Axono-cortical evoked potentials as a new method of IONM for preserving the motor control network: a first study in three cases.

BACKGROUND: White matter stimulation in an awake patient is currently the gold standard for identification of functional pathways. Despite the robustness and reproducibility of this method, very little is known about the electrophysiological mechanisms underlying the functional disruption. Axono-cortical evoked potentials (ACEPs) provide a reliable technique to explore these mechanisms. OBJECTIVE: To describe the shape and spatial patterns of ACEPs recorded when stimulating the white matter of the caudal part of the right superior frontal gyrus while recording in the precentral gyrus. METHODS: We report on three patients operated on under awake condition for a right superior frontal diffuse low-grade glioma. Functional sites were identified in the posterior wall of the cavity, whose 2-3-mA stimulation generated an arrest of movement. Once the resection was done, axono-cortical potentials were evoked: recording electrodes were put over the precentral gyrus, while stimulating at 1 Hz the white matter functional sites during 30-60 s. Unitary evoked potentials were averaged off-line. Waveform was visually analyzed, defining peaks and troughs, with quantitative measurements of their amplitudes and latencies. Spatial patterns of ACEPs were compared with patients' own and HCP-derived structural connectomics. RESULTS: Axono-cortical evoked potentials (ACEPs) were obtained and exhibited complex shapes and spatial patterns that correlated only partially with structural connectivity patterns. CONCLUSION: ACEPs is a new IONM methodology that could both contribute to elucidate the propagation of neuronal activity within a distributed network when stimulating white matter and provide a new technique for preserving motor control abilities during brain tumor resections.

Multinomial probabilistic fiber representation for connectivity driven clustering.

The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and thus only provides very limited information about the neuroanatomical function of the brain. We advance this issue by proposing a multinomial representation of fibers decoding their connectivity to gray matter regions. We then simplify the clustering task by first deriving a compact encoding of our representation via the logit transformation. Furthermore, we define a distance between fibers that is in theory invariant to parcellation biases and is equivalent to a family of Riemannian metrics on the simplex of multinomial probabilities. We apply our method to longitudinal scans of two healthy subjects showing high reproducibility of the resulting fiber bundles without needing to register the corresponding scans to a common coordinate system. We confirm these qualitative findings via a simple statistical analyse of the fiber bundles.