Biology subjects

Pedersen, N. P.

Publications and source records attributed to Pedersen, N. P..

5 recordsLinked to original sources

bioRxiv2026

Directed Human Structural Connectome Reveals Hierarchical Organization and Shapes Large-Scale Brain Dynamics

The human structural connectome, most commonly derived from diffusion-weighted imaging (DWI) and tractography, provides a macroscopic description of whole-brain wiring and serves as the structural foundation of network neuroscience, large-scale brain simulations, and personalized digital brain twins. However, tractography-derived connectomes are fundamentally limited by their inability to distinguish afferent from efferent connections, yielding networks that are undirected and therefore blind to the hierarchical organization imposed by the directionality of anatomical connections. In this study, we introduce a directed human structural connectome (DHSC) by transferring tracer-derived projection patterns from macaque to human using cross-species connectivity blueprints. Topological analysis of the DHSC manifests biological plausibility, a small-world network organization, and a directionality-based hierarchy, which offer the hierarchical organization of human brain networks. In the context of brain dynamics, the introduction of directionality reshapes the propagation and persistence of sensory inputs. DHSC also best captures the empirical spatiotemporal dynamics of stimulus-evoked brain activity. The findings demonstrate that anatomical directionality is a critical determinant of large-scale brain organization and dynamics. This provides evidence that directed connectome may offer potential advantages in large-scale simulations of the human brain. The resulting DHSC, along with all related analyses and data are openly available.

neuroscience↗

bioRxiv2025

Temporal Interference Stimulation Can Enhance or Disrupt Human Memory Encoding as a Function of Brain Location and Frequency

Visual memory relies on synchronized interactions and rhythms between the medial temporal lobes and neocortical brain regions. Non-invasive manipulation of memory-related brain regions, specifically deeper temporal lobe regions, has been limited by the lack of precision of non-invasive neuromodulation - when targeting deeper structures, the cortex is always stimulated, never deeper structures in isolation. Temporal Interference (TI) stimulation, a novel non-invasive technique, uses high-frequency carrier fields to deliver targeted, physiologically relevant neuromodulation via amplitude-modulated envelopes at specific brain regions. Here, we investigate TIs impact on figure memory encoding in 70 healthy participants using the Rey-Osterrieth and Taylor Complex Figure tasks, with TI applied in several brain regions independently and simultaneously - allowing investigation of combinations of medial temporal lobe and neocortical brain regions. Interestingly, higher frequency TI envelopes (130 Hz offset) targeting bilateral hippocampi and temporal cortices significantly impair recall (p = 6.54e-04), while lower frequency TI envelopes (5 Hz offset) targeting only the bilateral hippocampi significantly enhance recall (p = 0.0447). Stimulation using other combinations of medial temporal lobe and neocortical regions showed no effect, underscoring the critical role of frequency and focality of non-invasive brain stimulation and correct target selection. Finally, functional MRI reveals strong differences between the effects of 130 Hz and 5 Hz envelopes, specifically in hippocampal BOLD signals, brain connectivity, default mode, and attentional networks. These findings demonstrate TIs ability to bidirectionally modulate memory encoding through precise frequency and target tuning, offering a powerful tool for cognitive neuroscience and potential therapeutic applications for memory disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/677714v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@8e9db3org.highwire.dtl.DTLVardef@1135a3corg.highwire.dtl.DTLVardef@189345dorg.highwire.dtl.DTLVardef@ccca8f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Temporal Interference stimulation for bidirectional memory modulation assessed via behavioral testing and MRI scanning. C_FIG

neuroscience↗

bioRxiv2023

Classifier for the Rapid Simultaneous Determination of Sleep-Wake States and Seizures in Mice

