Functional connectivity during working memory maintenance

Timescales of learning in prefrontal cortex

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

Internal Representations Are Prioritized by Frontoparietal Theta Connectivity and Suppressed by alpha Oscillation Dynamics: Evidence from Concurrent Transcranial Magnetic Stimulation EEG and Invasive EEG

Control over internal representations requires the prioritization of relevant information and suppression of irrelevant information. The frontoparietal network exhibits prominent neural oscillations during these distinct cognitive processes. Yet, the causal role of this network-scale activity is unclear. Here, we targeted theta-frequency frontoparietal coherence and dynamic alpha oscillations in the posterior parietal cortex using online rhythmic transcranial magnetic stimulation (TMS) in women and men while they prioritized or suppressed internally maintained working memory (WM) representations. Using concurrent high-density EEG, we provided evidence that we acutely drove the targeted neural oscillation and TMS improved WM capacity only when the evoked activity corresponded with the desired cognitive process. To suppress an internal representation, we increased the amplitude of lateralized alpha oscillations in the posterior parietal cortex contralateral to the irrelevant visual field. For prioritization, we found that TMS to the prefrontal cortex increased theta-frequency connectivity in the prefrontoparietal network contralateral to the relevant visual field. To understand the spatial specificity of these effects, we administered the WM task to participants with implanted electrodes. We found that theta connectivity during prioritization was directed from the lateral prefrontal to the superior posterior parietal cortex. Together, these findings provide causal evidence in support of a model where a frontoparietal theta network prioritizes internally maintained representations and alpha oscillations in the posterior parietal cortex suppress irrelevant representations.

Inhibitory control in WM gate-opening: Insights from alpha desynchronization and norepinephrine activity under atDCS stimulation

Our everyday activities require the maintenance and continuous updating of information in working memory (WM). To control this dynamic, WM gating mechanisms have been suggested to be in place, but the neurophysiological mechanisms behind these processes are far from being understood. This is especially the case when it comes to the role of oscillatory neural activity. In the current study we combined EEG recordings, and anodal transcranial direct current stimulation (atDCS) and pupil diameter recordings to triangulate neurophysiology, functional neuroanatomy and neurobiology. The results revealed that atDCS, compared to sham stimulation, affected the WM gate opening mechanism, but not the WM gate closing mechanism. The altered behavioral performance was associated with specific changes in alpha band activities (reflected by alpha desynchronization), indicating a role for inhibitory control during WM gate opening. Functionally, the left superior and inferior parietal cortices, were associated with these processes. The findings are the first to show a causal relevance of alpha desynchronization processes in WM gating processes. Notably, pupil diameter recordings as an indirect index of the norepinephrine (NE) system activity revealed that individuals with stronger inhibitory control (as indexed through alpha desynchronization) showed less pupil dilation, suggesting they needed less NE activity to support WM gate opening. However, when atDCS was applied, this connection disappeared. The study suggests a close link between inhibitory controlled WM gating in parietal cortices, alpha band dynamics and the NE system.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Frontal adenosine triphosphate markers from 31P MRS are associated with cognitive performance in healthy older adults: preliminary findings

Aging is associated with declines in mitochondrial efficiency and energy production which directly impacts the availability of adenosine triphosphate (ATP), which contains high energy phosphates critical for a variety of cellular functions. Previous phosphorous magnetic resonance spectroscopy (31P MRS) studies demonstrate cerebral ATP declines with age. The purpose of this study was to explore the functional relationships of frontal and posterior ATP levels with cognition in healthy aging. Here, we measured frontal and posterior ATP levels using 31P MRS at 3 Tesla (3 T) and assessed cognition using the Montreal Cognitive Assessment (MoCA) in 30 healthy older adults. We found that greater frontal, but not posterior, ATP levels were significantly associated with better MoCA performance. This relationship remained significant after controlling for age, sex, years of education, and brain atrophy. In conclusion, our findings indicate that cognition is related to ATP in the frontal cortex. These preliminary findings may have important implications in the search for non-invasive markers of in vivo mitochondrial function and the impact of ATP availability on cognition. Future studies are needed to confirm the functional significance of regional ATP and cognition across the lifespan.

