Development and Retrospective Application of Novel Outcome Measure: Sturge-Weber Syndrome Acute Crisis (SWAC) Index.

Autism spectrum disorder (ASD) is a disorder of social communication and altered behavior affecting 1 in 36 children in the United States [, ]. However, despite decades of significant popular interest and research, the neuroanatomic circuitry underlying autism is still not well understood. Mapping idiopathic ASD symptoms to specific neuroanatomy in human neuroimaging studies remains difficult, partly because idiopathic/nonsyndromic ASD patients as a population lack obvious lesions or specific neuroanatomical abnormalities on standard brain imaging []. While advanced functional neuroimaging can reveal more subtle abnormalities, attempts to associate these subtle findings with ASD severity in patients have produced inconsistent results across different studies: recent reviews discuss the variable implication of several areas ranging from the frontal lobes, to the amygdala, to the cerebellum [], and to the temporal lobe []. This variability is also compounded by the high degree of phenotypic variability across idiopathic ASD. Resolving this variability could enable targeted modification of brain networks. Using techniques such as transcranial magnetic stimulation (TMS) and real‐time functional magnetic resonance imaging neurofeedback (fMRI NF) may become feasible. These techniques have shown promise in both increasing network functional connectivity in desired networks and increasing inhibition of areas with maladaptive overactivation []. Unfortunately, identifying target regions for ASD therapies using TMS and fMRI NF remains challenging []. One promising approach is to leverage lesion network mapping (LNM), a recently validated method that combines clinical lesion data with separately acquired normative functional connectivity data to link lesioned brain regions with networks related to symptomatology []. LNM has defined circuitry related to various diseases including infantile spasms [], prosopagnosia [], criminality [], lesional epilepsy [], depression [], and emotional dysregulation []. This methodology is useful in clinical cohorts with a specific symptom profile, whether this is a binary diagnosis or a spectrum/continuum. LNM is particularly useful for studying clinical cohorts as it does not require functional MRI from the lesioned cohort itself []. Patient lesions are solely used as seed regions for resting‐state fMRI connectivity analyses using a large control cohort of typically developing participants, comprising a “normative connectome.” [] Resulting statistical analyses of these maps thus reveal the networks of regions likely to be impacted by a lesioned location. An additional benefit of LNM is that the connectome cohort does not need to be diagnosis‐ or age‐matched to the lesion cohort as the intention is to simulate the “pre‐lesional/unaffected trajectory” of a lesion's network and not subsequent reorganization. As such, all comparisons are based on differences in lesion location while a consistent connectome is used throughout the analysis [].