Sleep-wake states bi-directionally interact with epilepsy and seizures, but the mechanisms are unknown. A barrier to comprehensive characterization and the study of mechanisms has been the difficulty of annotating large chronic recording datasets. To overcome this barrier, we sought to develop an automated method of classifying sleep-wake states, seizures, and the post-ictal state in mice ranging from controls to mice with severe epilepsy with accompanying background EEG abnormalities. We utilized a large dataset of recordings, including EMG, EEG, and hippocampal local field potentials, from control and intra-amygdala kainic acid-treated mice. We found that an existing sleep-wake classifier performed poorly, even after retraining. A support vector machine, relying on typically used scoring parameters, also performed below our benchmark. We then trained and evaluated several multi-layer neural network architectures and found that a bidirectional long short-term memory-based model performed best. This Sleep-Wake and Ictal State Classifier (SWISC) showed high agreement between ground-truth and classifier scores for all sleep and seizure states in an unseen and unlearned epileptic dataset (average agreement 96.41% {+/-} SD 3.80%), and saline animals (97.77% {+/-} 1.40%). Channel elimination and feature selection provided interpretability and demonstrated that SWISC was primarily dependent on hippocampal signals, yet still maintained good performance ([~]90% agreement) with EEG alone, thereby expanding the classifiers applicability to other epilepsy datasets. SWISC enables the efficient combined scoring of sleep-wake and seizure states in mouse models of epilepsy and healthy controls, facilitating comprehensive and mechanistic studies of sleep-wake and biological rhythms in epilepsy.

neuroscience↗

bioRxiv2021

Ultrafast large-scale simulations of biophysically realistic neurons using deep learning

Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. To understand how multiple parameters contribute synergistically to circuit behavior, neuronal computational models are regularly employed. However, traditional models containing anatomically and biophysically realistic neurons are computationally demanding even when scaled to model local circuits. To overcome this limitation, we trained several artificial neural net (ANN) architectures to model the activity of realistic, multicompartmental neurons. We identified a single ANN that accurately predicted both subthreshold and action potential firing and correctly generalized its responses to previously unobserved synaptic input. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach that allows for rapid, detailed network experiments using inexpensive, readily available computational resources.

neuroscience↗

bioRxiv2019

Noninvasive Sleep Scoring in Mice using Electric Field Sensors

BackgroundRodent sleep scoring in principally reliant on electroencephalogram (EEG) and electromyogram (EMG), but this approach is invasive, can be expensive, and requires expertise and specialized equipment. Affordable, simple to use, and noninvasive ways to accurately quantify rodent sleep are needed.\n\nNew methodWe developed and validated a new method for sleep-wake staging in mice using cost-effective, noninvasive electric field (EF) sensors that detect respiration and other movements. We validated recordings from EF sensors attached to the exterior of specialty chambers used to continuously capture sleep with EEG/EMG, then compared this to EF sensors attached to vivarium home-cages.\n\nResultsEF sensors quantified 3-state sleep architecture (wake, rapid eye movement - REM - sleep, and non-REM sleep) with high agreement (>93%) and comparable inter- and intra-scorer error as expert EEG/EMG scoring. Novices given an instruction document with examples were able to score sleep comparable to expert scorers (>91% agreement). Additionally, EF sensors were able to quantify 3-state sleep scoring in traditional mouse home cages.\n\nComparison with existing methodMost noninvasive sleep assessment technology requires animal contact, altered cage environments, and/or can only discern 2 states of arousal (wake or asleep). The EF sensors are able to discriminate REM from non-REM sleep accurately and from outside the animals home cage.\n\nConclusionsEF sensors provide a simple and reliable method to accurately score 3-state sleep architecture; (i) from outside the typical home cage, (ii) where noninvasive approaches are preferred, or (iii) which EEG/EMG is not possible.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR/small/794552v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (43K):\norg.highwire.dtl.DTLVardef@1b36cf3org.highwire.dtl.DTLVardef@b5fdc9org.highwire.dtl.DTLVardef@28a6dforg.highwire.dtl.DTLVardef@e39a5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