Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory

Complexity measures reveal age-dependent changes in electroencephalogram during working memory task

The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.

Predicting creative behavior using resting-state electroencephalography

Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.

Cortical and white matter substrates supporting visuospatial working memory

OBJECTIVE

In tasks involving new visuospatial information, we rely on working memory, supported by a distributed brain network. We investigated the dynamic interplay between brain regions, including cortical and white matter structures, to understand how neural interactions change with different memory loads and trials, and their subsequent impact on working memory performance.

METHODS

Patients undertook a task of immediate spatial recall during intracranial EEG monitoring. We charted the dynamics of cortical high-gamma activity and associated functional connectivity modulations in white matter tracts.

RESULTS

Elevated memory loads were linked to enhanced functional connectivity via occipital longitudinal tracts, yet decreased through arcuate, uncinate, and superior-longitudinal fasciculi. As task familiarity grew, there was increased high-gamma activity in the posterior inferior-frontal gyrus (pIFG) and diminished functional connectivity across a network encompassing frontal, parietal, and temporal lobes. Early pIFG high-gamma activity was predictive of successful recall. Including this metric in a logistic regression model yielded an accuracy of 0.76.

CONCLUSIONS

Optimizing visuospatial working memory through practice is tied to early pIFG activation and decreased dependence on irrelevant neural pathways.

SIGNIFICANCE

This study expands our knowledge of human adaptation for visuospatial working memory, showing the spatiotemporal dynamics of cortical network modulations through white matter tracts.

Functional brain connectivity predicts sleep duration in youth and adults

Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includes n-back task-based and resting-state fMRI data from adults aged 22-35 years (task n = 896; rest n = 898). We applied connectome-based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10-fold cross-validation predicted self-reported average sleep duration for the past month from n-back task and resting-state connectivity patterns. We replicated this finding in data from the 2-year follow-up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includes n-back task and resting-state fMRI for adolescents aged 11-12 years (task n = 786; rest n = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10-fold cross-validation again predicted sleep duration from n-back task and resting-state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting-state functional brain connectivity patterns reflect sleep duration in youth and young adults.

ConnSearch: A framework for functional connectivity analysis designed for interpretability and effectiveness at limited sample sizes

Functional connectivity studies increasingly turn to machine learning methods, which typically involve fitting a connectome-wide classifier, then conducting post hoc interpretation analyses to identify the neural correlates that best predict a dependent variable. However, this traditional analytic paradigm suffers from two main limitations. First, even if classifiers are perfectly accurate, interpretation analyses may not identify all the patterns expressed by a dependent variable. Second, even if classifiers are generalizable, the patterns implicated via interpretation analyses may not replicate. In other words, this traditional approach can yield effective classifiers while falling short of most neuroscientists' goals: pinpointing the neural correlates of dependent variables. We propose a new framework for multivariate analysis, ConnSearch, which involves dividing the connectome into components (e.g., groups of highly connected regions) and fitting an independent model for each component (e.g., a support vector machine or a correlation-based model). Conclusions about the link between a dependent variable and the brain are based on which components yield predictive models rather than on interpretation analysis. We used working memory data from the Human Connectome Project (N = 50-250) to compare ConnSearch with four existing connectome-wide classification/interpretation methods. For each approach, the models attempted to classify examples as being from the high-load or low-load conditions (binary labels). Relative to traditional methods, ConnSearch identified neural correlates that were more comprehensive, had greater consistency with the WM literature, and better replicated across datasets. Hence, ConnSearch is well-positioned to be an effective tool for functional connectivity research.

Effects of indoor exposure to low level toluene on neural network alterations during working memory encoding

OBJECTIVE

While high concentrations of toluene are known to affect multiple human organ systems, research concerning the influence of immediate, short-term exposure to toluene indoors and at low concentrations is scarce. Here, we studied effects of indoor toluene exposure on neural network alterations during working memory (WM) encoding.

METHODS

A total of 23 healthy college students were recruited. All participants were situated in a closed environmental chamber with a full fresh air system. Each participant was subjected to four exposure experiments with different toluene concentrations (0, 17.5, 35, and 70 ppb, named Group A, B, C and D, respectively), with at least one week between each experiment. WM Behavioral and 19-channel electroencephalogram (EEG) recordings in a pre-set environmental chamber were conducted simultaneously during each toluene exposure experiment. Neural networks relevant to WM encoding were visualized analyzing the obtained data.

