Biology subjects

Saal, J.

Publications and source records attributed to Saal, J..

5 recordsLinked to original sources

bioRxiv2026

Decoding Chronic Pain States from Distributed Intracranial Recordings

Chronic pain engages distributed cortical and subcortical circuits, and large-scale intracranial recordings in humans offer a valuable opportunity to characterize its neural signatures. Here, we recorded multi-day stereoelectroencephalography (sEEG) from six participants with refractory chronic neuropathic pain, each implanted with sEEG electrodes spanning dozens of cortical and subcortical structures. Using simultaneous chronic pain ratings, we decoded spontaneous high versus low pain states within individuals (median area under the curve = 0.72; five of six participants performed above chance). Pain-predictive signals were broadly distributed and highly participant-specific. However, mapping the spatial distribution of pain-predictive features revealed preferential representation within canonical macroscale networks: beta-band activity in the default mode network and high-gamma activity in the salience network. These results demonstrate that intracranial recordings can capture distributed, network-organized representations of spontaneous chronic pain states.

neuroscience↗

bioRxiv2025

Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

ObjectivesDeep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets. Materials and MethodsFourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. ResultsEffect sizes (Hedges g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The standard deviation of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p<0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately 10 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes [≥] 1.1, roughly 10 trials should be used per stimulation site for sufficiently powered results. ConclusionsThe presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.

neuroscience↗

bioRxiv2025

Caecilians maintain a functional long-wavelength-sensitive cone opsin gene despite signatures of relaxed selection and more than 200 million years of fossoriality

Vision is tuned to animals ecologies, evolving in response to specific light environments and visual needs. Transitions to fossorial lifestyles impose strong selective pressures favoring adaptations for underground life, such as increased skull ossification and reduced eye protrusion. Fossoriality may simultaneously relax constraints on vision leading to diminished visual capabilities. Caecilians (Gymnophiona)--specialized, fossorial amphibians--possess reduced eyes covered by skin or bone. For years, these traits, along with the presence of a single photoreceptor expressing one functional opsin gene, have been interpreted as evidence of limited vision, including an inability to focus or perceive color. Our results challenge these assumptions: we identified the long-wavelength-sensitive (LWS) opsin gene in 13 species of caecilians spanning 8 of 10 recognized families. Molecular evidence indicates that LWS is intact and transcribed in the eye of at least one species (Caecilia orientalis). However, the specific photoreceptor type expressing LWS remains uncertain, as our survey of cone phototransduction genes revealed a mosaic of losses, and anatomical observations from five families did not conclusively identify cone-like cells, though they revealed highly organized retinae even in families with vestigial eyes. Altogether, our results suggest that vision in caecilians may be underestimated and the role of light perception in their ecology is possible. TEASER TEXTThe colonization of low-light environments exerts selective pressure to enhance non-visual senses while relaxing selection on vision, often leading to opsin gene loss. We present compelling evidence for the retention of a cone opsin gene (LWS) in caecilians--a fossorial amphibian lineage with a 200-million-year history of burrowing and reduced eyes. Despite signs of relaxed selection, LWS appears functional, echoing patterns in other low-light-adapted vertebrates. Additionally, the loss of key cone phototransduction genes and absence of clear cone cells raises the possibility of LWS expression in rods or a functional change in its use. Our study provides an evolutionary framework for investigating LWS retention in fossorial vertebrates.

evolutionary biology↗

bioRxiv2024

Robust Detection of Brain Stimulation Artifacts in iEEG Using Autoencoder-Generated Signals and ResNet Classification

BackgroundIntracranial EEG (iEEG) is crucial for understanding brain function, but stimulation-induced noise complicates data interpretation. Traditional artifact detection methods require manual user input or struggle with noise variability, especially with limited labeled data. ObjectiveWe developed a supervised method to automatically detect stimulation-induced noise in human iEEG recordings using synthetic data generated by Variational Autoencoders (VAEs) to train a ResNet-18 classifier. MethodsMulti-lead iEEG data were collected, preprocessed, and used to train VAEs for generating synthetic clean and noisy signals. The ResNet-18 model was trained on images of spectra generated from these synthetic signals and validated on real iEEG data from five participants. ResultsThe classifier, trained exclusively on synthetic data, demonstrated high accuracy, precision, and recall when applied to real iEEG recordings, with AUC values greater than 0.99 across all participants. ConclusionWe present a novel approach to effectively detect stimulation-induced noise in iEEG, offering a robust solution for improving data interpretation in scenarios with limited labeled data. Additionally, the pre-trained ResNet-18 model is available for the community to use, facilitating further research and application in similar datasets.

neuroscience↗

bioRxiv2020

Real-time Synthesis of Imagined Speech Processes from Minimally Invasive Recordings of Neural Activity

Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and significantly improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which neglects the critical human-in-the-loop aspect of a practical speech neuroprosthetic. Here we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. Our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis, as well as the development of techniques that incorporate co-adaptation of the user and system for optimized performance.

neuroscience↗