Auto-QCLF: A Quick and Reliable EEG Lead Field Quality Control for Big Databases

Vega, María Luisa Bringas; Riaz, Usama; Razzaq, Fuleah A.; Gonzalez, Ariosky Areces; Linares, Deirel Paz; Sosa, Pedro Valdés

doi:10.1016/j.ijpsycho.2021.07.503

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results were subjected to visual quality control, obtaining 60 Lead Fields marked as doubtful and the rest as good. All 60 LFs declared as "doubtful," and another 40 goods LF samples from the 1251 LFs of the CMI dataset were used as the sample in the Automatic Quality Control of the numerical accuracy of EEG Lead Fields (LF-AQI) (Vega et al, 2021). This analysis found three main reasons why the LFs were screened as "doubtful": the first reason was the blurred/noisy in the MRI causing inaccurate segmentation, the second one was an evident misalignment of the Cortex inside the head, and the bad alignment of the sensor's layout caused thirst.…”

Section: Discussionmentioning

confidence: 99%

“…Since we have already performed automatic sensor correction during the head modeling step, we manually correct the electrode alignment and placement on the scalp and recompute the Lead Field again after the manual correction and processed by the aftermanual-quality-control process. Once the corrections were completed, the LFs marked as bad were passed through the LF-AQI pipeline (Vega et al, 2021), getting all then corrected and declared as goods, and LF-AQI then accepted these LFs.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal pipeline for HCP-compatible processing and registration of legacy datasets (MRI, MEG, and EEG)

Gonzalez¹,

Paz-Linares²,

Riaz³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>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.</p>

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multimodal pipeline for HCP-compatible processing and registration of legacy datasets (MRI, MEG, and EEG)

Gonzalez¹,

Paz-Linares²,

Riaz³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These artifact annotations follow a standard questionnaire prepared following the HCP quality control recommendations (Marcus et al, 2013). We conducted a final trial on the annotated artifacts, calculating their scores using item response theory (Vega et al, 2021;Riaz et al, 2023).…”

Section: Geometrical Artifact Correctionsmentioning

confidence: 99%

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics

Areces-Gonzalez,

Paz-Linares,

Riaz

et al. 2024

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

show abstract