Biology subjects

Miller, K. J.

Publications and source records attributed to Miller, K. J..

At least 19 recordsLinked to original sources

bioRxiv2026

A Translational Platform for Brain-Computer Interfaces and Adaptive Neuromodulation: Technical Characterization, Long-Term Validation, and Implementation of the CorTec Brain Interchange--BCI2000 Ecosystem

Objective: Adaptive neuromodulation systems and implantable brain-computer interfaces (BCIs) are promising therapies for neurological and psychiatric disorders. However, their broader translation into research and clinical practice remains limited by technological complexity, restricted access to implantable research platforms, and the lack of standardized, reproducible experimental workflows. We therefore aimed to develop and validate an open, general-purpose translational ecosystem that enables rapid development, evaluation, and dissemination of novel neuromodulation and implantable BCI paradigms. Approach: The CorTec Brain Interchange (BIC) implantable neural sensing and stimulation device was integrated with the open-source BCI2000 platform to create a modular, extensible neuromodulation ecosystem. We established a standardized battery of quantitative assessments to characterize implantable neuromodulation systems to comprehensively evaluate the CorTec BIC device through benchtop characterization, long-term preclinical in vitro and in vivo validation, and a human proof-of-concept demonstration. Results: Benchtop and saline testing provided a comprehensive technical ex vivo characterization of the BIC device, independently validating previously reported performance while extending its characterization through quantification of the recording noise floor, stimulation and acquisition latencies and impedance measurement accuracy. Long-term in vivo validation in five canines, with the longest implantation exceeding three years, demonstrated stable chronic recordings while capturing progressive channel deterioration and its underlying mechanical causes. The ecosystem enabled active functional decoding more than two years after implantation, implementation of closed-loop stimulation using arbitrary spectral biomarkers, detection and modulation of epilepsy-associated biomarkers, and brain stimulation evoked potential recordings. In addition, we translated an established one-dimensional BCI cursor control paradigm to the BIC benchtop evaluation kit and demonstrated its feasibility in a human participant. Finally, we openly provide standardized surgical, imaging, and analysis pipelines together with datasets and software to facilitate reproducible neuromodulation research. Significance: We present a versatile, open-source translational ecosystem that supports a wide range of neuromodulation and implantable BCI applications with minimal modification. This battery of quantitative assessments can be applied generally as a blueprint for systematic characterization of implantable neuromodulation systems. By combining comprehensive hardware characterization with standardized software tools and experimental workflows, this work provides both an essential reference for researchers adopting the Brain Interchange platform. The ecosystem lowers technical barriers to implantable neurotechnology research, promotes reproducibility, and provides a foundation for accelerating the development and clinical translation of next-generation adaptive neuromodulation and implantable BCI therapies for patients with neurological and psychiatric disorders.

