.. _AFNI_05_1stLevelAnalysis:

=========================================
AFNI Tutorial #5: Statistics and Modeling
=========================================

-----------

Overview
********

Now that the first functional run has been preprocessed, we can **fit a model** to the data. To understand how model fitting works, we need to review some fundamentals such as the General Linear Model, the BOLD response, and what a time-series is. Each of these topics are discussed in the following table of contents.

After you have reviewed those concepts, you are then ready to run a first-level analysis. The figure below illustrates how we will be fitting a model to the data.

.. figure:: 05_1stLevelAnalysis_Pipeline.png

   After a model has been constructed indicating what the BOLD response should look like (A), that model is then fit to the time-series at each voxel (B). How well the model fits (also known as the **goodness of fit**) can then be represented on the brain with statistical maps, with brighter intensities signifying a better model fit. These statistical maps can then be thresholded to show only the voxels with a statistically significant model fit (C).

.. toctree::
   :maxdepth: 1
   :caption: First-Level Analysis

   AFNI_Statistics/AFNI_01_Stats_TimeSeries
   AFNI_Statistics/AFNI_02_Stats_HRF_History
   AFNI_Statistics/AFNI_03_Stats_HRF_Overview
   AFNI_Statistics/AFNI_04_Stats_General_Linear_Model
   AFNI_Statistics/AFNI_05_Creating_Timing_Files
   AFNI_Statistics/AFNI_06_Stats_Running_1stLevel_Analysis


.. note::

   Understanding model fitting and first-level analysis can be challenging. Don't be discouraged if you don't understand everything the first time you read the chapters; keep at it, and the concepts will become clearer with time and practice.