RESULTS

1. No significant difference in WM behavioral performance among the four groups was found. However, a significant increase in whole brain neural network functional connectivity was noted, especially in the frontal region. 2. An outflow directional transfer function (DTF_outflow) revealed higher frontal region values among Group D (the 70 ppb group) as compared to Group A, B and C (the0, 17.5 ppb and 35 ppb groups, respectively), although no differences in frontal region DTF_inflow values among the four groups were noted. 3. The DTF_FZ-F7, DTF_FZ-T5, DTF_FZ-P4, DTF_FZ-P3, DTF_FP2-O2, DTF_P3-T4, DTF_P3-F4, DTF_P4-CZ and DTF_P4-T4 values of Group D were found to be higher as compared to those of Group A and B. Furthermore, DTF_FZ-F7 and DTF_P4-T4 values of Group C were higher as compared to those of Group A. The DTF_FZ-F7 values of Group D were higher as compared to those of the Group C.

CONCLUSION

Short-term toluene exposure significantly influences neural networks during cognitive processes such as WM encoding, even at low concentration.

The medial temporal lobe supports the quality of visual short-term memory representation

The quality of short-term memory (STM) underlies our ability to recall the exact details of a recent event, yet how the human brain enables this core cognitive function remains poorly understood. Here we use multiple experimental approaches to test the hypothesis that the quality of STM, such as its precision or fidelity, relies on the medial temporal lobe (MTL), a region commonly associated with the ability to distinguish similar information remembered in long-term memory. First, with intracranial recordings, we find that delay-period MTL activity retains item-specific STM content that is predictive of subsequent recall precision. Second, STM recall precision is associated with an increase in the strength of intrinsic MTL-to-neocortical functional connections during a brief retention interval. Finally, perturbing the MTL through electrical stimulation or surgical removal can selectively reduce STM precision. Collectively, these findings provide converging evidence that the MTL is critically involved in the quality of STM representation.

Exploring construction workers' brain connectivity during hazard recognition: a cognitive psychology perspective

Monitoring brain activity is a novel development for hazard recognition in the construction industry. However, very few empirical studies have investigated the causal connections within the brain. This study aimed to explore the brain connectivity of construction workers during hazard recognition. Electroencephalogram data were collected from construction workers to perform image-based hazard recognition tasks. The Granger causality-based adaptive directed transfer function was used to simulate directed and time-variant information flow across the observed brain activity from the perspective of cognitive psychology. The results suggested a top-down modulation of behavioral goals originating from the dorsal attention network during hazard relocation. The sensory cortex predominantly serves as the information outlet center and interacts extensively with the frontal and visual cortices, reflecting a top-down attention reorientation mechanism for processing threatening stimuli. Our findings of brain effective connectivity supplement new evidence underpinning parallel distributed processing theory for workplace hazard recognition.

Brain activity during visuospatial working memory in congenital adrenal hyperplasia

CONTEXT

Patients with congenital adrenal hyperplasia (CAH) require life-long replacement of cortisol. Problems with cognitive function, especially working memory, have previously been identified, but the long-term effects of this disease on brain function are unknown.

OBJECTIVE

We investigate brain activity during working memory in CAH compared to controls.

DESIGN, SETTING, AND PARTICIPANTS

Twenty-nine individuals with CAH (17 females) and 40 healthy controls (24 females), 16-33 years, from a single research institute, underwent functional magnetic resonance imaging while doing a verbal and visuospatial working memory task.

RESULTS

Individuals with CAH responded faster on the verbal task. Although we found no differences in BOLD response over the whole group, there were significant interactions with sex: CAH males had increased activity in the bilateral lateral superior occipital cortex, left supramarginal and angular gyri, left precuneus, left posterior cingulate cortex and bilateral cerebellum during decoding of the visuospatial task, while females showed decreased activity in these regions.

CONCLUSIONS

Long-term cortisol imbalances do not seem to have a major impact on the functional brain responses during working memory in CAH. However, activity of the left dorsal visual stream in particular might be affected depending on sex. As the task employed may have been relatively easy, larger studies using more complex tasks are needed to further investigate this.

