NSCI 801 - Quantitative Neuroscience

NSCI 801 (Queen's U) Quantitative Neuroscience course materials

This course is in tutorial format using Python and Google Colab.

You can find the course materials in a Jupyter Book here: StatsBook

Syllabus

Introduction (Gunnar)

• The research process

• Statistics and models in scientific discovery (Pearl)

• Study design (power, sample size, effect size)

  Intro slides (NSCI801_Intro.ipynb)

Intro Python (Joe)

• Google Colab interface

• Basic syntax and commands

• Importing and manipulating data

• Graphics

  Intro Python (NSCI801_Intro_python.ipynb)

Advanced Python (Joe)

• Vectors and Matrices

• Functions

  Advanced Python (NSCI801_Advanced_python.ipynb)

Data collection / signal processing (Joe)

• Data types

• Sampling

• DAQ

• Filtering (noise, differentiation, integration)

• Time vs frequency analysis

  Data Collection/Signal Processing (NSCI801_acquisition_filters.ipynb)

Statistics and Hypothesis testing - basics (Joe)

• Descriptors: central tendencies (mean, median, mode), Spread (Range, variance, percentiles), Shape (skew, kurtosis)

• Correlation / regression

• The logic of hypothesis testing

• Statistical significance

• Multiple comparisons

• Different test statistics

• Confidence intervals

  Descriptive Statistic (NSCI801_Descriptive_stats.ipynb)

Statistics and Hypothesis testing - advanced (Joe)

• ANOVA (between-subject, factorial, within-subject/repeated measures)

• Measuring effect size

• Multiple regression

• Non-parametric tests

  Statistics and hypothesis testing (NSCI801_Advanced_stats.ipynb)

Quantitative wet lab / bench methods (Joe)

• Image processing

  Image Processing (NSCI801_Image_Processing_Proper_v2.ipynb)

Statistics and Hypothesis testing - Bayesian (Gunnar)

• Motivation and pitfalls of classic methods

• Conditional probabilities and Bayes rule

• Bayes Factor

• Maximum A Posteriori (MAP) estimation

• Bayesian ANOVA

  Bayesian statistics (NSCI801_Bayesian-stats.ipynb)

Models in Neuroscience (Gunnar)

• Models in scientific discovery (Pearl)

• Usefulness of models

• Model fitting

• bootstrap

  Model fitting (NSCI801_ModelFitting.ipynb)

Data Neuroscience overview (Gunnar)

• Promises and limitations (Pearl)

• Data organization (format, DB)

• Blind data processing: machine learning techniques (classification, dimensionality reduction, decoding)

  Data Neuroscience (NSCI801_DataNeuroscience.ipynb)

Correlation vs causality (Gunnar)

• What’s causality?

• How to achieve causality

• Problem of unobserved variables in high-dimensional problems

  Correlation vs causality (NSCI801_CorrelationVsCausality.ipynb)

Reproducibility, reliability, validity (Gunnar)

• Statistical considerations (multiple comparisons, exploratory analysis, hypothesis testing)

• Open Science methods

• Open science vs patents (required for drug discovery)

  Reproducibility, reliability, validity (NSCI801_Reproducibility.ipynb)

Further readings

• 10 common stats mistakes paper
• Statistical Thinking for the 21st Century free online book by Russell A. Poldrack
• see more here