Biology subjects

Palva, M.

Publications and source records attributed to Palva, M..

4 recordsLinked to original sources

bioRxiv2025

A Surgical Protocol for a Large, Resealable Cranial Window Enabling Longitudinal, Multi-Modal Electrophysiological Recordings of the Mouse Default Mode Network

The Default Mode Network (DMN) is a central large-scale brain network implicated in a range of cognitive functions and neuropsychiatric disorders, such as major depressive disorder (MDD). Studying the DMNs complex dynamics in animal models provides invaluable insights into its function in both healthy and pathological states. However, performing a stable, long-term, and large-scale electrophysiological recordings from the multiple, deep, and distributed nodes of the DMN in awake, behaving mice has been a significant challenge. Here, we present a novel, two-phase surgical protocol developed to create a large (4x7.6 mm), durable, and resealable cranial window in mice. The procedure is designed to preserve the integrity of the dura mater, which is paramount for long-term brain health and recording stability. This window facilitates repeated, longitudinal recordings from over 1,000 electrodes simultaneously by combining surface-level micro-electrocorticography ({micro}ECoG) with two high-density intracranial electrode probes, allowing unprecedented access to the DMN. This technique provides a robust platform for multi-modal, multi-scale interrogation of network-wide electrophysiological dynamics over several weeks, opening new avenues for investigating the neuroplastic changes underlying the pathophysiology of brain disorders and for evaluating the chronic effects of novel therapeutics. SUMMARYThis protocol describes a two-phase surgical method to create a large, resealable, dura-sparing cranial window in mice. This technique enables chronic, multi-modal electrophysiological recordings from distributed, deep brain networks, such as the Default Mode Network, over several weeks.

neuroscience↗

bioRxiv2025

Low-dimensional brain-symptom associations delineate depression phenotypes with distinct connectivity biomarkers and symptom profiles

Depression is neurobiologically and clinically heterogeneous. New approaches using resting-state functional MRI (rs-fMRI) functional connectivity (FC) data have modeled the neural basis of depression heterogeneity and revealed unique neural phenotypes. Yet, no studies have identified depression phenotypes from electrophysiological magnetoencephalography (MEG) data although MEG measures human brain dynamics at millisecond precision. We demonstrate here unique depression phenotypes based on MEG-oscillation FC. We collected resting-state MEG, MRI, and clinical symptom data from 263 patients with unipolar depression and 75 healthy controls. We assessed MEG-FC with two oscillatory coupling-mode measures that are fundamental for information processing. To define normative phenotypes, we computed their latent-space low-dimensional brain-symptom associations, and used these components to identify phenotypes using unsupervised machine learning. We identified five stable depression phenotypes that were characterized by unique symptom profiles and distinct spectral patterns. Our results demonstrate new neural underpinnings of depression heterogeneity and reveal unique neural phenotypes with potential personalized diagnostic value.

neuroscience↗

bioRxiv2025

Toward Unified Biomarkers for Focal Epilepsy

Accurately localizing the epileptogenic network (EpiNet) remains a major barrier to effective epilepsy treatment, largely due to limited mechanistic understanding. The EpiNet is a patient-specific brain network shaped by complex, overlapping pathology. While combining biomarkers can improve localization, it also generates high-dimensional feature data that increases the risk of overfitting and reduces interpretability. We hypothesized that the core epileptogenic dynamics could be captured in a low-dimensional latent space derived from empirical data, without the need to record seizures. From interictal stereo-EEG (SEEG) recordings in 64 patients (29 females), we extracted 260 neuronal features and reduced them to 10 latent components using singular value decomposition. A classifier trained on these 10 components was then simplified into a probabilistic EpiNet model requiring only two components as input. Individual position in this two-dimensional latent space correlated with previously reported classification accuracy (r2=0.5), supporting its functional relevance. In three independent patients, the probabilistic model captured time-varying epileptogenic dynamics during sleep-SEEG recordings, corroborated clinical assessments, and achieved peak classification accuracies of 0.63, 0.85, and 0.94. These predictions were independently validated by tensor component analysis. Together, these results provide evidence for a robust low-dimensional representation of epileptogenicity across brain states and pathological substrates. This approach simplifies interpretation, facilitates integration of additional biomarkers, and enables large-scale cohort analyses, establishing a proof of concept for a unified framework for epilepsy biomarkers. Significance StatementTo advance mechanistic understanding of large-scale brain dynamics underlying epilepsy, we combined novel epilepsy biomarkers with interpretable machine learning. From interictal SEEG, we extracted 260 connectivity and criticality features. Dimensionality reduction of these raw features showed that only two components were needed to identify epileptogenic networks, reducing the feature space by >99%. These components were highlighted by abnormal power-law scaling, bistability, and strong inhibition or excitation in the 15-200 Hz range, consistent with prior findings. The model tracked neuropathological dynamics over hours and was validated through tensor component analysis, suggesting that epileptogenic activity is dynamic, subject-specific, and sparsely represented in both state space and cortical networks.

neuroscience↗

bioRxiv2024

Node centrality in MEG resting-state networks covaries with neurotransmitter receptor and transporter density

Neuronal oscillations are central mechanisms in the regulation of neuronal processing and communication (Deco et al., 2011; Fries, 2015; S. Palva & Palva, 2012; Siegel et al., 2012; Singer, 1999), but the relationship between the emergent inter-areal synchronization of oscillations and their underlying synaptic and neuromodulatory mechanisms - the dynome - has remained poorly understood (Kopell et al., 2014). While oscillations are largely generated by fast synaptic neurotransmission among pyramidal cells and interneurons (Traub et al., 2004), these microcircuits are subject to slower neuromodulation in a frequency- and region-specific manner (Batista-Brito et al., 2018; Roopun et al., 2010). While the efferent connections, receptor densities, and neurotransmitter reuptake regulation of neuromodulatory systems are highly heterogeneous across the cortical mantle (Avery & Krichmar, 2017; Deco et al., 2017; Hansen et al., 2022), it has remained unresolved how this variability shapes inter-areal connectivity of neuronal oscillations. Here, we used source-reconstructed human magnetoencephalography (MEG) data to assess how the centrality of brain areas (nodes) in large-scale networks of phase synchrony and amplitude correlations covaries with neurotransmitter receptor and transporter densities. Node centrality strongly covaried with receptor and transporter densities in a coupling- and frequency-specific manner both at the level of individual receptors and transporters and that of principal components. In delta, theta, and gamma frequencies, node centrality in phase-synchronization networks covaried positively, and in high-alpha and beta bands negatively, with dopaminergic, GABA, NMDA, muscarinic, and most serotonergic receptor densities. In amplitude-correlation networks, node centrality in delta and gamma bands covaried positively, and in theta to beta bands negatively, with most receptor and transporter densities. These results establish the contribution of neurotransmitter receptor and transporter densities for shaping connectivity of neuronal oscillations in the human brain and demonstrate a link between coupling of neuronal oscillations with the underlying biological details.

neuroscience↗