synapsin promoter to restrict expression to neurons, and (iii) incorporating a novel variant of AAV2 (AAV-TT) with greater biodistribution (here termed Proteostaser-1). Treatment of 5xFAD mice with Proteostaser-1 improved spatial learning and synaptic plasticity, and reduced the deposition of amyloid plaques in the brain. Proteostaser-1 administration also improved cognition in a model of sporadic AD based on the intracerebral injection of amyloid {beta} oligomers. Our results further support the therapeutic potential of the UPR as a strategy to ameliorate AD features and sustain synaptic function.

cell biology↗

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

Cognitive Resilience in Aging Degus is Linked to CA3 Hippocampal GABAergic Integrity

The preservation of cognitive function during aging remains a key challenge in neuroscience. In this study, we applied an integrative approach, combining behavioral assays with neurophysiological recordings, to investigate hippocampal circuit integrity. We used Octodon degus, a rodent with exceptional longevity (up to 10 years in laboratory conditions), as a natural model of aging and neurodegenerative disease such as Alzheimer. To assess agerelated cognitive changes, we employed three behavioral tasks: Novel Object Recognition (NOR), Open Field (OF), and the Burrowing Test (BT). The BT reflects Activities of Daily Living (ADLs) and is based on species-typical spontaneous burrowing behavior, which has been linked to neurodegenerative markers in degus. We also performed multielectrode electro-physiological recordings to assess GABAergic function in the hippocampus. Aged degus with high BT performance (classified as good burrowers, or GB) showed robust hippocampal activity, especially in the CA3 region, a key hub for signal integration and memory encoding. In contrast, degus with poor BT performance (bad burrowers, or BB) exhibited reduced spontaneous hippocampal activity, suggesting potential compensation via GABA-independent synaptic mechanisms. Altogether, our findings suggest that preserved GABAergic function supports cognitive resilience in aging degus. These results offer new insights into the neural mechanisms underlying healthy cognitive aging and may inform future strategies for preventing or mitigating neurodegeneration.

neuroscience↗

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↗

bioRxiv2022

The unfolded protein response transcription factor XBP1s ameliorates Alzheimer`s disease by improving synaptic function and proteostasis

Alteration in the buffering capacity of the proteostasis network is an emerging feature of Alzheimers disease (AD), highlighting the occurrence of endoplasmic reticulum (ER) stress. The unfolded protein response (UPR) is the main adaptive pathway to cope with protein folding stress at the ER. Inositol requiring enzyme-1 (IRE1) is an ER-located kinase and endoribonuclease that operates as a central ER stress sensor, enabling the establishment of adaptive and repair programs through the control of the expression of the transcription factor X-Box binding protein 1 (XBP1). A polymorphism in the XBP1 promoter has been suggested as a risk factor for AD. To artificially enforce the adaptive capacity of the UPR in the AD brain, we developed strategies to express the active form of XBP1 in neurons using preclinical models. Overexpression of an active form of XBP1 in the nervous system using transgenic mice significantly reduced the load of amyloid deposits in the cerebral cortex and hippocampus and preserved synaptic and cognitive function. Moreover, local delivery of XBP1 into the hippocampus of an AD mice using adeno-associated vectors improved long-term potentiation, memory performance, and dendritic spine density. Quantitative proteomics of the hippocampus revealed that XBP1 expression corrects a large proportion of the alterations observed in the 5xFAD model, restoring the levels of several synaptic proteins and factors involved in actin cytoskeleton regulation and axonal growth. Our results illustrate the therapeutic potential of targeting UPR-dependent gene expression programs as a strategy to ameliorate AD features and sustain synaptic function.

animal behavior and cognition↗

bioRxiv2021

Parallel processing of natural images by overlapping retinal neuronal ensembles

Even though the retinal microcircuit organization has been described in detail at the single-cell level, little is known about how groups of retinal cells coordinated activity encode and process parallel information representing the spatial and temporal structure of changing environmental conditions. To describe the population dynamics of retinal neuronal ensembles, we used microelectrode array recordings that describe hundreds of retinal ganglion cells simultaneous activity in response to a short movie captured in the natural environment where our subject develops their visual behaviors. The vectorization of population activity allowed the identification of retinal neuronal ensembles that synchronize to specific segments of natural stimuli. These synchronous retinal neuronal ensembles were reliably activated by the same stimuli at different trials, indicating a robust population response of retinal microcircuits. The generation of asynchronous events required integrating a physiologically meaningful time window larger than 80 ms, demonstrating that retinal neuronal ensembles time integration filters non-structured visual information. Interestingly, individual neurons could be part of several ensembles indicating that parallel circuits could encode environmental conditions changes. We conclude that parallel neuronal ensembles could represent the functional unit of retinal computations and propose that the further study of retinal neuronal ensembles could reveal emergent properties of retinal circuits that individual cells activity cannot explain.

