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IDNE 701 · Introduction to Neuroengineering · FA26 Edition

Multimodal Neuroimaging &
Diagnostic Biomarker Systems

A 14-week semester trajectory from clinical neuropathology to physical sensor design, reconstruction algorithms, safety validation, and regulatory translation.

Discovery & Clinical Framing (1-3)
Biophysics & Sensors (4-6)
Architecture & Midterm (7-9)
Safety & Regulation (10-12)
Verification & Pitch (13-14)
Week 1 · Aug 26 Course Launch, Team Formation & Diagnostic Challenges
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Student Tasks
  • Form interdisciplinary teams (3–4 students blending engineering, computing, and neurobiology skills).
  • Select a target neurological or psychiatric disorder (e.g., Early Alzheimer's, Traumatic Brain Injury, Drug-Resistant Epilepsy, Parkinsonian Subtypes, Stroke Recovery).
  • Review the foundational Neuroengineering Knowledge Base and terminology in the Glossary.
  • Establish team collaboration protocols, GitHub repository, and shared documentation workspace.
Instructor Actions
Events
  • Course Kickoff: Scope, project charter, and team formation salon.
Time Division
40%
35%
25%
Team formation & charter Disease literature review Project scoping
Week 2 · Sep 02 Neuropathology, Clinical Stakeholders & Biomarker Definition
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Student Tasks
  • Map out specific pathological hallmarks (e.g., neuroinflammation, tau accumulation, microvascular disruption, epileptogenic spikes, demyelination).
  • Draft a stakeholder interview protocol for clinical neurologists, neuroradiologists, and neurosurgeons.
  • Establish quantitative sensitivity, specificity, and spatiotemporal target criteria for the proposed diagnostic system.
  • Explore contrast modalities: fMRI, DTI / Diffusion MRI, EEG, and MEG.
Instructor Actions
  • Identify clinical guest speaker: From Clinical Symptom to Imaging Biomarker — What Clinicians Need.
  • Provide stakeholder interview rubric and clinical needs specification template.
  • Confirm schedule and safety clearance for Week 3 Research MRI core visit.
Events
  • Guest Clinical Lecture: Neuroradiology clinical workflow and diagnostic bottlenecks.
Time Division
45%
35%
20%
Pathology & biomarker mapping Stakeholder needs interview Target specifications
Week 3 · Sep 09 Precedents & Lab Visit: UAB Research MRI Core Lab Tour I
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Student Tasks
  • Complete MRI Safety Training & Protocol Quiz prior to entering scanner suites.
  • Participate in the on-site walkthrough of the UAB Research MRI Core facilities (3T & 7T scanners, RF coil arrays, gradient cabinets).
  • Document state-of-the-art diagnostic precedents and benchmark their technical specifications against your project goals.
  • Finalize Phase 1 Deliverable: Target Disease & Clinical Needs Specification Dossier.
Instructor Actions
  • Lead guided lab tour of the UAB Research MRI Core facilities.
  • Demonstrate live pulse sequence execution, phantom scanning, and RF head coil hardware.
  • Review and provide formative feedback on Phase 1 team project charters.
Events
  • Lab Tour I: UAB Research MRI Core (High-Field Scanner Bay, RF Lab, Gradient Cabinets).
Time Division
40%
30%
30%
MRI Core on-site tour Precedent benchmarking Phase 1 dossier synthesis
Week 4 · Sep 16 Biophysical Contrast Mechanisms & Signal Transduction
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Student Tasks
  • Model the fundamental biophysics of your team's primary sensing modality:
    • Nuclear spin dynamics, \(T_1 / T_2 / T_2^*\) relaxation, and BOLD contrast in fMRI.
    • Photons in scattering tissue, Beer-Lambert law, and oxy/deoxy-hemoglobin absorption in fNIRS.
    • Dipole sources, volume conduction, and Poisson equation in EEG and MEG.
    • Acoustic wave propagation and microbubble dynamics in focused ultrasound.
  • Calculate theoretical Signal-to-Noise Ratio (SNR) and spatiotemporal resolution boundaries for your target tissue depth.
Instructor Actions
  • Lecture: Biophysical Foundations of Neural Contrast — From Quantum Spins to Photons & Ionic Currents.
  • Provide interactive simulation notebooks (Bloch equations, Monte Carlo photon scattering).