bioengineering↗

bioRxiv2026

Insulotaxy: Navigating the Human Insula with a Novel Stereotactic Framework

IntroductionThe insula is an increasingly important target for functional neurosurgery given its involvement in a range of neurological and neuropsychiatric disorders, including epilepsy and chronic pain. As this practice evolves, optimal targeting will require standardized outcome measures that relate electrode or laser trajectory to postprocedural outcome. Traditional whole- brain registration approaches fail to capture the substantial person-to-person variability in insular gyral configuration, including the relative internal rotation of the insular gyri with respect to standard stereotactic space. ObjectiveWe propose and validate a stereotactic coordinate system based on local anatomical landmarks to facilitate surgical planning and standardized outcome assessment within the insular cortex. MethodsOur approach transforms brain MRI first into standard AC-PC space, and then into an insular-specific space defined by five anatomical landmarks: four points along the central sulcus of the insula and one point at the middle cerebral artery (MCA) bifurcation (at the limen insulae). The system calculates two angles - {theta} (axial) and {varphi} (sagittal) - between the AC-PC line and the insular axis, and the brain volume undergoes sequential rotation through these angles followed by translation to place the coordinate systems origin along the insular axis. ResultsIn a sample of 32 patients, the angle between the AC-PC line and the insular axis ranged from -17{degrees} to 17{degrees} in the axial plane ({theta}) and 31{degrees} to 69{degrees} in the sagittal plane ({varphi}). In the resulting coordinate system, the insular axis defines z = 0 and the MCA turning point defines y = 0. We developed a custom, open-access MATLAB graphical interface that allows intuitive implementation of this system for both surgical planning and postoperative analysis; implanted electrodes, laser fiber position, and ablation geometry can each be localized within this common space. As a demonstration of its utility for pooling data across subjects, we applied the transformation to a previously acquired intracranial electrophysiology dataset and found that anatomically consistent, effector-specific motor representations emerged across 18 subjects once electrode positions were expressed in insular-specific coordinates. ConclusionAs stereotactic surgery for insular targets becomes more common with expanding scientific inquiry, an insular-specific coordinate system may facilitate operative planning and functional mapping, and may help standardize outcome assessment across patients and institutions. SIGNIFICANCE STATEMENTThe insular cortex represents an increasingly important surgical target for therapeutic interventions, yet substantial person-to-person anatomical variability hampers standardized targeting and outcome comparison. The insula is simultaneously the subject of expanding scientific inquiry -- into interoception, pain, autonomic regulation, salience processing, and sensorimotor representation -- much of it now pursued through intracranial recording and stimulation in humans, where cohorts are small, electrode sampling is idiosyncratic, and progress therefore depends on pooling data across patients in a frame that respects insular gyral architecture. We present "Insulotaxy," a stereotactic coordinate system built from consistent, easily identifiable local anatomical landmarks that accounts for the insulas unique rotational relationship to standard brain coordinates. An open-source MATLAB tool transforms imaging into insular-specific coordinates, facilitating surgical planning for ablation and electrode placement while enabling standardized outcome reporting across institutions. By providing locally anchored, anatomically aligned coordinates rather than relying on whole-brain registration, this framework addresses a practical gap in functional neurosurgery and lays a foundation for pooling clinical and electrophysiological data as insular interventions become more prevalent.

neuroscience↗

bioRxiv2026

White matter conduction in the human brain is mostly slow, with rare high velocity connections

White matter bundles play a crucial role in cognitive functions by rapidly transmitting information between brain regions. Inter-areal conduction guides the integration of information and conduction velocity is a fundamental parameter in theories and models of brain function. However, distributions of conduction velocity remain difficult to characterize in vivo in humans. In this work, we integrated diffusion magnetic resonance imaging (dMRI) tractography with intracranial electrical stimulation during clinical stereo-electroencephalography (sEEG) monitoring in 17 subjects to measure conduction velocity within four major white matter bundles. Our findings reveal that human brain conduction is characterized by high variability both within and between bundles, reflecting a predominance of slow connections alongside rare high-speed connections. Because conduction velocity in myelinated fibers follows an approximately linear relationship with axon diameter, we derive underlying axon diameter distributions and show that these estimates are comparable to previous post-mortem studies. These findings demonstrate a heavily skewed distribution of human neural conduction velocities and show that structural heterogeneity shapes the timing and integration of information in large-scale networks.

neuroscience↗

bioRxiv2026

Attention reshapes the information dynamics of thalamic and cortical prediction-error learning

Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.

neuroscience↗

bioRxiv2026

AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning

Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data [1]. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data [2, 3]. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains [4-7]. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models [8-11]. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.

