Response-time corrected averaging of event-related potentials.

OBJECTIVE: The present study presents a novel approach to averaging of event-related potentials (ERPs). Acknowledging latency variability of late ERP components as related to performance fluctuations across trials should improve the assessment of late portions of the ERP. METHODS: Prior to the averaging procedure stimulus-to-response epochs in the electroencephalogram (EEG) were expanded/compressed in time to match mean RT in a certain condition and participant. By means of several mathematical functions RT variability was differentially distributed over late vs. early portions of the ERP. Data from 20 participants from two conditions of an identity-based priming task were analyzed using traditional stimulus- and response-locked averaging, as well as four different RT-corrected averaging procedures. RESULTS: Area under the curve as an index of precision of LPC assessment was reliably enhanced for certain RT-corrected procedures relative to traditional ERP averaging. Moreover, a priming effect on amplitude of a distinct LPC subcomponent which could not be confirmed with traditional stimulus-locked averaging was reliably born out using a cubic RT-correction procedure. CONCLUSIONS: RT-corrected ERP averaging can outperform traditional ERP averaging in the assessment of late portions of the ERP, and experimental effects upon. SIGNIFICANCE: Cognitive ERP researchers may take advantage of the improved capability of RT-corrected averaging to establish experimental effects on amplitudes in the late ERP range.     

Altered gray matter structural covariance networks at both acute and chronic stages of mild traumatic brain injury

Cognitive and emotional impairments observed in mild traumatic brain injury (mTBI) patients may reflect variances of brain connectivity within specific networks. Although previous studies found altered functional connectivity (FC) in mTBI patients, the alterations of brain structural properties remain unclear. In the present study, we analyzed structural covariance (SC) for the acute stages of mTBI (amTBI) patients, the chronic stages of mTBI (cmTBI) patients, and healthy controls. We first extracted the mean gray matter volume (GMV) of seed regions that are located in the default-mode network (DMN), executive control network (ECN), salience network (SN), sensorimotor network (SMN), and the visual network (VN). Then we determined and compared the SC for each seed region among the amTBI, the cmTBI and the healthy controls. Compared with healthy controls, the amTBI patients showed lower SC for the ECN, and the cmTBI patients showed higher SC for the both DMN and SN but lower SC for the SMN. The results revealed disrupted ECN in the amTBI patients and disrupted DMN, SN and SMN in the cmTBI patients. These alterations suggest that early disruptions in SC between bilateral insula and the bilateral prefrontal cortices may appear in amTBI and persist into cmTBI, which might be potentially related to the cognitive and emotional impairments.

     

Multi-scale graph-based grading for Alzheimer’s disease prediction

The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.     

Brain multigraph prediction using topology-aware adversarial graph neural network

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches.     

Altered microstructural properties of superficial white matter in patients with Parkinson’s disease

Brain Imaging and Behavior - Parkinson’s disease (PD), a chronic neurodegenerative disease, is characterized by sensorimotor and cognitive deficits. Previous diffusion tensor imaging (DTI)...     

Changes in cognitive function among older adults: A latent profile transition analysis

Cognitive decline in late life is a crucial health problem. It is important to understand the consistency and change of older adults’ cognitive function in late life. Data for older adults (78 years and above) from the Health and Retirement Study (N = 1680) were used to explore meaningful subtypes of cognitive function and transitions patterns between those profiles across times. Age, gender, levels of education and nursing home were incorporated as covariates to explore the association between these variables and cognitive function transition pattern. Three cognitive function subgroups (Normal Cognitive Function, Fluid Intelligence Impairment and Cognitive Impairment) were identified. Individuals in Normal Cognitive Function status had a high probability to convert to the Fluid Intelligence Impairment status whereas the Cognitive Impairment status appeared a predominant tendency for stability. Increasing age played a significant role in fluid intelligence impairment and cognitive impairment process. Female and individuals with nursing home might be at higher risk of subsequent fluid intelligence impairment, while higher education did not protect against fluid intelligence impairment. These findings highlighted the usefulness to adopt a person-centered approach rather than a variable-centered approach, suggesting directions for future research and tailored interventions approaches to older adults with particular characteristics.     

Buffering the effects of peer victimization on adolescent non-suicidal self-injury: The role of self-compassion and family cohesion

Non-suicidal self-injury (NSSI) is a significant behavioral problem among adolescents all over the world. This study examined the longitudinal relationship between peer victimization and NSSI, as well as the buffering effects of self-compassion and family cohesion on this relationship. Data were collected at two time points from 525 secondary school students (226 girls; M_age = 12.97, SD = 1.02) in China. Results showed that peer victimization (marginally) significantly predicted NSSI over time even after controlling for Wave 1 NSSI. This association was weakened under the condition of high levels of self-compassion. Findings of this study emphasize the buffering effect of self-compassion in the relationship between peer victimization and NSSI, and are informative for prevention and intervention of this behavioral problem.