neuroscience↗

bioRxiv2020

Scalable and accurate automated method for neuronal ensemble detection in spiking neural networks

We propose a novel, scalable, and accurate automated method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity is already organized in clearly distinguishable functional ensembles. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. Additionally, we found that our method outperforms current alternative methodologies. Finally, we provide a Graphic User Interface, which aims to facilitate our methods use by the scientific community. Author summaryNeuronal ensembles are strongly interconnected groups of neurons that tend to fire together (Hebb 1949). However, even when this concept was proposed more than 70 years ago, only recent advances in multi-electrode arrays and calcium imaging, statistical methods, and computing power have made it possible to record and analyze multiple neurons activities spiking simultaneously, providing a unique opportunity to study how groups of neurons form ensembles spontaneously and under different stimuli scenarios. Using our method, we found that retinal ganglion cells show a consistent stimuli-evoked ensemble activity, and, when validated with synthetic data, the method shows good performance by detecting the number of ensembles, the activation times, and the core-cells for a wide range of firing rates and number of ensembles accurately.

neuroscience↗

bioRxiv2018

Speed-Selectivity in Retinal Ganglion Cells is Modulated by the Complexity of the Visual Stimulus

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, is remain unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently. Interestingly, these properties were observed throughout different populations of RGCs. Thus, our results reveal that the response at the level of RGCs is modulated by the naturalness of the stimulus - in particular for motion - which suggests that the tuning to the statistics of natural images already emerges at the level of the retina.

neuroscience↗

bioRxiv2018

Dimensionality Reduction on Spatio-Temporal Maximum Entropy Models of Spiking Networks

Maximum entropy models (MEM) have been widely used in the last 10 years to characterize the statistics of networks of spiking neurons. A major drawback of this approach is that the number of parameters used in the statistical model increases very fast with the network size, hindering its interpretation and fast computation. Here, we present a novel framework of dimensionality reduction for generalized MEM handling spatio-temporal correlations. This formalism is based on information geometry where a MEM is a point on a large-dimensional manifold. We exploit the geometrical properties of this manifold in order to find a projection on a lower dimensional space that best captures the high-order statistics. This allows us to define a quantitative criterion that we call the \"degree of compressibility\" of the neuronal code. A powerful aspect of this method is that it does not require fitting the model. Indeed, the matrix defining the metric of the manifold is computed directly via the data without parameters fitting. The method is first validated using synthetic data generated by a known statistics. We then analyze a MEM having more parameters than the underlying data statistics and show that our method detects the extra dimensions. We then test it on experimental retinal data. We record retinal ganglion cells (RGC) spiking data using multi-electrode arrays (MEA) under different visual stimuli: spontaneous activity, white noise stimulus, and natural scene. Using our method, we report a dimensionality reduction up to 50% for retinal data. As we show, this is quite a huge reduction compared to a randomly generated spike train, suggesting that the neuronal code, in these experiments, is highly compressible. This additionally shows that the dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics by modifying the level of redundancy.\n\nAuthor SummaryMaximum entropy models (MEM) have been widely used to characterize the statistics of networks of spiking neurons. However, as the network size increases, the number of model parameters increases rapidly, hindering its interpretation and fast computation. Here, we propose a method to evaluate the dimensionality reduction of MEM, based on the geometrical properties of the manifold best capturing the network high-order statistics. Our method is validated with synthetic data using independent or correlated neural responses. Importantly, we show that dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics modifying the level of redundancy.

neuroscience↗

bioRxiv2018

Functional Asymmetries between Central and Peripheral Retinal Ganglion Cells in a Diurnal Rodent

The segregated properties of the visual system processing central or peripheral regions of the visual field have been widely studied in the visual cortex and the LGN, but rarely reported in retina. The retina performs complex computational strategies to extract spatial-temporal features that are in coherence with animal behavior and survival. Even if a big effort has been done to functionally characterize different retinal ganglion cell (RGC) types, a clear account of the particular functionality of central and peripheral cells is still missing. Here, using electrophysiological data obtained with a 256-MEA recording system on female diurnal rodent retinas (Octodon degus), we evidenced that peripheral RGCs have larger receptive fields, more sustained, faster and shorter temporal responses and sensitive to higher temporal frequencies with a broader frequency bandwidth than the center. Additionally, we also compared the asymmetries between ON and OFF cell populations present in each region, reporting that these asymmetries are dependent on the eccentricity. Finally, the presence of the asymmetries here reported emphasizes even more the complexity of computational strategies performed by the retina, which could serve as inspiration for the development of artificial visual systems.

neuroscience↗