Carrying the past to the future: Distinct brain networks underlie individual differences in human spatial working memory capacity

Task-specific topology of brain networks supporting working memory and inhibition

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks

Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six functional magnetic resonance imaging studies, resulting in a sample of $155$ ($77$ women, $25 \pm 5$ years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared with categorical representations.

Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks

Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six fMRI studies, resulting in a sample of 155 (77 women, 25 ± 5 years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared to categorical representations.

Differential spatial working memory-related functional network reconfiguration in young and older adults

Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.

A domain-general frontoparietal network interacts with domain-preferential intermediate pathways to support working memory task

Working memory (WM) is essential for cognition, but the underlying neural mechanisms remain elusive. From a hierarchical processing perspective, this paper proposed and tested a hypothesis that a domain-general network at the top of the WM hierarchy can interact with distinct domain-preferential intermediate circuits to support WM. Employing a novel N-back task, we first identified the posterior superior temporal gyrus (pSTG), middle temporal area (MT), and postcentral gyrus (PoCG) as intermediate regions for biological motion and shape motion processing, respectively. Using further psychophysiological interaction analyses, we delineated a frontal-parietal network (FPN) as the domain-general network. These results were further verified and extended by a delayed match to sample (DMS) task. Although the WM load-dependent and stimulus-free activations during the DMS delay phase confirm the role of FPN as a domain-general network to maintain information, the stimulus-dependent activations within this network during the DMS encoding phase suggest its involvement in the final stage of the hierarchical processing chains. In contrast, the load-dependent activations of intermediate regions in the N-back task highlight their further roles beyond perception in WM tasks. These results provide empirical evidence for a hierarchical processing model of WM and may have significant implications for WM training.

Delay activity during visual working memory: A meta-analysis of 30 fMRI experiments

Visual working memory refers to the temporary maintenance and manipulation of task-related visual information. Recent debate on the underlying neural substrates of visual working memory has focused on the delay period of relevant tasks. Persistent neural activity throughout the delay period has been recognized as a correlate of working memory, yet regions demonstrating sustained hemodynamic responses show inconsistency across individual studies. To develop a more precise understanding of delay-period activations during visual working memory, we conducted a coordinate-based meta-analysis on 30 fMRI experiments involving 515 healthy adults with a mean age of 25.65 years. The main analysis revealed a widespread frontoparietal network associated with delay-period activity, as well as activation in the right inferior temporal cortex. These findings were replicated using different meta-analytical algorithms and were shown to be robust against between-study heterogeneity and publication bias. Further meta-analyses on different subgroups of experiments with specific task demands and stimulus types revealed similar delay-period networks, with activations distributed across the frontal and parietal cortices. The roles of prefrontal regions, posterior parietal regions, and inferior temporal areas are reviewed and discussed in the context of content-specific storage. We conclude that cognitive operations that occur during the unfilled delay period in visual working memory tasks can be flexibly expressed across a frontoparietal-temporal network depending on experimental parameters.

Spatial Recognition Memory: Differential Brain Strategic Activation According to Sex

Human spatial memory research has significantly progressed since the development of computerized tasks, with many studies examining sex-related performances. However, few studies explore the underlying electrophysiological correlates according to sex. In this study event-related potentials were compared between male and female participants during the performance of an allocentric spatial recognition task. Twenty-nine university students took part in the research. Results showed that while general performance was similar in both sexes, the brain of males and females displayed a differential activation. Males showed increased N200 modulation than females in the three phases of memory process (encoding, maintenance, and retrieval). Meanwhile females showed increased activation of P300 in the three phases of memory process compared to males. In addition, females exhibited more negative slow wave (NSW) activity during the encoding phase. These differences are discussed in terms of attentional control and the allocation of attentional resources during spatial processing. Our findings demonstrate that sex modulates the resources recruited to performed this spatial task.

