Biology subjects

de la Prida, L. M.

Publications and source records attributed to de la Prida, L. M..

9 recordsLinked to original sources

bioRxiv2026

Blade-dependent molecular identity and neurogenic potential in the adult dentate gyrus

The dentate gyrus (DG) is a key hippocampal gateway for cognition and emotion and a major site of adult neurogenesis, yet its organization along the transverse (suprapyramidal- infrapyramidal) axis remains poorly understood. Here, by integrating bulk RNA sequencing of microdissected mouse DG blades with spatial transcriptomics and single-nucleus RNA sequencing, we define the suprapyramidal and infrapyramidal blades (SB and IB) as distinct molecular compartments characterized by anterior-posterior-dependent gene expression programs. Functionally, the SB exhibits enhanced neurogenic activity, particularly in the anterior DG, including increased progenitor proliferation and neuronal differentiation, whereas the IB contains a larger pool of quiescent neural stem cells. Together, these findings reveal molecular and functional specialization along both transverse and longitudinal axes of the DG and provide a framework for interrogating hippocampal subregional organization in health and disease.

neuroscience↗

bioRxiv2026

Low-latency neuromorphic closed-loop control of hippocampal ripples in vivo

Real-time closed-loop neuromodulation, in which stimulation is precisely timed to ongoing brain dynamics, holds transformative potential for treating neurological disorders and probing neural circuit function. However, it requires low-latency, energy-efficient processing of high-bandwidth neural signals that conventional computing architectures struggle to deliver. Neuromorphic computing, which emulates the event-driven and massively parallel operation of biological neural circuits, offers a compelling alternative. Yet, its integration into closed-loop frameworks validated in vivo for fast, transient oscillations has not been demonstrated. Here, we present a fully integrated neuromorphic framework for real-time detection and manipulation of hippocampal ripples: brief (30-100 ms), high-frequency (100-250 Hz) oscillations that are critical for memory consolidation and implicated in neurological disorders. We train compact spiking neural networks comprising 41 neurons and 530 parameters using surrogate-gradient backpropagation, achieving detection performance competitive with deep learning models across 23 recording sessions while consuming up to 200-fold less energy when deployed on SpiNNaker neuromorphic hardware. Integration with the open-source Open Ephys platform yields total closed-loop latencies of approximately 50 ms, enabling intra-event stimulation in up to 80% of ripples. Validating the complete sensing-processing-stimulation pipeline in awake, head-fixed mice, we demonstrate that neuromorphic-triggered optogenetic inhibition significantly alters ripple dynamics and reduces oscillatory energy. This work establishes a practical and accessible neuromorphic framework for low-latency closed-loop control of fast brain dynamics in vivo.

neuroscience↗

bioRxiv2023

A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species

The study of sharp-wave ripples (SWRs) has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy and Alzheimers disease is considered a biomarker of dysfunction. SWRs exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine learning (ML) models for automatic detection and analysis of SWRs. The ML architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of SWR features recorded in the dorsal hippocampus of mice. When applied to data from the macaque hippocampus, these models were able to generalize detection and revealed shared SWR properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize SWR research, lowering the threshold for its adoption in biomedical applications.

neuroscience↗

bioRxiv2022

Quantifying the distribution of feature values over data represented in arbitrary dimensional spaces

BackgroundIdentifying the structured distribution (or lack thereof) of a given feature over a point cloud is a general research question. In the neuroscience field, this problem arises while investigating representations over neural manifolds (e.g., spatial coding), in the analysis of neurophysiological signals (e.g., auditory coding) or in anatomical image segmentation. New methodWe introduce the Structure Index (SI) as a graph-based topological metric to quantify the distribution of feature values projected over data in arbitrary D-dimensional spaces (neurons, time stamps, pixels). The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood. ResultsUsing model data clouds we show how the SI provides quantification of the degree of local versus global organization of feature distribution. SI can be applied to both scalar and vectorial features permitting quantification of the relative contribution of related variables. When applied to experimental studies of head-direction cells, it is able to retrieve consistent feature structure from both the high- and low-dimensional representations. Finally, we provide two general-purpose examples (sound and image categorization), to illustrate the potential application to arbitrary dimensional spaces. Comparison with existing methodsMost methods for quantifying structure depend on cluster analysis, which are suboptimal for continuous features and non-discrete data clouds. SI unbiasedly quantifies structure from continuous data in any dimensional space. ConclusionsThe method provides versatile applications in the neuroscience and data science fields HighlightsO_LIThe Structure Index is a graph-based topological metric C_LIO_LIIt quantifies the distribution of feature values in arbitrary dimensional spaces C_LIO_LIIt can be applied to both scalar and vectorial features C_LIO_LIWhen applied to the head-direction neural system, it extracts concordant information from high- and low-dimensional representations C_LIO_LIIt can be extended to sound and image categorization, expanding the range of applications C_LI

neuroscience↗

bioRxiv2022

Deep brain cancer metastasis detection with wide-volume Raman spectroscopy through a single tapered fiber

