### 6.5 Summary

The most important aspects of fMRI data analysis, as used on the data
in this thesis have been presented above. The pre-processing steps improve
the ability of the statistical analysis to detect activations. The choice
of an appropriate statistical test depends on the assumptions that can
be reasonably made, and the questions that need to be addressed. Decisions
about the statistical significance of the results must take into account
the multiple comparisons problem, and the reduction in degrees of freedom
resulting from smoothing. Finally the results must be presented in an appropriate
form, to enable the comparison of results between subjects.

In the whole process, many informed decisions need to be made, and it
is difficult to propose a single 'black box' approach to the analysis,
where data is fed in and an activation image is automatically fed out.
However in order to formalise the analysis procedure to some degree, a
standard analysis protocol was written. This is included as Appendix D.

There are many more issues in the analysis that have not been mentioned
here, and much work that can be done. For example the Fourier Analysis
methods for detecting periodic activations[32]
, principal component analysis[33],
clustering techniques which detect clump together pixels having time courses
which vary in the same way, and nonparametric tests[34].
There are other methods for solving the multiple comparison problem
[35] and for carrying out inter
subject comparisons. It is this last area which requires the most attention,
since, as fMRI becomes more and more a tool for neurologists to detect
abnormality in brain function, the reliability of inter subject comparisons
will become more important. It has been well shown that fMRI can give colourful
pictures of the brain working, and it is necessary to take the techniques
for data analysis onward if fMRI is to fulfil its clinical and neurological
research potential.

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