Dynamic functional brain networks underlying the temporal inertia of negative emotions

Affective inertia represents the lasting impact of transient emotions at one time point on affective state at a subsequent time point. Here we describe the neural underpinnings of inertia following negative emotions elicited by sad events in movies. Using a co-activation pattern analysis of dynamic functional connectivity, we examined the temporal expression and reciprocal interactions among brain-wide networks during movies and subsequent resting periods in twenty healthy subjects. Our findings revealed distinctive spatiotemporal expression of visual (VIS), default mode (DMN), central executive (CEN), and frontoparietal control (FPCN) networks both in negative movies and in rest periods following these movies. We also identified different reciprocal relationships among these networks, in transitions from movie to rest. While FPCN and DMN expression increased during and after negative movies, respectively, FPCN occurrences during the movie predicted lower DMN and higher CEN expression during subsequent rest after neutral movies, but this relationship was reversed after the elicitation of negative emotions. Changes in FPCN and DMN activity correlated with more negative subjective affect. These findings provide new insights into the transient interactions of intrinsic brain networks underpinning the inertia of negative emotions. More specifically, they describe a major role of FPCN in emotion elicitation processes, with prolonged impact on DMN activity in subsequent rest, presumably involved in emotion regulation and restoration of homeostatic balance after negative events.

Quantifying the variability of neural activation in working memory: A functional probabilistic atlas

Working memory is a fundamental cognitive ability that allows the maintenance and manipulation of information for a brief period of time. Previous studies found a set of brain regions activated during working memory tasks, such as the prefrontal and parietal cortex. However, little is known about the variability of neural activation in working memory. Here, we used functional magnetic resonance imaging to quantify individual, hemispheric, and sex differences of working memory activation in a large cohort of healthy adults (N = 477). We delineated subject-specific activated regions in each individual, including the frontal pole, middle frontal gyrus, frontal eye field, superior parietal lobule, insular, precuneus, and anterior cingulate cortex. A functional probabilistic atlas was created to quantify individual variability in working memory regions. More than 90% of the participants activated all seven regions in both hemispheres, but the intersection of regions across participants was markedly less (50%), indicating significant individual differences in working memory activations. Moreover, we found hemispheric and sex differences in activation location, extent, and magnitude. Most activation regions were larger in the right than in the left hemisphere, but the magnitude of activation did not follow a similar pattern. Men showed more extensive and stronger activations than women. Taken together, our functional probabilistic atlas quantified variabilities of neural activation in working memory, providing a robust spatial reference for standardization of functional localization.

Brain functional connectivity dynamics at rest in the aftermath of affective and cognitive challenges

Carry-over effects on brain states have been reported following emotional and cognitive events, persisting even during subsequent rest. Here, we investigated such effects by identifying recurring co-activation patterns (CAPs) in neural networks at rest with functional magnetic resonance imaging (fMRI). We compared carry-over effects on brain-wide CAPs at rest and their modulation after both affective and cognitive challenges. Healthy participants underwent fMRI scanning during emotional induction with negative valence and performed cognitive control tasks, each followed by resting periods. Several CAPs, overlapping with the default-mode (DMN), salience, dorsal attention, and social cognition networks were impacted by both the preceding events (movie or task) and the emotional valence of the experimental contexts (neutral or negative), with differential dynamic fluctuations over time. Temporal metrics of DMN-related CAPs were altered after exposure to negative emotional content (compared to neutral) and predicted changes in subjective affect on self-reported scores. In parallel, duration rates of another attention-related CAP increased with greater task difficulty during the preceding cognitive control condition, specifically in the negative context. These findings provide new insights on the anatomical organization and temporal inertia of functional brain networks, whose expression is differentially shaped by emotional states, presumably mediating adaptive homeostatic processes subsequent to behaviorally challenging events.

