Day 2 (Part 2) The BCI & Neurotechnology Spring School 2026

Scientific concepts, discoveries, and nature/medical phenomena

1) Intraoperative neurophysiology for brain tumor surgery (motor pathway protection)

Core phenomena

• Ischemia affecting the cortical/corticospinal tract can manifest as MEP (motor evoked potential) deterioration during surgery.
• MEP changes may be reversible warnings for impending injury.
• The severity of MEP deterioration correlates with postoperative deficits.

Clinical evidence described

• A debated study claimed no significant long-term neuro outcome effect, but confounding was discussed (e.g., higher rates of temporary clipping in one group).
• Reply studies suggested that intraoperative monitoring/mapping reduces overall deficits.

Methodological workflow described (in surgery)

1. Place a strip electrode for motor mapping.
2. Use:
   • Transcranial electric stimulation
   • Direct cortical stimulation (including subcortical stimulation)
3. Monitor with sEMPs/SAPs (described as sensory evoked potentials) and MEPs.
4. During resection:
   • Stimulate within the resection cavity to elicit responses in contralateral hand/face areas.
   • Apply a double stimulation paradigm:
     • first provide stimulation artifacts,
     • then use time-lagged responses to delineate functional boundaries.
   • Use results to decide where to stop resection.

Stimulation parameter concepts

• Goal: selective activation near the axon hillock / gray–white matter border to target the corticospinal tract while minimizing current spread.
• Monopolar vs bipolar stimulation:
  • Bipolar: highest current tends to be in the middle.
  • Monopolar: effects skew more toward layer 5.

MEP monitoring interpretation concepts

• Distinguish between:
  • Reversible vs irreversible MEP deterioration
• Transient MEP reductions may indicate impending ischemia in the corticospinal tract.

NAPS warning optimization (predictive thresholds)

• A large-cohort analysis (Tom from Germany mentioned) proposed a criterion combining:
  • Affected-side amplitude decrement > 80%
  • with increased stimulation intensity
• Reported outcome: higher sensitivity / positive predictive value for impending dysfunction.

Direct cortical stimulation (DCS) vs transcranial stimulation (tES) tradeoffs

• DCS is favorable when strip electrodes can be placed well on motor cortex.
• tES can produce false positives, e.g.:
  • parietal tumors and large resection cavities leading to “zaging”/subdural air.
• Irrigation / reestablishment of signals may help identify false positives.

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2) Subcortical distance estimation via stimulation thresholds

Scientific idea

• Subcortical stimulation intensity correlates approximately linearly with distance to the corticospinal tract within a usable range.

Quantitative relation described

• A near-linear region reported:
  • intensities < ~15 mA
  • distances to ~50 mm

Clinical use

• Use the minimum intensity that elicits contralateral MEPs as a surrogate for proximity to corticospinal fibers.

Bipolar vs monopolar in different tumor contexts

• Bipolar may be safer for “low-risk motor pathway” tumors.
• Monopolar may work better in more infiltrative or procedural-history scenarios.

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3) Awake/eloquent tumor surgery and maximization of gross total resection

Key principle

• More extensive gross total resection is linked to improved overall survival.

Neurophysiology as a guiding tool

• Mapping + monitoring add guidance beyond imaging alone:
  • navigation/visualization plus physiology-based boundaries (MEPs/SAPs/DCS/subcortical stimulation).

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4) Future outlook in neuro-oncology electrophysiology: cortical potentials (CPs)

New modality

• Cortical potentials (CPs) are discussed as a next step, requiring:
  • technical maturation
  • better understanding of mechanisms and connectivity
  • clarity on how CPs relate to tumor-relevant pathways (and ASEPs/AEPs)

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Brain–spine interfaces after spinal cord injury (epidurally driven locomotion + cortical decoding)

5) Implantable brain–spine interface for severe spinal cord injury

Target problem/phenomenon

• After spinal cord injury, brain commands to lumbar spinal locomotor circuits are disrupted, leading to paralysis.