SF-MVPA: A from raw data to statistical results and surface space-based MVPA toolbox

Compared with traditional volume space-based multivariate pattern analysis (MVPA), surface space-based MVPA has many advantages and has received increasing attention. However, surface space-based MVPA requires considerable programming and is therefore difficult for people without a programming foundation. To address this, we developed a MATLAB toolbox based on a graphical interactive interface (GUI) called surface space-based multivariate pattern analysis (SF-MVPA) in this manuscript. Unlike the traditional MVPA toolboxes, which often only include MVPA calculation processes after data preprocessing, SF-MVPA covers the complete pipeline of surface space-based MVPA, including raw data format conversion, surface reconstruction, functional magnetic resonance (fMRI) data preprocessing, comparative analysis, surface space-based MVPA, leave one-run out cross validation, and family-wise error correction. With SF-MVPA, users can complete the complete pipeline of surface space-based MVPA without programming. In addition, SF-MVPA is designed for parallel computing and hence has high computational efficiency. After introducing SF-MVPA, we analyzed a sample dataset of tonal working memory load. By comparison with another surface space-based MVPA toolbox named CoSMoMVPA, we found that the two toolboxes obtained consistent results. We hope that through this toolbox, users can more easily implement surface space-based MVPA.

Propofol selectively modulates functional connectivity signatures of sustained attention during rest and narrative listening

Sustained attention is a critical cognitive function reflected in an individual's whole-brain pattern of functional magnetic resonance imaging functional connectivity. However, sustained attention is not a purely static trait. Rather, attention waxes and wanes over time. Do functional brain networks that underlie individual differences in sustained attention also underlie changes in attentional state? To investigate, we replicate the finding that a validated connectome-based model of individual differences in sustained attention tracks pharmacologically induced changes in attentional state. Specifically, preregistered analyses revealed that participants exhibited functional connectivity signatures of stronger attention when awake than when under deep sedation with the anesthetic agent propofol. Furthermore, this effect was relatively selective to the predefined sustained attention networks: propofol administration modulated strength of the sustained attention networks more than it modulated strength of canonical resting-state networks and a network defined to predict fluid intelligence, and the functional connections most affected by propofol sedation overlapped with the sustained attention networks. Thus, propofol modulates functional connectivity signatures of sustained attention within individuals. More broadly, these findings underscore the utility of pharmacological intervention in testing both the generalizability and specificity of network-based models of cognitive function.

The bottom-up information transfer process and top-down attention control underlying tonal working memory

Tonal working memory has been less investigated by neuropsychological and neuroimaging studies and even less in terms of tonal working memory load. In this study, we analyzed the dynamic cortical processing process of tonal working memory with an original surface-space-based multivariate pattern analysis (sf-MVPA) method and found that this process constituted a bottom-up information transfer process. Then, the local cortical activity pattern, local cortical response strength, and cortical functional connectivity under different tonal working memory loads were investigated. No brain area’s local activity pattern or response strength was significantly different under different memory loads. Meanwhile, the interactions between the auditory cortex (AC) and an attention control network were linearly correlated with the memory load. This finding shows that the neural mechanism underlying the tonal working memory load does not arise from changes in local activity patterns or changes in the local response strength, but from top-down attention control. Our results indicate that the implementation of tonal working memory is based on the cooperation of the bottom-up information transfer process and top-down attention control.

A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis

Dynamic causal modeling (DCM) is a tool used for effective connectivity (EC) estimation in neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC network needs to be determined in advance based on a large amount of prior knowledge. This characteristic makes it difficult to apply DCM to the exploratory brain network analysis. The exploratory analysis of DCM can be realized from two perspectives: one is to reduce the computational cost of the model; the other is to reduce the model space. From the perspective of model space reduction, a model space exploration strategy is proposed, including two algorithms. One algorithm, named GreedyEC, starts with reducing EC from full model, and the other, named GreedyROI, start with adding EC from one node model. Then the two algorithms were applied to the task state functional magnetic resonance imaging (fMRI) data of visual object recognition and selected the best DCM model from the perspective of model comparison based on Bayesian model compare method. Results show that combining the results of the two algorithms can further improve the effect of DCM exploratory analysis. For convenience in application, the algorithms were encapsulated into MATLAB function based on SPM to help neuroscience researchers to analyze the brain causal information flow network. The strategy provides a model space exploration tool that may obtain the best model from the perspective of model comparison and lower the threshold of DCM analysis.

