Disentangling shared network-level dynamics from region-specific activity is a central challenge in modeling multi-region neural data. We introduce SPIRE (Shared-Private Inter-Regional Encoder), a deep multi-encoder autoencoder that factorizes recordings into shared and private latent subspaces with novel alignment and disentanglement losses. Trained solely on baseline data, SPIRE robustly recovers cross-regional structure and reveals how external perturbations reorganize it. On synthetic benchmarks with ground-truth latents, SPIRE outperforms classical probabilistic models under nonlinear distortions and temporal misalignments. Applied to intracranial deep brain stimulation (DBS) recordings, SPIRE shows that shared latents reliably encode stimulation-specific signatures that generalize across sites and frequencies. These results establish SPIRE as a practical, reproducible tool for analyzing multi-region neural dynamics under stimulation. Understanding how distributed brain regions coordinate--and how this coordination is reorganized by interventions such as deep brain stimulation (DBS)--remains a major challenge. Disorders like dystonia and Parkinson's involve dysfunction in basal ganglia-thalamo-cortical circuits (Galvan et al., 2015; Jinnah & Hess, 2006; Obeso et al., 2008; Zhuang et al., 2004), and while DBS of targets such as globus pallidus internus (GPi) and subthalamic nucleus (STN) is clinically effective (Ben-abid, 2003; Lozano et al., 2019; Larsh et al., 2021) its network-level mechanisms remain poorly understood. Latent variable models can capture such effects by reducing neural activity to low-dimensional subspaces, but existing methods have key limitations. Classical models such as Gaussian Process Factor Analysis (GPFA) (Y u et al., 2008) and Canonical Correlation Analysis (CCA) (Bach & Jordan, 2005) assume linearity. DLAG (Delayed Latents Across Groups) (Gokcen et al., 2022) disentangles shared vs. private dynamics but is restricted to linear-Gaussian structure and spiking data. Multimodal models (SharedAE (Yi et al.), MMV AE (Shi et al., 2019)) align shared spaces but are not designed for intracranial recordings under stimulation. Critically, none of these frameworks provide a nonlinear, disentangling model that can separate shared versus private dynamics in human local field potential (LFP) data under external perturbation. Addressing this gap is essential: understanding how stimulation reorganizes intrinsic cross-regional coordination could reveal circuit-level mechanisms of DBS that remain invisible to local analyses.

Neural decoding of pathological and physiological states can enable patient-individualized closed-loop neuromodulation therapy. Recent advances in pre-trained large-scale foundation models offer the potential for generalized state estimation without patient-individual training. Here we present a foundation model trained on chronic longitudinal deep brain stimulation recordings spanning over 24 days. Adhering to long time-scale symptom fluctuations, we highlight the extended context window of 30 minutes. We present an optimized pre-training loss function for neural electrophysiological data that corrects for the frequency bias of common masked auto-encoder loss functions due to the 1-over-f power law. We show in a downstream task the decoding of Parkinson's disease symptoms with leave-one-subject-out cross-validation without patient-individual training.

Transcranial magnetic stimulation (TMS) is a non-invasive and safe brain stimulation procedure with growing applications in clinical treatments and neuroscience research. However, achieving precise stimulation over prolonged sessions poses significant challenges. By integrating advanced robotics with conventional TMS, robot-assisted TMS (Robo-TMS) has emerged as a promising solution to enhance efficacy and streamline procedures. Despite growing interest, a comprehensive review from an engineering perspective has been notably absent. This paper systematically examines four critical aspects of Robo-TMS: hardware and integration, calibration and registration, neuronavigation systems, and control systems. We review state-of-the-art technologies in each area, identify current limitations, and propose future research directions. Our findings suggest that broader clinical adoption of Robo-TMS is currently limited by unverified clinical applicability, high operational complexity, and substantial implementation costs. Emerging technologies, including marker-less tracking, non-rigid registration, learning-based electric field (E-field) modelling, individualised magnetic resonance imaging (MRI) generation, robot-assisted multi-locus TMS (Robo-mTMS), and automated calibration and registration, present promising pathways to address these challenges.

"Oh my god, this arm is part of me," says Scott Imbrie, who was able to use it to feel objects Two people with paralysis in their hands were able to temporarily regain their sense of touch and feel the shape of objects, thanks to electrical brain stimulation. The approach could one day help people with spinal cord injuries to better carry out everyday activities by controlling a robotic arm that feels like their own. There have been previous efforts to restore touch through brain stimulation, but they were fairly crude.

First, the integration of artificial intelligence-facilitated algorithms for the early detection of cardiovascular illness, which will move us closer toward early prevention. We also envision a focus on using genetically informed treatments to reduce the risk of atherosclerotic heart disease, valvular heart disease and heart failure. Together, these important advances will usher in an era of personalized health care in cardiovascular disease."

Zapping the brain has allowed partially paralysed patients to walk again in a'major milestone' for wheelchair users. Deep brain stimulation has been found to improve walking and promote recovery in two people with a spinal cord injury. The surgical procedure involves implanting electrodes into the brain to produce electrical impulses. These can be easily switched'on' and'off'. Traditionally, it has been used to treat movement disorders like Parkinson's by targeting areas of the brain responsible for motor control.

Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFRL) holds promise in learning effective policies for a variety of similar control tasks, but is limited in domains like brain stimulation by a need for numerous costly environment interactions. In this work we introduce Coprocessor Actor Critic, a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation. Our key insight is that coprocessor policy learning is a combination of learning how to act optimally in the world and learning how to induce optimal actions in the world through stimulation of an injured brain. We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample efficiency and task success and outperforms baseline MBRL approaches in a neurologically realistic model of an injured brain.

There was a time before the turn of the millennium when Robert F. Kennedy Jr. gave a full-throated accounting of himself and the things he cared about. He recalls his voice then as "unusually strong," so much so that he could fill large auditoriums with his words. The independent presidential candidate recounts those times somewhat wistfully, telling interviewers that he "can't stand" the sound of his voice today -- sometimes choked, halting and slightly tremulous. Spasmodic dysphonia, a rare neurological condition, in which an abnormality in the brain's neural network results in involuntary spasms of the muscles that open or close the vocal cords. My my voice doesn't really get tired. "I feel sorry for the people who have to listen to me," Kennedy said in a phone interview with The Times, his voice sounding as strained as it does in his public appearances.