Core technology

• Epidural electrical stimulation (EES/ES) placed just below the lesion:
  • engages spinal circuits transinaptically via dorsal root afferents
  • activates motor neurons → muscle activation

Clinical transition described

• From earlier predefined stepping patterns (lower patient control)
• to patient intention decoding that drives stimulation.

Implant described

• Partner-developed implant:
  • ~64 epidural electrodes in some versions
  • later versions use a single medially placed unit for severe cases
• Wireless communication:
  • an external antenna/cap on the head
  • implant placed under the skull region

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6) Pilot/clinical trial structure and participant outcomes (high-level)

Trial logistics

• Enrollment → single surgery implantation → 2-week calibration → 14-week rehabilitation
• Afterward: patient can take the system home.

Stimulation parameter space

• Electrode configuration, frequency, passes per burst, amplitude
• Adapted in real time via wireless control.

Calibration/mapping methodology (explicit steps)

• Iterative algorithmic exploration:
  1. algorithm proposes parameter sets
  2. patient is stimulated
  3. record kinematics + EMG
  4. compute a reward
  5. iterate until achieving “primitive” movements for walking/standing
• Achieves multiple joint primitives within 1–2 days per phase (as stated).

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7) Decoding model: intention classification from brain signals

Model concept

• Recursive exponentially weighted switching multilinear model
• Updates every ~100 ms
• Uses prior ~400 ms to 1 s of data
• Classifies intention per joint (step left/right, hip flexion/extension, knee/ankle movements).

Online closed-loop calibration

• Additional labeling/cues every ~15 seconds
• Decoder updates during therapy if signal characteristics shift.

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8) “Brain GPT” self-supervised learning for robustness

Concept

• A self-supervised model learns latent structure of brain signals.

Method outline

• Convert signals into spectro-temporal-spatial feature tensors
• Convert features into tokens
• Mask tokens and train a transformer/encoder to reconstruct masked parts
• Use learned latent features to improve decoder stability.

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9) Results themes reported

Complete SCI participants

• No voluntary movement without stimulation (expected for complete lesions).
• With stimulation:
  • standing/walking enabled (prosthetic-like effect)
• Improvements maintained/enhanced with rehabilitation and home training.

Incomplete SCI participant

• More residual motor function on one side → asymmetric improvement:
  • incomplete side improves during rehab
  • more severe side less responsive
• With stimulation:
  • standing longer
  • walking with less need for body-weight support (as described)

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10) Neurofeedback and closed-loop motivation tools

• Neurofeedback provides real-time visualization (cursor/map) of brain feature states associated with intended joints.

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11) Practical use and decoder stability over time

Decoder recalibration frequency

• Early rehab: model updates about every ~4 days
• Later: decoder becomes more stable with fewer updates.

Home use

• Systems embedded in a walker enable independent training.

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Glial tumors, epilepsy, and neuronal activity fueling tumor growth (mechanistic and clinical links)

12) Clinical epidemiology: seizures co-evolve with tumor progression

Phenomenon

• Tumor growth and seizure control/frequency show parallel trajectories.

Key observations stated

• High fraction of patients with low-grade and glioblastoma develop seizures over time.
• Increasing seizure prevalence tracks increased pharmacoresistance.
• Epilepsy correlates with prognosis:
  • seizures at diagnosis/tumor evolution associate with different survival patterns (noted as shorter survival in some glioma series).

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13) Neuronal activity can biologically fuel glioblastoma growth (preclinical evidence)

Animal model described

• Xenograft human glioblastoma cells into mouse brain.

Manipulation #1: activate tumor-associated/parenchymal neuronal activity

• Via optogenetics/control of neuronal components
• Result: tumor growth ~doubled.

Manipulation #2: activate host neurons via optogenetics

• Result: tumor growth reduced (as described).

Manipulation #3: visual cortex light exposure

• Tumor implanted in visual cortex and exposed to light
• Result: altered tumor growth.