Characteristic functional cores revealed by hyperbolic disc embedding and k-core percolation on resting-state fMRI

Hyperbolic disc embedding and k-core percolation reveal the hierarchical structure of functional connectivity on resting-state fMRI (rsfMRI). Using 180 normal adults’ rsfMRI data from the human connectome project database, we visualized inter-voxel relations by embedding voxels on the hyperbolic space using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb{S}}^{1} /{\mathbb{H}}^{2}$$\end{document}S1/H2 model. We also conducted k-core percolation on 30 participants to investigate core voxels for each individual. It recursively peels the layer off, and this procedure leaves voxels embedded in the center of the hyperbolic disc. We used independent components to classify core voxels, and it revealed stereotypes of individuals such as visual network dominant, default mode network dominant, and distributed patterns. Characteristic core structures of resting-state brain connectivity of normal subjects disclosed the distributed or asymmetric contribution of voxels to the k_max-core, which suggests the hierarchical dominance of certain IC subnetworks characteristic of subgroups of individuals at rest.

The hippocampus supports high-precision binding in visual working memory

It is well established that the hippocampus is critical for long-term episodic memory, but a growing body of research suggests that it also plays a critical role in supporting memory over very brief delays as measured in tests of working memory (WM). However, the circumstances under which the hippocampus is necessary for WM and the specific processes that it supports remain controversial. We propose that the hippocampus supports WM by binding together high-precision properties of an event, and we test this claim by examining the precision of color-location bindings in a visual WM task in which participants report the precise color of studied items using a continuous color wheel. Amnestic patients with hippocampal damage were significantly impaired at retrieving these colors after a 1-s delay, and these impairments reflected a reduction in the precision of those memories rather than increases in total memory failures or binding errors. Moreover, a parallel fMRI study in healthy subjects revealed that neural activity in the head and body of the hippocampus was directly related to the precision of visual WM decisions. Together, these results indicate that the hippocampus is critical in complex high-precision binding that supports memory over brief delays.

Dynamic causal brain circuits during working memory and their functional controllability

Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe functional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto-parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition.

Dynamic functional connectome predicts individual working memory performance across diagnostic categories

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We created transdiagnostic predictive working memory models using connectome-based predictive modeling (CPM).

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Dynamic functional connectivity-based CPM models successfully predicted working memory.

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Static functional connectivity-based CPM models fell short in prediction.

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Frontoparietal, somato-motor, default mode and visual networks contributed most to prediction.

Working memory impairment is a common feature of psychiatric disorders. Although its neural mechanisms have been extensively examined in healthy subjects or individuals with a certain clinical condition, studies investigating neural predictors of working memory in a transdiagnostic sample are scarce. The objective of this study was to create a transdiagnostic predictive working memory model from whole-brain functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. Resting-state functional MRI data from 242 subjects across 4 diagnostic categories (healthy controls and individuals with schizophrenia, bipolar disorder, and attention deficit/hyperactivity) were used to construct dynamic and static functional connectomes. Spatial working memory was assessed by the spatial capacity task. CPM was conducted to predict individual working memory from dynamic and static functional connectivity patterns. Results showed that dynamic connectivity-based CPM models successfully predicted overall working memory capacity and accuracy as well as mean reaction time, yet their static counterparts fell short in the prediction. At the neural level, we found that dynamic connectivity of the frontoparietal and somato-motor networks were negatively correlated with working memory capacity and accuracy, and those of the default mode and visual networks were positively associated with mean reaction time. Moreover, different feature selection thresholds, parcellation strategies and model validation methods as well as diagnostic categories did not significantly influence the prediction results. Our findings not only are coherent with prior reports that dynamic functional connectivity encodes more behavioral information than static connectivity, but also help advance the translation of cognitive “connectome fingerprinting” into real-world application.

