Electrophysiological and neuroanatomical correlates of precision and capacity of working memory

Cognitive limits of working memory play a pivotal role in many varieties of mental operations in our daily life. The previously separate literatures on visual attention and on visual working memory are converging, with growing interest in how visual attention may relate to visual short-term memory and how hemispheric specificities constrain such higher cognitive functions. In addition, it has been debated whether the numbers of items (quantity) or the precision with which they are retained (quality) constrain human visual working memory. With psychophysical, electrophysiological, and neuroanatomical imaging approaches, I provide evidence for attentional and hemispheric interplays contributing to the maintenance of working memory in vision and audition. Here, I report exploratory analysis of how individual behavioural differences in separable aspects of attention may relate to particular aspects of visual working memory (in Chapter 2) and how structure of human parietal areas are associated with individual differences in the number and the precision of representations in vision (in Chapter 3) and audition (in Chapter 5). I further demonstrate that visual working memory resources can be flexibly allocated at will, providing evidence for a hybrid of discrete-slot and dynamic-resource models constraining working memory (in Chapter 4). Finally, I provide evidence of hemispheric differences during the maintenance of visual working memory (in Chapter 6). Rapprochement of rival accounts and hitherto ignored issues on the number and precision of human working memory are discussed. My thesis encourages further detailed investigations of human brain function and anatomy underlying attention and working memory

oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data

The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy

What contributes to individual differences in brain structure?

Individual differences in adult human brain structure have been found to reveal a great deal of information about variability in behaviors, cognitive abilities and mental and physical health. Driven by such evidence, what contributes to individual variation in brain structure has gained accelerated attention as a research question. Findings thus far appear to support the notion that an individual’s brain architecture is determined largely by genetic and environmental influences. This review aims to evaluate the empirical literature on whether and how genes and the environment contribute to individual differences in brain structure. It first considers how genetic and environmental effects may separately contribute to brain morphology, by examining evidence from twin, genome-wide association, cross-sectional and longitudinal studies. Next, evidence for the influence of the complex interplay between genetic and environmental factors, characterized as gene-environment interactions and correlations, is reviewed. In evaluating the extant literature, this review will conclude that both genetic and environmental factors play critical roles in contributing to individual variability in brain structure

Subjective discomfort of TMS predicts reaction times differences in published studies

Transcranial magnetic stimulation (TMS) was developed thirty years ago, in part to decrease the peripheral side-effects associated with transcranial electrical stimulation (Barker, 1991). TMS has been effective in that aim, and great advances have been made over the past 30 years. TMS can still be uncomfortable and painful, however, as it stimulates excitable superficial tissue including scalp muscles and peripheral nerves (Maizey et al., 2013). This causes annoyance, pain, and muscle twitches (i.e., discomfort) that vary systematically across the scalp (Meteyard & Holmes, 2018). For this Opinion, we investigated whether the TMS-related discomfort measured in our previous work could predict the reported differences in RT in studies published in the last 10 years. For single-pulse TMS studies, differences in RT between TMS and control conditions were significantly correlated with both the mean of median ratings of muscle twitches, and the mean effect of TMS on RT from Meteyard & Holmes (2018)

Contralateral delay activity tracks the storage of visually presented letters and words

Electrophysiological studies have demonstrated that the maintenance of items in visual working memory (VWM) is indexed by the contralateral delay activity (CDA), which increases in amplitude as the number of objects to remember increases, plateauing at VWM capacity. Previous work has primarily utilized simple visual items, such as colored squares or picture stimuli. Despite the frequent use of verbal stimuli in seminal investigations of visual attention and memory, it is unknown whether temporary storage of letters and words also elicit a typical load‐sensitive CDA. Given their close associations with language and phonological codes, it is possible that participants store these stimuli phonologically, and not visually. Participants completed a standard visual change‐detection task while their ERPs were recorded. Experiment 1 compared the CDA elicited by colored squares compared to uppercase consonants, and Experiment 2 compared the CDA elicited by words compared to colored bars. Behavioral accuracy of change detection decreased with increasing set size for colored squares, letters, and words. We found that a capacity‐limited CDA was present for colored squares, letters, and word arrays, suggesting that the visual codes for letters and words were maintained in VWM, despite the potential for transfer to verbal working memory. These results suggest that, despite their verbal associations, letters and words elicit the electrophysiological marker of VWM encoding and storage

Dynamic changes in prefrontal cortex involvement during verbal episodic memory formation

During encoding, the neural activity immediately before or during an event can predict whether that event will be later remembered. The contribution of brain activity immediately after an event to memory formation is however less known. Here, we used repetitive Transcranial Magnetic Stimulation (rTMS) to investigate the temporal dynamics of episodic memory encoding with a focus on post-stimulus time intervals. At encoding, rTMS was applied during the online processing of the word, at its offset, or 100, 200, 300 or 400 ms thereafter. rTMS was delivered to the left ventrolateral (VLPFC) or dorsolateral prefrontal cortex (DLPFC). VLPFC rTMS during the first few hundreds of milliseconds after word offset disrupted subsequent recognition accuracy. We did not observe effects of DLPFC rTMS at any time point. These results suggest that encoding-related VLPFC engagement starts at a relatively late processing stage, and may reflect brain processes related to the offset of the stimulus

Contralateral delay activity as a marker of visual working memory capacity: a multi-site registered replication

Visual working memory (VWM) is a temporary storage system capable of retaining information that can be accessed and manipulated by higher cognitive processes, thereby facilitating a wide range of cognitive functions. Electroencephalography (EEG) is used to understand the neural correlates of VWM with high temporal precision, and one commonly used EEG measure is an event-related potential called the contralateral delay activity (CDA). In a landmark study by Vogel and Machizawa (2004), the authors found that the CDA amplitude increases with the number of items stored in VWM and plateaus around three to four items, which is thought to represent the typical adult working memory capacity. Critically, this study also showed that the increase in CDA amplitude between two-item and four-item arrays correlated with individual subjects’ VWM performance. Although these results have been supported by subsequent studies, a recent study suggested that the number of subjects used in experiments investigating the CDA may not be sufficient to detect differences in set size and to provide a reliable account of the relationship between behaviorally measured VWM capacity and the CDA amplitude. To address this, the current study, as part of the #EEGManyLabs project, aims to conduct a multi-site replication of Vogel and Machizawa's (2004) seminal study on a large sample of participants, with a pre-registered analysis plan. Through this, our goal is to contribute to deepening our understanding of the neural correlates of visual working memory

One hundred years of EEG for brain and behaviour research

On the centenary of the first human EEG recording, more than 500 experts reflect on the impact that this discovery has had on our understanding of the brain and behaviour. We document their priorities and call for collective action focusing on validity, democratization and responsibility to realize the potential of EEG in science and society over the next 100 years