Interpretation

• Neuronal activity can modulate tumor growth (“fuel” concept).

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14) Spatial relationship: brain activity predicts tumor location probability (MEG-based)

• High baseline MEG activity correlates with likely glioblastoma locations.
• Stronger correlation for glioblastoma than for astrocytomas.
• Weaker for low-grade oligomas.

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15) Epilepsy generation occurs in peritumoral cortex rather than the tumor core

Historical evidence

• Jackson (1882) proposed epilepsy is the initial symptom and relates to cortical involvement.

Surgical recordings described

• Intraoperative recordings:
  • tumor sites may show flat activity
  • epileptiform sharp activity appears in cortex surrounding the tumor

Modern tissue experiments described

• Postoperative maintained tissue slices:
  • epileptiform activity in peritumoral cortices
  • absent in tumor tissue and remote cortex slices

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16) Surgical implication: supramarginal resection and mapping needs

Concept

• “Supramarginal surgery” aims to remove tissue beyond MRI-visible boundaries due to:
  • infiltration
  • epileptogenic cortex

Constraint

• Lack of reliable biomarkers for:
  • tumor cell infiltration vs
  • epileptogenic zones
• Spike hunting alone may not reliably map:
  • irritative vs
  • seizure-generating zones.

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17) High-frequency oscillations (HFOs) as biomarkers for epilepsy and infiltration

Phenomenon

• Ripples and fast ripples correlate with epileptogenic tissue and can align with tumor infiltration.

Tissue/in vitro mechanistic description

• In living human tumor tissue on MEAs:
  • epileptogenic progression includes:
    • low-frequency events (interictal → preictal discharges)
    • increasing HFO activity
  • HFOs emerge in microdomains that accumulate until seizure onset.

Microdomain hypothesis

• Macro electrodes may miss localized HFO microdomains due to spatial averaging.

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18) New electrode technology to detect HFOs (conductive polymer electrodes)

Problem with classical macro electrodes

• Example: ~5 mm diameter
• Limited spatial sampling and high noise.

Conductive polymer electrode approach (POT / Panaxium)

• Noise reduced by ~10× (as stated)
• Detect high-frequency components embedded in noise
• Very small contact diameters: ~10–30 µm
• Thin electrodes: ~4–10 µm, with tunable spacing

Planned trial concept

• Record intraoperatively, analyze electrophysiology, and decide infiltration/high vs low/no infiltration after resection.

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19) Synaptogenic signaling link: TSP1 and gamma power association

Reported chain

• Infiltrated regions show increased gamma power and altered functional connectivity.
• Synaptogenic factor thrombospondin-1 (TSP1) upregulated in highly connected tissue.
• Animal models:
  • faster tumor growth and shorter survival when grafts originate from high-connectivity regions.

Drug repurposing

• Gabapentin reduces proliferation (via reduced TSP1/proliferation mechanisms as described).
• Clinical cohorts:
  • glioblastoma patients treated with gabapentin show improved survival (Boston and San Francisco cohorts, as reported).

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20) Shared biological pathways: glutamate, synapses onto glia, calcium oscillations

Mechanisms described

• Tumor cells release high glutamate → increases neuronal activity → epileptic activity.
• Tumor cells express glutamate receptors; glutamate supports growth/migration.
• Neurons synapse onto glioma cells (fraction reported ~10–15%).
• Synaptic events generate calcium oscillations in tumor cells; calcium activity propagates through tumor networks.
• Neural activity influences tumor via growth factors (e.g., NLGN3 mentioned).
• Tumor connectivity via microtubes supports survival/extension.

Chemogenetics evidence concept

• Activating specific neurons accelerates tumor growth and drives spread toward more active regions → suggests activity-dependent tumor invasion.