General Intelligence Is Associated with Working Memory-Related Functional Connectivity Change: Evidence from a Large-Sample Study

Background/Purpose: Psychometric intelligence is closely related to working memory (WM) and the associated brain activity. We aimed to clarify the associations between psychometric intelligence and WM-induced functional connectivity changes. Materials and Methods: Here we determined the associations between psychometric intelligence measured by nonverbal reasoning (using the Raven's Advanced Progressive Matrices) and WM-induced changes in functional connectivity during the N-back paradigm, in a large cohort of 1221 young adults. Results: We observed that the measures of general intelligence showed a significant positive correlation with WM-induced changes in the functional connectivity with the key nodes of the frontoparietal network, such as the bilateral premotor cortices and the presupplementary motor area. Those significant correlations were observed for (1) areas showing a WM-induced increase of the functional connectivity with the abovementioned key nodes, such as the lateral parietal cortex; (2) areas showing a WM-induced decrease of the functional connectivity with the abovementioned key nodes (2-a) such as left perisylvian areas and cuneus, the fusiform gyrus, and the lingual gyrus, which play key roles in language processing, (2-b) hippocampus and parahippocampal gyrus, which play key roles in memory processing, and (2-c) the key node of the default mode network such as the medial prefrontal cortex; as well as (3) the border areas between (1) and (2). Conclusion: Psychometric intelligence is associated with WM-induced changes in functional connectivity, influencing the way in which WM key nodes dynamically modulate the interaction with other brain nodes in response to WM.

Consequence of stroke for feature recall and binding in visual working memory

Visual memory for objects involves the integration, or binding, of individual features into a coherent representation. We used a novel approach to assess feature binding, using a delayed-reproduction task in combination with computational modeling and lesion analysis. We assessed stroke patients and neurotypical controls on a visual working memory task in which spatial arrays of colored disks were presented. After a brief delay, participants either had to report the color of one disk cued by its location or the location of one disk cued by its color. Our results demonstrate that, in the controls, report imprecision and swap errors (non-target reports) can be explained by a single source of variability. Stroke patients showed an overall decrease in memory precision for both color and location, with only limited evidence for deviations from the predicted relationship between report precision and swap errors. These deviations were primarily deficits in reporting items rather than selecting items based on the cue. Atlas-based lesion-symptom mapping showed that selection and reporting deficits, precision in reporting color, and precision in reporting location were associated with different lesion profiles. Deficits in binding are associated with lesions in the left somatosensory cortex, deficits in the precision of reporting color with bilateral fronto-parietal regions, and no anatomical substrates were identified for precision in reporting location. Our results converge with previous reports that working memory representations are widely distributed in the brain and can be found across sensory, parietal, temporal, and prefrontal cortices. Stroke patients demonstrate mostly subtle impairments in visual working memory, perhaps because representations from different areas in the brain can partly compensate for impaired encoding in lesioned areas. These findings contribute to understanding of the relation between memorizing features and their bound representations.

Color Naming and Categorization Depend on Distinct Functional Brain Networks

Naming a color can be understood as an act of categorization, that is, identifying it as a member of a category of colors that are referred to by the same name. But are naming and categorization equivalent cognitive processes and consequently rely on same neural substrates? Here, we used task and resting-state functional magnetic resonance imaging as well as behavioral measures to identify functional brain networks that modulated naming and categorization of colors. We first identified three bilateral color-sensitive regions in the ventro-occipital cortex. We then showed that, across participants, color naming and categorization response times (RTs) were correlated with different resting state connectivity networks seeded from the color-sensitive regions. Color naming RTs correlated with the connectivity between the left posterior color region, the left middle temporal gyrus, and the left angular gyrus. In contrast, color categorization RTs correlated with the connectivity between the bilateral posterior color regions, and left frontal, right temporal and bilateral parietal areas. The networks supporting naming and categorization had a minimal overlap, indicating that the 2 processes rely on different neural mechanisms.

Abnormalities of Cortical Thickness in Pediatric Mesial Temporal Lobe Epilepsy with Hippocampal Sclerosis

OBJECTIVE

Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is the most common intractable seizure type of pediatric epilepsy, with alterations in the cortex across the whole brain. The aim of this study is to investigate the abnormalities of cortical thickness in pediatric MTLE-HS.

METHODS

Subjects were recruited from Shenzhen Children's Hospital between September 2015 and December 2016. MTLE was confirmed by the experienced neurological physician based on International League Against Epilepsy (ILAE) diagnosis criteria, and structural magnetic resonance imaging (MRI) was performed at 3T for quantitative assessment of cortical thickness. A general linear model with age and gender as covariates was used to examine the vertex-wise differences in cortical thickness between 1) left MTLE-HS (LMTLE-HS) and healthy controls (HC), and 2) right MTLE-HS (RMTLE-HS) and HC. The family-wise error corrected significance threshold was set at P < 0.05. Through a combination of probability and cluster-size thresholding, cluster-wise P values were obtained for the resulting clusters.

