  The distributed nature of the neural substrate, and the difficulty of
establishing necessity from correlative data, combine to render the mapping of
brain function a far harder task than it seems. Methods capable of combining
connective anatomical information with focal disruption of function are needed
to disambiguate local from global neural dependence, and critical from merely
coincidental activity. Here we present a comprehensive framework for focal and
connective spatial inference based on sparse disruptive data, and demonstrate
its application in the context of transient direct electrical stimulation of
the human medial frontal wall during the pre-surgical evaluation of patients
with focal epilepsy. Our framework formalizes voxel-wise mass-univariate
inference on sparsely sampled data within the statistical parametric mapping
framework, encompassing the analysis of distributed maps defined by any
criterion of connectivity. Applied to the medial frontal wall, this transient
dysconnectome approach reveals marked discrepancies between local and
distributed associations of major categories of motor and sensory behaviour,
revealing differentiation by remote connectivity to which purely local analysis
is blind. Our framework enables disruptive mapping of the human brain based on
sparsely sampled data with minimal spatial assumptions, good statistical
efficiency, flexible model formulation, and explicit comparison of local and
distributed effects.
