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Lapatrie, M.

Publications and source records attributed to Lapatrie, M..

2 recordsLinked to original sources

Low-cost monophasic transcranial magnetic stimulator

Transcranial magnetic stimulation (TMS) excites neurons noninvasively by electromagnetic induction and is used in neurophysiology research and in approved therapy for depression. Commercial stimulators cost tens of thousands of dollars. Existing open-source designs are either low-energy and unvalidated or rely on expensive switches and laboratory infrastructure. We present a monophasic, fixed-pulse-shape TMS device built at a parts cost of ~USD 700 which, under specific modeling assumptions, can exceed average human motor thresholds. Our design assumes access to basic, off-the-shelf equipment such as a 24 V power supply unit, an oscilloscope, and a few basic tools. The device charges a 230 F film-capacitor bank and discharges it through a self-wound figure-of-eight coil using a thyristor, producing a fixed pulse with a positive lobe lasting approximately 90 s. A Zero-Voltage Switching (ZVS) driver-based charging circuit charges the capacitor bank up to 1460 V from a 24 V bench supply. Three galvanically isolated voltage domains, redundant interlocks, and passive and active discharge paths help mitigate the safety risks involved with handling lethal energy levels. We also present a low-cost way to characterize the device by reconstructing coil di/dt from pickup-coil dB/dt maps to estimate the induced cortical E-fields. At the maximum capacitor voltage, the recovered maximal di/dt is 110.86 A/s, giving estimated 99.9th percentile cortical E-fields of 159 V/m at Oz and 196 V/m at C3 on an example anatomy. Although not yet approved for clinical trials and routine stimulation, the device demonstrated the possibility of a cost-effective TMS unit.

bioengineering↗

Unsupervised Representation Learning Generates Differentiable Neurophysiological Profiles

Human brain activity contains stable, individual-specific features that persist over months to years, forming neurophysiological profiles. Most model-based profiling approaches use participant labels or supervised objectives, making it difficult to determine whether successful differentiation reflects stable biology or exploitable idiosyncrasies. We introduce a participant-agnostic autoencoder framework that derives profiles from brief resting-state magnetoencephalography (MEG) segments using reconstruction as sole training objective. Discriminative profiles emerged from the learned latent space without participant labels. Within-session, autoencoder profiles reached 93.3% accuracy at 120 s, exceeding functional-connectivity, spectral, and contrastive baselines with recordings as short as 14 s when participant-specific anatomy was withheld from source reconstruction. Differentiation generalized above chance across recording sessions (between-session accuracy 49.5% for the pretrained autoencoder). Profiles also predicted age more accurately than baselines (r2=0.318), and the decoder enabled perturbation-based sensitivity analyses in spectral and connectivity spaces. This establishes participant-agnostic representation learning as a scalable and interpretable profiling.

neuroscience↗