Liset Menendez de la Prida

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.

High frequency oscillations (HFOs) are being incorporated into the presurgical evaluation of patients with epilepsy and also represent a rapidly evolving field in basic epilepsy research. Animal models have been pivotal in uncovering the mechanisms underlying HFOs and their role in epilepsy. In this review, we provide an overview of HFOs recorded in animal models, highlighting their relevance not only to epilepsy but also to neuropsychiatric and Alzheimer's diseases, suggesting a broader role in brain disorders. Then we explore recent advances, including innovative computational methods for the automated detection and analysis of HFOs. Building on this analysis, we propose guidelines for HFO identification and reporting with translational potential from animal models to humans. Finally, we discuss existing knowledge gaps and outline future directions for research in this rapidly evolving field.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re‐examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting‐edge, large‐scale techniques using micro‐electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra‐slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long‐term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy‐related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long‐term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

Optical approaches to monitor neural activity are transforming neuroscience, owing to a fast-evolving palette of genetically encoded molecular reporters. However, the field still requires robust and label-free technologies to monitor the multifaceted biomolecular changes accompanying brain development, aging or disease. Here, we have developed vibrational fiber photometry as a low-invasive method for label-free monitoring of the biomolecular content of arbitrarily deep regions of the mouse brain in vivo through spontaneous Raman spectroscopy. Using a tapered fiber probe as thin as 1 µm at its tip, we elucidate the cytoarchitecture of the mouse brain, monitor molecular alterations caused by traumatic brain injury, as well as detect markers of brain metastasis with high accuracy. We view our approach, which introduces a deep learning algorithm to suppress probe background, as a promising complement to the existing palette of tools for the optical interrogation of neural function, with application to fundamental and preclinical investigations of the brain and other organs.

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

Identifying 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., sensory coding) or in anatomical image segmentation. We introduce the Structure Index (SI) as a directed graph-based metric to quantify the distribution of feature values projected over data in arbitrary D-dimensional spaces (defined from neurons, time stamps, pixels, genes, etc). The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood of the cloud. Using arbitrary data clouds, we show how the SI provides quantification of the degree and directionality of the 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, and to disclose the local and global structure of the angle and speed represented in different brain regions. Finally, we provide two general-purpose examples (sound and image categorization), to illustrate the potential application to arbitrary dimensional spaces. Our method provides versatile applications in the neuroscience and data science fields.