Functional specialization and interaction in the amygdala-hippocampus circuit during working memory processing

Both the hippocampus and amygdala are involved in working memory (WM) processing. However, their specific role in WM is still an open question. Here, we simultaneously recorded intracranial EEG from the amygdala and hippocampus of epilepsy patients while performing a WM task, and compared their representation patterns during the encoding and maintenance periods. By combining multivariate representational analysis and connectivity analyses with machine learning methods, our results revealed a functional specialization of the amygdala-hippocampal circuit: The mnemonic representations in the amygdala were highly distinct and decreased from encoding to maintenance. The hippocampal representations, however, were more similar across different items but remained stable in the absence of the stimulus. WM encoding and maintenance were associated with bidirectional information flow between the amygdala and the hippocampus in low-frequency bands (1-40 Hz). Furthermore, the decoding accuracy on WM load was higher by using representational features in the amygdala during encoding and in the hippocampus during maintenance, and by using information flow from the amygdala during encoding and that from the hippocampus during maintenance, respectively. Taken together, our study reveals that WM processing is associated with functional specialization and interaction within the amygdala-hippocampus circuit.

Associations among body mass index, working memory performance, gray matter volume, and brain activation in healthy children

To investigate the neural mechanisms underlying the association between poorer working memory performance and higher body mass index (BMI) in children. We employed structural-(sMRI) and functional magnetic resonance imaging (fMRI) with a 2-back working memory task to examine brain abnormalities and their associations with BMI and working memory performance in 232 children with overweight/obesity (OW/OB) and 244 normal weight children (NW) from the Adolescent Brain Cognitive Development dataset. OW/OB had lower working memory accuracy, which was associated with higher BMI. They showed smaller gray matter (GM) volumes in the left superior frontal gyrus (SFG_L), dorsal anterior cingulate cortex, medial orbital frontal cortex, and medial superior frontal gyrus, which were associated with lower working memory accuracy. During the working memory task, OW/OB relative to NW showed weaker activation in the left superior temporal pole, amygdala, insula, and bilateral caudate. In addition, caudate activation mediated the relationship between higher BMI and lower working memory accuracy. Higher BMI is associated with smaller GM volumes and weaker brain activation in regions involved with working memory. Task-related caudate dysfunction may account for lower working memory accuracy in children with higher BMI.

Spatiotemporal Alterations in Working Memory-Related Beta Band Neuromagnetic Activity of Patients With Schizophrenia On and Off Antipsychotic Medication: Investigation With MEG

BACKGROUND AND HYPOTHESIS

We used the uniquely high combined spatial and temporal resolution of magnetoencephalography to characterize working memory (WM)-related modulation of beta band activity in neuroleptic-free patients with schizophrenia in comparison to a large sample of performance-matched healthy controls. We also tested for effects of antipsychotic medication on identified differences in these same patients.

STUDY DESIGN

Inpatients with schizophrenia (n = 21) or psychotic disorder not otherwise specified (n = 4) completed N-back and control tasks during magnetoencephalography while on placebo and during antipsychotic medication treatment, in a blinded, randomized, counterbalanced manner. Healthy, performance-matched controls (N = 100) completed the same tasks. WM-related neural activation was estimated as beta band (14-30 Hz) desynchronization throughout the brain in successive 400 ms time windows. Voxel-wise statistical comparisons were performed between controls and patients while off-medication at each time window. Significant clusters resulting from this between-groups analysis were then used as regions-of-interest, the activations of which were compared between on- and off-medication conditions in patients.

STUDY RESULTS

Controls showed beta-band desynchronization (activation) of a fronto-parietal network immediately preceding correct button press responses-the time associated with WM updating and task execution. Altered activation in medication-free patients occurred largely during this time, in prefrontal, parietal, and visual cortices. Medication altered patients' neural responses such that the activation time courses in these regions-of-interest more closely resembled those of controls.

CONCLUSIONS

These findings demonstrate that WM-related beta band alterations in schizophrenia are time-specific and associated with neural systems targeted by antipsychotic medications. Future studies may investigate this association by examining its potential neurochemical basis.

Influence of goals on modular brain network organization during working memory

Introduction

Top-down control underlies our ability to attend relevant stimuli while ignoring irrelevant, distracting stimuli and is a critical process for prioritizing information in working memory (WM). Prior work has demonstrated that top-down biasing signals modulate sensory-selective cortical areas during WM, and that the large-scale organization of the brain reconfigures due to WM demands alone; however, it is not yet understood how brain networks reconfigure between the processing of relevant versus irrelevant information in the service of WM.