RESULTS

13 LMTLE-HS, 6 RMTLE-HS, and 20 age-matched HC were finally enrolled in the study. No significant difference in the mean age (LMTLE-HS vs. HC, p=0.57; RMTLE-HS vs. HC, p=0.39) and gender ratio (LMTLE-HS vs. HC, p=0.24; RMTLE-HS vs. HC, p=0.72) was found between MTLE-HS and HC. In LMTLE-HS, cortical thickness was found significantly decreased in the ipsilateral caudal middle frontal gyrus (p=0.012) and increased in the contralateral inferior temporal gyrus (p=0.020). In RMTLE-HS, cortical thickness significantly decreased in the ipsilateral posterior parietal lobe (superior, p<0.001 and inferior parietal gyrus, p=0.03), the anterior parietal lobe (postcentral gyrus, p=0.006), the posterior frontal lobe (precentral gyrus, p=0.04 and the lateral occipital gyrus, p<0.001), and the contralateral lateral occipital gyrus, middle frontal (p<0.0001) and superior frontal gyrus (p<0.001), and pericalcarine cortex (p=0.020).

CONCLUSION

We detected significant cortical abnormalities in pediatric MTLE-HS patients compared with HC. These cortical abnormalities could be explained by specific pathogenesis in MTLE-HS, and may finally contribute to understanding the intrinsic mechanism of MTLE-HS.

Behavioral and Neural Signatures of Working Memory in Childhood

Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during human development, however, are not well understood. Here, we establish associations between working memory, other cognitive abilities, and functional MRI (fMRI) activation in data from over 11,500 9- to 10-year-old children (both sexes) enrolled in the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity during a working memory challenge indexes working memory performance. This relationship is domain specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task (SST), and reward processing during a monetary incentive delay (MID) task does not track memory abilities. Together, these results inform our understanding of individual differences in working memory in childhood and lay the groundwork for characterizing the ways in which they change across adolescence.SIGNIFICANCE STATEMENT Working memory is a foundational cognitive ability that changes over time and varies across individuals. Here, we analyze data from over 11,500 9- to 10-year-olds to establish relationships between working memory, other cognitive abilities, and frontoparietal brain activity during a working memory challenge, but not during other cognitive challenges. Our results lay the groundwork for assessing longitudinal changes in working memory and predicting later academic and other real-world outcomes.

Functional connectivity predicts changes in attention observed across minutes, days, and months

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

Neural Correlates Underlying the Precision of Visual Working Memory

The neural mechanisms associated with the limited capacity of working memory (WM) has long been studied, but it is still unclear which neural regions are associated with the precision of visual WM. Here, an orientation recall task for estimating the trial-wise precision of visual WM was performed and then repeated two weeks later in an fMRI scanner. Results showed that activity in frontal and parietal regions during WM maintenance scaled with WM load, but not with the precision of WM (i.e., recall error in radians). Conversely, activity in the lateral occipital complex (LOC) during WM maintenance was not affected by memory load, but rather, correlated with WM precision on a trial-by-trial basis. Moreover, activity in LOC also correlated with the individual participant's precision of WM from a separate behavioral experiment. Interestingly, a region within the prefrontal cortex, the inferior frontal junction (IFJ), exhibited greater functional connectivity with LOC when the WM load increased. Together, our findings provide unique evidence that the LOC supports visual WM precision, while communication between the IFJ and LOC varies based on WM load demands. These results suggest an intriguing possibility that distinct neural mechanisms may be associated with general content (load) or detailed information (precision) of WM.