animal behavior and cognition↗

bioRxiv2026

A Systematic Characterization of Causal Interactions Between Human Visual Areas

Human visual cortex comprises three interacting streams, but anatomy and correlated activity cannot determine the direction or reliability of interareal influence. Characterizing directional interactions among these streams can reveal the architecture through which activity can propagate across the visual system. Here, we used single-pulse electrical stimulation during intracranial EEG recordings in 23 patients to map directed effective connectivity among 22 atlas-defined visual cortical areas. The resulting effective connectivity matrix revealed a selective and asymmetric architecture of interareal influence. Feedforward influences from early visual areas to the dorsal and lateral streams were more reliable and more prevalent than the corresponding feedback influences, whereas interactions between early visual and ventral temporal areas were comparatively balanced. Cross-stream interactions favored temporal-to-parietal over parietal-to-temporal influence, with higher response reliability and a greater proportion of significant responses among sampled connections. Network-level profiles were consistent with source-like organization in early visual areas and the ventral stream, and integrator-like organization in the dorsal and lateral streams. Visual-task data collected in three of the same patients illustrated how stimulation-derived connectivity may relate to functional responses across connected visual regions. These findings reveal a directional architecture for activity propagation across the human visual cortex and provide empirical constraints for biologically grounded models of visual processing.

neuroscience↗

bioRxiv2025

Integrative Genomic and Functional Analyses Reveal NINL as a Modulator of Tau Aggregation

IntroductionProteostasis dysfunction is a hallmark of frontotemporal dementia (FTD) and Alzheimers disease (AD), yet the genetic and molecular pathways that disrupt protein homeostasis remain poorly understood. MethodsWe integrated human genetics, transcriptomics, and functional studies to identify proteostasis network components involved in tauopathy. ResultsWe identified 18 proteostasis network genes harboring 75 rare, damaging variants enriched in FTD and/or AD. These genes, spanning multiple proteostasis pathways, were differentially expressed in MAPT mutant neurons and dysregulated in FTD and AD brains. NINL, which encodes Nlp, emerged as the only gene consistently upregulated across all datasets. NINL overexpression reduced tau seeding and enhanced lysosomal proteolytic activity, whereas two FTD-enriched NINL frameshift variants impaired Nlp expression and abolished these protective effects. DiscussionOur findings identify a set of proteostasis genes with genetic and transcriptional links to neurodegeneration and reveal NINL as a novel regulator of tau aggregation, potentially upregulated as an adaptive response to proteotoxic stress.

neuroscience↗

bioRxiv2025

Characterizing neuronal population geometry with AI equation discovery

The activity of visual cortical neurons forms a population code representing image stimuli. There is, however, a discrepancy between our understanding of this code at the single-cell and population levels: direct measurements indicate the population code is high-dimensional, but established models of single-cell tuning give rise to low-dimensional codes. We reconciled this discrepancy by developing an AI science system to find a new parsimonious, interpretable equation for visual cortical orientation tuning. Candidate equations were expressed as short computer programs and evolved by Large Language Models (LLMs) using graphical diagnostics. The resulting equation not only improved single-cell fits, but also accurately modelled the population codes high-dimensional geometry. A novel parameter of the AI-discovered equation, which controls single-cell tuning smoothness, gives rise to high-dimensional population codes. The same parameter drives high-dimensional coding in head-direction cells, suggesting a common coding strategy across brain regions. We used this equation to hypothesize a circuit mechanism generating high-dimensional population codes, and to demonstrate the advantages of these codes in a simulated hyperacuity task. These results show that tuning smoothness has a key role in controlling population code geometry, and demonstrate how AI equation discovery can deliver interpretable models accelerating scientific understanding in neuroscience and beyond.

neuroscience↗

bioRxiv2025

The Human Insula Encodes Somatotopic Representation of Motor Execution with an Effector-Specific Connectomic Map to Primary Motor Cortex

