Multimodal pipeline for HCP-compatible processing and registration of
legacy datasets (MRI, MEG, and EEG)
Abstract
Extracting cortical features, which are the most relevant at
characterizing structure and function for normal or abnormal brain
conditions, would greatly benefit from multimodal neuroimage processing
following the surface-based style. This style recognizes the natural
definition space for such features due to the layered (surface-based)
Cortex structural and functional organization. It may therefore be more
sensitive and specific than the former volume-based style. The Human
Connectome Project (HCP) multimodal pipelines render high-quality
surface-based processing for some of the most consistently acquired
neuroimaging modalities, with the quality too reliant on their precise
acquisition requirements. Relevant international brain initiatives are
espoused to develop an HCP-compatible neuroinformatic facility for the
quality-ensured processing of international neuroimaging datasets, which
may not follow the specific HCP acquisition requirements, also coined as
legacy datasets. We appointed some initiatives to introduce multimodal
pipelines in two HCP-compatible processing branches. a) Structural:
forward- modeling with geometry (sources and head) and Lead Fields
defined for legacy MEG, or EEG, in the HCP individual cortical space
(Cifti) obtained from legacy MRI. Our pipeline (Ciftify-MEEG) leverages
a more diverse neuroinformatic repository than the HCP structural or MEG
pipelines. Ciftify-MEEG produces substantial processing illustrated here
with EEG examples, incorporating alternative processing paths and
corrections based on a quality control loop and upon qualitative and
quantitative indicators. b) Functional: identifying spectral
topographies and connectomes for the cortical oscillatory activity
observed in the MEG or EEG frequency bands. We leverage a novel
repository of Bayesian sparse inverse methods that target identifying
the topographies and connectomes with actual statistical guarantees, the
brain connectivity Variable Resolution Electromagnetic Tomographic
Analysis (BC-VARETA). Our pipeline design for BC-VARETA, which we
denominate Ciftify-bcVARETA, is integrated into the Ciftify- MEEG
outputs and with Bayesian sparse priors structured upon Cifti space
information. Ciftify-bcVARETA identification is less biased to forward
models and more biased to the observations than the HCP pipeline
illustrated here with topographies obtained for MEG and EEG legacy
databases.