Neural measures of working memory in a bilateral change detection task

The change detection task is a widely used paradigm to examine visual working memory processes. Participants memorize a set of items and then, try to detect changes in the set after a retention period. The negative slow wave (NSW) and contralateral delay activity (CDA) are event-related potentials in the EEG signal that are commonly used in change detection tasks to track working memory load, as both increase with the number of items maintained in working memory (set size). While the CDA was argued to more purely reflect the memory-specific neural activity than the NSW, it also requires a lateralized design and attention shifts prior to memoranda onset, imposing more restrictions on the task than the NSW. The present study proposes a novel change detection task in which both CDA and NSW can be measured at the same time. Memory items were presented bilaterally, but their distribution in the left and right hemifield varied, inducing a target imbalance or "net load." NSW increased with set size, whereas CDA increased with net load. In addition, a multivariate linear classifier was able to decode the set size and net load from the EEG signal. CDA, NSW, and decoding accuracy predicted an individual's working memory capacity. In line with the notion of a bilateral advantage in working memory, accuracy, and CDA data suggest that participants tended to encode items relatively balanced. In sum, this novel change detection task offers a basis to make use of converging neural measures of working memory in a comprehensive paradigm.

Differences in prefrontal cortex activity based on difficulty in a working memory task using near-infrared spectroscopy

The Prefrontal cortex (PFC) has been highly related to executive functions such as working memory (WM). This study assesses the activity of the PFC in performing the Sternberg WM task (ST) with three levels of difficulty (easy, medium and hard) using the near-infrared spectroscopy (fNIRS) technique. Participants were 43 young and healthy right-handed women. Nine WM task blocks were pseudo randomly presented, three for each difficulty task. The results showed that the participant's performance was better in the easy trials than in the medium and hard trials. Performance in the medium trials was also better than in the hard ones. Bonferroni-corrected paired post-hoc t-tests indicated higher oxygenation in medium and hard tasks than in the easy ones for times between 13 and 42 s in the left lateral PFC and in both, medial and lateral, right PFC. Significant differences in Oxygenated hemoglobin (HbO), Total hemoglobin (HbT) and oxygenation (Oxy) changes depending on the Sternberg WM task were found. Unlike previous studies with fNIRS and WM, the current study uses a highly controlled WM task that differentiates between encoding, retention and retrieval phases, comparing different levels of task load.

Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG

Mind‐wandering is a ubiquitous mental phenomenon that is defined as self‐generated thought irrelevant to the ongoing task. Mind‐wandering tends to occur when people are in a low‐vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind‐wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self‐reported mind‐wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind‐wandering above‐chance level, while a classifier trained on self‐reports of mind‐wandering was able to do so. This suggests that mind‐wandering is a mental state different from low vigilance or performing tasks with low demands—both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source‐localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine‐learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique.

Multimodal network dynamics underpinning working memory

Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.

Multi-modal normalization of resting-state using local physiology reduces changes in functional connectivity patterns observed in mTBI patients

Blood oxygenation level dependent (BOLD) resting-state functional magnetic resonance imaging (rs-fMRI) may serve as a sensitive marker to identify possible changes in the architecture of large-scale networks following mild traumatic brain injury (mTBI). Differences in functional connectivity (FC) measurements derived from BOLD rs-fMRI may however be confounded by changes in local cerebrovascular physiology and neurovascular coupling mechanisms, without changes in the underlying neuronally driven connectivity of networks. In this study, multi-modal neuroimaging data including BOLD rs-fMRI, baseline cerebral blood flow (CBF0) and cerebrovascular reactivity (CVR; acquired using a hypercapnic gas breathing challenge) were collected in 23 subjects with reported mTBI (14.6±14.9 months post-injury) and 27 age-matched healthy controls. Despite no group differences in CVR within the networks of interest (P > 0.05, corrected), significantly higher CBF0 was documented in the mTBI subjects (P < 0.05, corrected), relative to the controls. A normalization method designed to account for differences in CBF0 post-mTBI was introduced to evaluate the effects of such an approach on reported group differences in network connectivity. Inclusion of regional perfusion measurements in the computation of correlation coefficients within and across large-scale networks narrowed the differences in FC between the groups, suggesting that this approach may elucidate unique changes in connectivity post-mTBI while accounting for shared variance with CBF0. Altogether, our results provide a strong paradigm supporting the need to account for changes in physiological modulators of BOLD in order to expand our understanding of the effects of brain injury on large-scale FC of cortical networks.

