Stability of ICA decomposition across within-subject EEG datasets

Removal of eye-blinking artifacts by ICA in cross-modal long-term EEG recording

Independent Component Analysis (ICA) has became the most popular method to remove eye-blinking artifacts from electroencephalogram (EEG) recording. For long term EEG recording, ICA was commonly considered to costing a lot of computation time. Furthermore, with no ground truth, the discussion about the quality of ICA decomposition in a nonstationary environment was specious. In this study, we investigated the "signal" (P300 waveform) and the "noise" (averaged eye-blinking artifacts) on a cross-modal long-term EEG recording to evaluate the efficiency and effectiveness of different methods on ICA eye-blinking artifacts removal. As a result, it was found that, firstly, down sampling is an effective way to reduce the computation time in ICA. Appropriate down sampling ratio could speed up ICA computation 200 times and keep the decomposition performance stable, in which the computation time of ICA decomposition on a 2800 s EEG recording was less than 5 s. Secondly, dimension reduction by PCA was also a way to improve the efficiency and effectiveness of ICA. Finally, the comparison by cropping the dataset indicated that performing ICA on each run of the experiment separately would achieve a better result for eye-blinking artifacts removal than using all the EEG data input for ICA.

Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography

BACKGROUND

Amyloid β (Aβ) and tau proteins are considered as critical factors that affect Alzheimer's disease (AD) and mild cognitive impairment (MCI). Although many studies have conducted on these two proteins, little study has investigated the relationship between their spatial distributions. This study aims to explore the associations of spatial patterns between Aβ deposition and tau deposition in patients with MCI and normal control (NC).

METHODS

We used multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with MCI and NC. All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database containing information of 65 patients with MCI and 75 NC who both had undergone AV45 (Aβ) and AV1451 (tau) PET. To assess the spatial distribution of Aβ and tau deposition, we employed parallel independent component analysis (pICA), which enabled the joint analysis of multimodal imaging data. pICA was conducted to identify the significant difference and correlation relationship of brain networks between Aβ PET and tau PET in MCI and NC groups.

RESULTS

Our results revealed the strongly correlated network between Aβ PET and tau PET were colocalized with the default-mode network (DMN). Simultaneously, in comparison of the spatial distribution between Aβ PET and tau PET, it was found that the significant differences between MCI and NC were mainly distributed in DMN, cognitive control network and visual networks. The altered brain networks obtained from pICA analysis are consistent with the abnormalities of brain network in MCI patients.

CONCLUSIONS

Findings suggested the abnormal spatial distribution regions of tau PET were correlated with the abnormal spatial distribution regions of Aβ PET, and both of which were located in DMN network. This study revealed that combining pICA with multimodal imaging data is an effective approach for distinguishing MCI patients from NC group.

Brain Network Alterations in Alzheimer's Disease Identified by Early-Phase PIB-PET

The aim of this study was to identify the brain networks from early-phase ¹¹C-PIB (perfusion PIB, pPIB) data and to compare the brain networks of patients with differentiating Alzheimer's disease (AD) with cognitively normal subjects (CN) and of mild cognitively impaired patients (MCI) with CN. Forty participants (14 CN, 12 MCI, and 14 AD) underwent ¹¹C-PIB and ¹⁸F-FDG PET/CT scans. Parallel independent component analysis (pICA) was used to identify correlated brain networks from the ¹¹C-pPIB and ¹⁸F-FDG data, and a two-sample t-test was used to evaluate group differences in the corrected brain networks between AD and CN, and between MCI and CN. Our study identified a brain network of perfusion (early-phase ¹¹C-PIB) that highly correlated with a glucose metabolism (¹⁸F-FDG) brain network and colocalized with the default mode network (DMN) in an AD-specific neurodegenerative cohort. Particularly, decreased ¹⁸F-FDG uptake correlated with a decreased regional cerebral blood flow in the frontal, parietal, and temporal regions of the DMN. The group comparisons revealed similar spatial patterns of the brain networks derived from the ¹¹C-pPIB and ¹⁸F-FDG data. Our findings indicate that ¹¹C-pPIB derived from the early-phase ¹¹C-PIB could provide complementary information for ¹⁸F-FDG examination in AD.

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

Mirror neuron activation as a function of explicit learning: changes in mu-event-related power after learning novel responses to ideomotor compatible, partially compatible, and non-compatible stimuli

Questions regarding the malleability of the mirror neuron system (MNS) continue to be debated. MNS activation has been reported when people observe another person performing biological goal-directed behaviors, such as grasping a cup. These findings support the importance of mapping goal-directed biological behavior onto one's motor repertoire as a means of understanding the actions of others. Still, other evidence supports the Associative Sequence Learning (ASL) model which predicts that the MNS responds to a variety of stimuli after sensorimotor learning, not simply biological behavior. MNS activity develops as a consequence of developing stimulus-response associations between a stimulus and its motor outcome. Findings from the ideomotor literature indicate that stimuli that are more ideomotor compatible with a response are accompanied by an increase in response activation compared to less compatible stimuli; however, non-compatible stimuli robustly activate a constituent response after sensorimotor learning. Here, we measured changes in the mu-rhythm, an EEG marker thought to index MNS activity, predicting that stimuli that differ along dimensions of ideomotor compatibility should show changes in mirror neuron activation as participants learn the respective stimulus-response associations. We observed robust mu-suppression for ideomotor-compatible hand actions and partially compatible dot animations prior to learning; however, compatible stimuli showed greater mu-suppression than partially or non-compatible stimuli after explicit learning. Additionally, non-compatible abstract stimuli exceeded baseline only after participants explicitly learned the motor responses associated with the stimuli. We conclude that the empirical differences between the biological and ASL accounts of the MNS can be explained by Ideomotor Theory.