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Adaptive/Network-level deep brain stimulation (DBS) and translational neurotechnology

21) DBS targets and mechanistic levels of analysis

Targets discussed

• Thalamic targets (e.g., VIM)
• GPi (Tourette syndrome / Parkinson’s)
• STN for Parkinson’s

DBS parameters (typical clinical examples described)

• Pulse width: ~60 µs
• Frequency: ~130 Hz
• Amplitude: ~1–5 V

Mechanistic framing

• Move from:
  • single-neuron spike changes
  • to oscillatory rhythms (LFP)
  • to whole-brain connectivity patterns
• Goal: identify oscillatory/network biomarkers for symptom states.

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22) Multimodal human neuroscience for network biomarkers

Data sources

• ECoG + DBS LFP + MEG/EEG + imaging

Examples stated

• STN/basal ganglia rhythms and cortical rhythms:
  • beta/alpha/theta spectral roles discussed
• Relationship between dopamine receptors and connectivity/tracer uptake.
• Dopamine medication vs STN DBS produce spectral differences (site- and frequency-specific effects).

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23) Adaptive DBS as a “clinical BCI”

Core concept

• Real-time detection of biomarkers (e.g., beta power) drives stimulation on/off or amplitude changes.

Translational status described

• Fully implantable adaptive DBS reported as FDA/CE-approved (as stated).

Technical/clinical issues

• Some patients fail due to insufficient signal quality (noise/distance to target).
• Cardiac dipole proximity can create ECG noise, impacting eligibility.

Beyond beta power

• Sleep disturbance and state-dependent offsets may break fixed-threshold adaptive schemes.
• Proposal: stimulation driven by behavioral/context triggers (e.g., movement speed).

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24) Movement-speed responsive stimulation idea

• Stimulation effects depend on patient behavior at millisecond timescales.
• Translational experiment:
  • trigger stimulation during fast vs slow voluntary movements
  • reported finding: fast-movement stimulation counteracts pathological slowing.

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25) “Therapeutic BCI” definition and AI-driven adaptive circuits

• Adaptive DBS + machine learning decoding can be framed as a therapeutic BCI.
• Aims toward:
  • scalable cross-patient models
  • connectomic circuit targeting
  • real-time symptom-specific adaptive stimulation

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26) Real-time AI steering and “plug-and-play” adaptive stimulation

• Pre-trained models may interpret signals without long individual training.
• Validation described as contrasting against approaches requiring extended patient-specific calibration.

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Researchers / sources mentioned (as featured)

(Only names explicitly present in the subtitles are included.)

• Mark McDonald
• Tom (mentioned: “Tom from Ging here in Germany”)
• Lorenzo (Lorenzo group; surname unclear)
• Emanuel Mand
• Kathleen Zaidder (from Bon/Bochum region; subtitles unclear)
• Catlin (Kathleen Catlin? name appears as “Catlin summarized her data”)
• Manuel (mentioned repeatedly)
• Ricky Matsumoto
• Fran Don BL (surname unclear)
• Michelle Leon
• Navarete (associated with “ripple lab”)
• John Pal
• Jackson (historical paper, 1882)
• (Paris) Pitié-Salpêtrière team (1966 study; individual names not given)
• Akim Purakbari
• Mera Chikaban
• Sao Yin
• Simon Little
• Philip Star
• Tim Denson
• Tibokan Bianca Yu
• Ari (supervisor name partially unclear)
• Josine (supervisor name partially unclear)
• Kate Schneider
• Katalina P.
• Eduardo Mug’s lab (adaptive DBS + gate signatures preprint mentioned; surname timing unclear)
• Allesia Cavalo
• David Martin
• Adam (questioner; neurosurgeon in Denver; surname unclear)
• Oscar
• Clara / Clara wants… (questioner; not clearly tied to a researcher)

Go-to collaborators/partners

• Onward (company mentioned; adaptive DBS system)
• Clean tech / GTech / GTech amplify (company mentioned; multiple instances)
• Panaxium (company mentioned; POT electrodes)
• Avo (software company; “Alysia” mentioned)
• Gilles Huberfeld / Uberfeld (pronunciation discussion; exact spelling uncertain)