Poster Keerthana Deepti Karunakaran BioMedical Engineering And Imaging Institute
Title:
Integrating Machine Learning and Big‑Data Analytics into Neuroscience Research: A Roadmap for Emerging Investigators

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1 Introduction

Neuroscience has entered a data‑rich era in which multi‑modal recordings—electrophysiology, functional imaging, genomics, behavior tracking, and even virtual reality environments—produce terabytes of information daily. Traditional statistical tools often fall short when confronted with such complexity: high dimensionality, non‑linear relationships, temporal dependencies, and heterogeneous noise sources. Machine learning (ML) and big‑data analytics offer scalable, flexible frameworks that can uncover hidden structure, predict neural states, and guide experimental design.

This primer is written for graduate students and postdocs who have mastered basic statistical methods but are new to ML pipelines and large‑scale data processing. It will walk through a typical workflow: from raw acquisition to deployment of predictive models, highlighting key concepts, pitfalls, and best practices in a research setting.

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1. From Raw Acquisition to Feature Engineering

1.1 Data Acquisition Modalities

Common neural recording modalities include:
Electrophysiology (single‑unit spikes, local field potentials) Calcium imaging (fluorescent indicators like GCaMP) Magnetoencephalography / EEG (non‑invasive signals) Functional MRI (hemodynamic responses)

Each modality has distinct sampling rates, signal characteristics, and noise profiles. For maids-station.com instance, calcium imaging typically samples at 10–30 Hz, while electrophysiology can reach tens of kHz.

1.2 Preprocessing Steps

Typical preprocessing pipelines involve:
Signal Filtering: Band‑pass filters to isolate frequency bands (e.g., 0.1–40 Hz for EEG). Artifact Rejection: Removing eye blinks, muscle activity via ICA or regression. Baseline Correction: Subtracting a pre‑stimulus mean. Downsampling/Resampling: To match analysis resolution. Normalization: Z‑scoring across trials.

1.3 Feature Extraction

Depending on the modeling approach, features may include:
Time‑Domain Features: ERP amplitudes at specific latencies (e.g., P300). Frequency‑Domain Features: Power spectral density in bands. Temporal Sequences: Raw voltage traces over a window.

1. Modeling Approaches

ApproachCore IdeaStrengthsWeaknesses Classical Linear Regression / Logistic RegressionPredict continuous or binary outcome from features using linear mapping (possibly with interaction terms).Simple, interpretable, fast to train.Assumes linearity