Abnormal distraction and load-specific connectivity during working memory in cognitively normal Parkinson's disease

Visuospatial working memory impairments are common in Parkinson's disease (PD), yet the underlying neural mechanisms are poorly understood. The present study investigated abnormalities in context‐dependent functional connectivity of working memory hubs in PD. Cognitively normal PD and control participants underwent fMRI while performing a visuospatial working memory task. To identify sources of dysfunction, distraction, and load‐modulated connectivity were disentangled for encoding and retrieval phases of the task. Despite normal working memory performance in PD, two features of abnormal connectivity were observed, one due to a loss in normal context‐related connectivity and another related to upregulated connectivity of hubs for which the controls did not exhibit context‐dependent connectivity. During encoding, striatal‐prefrontal coupling was lost in PD, both during distraction and high memory loads. However, long‐range connectivity of prefrontal, medial temporal and occipital hubs was upregulated in a context‐specific manner. Memory retrieval was characterized by different aberrant connectivity patterns, wherein precuneus connectivity was upregulated during distraction, whereas prefrontal couplings were lost as memory load approached capacity limits. Features of abnormal functional connectivity in PD had pathological and compensatory influences as they correlated with poorer working memory or better visuospatial skills. The results offer new insights into working memory‐related signatures of aberrant cortico–cortical and corticostriatal functional connections, which may portend future declines in different facets of working memory.

Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

Resting-state dynamics as a neuromarker of dopamine administration in healthy female adults

BACKGROUND

Different neuromarkers of people's emotions, personality traits and behavioural performance have recently been identified. However, not much attention has been devoted to neuromarkers of neural responsiveness to drug administration.

AIMS

We investigated the predictive neuromarkers of acute dopamine (DA) administration.

METHODS

In a double-blind, within-subject study, we administrated a DA agonist (pramipexole) or placebo to 27 healthy female subjects. Using multivariate classification and prediction analyses, we examined whether dopaminergic modulations of task-free resting-state brain dynamics predict individual differences in pramipexole's modulation of facial attractiveness evaluations.

RESULTS

Our results demonstrate that pramipexole's effects on brain dynamics could be successfully discriminated from resting-state functional connectivity (accuracy: 78.9%; p < 0.0001). On the behavioural level, pramipexole increased facial attractiveness evaluations (t₍₃₉₎ = 4.44; p < 0.0001). In particular, pramipexole administration enhanced connectivity strength of the cinguloopercular network (t₍₂₃₎ = 3.29; p = 0.003) and increased brain signal variability in subcortical and prefrontal brain areas (t₍₁₃₎ = 3.05, p = 0.009). Importantly, multivariate predictive models reveal that pramipexole-dependent modulation of resting-state dynamics predicted the increase of facial attractiveness evaluations after pramipexole (connectivity strength: standardized mean squared error, smse = 0.65; p = 0.0007; brain signal variability: smse = 0.94, p = 0.015).

CONCLUSION

These results demonstrate that modulations of resting-state brain dynamics induced by a DA agonist predict drug-related effects on evaluation processes, providing a neuromarker of the neural responsiveness of specific brain networks to DA administration.

Electrical Stimulation Over Human Posterior Parietal Cortex Selectively Enhances the Capacity of Visual Short-Term Memory

Visual short-term memory (VSTM) provides an on-line mental space for incoming sensory information to be temporally maintained to carry out complex behavioral tasks. Despite its essential functions, the capacity at which VSTM could maintain sensory information is limited (i.e., VSTM can hold only about three to four visual items at once). Moreover, the quality of sensory representation (i.e., precision) degrades as more information has to be maintained in VSTM. Correlational evidence suggests that the level and the pattern of neural activity measured in the posterior parietal cortex (PPC) track both VSTM capacity and precision. However, the causal contributions of the PPC to these different VSTM operations are unclear. Here, we tested whether stimulating the PPC with transcranial direct current stimulation (tDCS) could increase VSTM capacity or precision. We found that stimulating the PPC in male and female human participants selectively enhanced VSTM capacity when the number of memory items exceeded capacity limit, without significant effects on VSTM precision. Moreover, this enhancement of VSTM capacity is region specific as stimulating the prefrontal cortex did not change VSTM capacity or precision. Null stimulation effects in the sensory memory condition confirmed that the tDCS-induced enhancement of VSTM capacity was not simply due to changes in sensory or attentional processes. Altogether, these results provide causal evidence suggesting that the PPC has a more dominant role in supporting the storage capacity of VSTM compared with maintaining the quality of sensory representations. Furthermore, tDCS could be used as a promising noninvasive method to enhance this PPC VSTM-related function.SIGNIFICANCE STATEMENT Correlational evidence from neuroimaging and electrophysiology suggests that the posterior parietal cortex (PPC) supports the storage capacity of visual short-term memory (VSTM) and the precision of sensory representations maintained in VSTM. However, the causal contributions of the PPC to these different VSTM functions were unclear. Here, we found that electrical stimulation over the PPC selectively enhanced VSTM capacity without changing VSTM precision. Overall, our findings suggest that the PPC has a dominant and causal role in supporting the storage capacity of VSTM.