Understanding motor representation in the human brain requires mapping beyond the primary motor cortex, into the distributed networks that coordinate complex movements. The insular cortex, a multifunctional hub buried within the Sylvian fissure, has been implicated in motor control through clinical observations and neuroimaging. Yet its precise relation to movement processing remains one of the least understood aspects of motor neurophysiology. To address this gap, we quantified electrophysiological changes from implanted depth electrodes in patients performing simple movement tasks combined with single-pulse electrical stimulation (SPES) to map functional connectivity. The movement data reveal distinct somatotopic representation bilaterally, as well as inter-effector regions that are active for different movement types. Hand representation is centered along the ventral aspect of the middle and posterior short gyri bilaterally, while tongue/mouth tuned sites cluster in the dorsal posterior short gyrus and the dorsal long gyri. Insular activity temporally follows the primary motor cortex (M1) and precedes movement onset. SPES revealed somatotopically-specific connectivity between corresponding sites in M1 and insula (hand-to-hand, tongue-to-tongue) and between bilateral insulae. These observations establish that somatotopy is a conserved property of distributed motor control incorporating the insula, with direct implications for basic and clinical neuroscience.

neuroscience↗

bioRxiv2025

Single pulse electrical stimulation in white matter modulates iEEG visual responses in human early visual cortex

Electrical stimulation is increasingly used to modulate brain networks for clinical purposes. The basic unit of neurostimulation, a single electrical pulse, can travel through white matter to influence connected neuronal populations. However, the mechanisms by which it influences connected populations is not well understood: stimulation may excite, inhibit, or add noise to neuronal population activity. In this study, we investigated how single pulses modulate the neuronal processing of images in a well-controlled visual paradigm. In two human subjects implanted with iEEG electrodes for clinical purposes, single pulses were delivered to electrodes in white matter tracts connected to measurement electrodes in visual cortex. Images appeared on-screen at 0, 100, or 200 ms after each pulse. Using finite impulse response modeling, we decomposed the broadband and evoked potential responses into separate components induced by electrical stimulation and by visual processing. Single pulses induced transient broadband increases followed by suppression, but they did not modulate the visual broadband responses (i.e., stimulation response was additive to visual response). In contrast, single pulses elicited prominent brain stimulation evoked potentials and they modulated the visual evoked potentials. Specifically, visual evoked potentials were larger when stimulation occurred closer to visual onset. This indicates that a single electrical pulse can increase the strength or synchrony of visual inputs. Overall, these findings suggest that the effects of electrical stimulation in the visual system are two-fold: stimulation induces additive effects on broadband power, possibly by adding noise, and it interacts with synchronous visual inputs to amplify them.

neuroscience↗

bioRxiv2025

Human pulvinar stimulation engages select cortical pathways in epilepsy

The pulvinar has been proposed as an effective neuromodulation target for patients with posterior quadrant and temporal epilepsies. However, the pulvinar has a large tissue volume, multiple subnuclei, and widespread cortical connections. It remains unknown whether electrical stimulation of distinct pulvinar subregions affects the temporal, occipital, and parietal areas differently. To address this gap, we delivered single-pulse electrical stimulation to the pulvinar and measured the resulting brain stimulation evoked potentials in twelve patients undergoing stereotactic EEG for drug-resistant epilepsy. Brain stimulation evoked potentials were parameterized across the occipital, temporal and parietal cortex. Stimulation of the lateral pulvinar elicited significant brain stimulation evoked potentials in striate and extrastriate areas that diminish as stimulation shifts towards the medial pulvinar. Conversely, stimulation of the ventral aspect of the medial pulvinar produced significant lateral temporal evoked potentials, which diminish with lateral pulvinar stimulation. We also found that stimulation of the dorsomedial pulvinar evoked significant parietal responses with limited striate/extrastriate and lateral temporal responses. These results demonstrate that electrical stimulation of specific pulvinar subregions influences distinct occipital, parietal and lateral temporal areas. Selective targeting of pulvinar subregions to maximize seizure network engagement may be essential for individualized treatment of posterior quadrant and temporal epilepsies.