Emergence of the Affect from the Variation in the Whole-Brain Flow of Information

Over the past few decades, the quest for discovering the brain substrates of the affect to understand the underlying neural basis of the human's emotions has resulted in substantial and yet contrasting results. Whereas some point at distinct and independent brain systems for the Positive and Negative affects, others propose the presence of flexible brain regions. In this respect, there are two factors that are common among these previous studies. First, they all focused on the change in brain activation, thereby neglecting the findings that indicate that the stimuli with equivalent sensory and behavioral processing demands may not necessarily result in differential brain activation. Second, they did not take into consideration the brain regional interactivity and the findings that identify that the signals from individual cortical neurons are shared across multiple areas and thus concurrently contribute to multiple functional pathways. To address these limitations, we performed Granger causal analysis on the electroencephalography (EEG) recordings of the human subjects who watched movie clips that elicited Negative, Neutral, and Positive affects. This allowed us to look beyond the brain regional activation in isolation to investigate whether the brain regional interactivity can provide further insights for understanding the neural substrates of the affect. Our results indicated that the differential affect states emerged from subtle variation in information flow of the brain cortical regions that were in both hemispheres. They also showed that these regions that were rather common between affect states than distinct to a specific affect were characterized with both short- as well as long-range information flow. This provided evidence for the presence of simultaneous integration and differentiation in the brain functioning that leads to the emergence of different affects. These results are in line with the findings on the presence of intrinsic large-scale interacting brain networks that underlie the production of psychological events. These findings can help advance our understanding of the neural basis of the human's emotions by identifying the signatures of differential affect in subtle variation that occurs in the whole-brain cortical flow of information.

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.

Multi-parametric analysis reveals metabolic and vascular effects driving differences in BOLD-based cerebrovascular reactivity associated with a history of sport concussion

Objective: Identify alterations in cerebrovascular reactivity (CVR) based on the history of sport-related concussion (SRC). Further explore possible mechanisms underlying differences in vascular physiology using hemodynamic parameters modeled using calibrated magnetic resonance imaging (MRI). Method: End-tidal targeting and dual-echo MRI were combined to probe hypercapnic and hyperoxic challenges in athletes with (n = 32) and without (n = 31) a history of SRC. Concurrent blood oxygenation level dependent (BOLD) and arterial spin labeling (ASL) data were used to compute BOLD-CVR, ASL-CVR, and other physiological parameters including resting oxygen extraction fraction (OEF0) and cerebral blood volume (CBV0). Multiple linear and logistic regressions were then used to identify dominant parameters driving group-differences in BOLD-CVR. Results: Robust evidence for elevated BOLD-CVR were found in athletes with SRC history spreading over parts of the cortical hemispheres. Follow-up analyses showed co-localized differences in ASL-CVR (representing modulation of cerebral blood flow) and hemodynamic factors representing static vascular (i.e., CBV0) and metabolic (i.e., OEF0) effects suggesting that group-based differences in BOLD-CVR may be driven by a mixed effect from factors with vascular and metabolic origins. Conclusion: These results emphasize that while BOLD-CVR offers promises as a surrogate non-specific biomarker for cerebrovascular health following SRC, multiple hemodynamic parameters can affect its relative measurements. Abbreviations: [dHb]: concentration of deoxyhemoglobin; AFNI: Analysis of Functional NeuroImages ( https://afni.nimh.nih.gov ); ASL: arterial spin labeling; BIG: position group: defensive and offensive linemen; BIG-SKILL: position group: full backs, linebackers, running backs, tight-ends; BOLD: blood oxygen level dependent; CBF: cerebral blood flow; CMRO2: cerebral metabolic rate of oxygen consumption; CTL: group of control subjects; CVR: cerebrovascular reactivity; fMRI: functional magnetic resonance imaging; FSL: FMRIB software library ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ ); HC: hypercapnia; HO: hyperoxia; HX: group with history of concussion; M: maximal theoretical BOLD signal upon complete removal of venous dHb; pCASL: pseudo-continuous arterial spin labeling; PETCO2: end-tidal carbon dioxide; PETO2: end-tidal oxygen; SCAT: sport-concussion assessment tool; SKILL: position group: defensive backs, kickers, quarterbacks, safeties, wide-receivers; SRC: sport-related concussion.

Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information

Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.