Spectral Imprints of Working Memory for Everyday Associations in the Frontoparietal Network

How does the human brain rapidly process incoming information in working memory? In growing divergence from a single-region focus on the prefrontal cortex (PFC), recent work argues for emphasis on how distributed neural networks are rapidly coordinated in support of this central neurocognitive function. Previously, we showed that working memory for everyday “what,” “where,” and “when” associations depends on multiplexed oscillatory systems, in which signals of different frequencies simultaneously link the PFC to parieto-occipital and medial temporal regions, pointing to a complex web of sub-second, bidirectional interactions. Here, we used direct brain recordings to delineate the frontoparietal oscillatory correlates of working memory with high spatiotemporal precision. Seven intracranial patients with electrodes simultaneously localized to prefrontal and parietal cortices performed a visuospatial working memory task that operationalizes the types of identity and spatiotemporal information we encounter every day. First, task-induced oscillations in the same delta-theta (2–7 Hz) and alpha-beta (9–24 Hz) frequency ranges previously identified using scalp electroencephalography (EEG) carried information about the contents of working memory. Second, maintenance was linked to directional connectivity from the parietal cortex to the PFC. However, presentation of the test prompt to cue identity, spatial, or temporal information changed delta-theta coordination from a unidirectional, parietal-led system to a bidirectional, frontoparietal system. Third, the processing of spatiotemporal information was more bidirectional in the delta-theta range than was the processing of identity information, where alpha-beta connectivity did not exhibit sensitivity to the contents of working memory. These findings implicate a bidirectional delta-theta mechanism for frontoparietal control over the contents of working memory.

Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies

Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10-60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions.

Reevaluating the Sensory Account of Visual Working Memory Storage

Recent human fMRI pattern-decoding studies have highlighted the involvement of sensory areas in visual working memory (VWM) tasks and argue for a sensory account of VWM storage. In this review, evidence is examined from human behavior, fMRI decoding, and transcranial magnetic stimulation (TMS) studies, as well as from monkey neurophysiology studies. Contrary to the prevalent view, the available evidence provides little support for the sensory account of VWM storage. Instead, when the ability to resist distraction and the existence of top-down feedback are taken into account, VWM-related activities in sensory areas seem to reflect feedback signals indicative of VWM storage elsewhere in the brain. Collectively, the evidence shows that prefrontal and parietal regions, rather than sensory areas, play more significant roles in VWM storage.

Bidirectional Frontoparietal Oscillatory Systems Support Working Memory

The ability to represent and select information in working memory provides the neurobiological infrastructure for human cognition. For 80 years, dominant views of working memory have focused on the key role of prefrontal cortex (PFC) [1-8]. However, more recent work has implicated posterior cortical regions [9-12], suggesting that PFC engagement during working memory is dependent on the degree of executive demand. We provide evidence from neurological patients with discrete PFC damage that challenges the dominant models attributing working memory to PFC-dependent systems. We show that neural oscillations, which provide a mechanism for PFC to communicate with posterior cortical regions [13], independently subserve communications both to and from PFC-uncovering parallel oscillatory mechanisms for working memory. Fourteen PFC patients and 20 healthy, age-matched controls performed a working memory task where they encoded, maintained, and actively processed information about pairs of common shapes. In controls, the electroencephalogram (EEG) exhibited oscillatory activity in the low-theta range over PFC and directional connectivity from PFC to parieto-occipital regions commensurate with executive processing demands. Concurrent alpha-beta oscillations were observed over parieto-occipital regions, with directional connectivity from parieto-occipital regions to PFC, regardless of processing demands. Accuracy, PFC low-theta activity, and PFC → parieto-occipital connectivity were attenuated in patients, revealing a PFC-independent, alpha-beta system. The PFC patients still demonstrated task proficiency, which indicates that the posterior alpha-beta system provides sufficient resources for working memory. Taken together, our findings reveal neurologically dissociable PFC and parieto-occipital systems and suggest that parallel, bidirectional oscillatory systems form the basis of working memory.