neuroscience↗

bioRxiv2025

Broadband synergy versus oscillatory redundancy in the visual cortex

The cortex generates diverse neural dynamics, ranging from broadband fluctuations to narrowband oscillations at specific frequencies. Here, we investigated whether broadband and oscillatory dynamics play different roles in the encoding and transmission of visual information. We used information-theoretical measures to dissociate neural signals sharing common information (i.e., redundancy) from signals encoding complementary information (i.e., synergy). We analyzed electrocorticography (ECoG) and local field potentials (LFP) in the visual cortex of human and non-human primates (macaque) to investigate the extent to which broadband signals (BB) and narrowband gamma (NBG) oscillations conveyed synergistic or redundant information about images. In both species, the information conveyed by BB signals was highly synergistic within and between visual areas. By contrast, the information carried by NBG was primarily redundant within and between the same visual areas. Finally, the information conveyed by BB signals emerged early after stimulus onset, while NBG sustained information at later time points. These results suggest a potential dual role of BB and NBG cortical dynamics in visual processing, with broadband dynamics supporting nonlinear pattern recognition and oscillations facilitating information maintenance across the cortex.

neuroscience↗

bioRxiv2025

Discovering Symbolic Cognitive Models from Human and Animal Behavior

Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch Romera-Paredes et al. (2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture human and animal behavior. We consider datasets from three species performing a classic reward-learning task that has been the focus of substantial modeling effort, and find that the discovered programs outperform state-of-the-art cognitive models for each. The discovered programs can readily be interpreted as hypotheses about human and animal cognition, instantiating interpretable symbolic learning and decision-making algorithms. Broadly, these results demonstrate the viability of using LLM-powered program synthesis to propose novel scientific hypotheses regarding mechanisms of human and animal cognition.

animal behavior and cognition↗

bioRxiv2025

Parameterization of intraoperative human microelectroderecordings: Linking action potential morphology to brainanatomy

Deep brain stimulation (DBS) is a targeted manipulation of brain circuitry to treat neurological and neuropsychiatric conditions. Optimal DBS lead placement is essential for treatment efficacy. Current targeting practice is based on preoperative and intraoperative brain imaging, intraoperative electrophysiology, and stimulation mapping. Electrophysiological mapping using extracellular microelectrode recordings aids in identifying functional subdomains, anatomical boundaries, and disease-correlated physiology. The shape of single-unit action potentials may differ due to different biophysical properties between cell-types and brain regions. Here, we describe a technique to parameterize the structure and duration of sorted spike units using a novel algorithmic approach based on canonical response parameterization, and illustrate how it may be used on DBS microelectrode recordings. Isolated spike shapes are parameterized then compared using a spike similarity metric and grouped by hierarchical clustering. When spike morphology is associated with anatomy, we find regional clustering in the human globus pallidus. This method is widely applicable for spike removal and single-unit characterization and could be integrated into intraoperative array-based technologies to enhance targeting and clinical outcomes in DBS lead placement. Author summaryWe developed a new algorithmic approach to capture the structure of single neuron electrophysiology from microelectrode recordings during deep brain stimulation surgery. This method has broad applications for spike characterization, clustering, removal from the background field potential, and comparison across recording sites. Using this technique, we illustrate regional clustering of spike morphologies in the human globus pallidus.

neuroscience↗

bioRxiv2025

Large Animal Epilepsy Model Platform: Kainic Acid Porcine Model of Mesial Temporal Lobe Epilepsy

