A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies

Locus Coeruleus Shows a Spatial Pattern of Structural Disintegration in Parkinson's Disease

BACKGROUND: Parkinson's disease (PD) causes a loss of neuromelanin‐positive, noradrenergic neurons in the locus coeruleus (LC), which has been implicated in nonmotor dysfunction. OBJECTIVES: We used “neuromelanin sensitive” magnetic resonance imaging (MRI) to localize structural disintegration in the LC and its association with nonmotor dysfunction in PD. METHODS: A total of 42 patients with PD and 24 age‐matched healthy volunteers underwent magnetization transfer weighted (MTw) MRI of the LC. The contrast‐to‐noise ratio of the MTw signal (CNR(MTw)) was used as an index of structural LC integrity. We performed slicewise and voxelwise analyses to map spatial patterns of structural disintegration, complemented by principal component analysis (PCA). We also tested for correlations between regional CNR(MTw) and severity of nonmotor symptoms. RESULTS: Mean CNR(MTw) of the right LC was reduced in patients relative to controls. Voxelwise and slicewise analyses showed that the attenuation of CNR(MTw) was confined to the right mid‐caudal LC and linked regional CNR(MTw) to nonmotor symptoms. CNR(MTw) attenuation in the left mid‐caudal LC was associated with the orthostatic drop in systolic blood pressure, whereas CNR(MTw) attenuation in the caudal most portion of right LC correlated with apathy ratings. PCA identified a bilateral component that was more weakly expressed in patients. This component was characterized by a gradient in CNR(MTw) along the rostro‐caudal and dorso‐ventral axes of the nucleus. The individual expression score of this component reflected the overall severity of nonmotor symptoms. CONCLUSION: A spatially heterogeneous disintegration of LC in PD may determine the individual expression of specific nonmotor symptoms such as orthostatic dysregulation or apathy. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society