  • Hold technical office hours on physics modeling and parameter selection.
Events
  • Hands-on Workshop: Numerical modeling of contrast physics in Python/MATLAB.
Time Division
50%
30%
20%
Biophysical modeling SNR & resolution bounds Literature calibration
Week 5 · Sep 23 Sensor Hardware, Transduction Physics & Array Architecture
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Student Tasks
  • Design the physical transducer / sensor geometry (e.g., phased-array RF coils, optode source-detector geometry, active EEG electrode layouts, capacitive sensors).
  • Analyze impedance matching, geometric decoupling, optical fiber coupling, or analog front-end (AFE) amplification stages.
  • Simulate the spatial sensitivity profile (e.g., \(B_1^-\) receive sensitivity, photon banana distribution in brain tissue, lead field matrices).
  • Review comparative trade-offs between invasive vs non-invasive sensing modalities (ECoG vs EEG vs fNIRS).
Instructor Actions
  • Lecture: Sensor Engineering: Front-End Noise, Geometric Decoupling, and Multi-Channel Array Design.
  • Demonstrate RF bench test equipment (Network Analyzer, S-parameter measurement, Q-factor calculation).
  • Distribute hardware specification checklist and design matrix template.
Events
  • Hardware Design Studio: Array geometry and front-end noise budget optimization.
Time Division
45%
35%
20%
Sensor array design Sensitivity field modeling Front-end electronics specs
Week 6 · Sep 30 Optical Imaging & In-Class fNIRS Demonstration Live Demo
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Student Tasks
  • Participate in live in-class fNIRS system demonstration (continuous-wave & frequency-domain optical topography).
  • Collect and inspect real-time optical brain hemodynamic signals during a cognitive motor/verbal task.
  • Compare optical absorption curves of HbO vs HbR against functional magnetic resonance (fMRI) BOLD responses.
  • Prepare preliminary presentation slides and system diagrams for next week's Mid-Semester Design Review.
Instructor Actions
  • Conduct live in-class fNIRS demonstration using multi-channel research cap system.
  • Demonstrate real-time modified Beer-Lambert transformation, motion artifact detection, and optode placement.
  • Release evaluation rubric for the Week 7 Mid-Semester Design Review milestone.
Events
  • Live In-Class Demonstration: Multi-channel fNIRS hemodynamic recording and live signal analysis.
Time Division
40%
35%
25%
fNIRS demo & data parsing Midterm presentation prep Design dossier compilation
Week 7 · Oct 07 Mid-Semester Milestone: Concept Pin-Up & Preliminary Design Review ★ Milestone
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Student Tasks
  • Present 12-minute team pitch with visual concept boards and preliminary technical block diagrams.
  • Defend selection of biophysical contrast mechanism, sensor geometry, and expected biomarker resolution.
  • Engage in peer critique and faculty Q&A panel.
  • Submit Phase 1 & 2 Deliverable: Preliminary Design Concept (PDC) & Biomarker Architecture Dossier.
Instructor Actions
  • Convene faculty and clinical review panel for the mid-semester design critique.
  • Evaluate presentations using the PDR rubric (Innovation, Biophysical Rigor, Feasibility, Stakeholder Alignment).
  • Provide written milestone synthesis and actionable pivots for Phase 3 engineering.
Events
  • Mid-Semester Review Colloquium: Team presentations, concept pin-up gallery, and feedback reception.
Time Division
50%
30%
20%
Design presentation & defense Panel Q&A & critique Peer review assessment
Week 8 · Oct 14 Signal Acquisition, Sampling Schemes & Pulse Sequences
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Student Tasks
  • Design the spatial encoding and temporal sampling scheme (e.g., 2D/3D k-space trajectories, EPI, spiral readouts, EEG temporal montage sampling, multiplexed optical source modulation).
  • Evaluate the Nyquist-Shannon sampling limits, spatial aliasing, and gradient timing constraints.
  • Calculate scan time / acquisition latency vs point spread function (PSF) blurring trade-offs.
  • Draft the mathematical signal model: \(S(t) = \int \rho(\mathbf{r}) e^{-i \mathbf{k}(t) \cdot \mathbf{r}} d\mathbf{r}\).
Instructor Actions
  • Lecture: Spatial Encoding, K-Space Dynamics, and Pulse Sequence Engineering.