Translational large-animal models that can accommodate human-scale implantable devices are essential for advancing neuromodulation therapies in epilepsy. This study establishes a kainic acid (KA)-induced porcine model of mesial temporal lobe epilepsy (mTLE) using clinical imaging, stereotactic surgery, and a fully implantable neural stimulation and recording (INSR) device designed for humans. Seven pigs (six KA-treated and one saline control) underwent MRI-guided stereotactic implantation of electrodes targeting bilateral hippocampus (HPC) and anterior thalamus (ANT), followed by intra-hippocampal KA or saline infusion. Local field potentials (LFP) were recorded continuously with synchronized video monitoring. Seizures and LFP interictal epileptiform-like discharges (IEDs) were quantified using validated automated detectors. Histology was performed in the saline control and the longest surviving KA-treated pig. Intra-hippocampal KA infusion induced acute status epilepticus in all (6/6) treated pigs. Four animals survived to chronic monitoring with spontaneous seizures observed in three pigs (2,733 seizures; mean duration of 27.2 {+/-} 17.6 seconds). IEDs were observed in bilateral HPC of all animals, including saline control, with higher rates in the lesioned HPC (p <0.0001). While the IED morphology is consistent with epileptiform activity; IEDs alone are not specific for epilepsy and physiological transients (e.g. sharp-wave ripples) and injury-related hyperexcitability or strain-specific hyperexcitability cannot be excluded. Histological analysis revealed patchy neuronal loss and cytoarchitectural changes in HPC. This porcine model reproduces electrophysiological features of human mTLE. This approach provides a powerful translational bridge for developing and testing next-generation INSR and neuromodulation strategies in freely behaving large animals.

neuroscience↗

bioRxiv2024

Proper reference selection and re-referencing to mitigate bias in single pulse electrical stimulation data

Single pulse electrical stimulation experiments produce pulse-evoked potentials used to infer brain connectivity. The choice of recording reference for intracranial electrodes remains non-standardized and can significantly impact data interpretation. When the reference electrode is affected by stimulation or evoked brain activity, it can contaminate the pulse-evoked potentials recorded at all other electrodes and influence interpretation of findings. We highlight this specific issue in intracranial EEG datasets from two subjects recorded at separate institutions. We present several intuitive metrics to detect the presence of reference contamination and offer practical guidance on different mitigation strategies. Either switching the reference electrode or re-referencing to an adjusted common average effectively mitigated the reference contamination issue, as evidenced by increased variability in pulse-evoked potentials across the brain. Overall, we demonstrate the importance of clear quality checks and preprocessing steps that should be performed before analysis of single pulse electrical stimulation data. HighlightsO_LIA reference electrode close to active tissue can contaminate intracranial EEG signals C_LIO_LIInterpretation of pulse-evoked potentials can be biased by reference contamination C_LIO_LILow response variability between channels is indicative of reference contamination C_LIO_LIReference contamination can be resolved through data recollection or re-referencing C_LI

neuroscience↗

bioRxiv2024

Spatial and Spectral Changes in Cortical Surface Potentials during Pinching versus Thumb and Index Finger Flexion

Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types, and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics. SIGNIFICANCE STATEMENTThis study measured brain activity actively during finger movement and found that pinching movements show higher cortical overlap with index finger flexion and uniquely engage parietal regions, advancing our understanding of motor control hierarchy and sensorimotor integration, while suggesting improvements for more naturalistic brain-computer interfaces through prioritized index finger decoding and integration of parietal lobe measurements.

neuroscience↗

bioRxiv2024

Cross-species brain-wide mapping reveals a conserved and coordinated network engaged by NAc DBS

SummaryNucleus accumbens (NAc) deep brain stimulation (DBS) has been increasingly explored as a treatment modality for refractory neuropsychiatric disorders. Uncovering the accumbens network that is engaged by DBS is a critical step forward in understanding how modulating this important node impacts the broader mesocorticolimbic circuit. Using whole-brain clearing and unbiased, brain-wide neural activity mapping, we found that NAc DBS increases neural activity in a coordinated mesocorticolimbic network in mice. Simultaneous intracranial electrophysiology recordings from the human NAc and brief stimulation epochs of homologous mesocorticolimbic nodes revealed similar connectivity. Altogether, these results identify specific connectivity conserved across species within the mesocorticolimbic circuit that may underlie mechanisms of NAc DBS.

neuroscience↗