MODELING DISCOORDINATION OF CORTICAL NEUROACTIVITY: RELEVANCE FOR THE EXECUTIVE CONTROL OF ATTENTION IN ALZHEIMER'S DISEASE

An Adaptive Joint CCA-ICA Method for Ocular Artifact Removal and its Application to Emotion Classification

BACKGROUND

The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research.

NEW METHOD

We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals.

RESULTS

We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition.

COMPARISON WITH EXISTING METHODS

Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition.

CONCLUSIONS

The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.

Are computational models of any use to psychiatry?

Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to approach psychiatric problems; and (d) the issue of communicating clinical concepts to theoreticians and vice versa. We argue that CMs have yet to influence psychiatric practice, but that they help psychiatric research in two fundamental ways: (a) to build better theories integrating psychiatry with neuroscience; and (b) to enforce explicit, global and efficient testing of hypotheses through more powerful analytical methods. CMs allow the complexity of a hypothesis to be rigorously weighed against the complexity of the data. The paper concludes with a discussion of the path ahead. It points to stumbling blocks, like the poor communication between theoretical and medical communities. But it also identifies areas in which the contributions of CMs will likely be pivotal, like an understanding of social influences in psychiatry, and of the co-morbidity structure of psychiatric diseases.

White matter pathways associated with working memory in normal aging

INTRODUCTION

Previous studies by our group have found that white matter integrity as determined by Diffusion Tensor Imaging (DTI) is associated with working memory decline. It has been proposed that subtle white matter integrity loss may lead to the disruption of working memory in particular because it relies on the dynamic and reiterative activity of cortico-cortical pathways.

METHODS

DTI and working memory measurement were acquired for 99 adults from our GENIE study of healthy middle aged and elderly individuals. Voxel-based statistics were used to identify clusters of voxels in mean diffusivity images specifically associated with variations in working memory performance. Tractography then identified the cortico-cortical white matter pathways passing through these clusters, between the temporal, parietal and frontal cortices.

RESULTS

Significant clusters were identified which were associated with working memory in the white matter of the temporal and frontal lobes, the cingulate gyrus, and in the thalamus. The tracts that passed through these clusters included the superior parietal lobule pathway, the medial temporo-frontal pathway, the uncinate fasciculus, the fronto-parietal fasciculus, and the cingulum.

CONCLUSIONS

Significant clusters were identified in the white matter that were associated with working memory performance. Tractography performed through these clusters identified white matter fiber tracts which pass between grey matter regions known to be activated by working memory tasks and also mirror working memory pathways suggested by previous functional connectivity imaging.