4.6. General challenges in integrating head models with fMRI
Integrating EFs with fMRI data presents several challenges that impact result interpretation and accuracy. First, combining a static map (head model) with a dynamic map (fMRI) is difficult due to the complex interactions between targeted and non-targeted brain regions. This complexity makes it challenging to establish a direct relationship between EFs and changes in brain activity measured by BOLD signals. Interpreting the results of brain integration studies can be complex in part due to the intricate interconnections between brain regions. When analyzing the relationship between EFs and BOLD signals, it’s crucial to consider both positive and negative correlations. For instance, higher EF strengths might reduce functional connectivity, while lower strengths could enhance it. This complexity calls into question the specific mechanisms through which EFs influence brain networks. Additionally, it’s important to recognize that the commonly used statistical measures of connectivity lack a robust biophysical basis. While there are plausible reasons why higher EFs could reduce connectivity, such as the orientation of EFs and neurons, the concept of connectivity itself is largely statistical. Therefore, further research is needed to elucidate the biophysical foundations of connectivity and its interplay with EFs for a more nuanced understanding of brain stimulation outcomes.Another challenge is selecting appropriate measures for EF and fMRI data, as different metrics provide distinct information about EF distribution patterns and brain activity. In addition to EF strength, the orientation of the EF plays a role in adjusting the "effective" intensity when it is not aligned with the axis from the dendrites to the axon of a neuron. It can also indirectly indicate which neurons are recruited, as those that are most aligned with the vector [