We propose a low-invasive method to enable implantable, large volume Raman spectroscopy in arbitrarily deep brain regions of the mouse brain. Using a single tapered fiber as thin as 1 m at the tip, we identified diagnostic markers of brain metastasis - the most frequent brain tumor in human adults - with overall accuracy [≥] 90%. We view our approach as a promising complement to the existing palette of tools for optical interrogation of the brain.

neuroscience↗

bioRxiv2022

Deep learning based feature extraction for prediction and interpretation of sharp- wave ripples

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWR), one of the most synchronous events of the brain. SWR reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. Current spectral methods fail to capture their mechanistic complexity, thus limiting progress. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for automatic identification of SWR. When applied to ultra-dense hippocampus-wide recordings, we discovered physiologically relevant processes associated to the emergence of SWR, prompting for novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied in feature-based specialization, which permit identification of spatially segregated oscillations and deflections, as well as synchronous population firing. Thus, using deep learning based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.

neuroscience↗

bioRxiv2021

Sublayer- and cell-type-specific neurodegenerative transcriptional trajectories in hippocampal sclerosis

Hippocampal sclerosis, the major neuropathological hallmark of temporal lobe epilepsy, is characterized by different patterns of neuronal loss. The mechanisms of cell-type specific vulnerability, their progression and histopathological classification remain controversial. Here using single-cell electrophysiology in vivo and immediate early gene expression, we reveal that superficial CA1 pyramidal neurons are overactive in epileptic rats and mice in vivo. Bulk tissue and single-nucleus expression profiling disclosed sublayer-specific transcriptomic signatures and robust microglial pro-inflammatory responses. Transcripts regulating neuronal processes such as voltage-channels, synaptic signalling and cell adhesion molecules were deregulated by epilepsy differently across sublayers, while neurodegenerative signatures primarily involved superficial cells. Pseudotime analysis of gene expression in single-nuclei and in situ validation revealed separated trajectories from health to epilepsy across cell types, and identified a subset of superficial cells undergoing a later stage in neurodegeneration. Our findings indicate sublayer- and cell type-specific changes associated with selective CA1 neuronal damage contributing to progression of hippocampal sclerosis.

neuroscience↗

bioRxiv2020

Multimodal determinants of phase-locked dynamics across deep-superficial hippocampal sublayers during theta oscillations

Theta oscillations play a major role in temporarily defining the hippocampal rate code by translating behavioural sequences into neuronal representations. However, mechanisms constraining phase timing and cell-type specific phase preference are unknown. Here, we employ computational models tuned with evolutionary algorithms to evaluate phase preference of individual CA1 pyramidal cells recorded in mice and rats not engaged in any particular memory task. We applied unbiased and hypothesis-free approaches to identify effects of intrinsic and synaptic factors, as well as cell morphology, in determining phase preference. We found that perisomatic inhibition delivered by complementary populations of basket cells interacts with input pathways to shape phase-locked specificity of deep and superficial pyramidal cells. Somatodendritic integration of fluctuating glutamatergic inputs defined cycle-by-cycle by unsupervised methods demonstrated that firing selection is tuneable across sublayers. Our data identify different mechanisms of phase-locking selectivity that are instrumental for high-level flexible dynamical representations.

neuroscience↗

bioRxiv2018

KCC2 regulates neuronal excitability and hippocampal activity via interaction with Task-3 channels

The K+/Cl- co-transporter KCC2 (SLC12A5) regulates neuronal transmembrane chloride gradients and thereby controls GABA signaling in the brain. KCC2 downregulation is observed in several neurological and psychiatric disorders including epilepsy, neuropathic pain and autism spectrum disorders. Paradoxical, excitatory GABA signaling is usually assumed to contribute to abnormal network activity underlying the pathology. We tested this hypothesis and explored the functional impact of chronic KCC2 downregulation in the rat dentate gyrus. Although the reversal potential of GABAA receptor currents was depolarized in KCC2 knockdown neurons, this shift was fully compensated by depolarization of their resting membrane potential. This effect was due to downregulation of Task-3 leak potassium channels that we show require KCC2 for membrane trafficking. Increased neuronal excitability upon KCC2 suppression altered dentate gyrus rhythmogenesis that could be normalized by chemogenetic hyperpolarization. Our data reveal KCC2 downregulation engages complex synaptic and cellular alterations beyond GABA signaling that concur to perturb network activity, thus offering novel targets for therapeutic intervention.

neuroscience↗