  • Demonstrate sequence simulation toolboxes (Pulseq, JEMRIS, Brainstorm).
  • Hold sequence optimization lab session with team breakout consultations.
Events
  • Workshop: Pulse sequence timing diagrams and k-space trajectory design.
Time Division
45%
35%
20%
Acquisition & sequence design K-space / sampling simulation Timing budget analysis
Week 9 · Oct 21 Reconstruction Pipelines & Machine Learning Biomarker Extraction
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Student Tasks
  • Implement reconstruction algorithms (Inverse Fast Fourier Transform [IFFT], SENSE/GRAPPA parallel imaging, or minimum norm estimate [MNE] source localization).
  • Build computational pipeline for feature extraction, dimensionality reduction, or deep neural network biomarker classification (see Machine Learning in BCI and Kalman Filtering).
  • Quantify reconstruction error (RMSE, SSIM) and biomarker classification accuracy (ROC-AUC, Confusion Matrix).
  • Implement baseline synthetic data test bench to benchmark reconstruction fidelity.
Instructor Actions
  • Lecture: Inverse Problems, Compressed Sensing, and Deep Learning in Neuroimage Reconstruction.
  • Provide open neuroimaging benchmark datasets (OASIS, ADNI, HCP, PhysioNet).
  • Code walkthrough: PyTorch / TensorFlow pipelines for accelerated image reconstruction and biomarker inference.
Events
  • Hackathon Session: Algorithm sprint for reconstruction acceleration and biomarker detection.
Time Division
50%
30%
20%
Reconstruction algorithm coding ML biomarker training Validation on public datasets
Week 10 · Oct 28 Artifact Mitigation, Motion Correction & SNR Optimization
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Student Tasks
  • Identify system-specific noise sources (e.g., patient motion, cardiac/respiratory pulsatility, eddy currents, \(B_0\) field inhomogeneities, eye blinks).
  • Incorporate real-time or prospective motion correction (navigators, optical tracking, ICA filtering, retrospective phase correction).
  • Demonstrate robustness of biomarker detection in the presence of synthetic noise and patient movement.
  • Formulate the signal processing flow chart and computational latency budget.
Instructor Actions
  • Lecture: The Dirty Reality of Brain Signals: Noise, Physiological Artifacts, and Mitigation Strategies.
  • Demonstrate artifact removal toolboxes (FSL, SPM, MNE-Python, EEGLAB).
  • Confirm logistics for Week 11 Neuroengineering EEG Core lab visit.
Events
  • Clinic Simulation: Stress-testing student algorithms against corrupt and noisy clinical datasets.
Time Division
40%
35%
25%
Artifact filtering & motion correction Algorithm stress testing Latency optimization
Week 11 · Nov 04 Lab Visit: Neuroengineering EEG Core & Electrophysiology Lab Tour II
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Student Tasks
  • Visit the Neuroengineering EEG Core for hands-on high-density EEG (128/256 channel) setup and impedance measurement.
  • Observe real-time evoked potential (ERP) paradigms, spectral power decomposition, and source localization.
  • Compare electrophysiological temporal resolution against metabolic neuroimaging modalities.
  • Integrate high-density electrophysiology considerations into multimodal sensing designs.
Instructor Actions
  • Host guided tour of the Neuroengineering EEG Core facilities.
  • Demonstrate electrode impedance checking, active shielding, noise cancellation, and ERP data acquisition.
  • Distribute FDA regulatory guidance documents (510(k) vs PMA, Early Feasibility, Q-Submission protocol).
Events
  • Lab Tour II: Neuroengineering EEG Core (Electrophysiology Suites, Faradaic Shielded Rooms).
Time Division
40%
30%
30%
EEG Core lab tour Electrophysiology signal analysis Regulatory framework drafting
Week 12 · Nov 11 Safety Limits (SAR, Thermal, Optical) & FDA Regulatory Strategy
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Student Tasks
  • Perform quantitative safety and dose modeling for your device:
    • Specific Absorption Rate (SAR in W/kg), \(B_1^+\) RMS, and peripheral nerve stimulation (PNS) thresholds in MRI.
    • Maximum Permissible Exposure (MPE) and tissue heating limits (ANSI Z136.1) in optical / fNIRS systems.
    • Thermal index (TI), mechanical index (MI), and spatial-peak temporal-average intensity (\(I_{\text{spta}}\)) in ultrasound.
    • Electrical leakage current and patient isolation (IEC 60601-1) in EEG.
  • Formulate FDA classification strategy (Class II 510(k) predicate vs De Novo vs Class III PMA) and draft an Early Feasibility / IDE plan.
  • Address ethical considerations in diagnostic neuroimaging (see Neuroethics & Translation).
Instructor Actions
  • Lecture: Safety Limits, Standards (IEC 60601 / ISO 14971), and the FDA Regulatory Pathway for Neurotechnologies.
  • Provide risk management matrix template (Failure Mode & Effects Analysis [FMEA]).
  • Conduct regulatory strategy clinics with student teams.
Events
  • Regulatory Clinic: Risk analysis, predicate device search, and FDA Pre-Submission strategy.
Time Division
45%
30%
25%
SAR & safety physics FDA regulatory roadmap Risk matrix & FMEA
Week 13 · Nov 18 Verification, Virtual Prototyping & Clinical Protocol (IRB)
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Student Tasks
  • Synthesize complete end-to-end virtual simulation demo showing:
    • Simulated diseased vs healthy neural substrate.
    • Sensor array signal acquisition with noise models.
    • Reconstruction and ML diagnostic output dashboard.
  • Draft a full Human Subjects Clinical Validation Protocol (IRB proposal with inclusion/exclusion criteria, primary endpoints, statistical power).
  • Compile the final Technical Design Dossier chapters and prepare pitch slides.
Instructor Actions
  • Review draft clinical validation protocols and provide IRB compliance feedback.
  • Conduct 1-on-1 pitch coaching sessions with each team.
  • Distribute symposium presentation guidelines and judging criteria.
Events
  • Dry Run & Pitch Coaching: Rapid-fire feedback on pitch structure and technical demonstration.
Time Division
50%
30%
20%
Virtual prototype simulation IRB clinical protocol draft Dossier compilation
🦃 Nov 25: Thanksgiving Break · No Scheduled Class Sessions · Final Polish Week
Week 14 · Dec 02–09 Final Milestone: Grand Pitch Symposium & Deliverable Showcase ★ Final Milestone
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Student Tasks
  • Deliver 15-minute public pitch + live interactive simulation demonstration to guest faculty, clinicians, and industry evaluators.
  • Defend the end-to-end design: Clinical impact, biophysical validation, hardware architecture, reconstruction algorithm, safety, and regulatory pathway.
  • Submit the final course deliverable package:
    • Deliverable 1: Complete Technical Design Dossier & Regulatory Strategy (PDF).
    • Deliverable 2: Reproducible Simulation & Reconstruction Codebase (GitHub repo with documentation).
    • Deliverable 3: Executive Pitch Deck & One-Page Clinical Brief.
  • Complete peer team evaluations and course retrospectives.
Instructor Actions
  • Host and moderate the IDNE 701 Grand Pitch Symposium with invited panel judges.
  • Tabulate final evaluations and award Best Innovation, Best Clinical Translation, and Best Engineering Design awards.
  • Publish selected project showcases to the program website and archive materials.
Events
  • Grand Pitch Symposium: Final formal presentations, live software demos, and award ceremony.
Time Division
60%
40%
Final symposium pitch & demo Dossier submission & debrief

Biophysical Contrast & Transduction

Grounding diagnostic measurements in biological reality — from nuclear spin relaxation and photon scattering to ionic dipole potentials.

Spatiotemporal Resolution Bounds

Navigating the fundamental physical trade-offs across penetration depth, temporal sampling frequency, and voxel/sensor volume.

K-Space & Inverse Problems

Mathematical foundations of spatial encoding, non-Cartesian sampling trajectories, and regularized / compressed sensing reconstruction.

Machine Learning & Biomarker Specificity

Validating feature extraction algorithms against class imbalance, motion confounders, and rigorous out-of-distribution generalization.

SAR, Thermal & Laser Safety

Ensuring patient safety through strict adherence to RF specific absorption rates, optical maximum permissible exposure, and IEC standards.

Regulatory & Clinical Translation

Navigating FDA 510(k)/PMA classification, Early Feasibility IDE protocols, and clinical workflow integration in medical centers.