Methods

Here, we investigated the effects of task goals on brain network organization while participants performed a WM task that required participants to detect repetitions (e.g., 0-back or 1-back) and had varying levels of visual interference (e.g., distracting, irrelevant stimuli). We quantified changes in network modularity-a measure of brain sub-network segregation-that occurred depending on overall WM task difficulty as well as trial-level task goals for each stimulus during the task conditions (e.g., relevant or irrelevant).

Results

First, we replicated prior work and found that whole-brain modularity was lower during the more demanding WM task conditions compared to a baseline condition. Further, during the WM conditions with varying task goals, brain modularity was selectively lower during goal-directed processing of task-relevant stimuli to be remembered for WM performance compared to processing of distracting, irrelevant stimuli. Follow-up analyses indicated that this effect of task goals was most pronounced in default mode and visual sub-networks. Finally, we examined the behavioral relevance of these changes in modularity and found that individuals with lower modularity for relevant trials had faster WM task performance.

Discussion

These results suggest that brain networks can dynamically reconfigure to adopt a more integrated organization with greater communication between sub-networks that supports the goal-directed processing of relevant information and guides WM.

Spectrotemporal content of human auditory working memory represented in functional connectivity patterns

Recent research suggests that working memory (WM), the mental sketchpad underlying thinking and communication, is maintained by multiple regions throughout the brain. Whether parts of a stable WM representation could be distributed across these brain regions is, however, an open question. We addressed this question by examining the content-specificity of connectivity-pattern matrices between subparts of cortical regions-of-interest (ROI). These connectivity patterns were calculated from functional MRI obtained during a ripple-sound auditory WM task. Statistical significance was assessed by comparing the decoding results to a null distribution derived from a permutation test considering all comparable two- to four-ROI connectivity patterns. Maintained WM items could be decoded from connectivity patterns across ROIs in frontal, parietal, and superior temporal cortices. All functional connectivity patterns that were specific to maintained sound content extended from early auditory to frontoparietal cortices. Our results demonstrate that WM maintenance is supported by content-specific patterns of functional connectivity across different levels of cortical hierarchy.

Transcranial Direct Current Stimulation Modulates Working Memory Maintenance Processes in Healthy Individuals

The effects of transcranial direct current stimulation (tDCS) at the pFC are often investigated using cognitive paradigms, particularly working memory tasks. However, the neural basis for the neuromodulatory cognitive effects of tDCS, including which subprocesses are affected by stimulation, is not completely understood. We investigated the effects of tDCS on working memory task-related spectral activity during and after tDCS to gain better insights into the neurophysiological changes associated with stimulation. We reanalyzed data from 100 healthy participants grouped by allocation to receive either sham (0 mA, 0.016 mA, and 0.034 mA) or active (1 mA or 2 mA) stimulation during a 3-back task. EEG data were used to analyze event-related spectral power in frequency bands associated with working memory performance. Frontal theta event-related synchronization (ERS) was significantly reduced post-tDCS in the active group. Participants receiving active tDCS had slower RTs following tDCS compared with sham, suggesting interference with practice effects associated with task repetition. Theta ERS was not significantly correlated with RTs or accuracy. tDCS reduced frontal theta ERS poststimulation, suggesting a selective disruption to working memory cognitive control and maintenance processes. These findings suggest that tDCS selectively affects specific subprocesses during working memory, which may explain heterogenous behavioral effects.

Spontaneity matters! Network alterations before and after spontaneous and active facial self-touches: An EEG functional connectivity study

BACKGROUND

Despite humans frequently performing spontaneous facial self-touches (sFST), the function of this behavior remains speculative. sFST have been discussed in the context of self-regulation, emotional homeostasis, working memory processes, and attention focus. First evidence indicates that sFST and active facial self-touches (aFST) are neurobiologically different phenomena. The aim of the present analysis was to examine EEG-based connectivity in the course of sFST and aFST to test the hypotheses that sFST affect brain network interactions relevant for other than sensorimotor processes.

METHODS

To trigger spontaneous FST a previously successful setting was used: 60 healthy participants manually explored two haptic stimuli and held the shapes of the stimuli in memory for a 14 min retention interval. Afterwards the shapes were drawn on a sheet of paper. During the retention interval, artifact-free EEG-data of 97 sFST by 32 participants were recorded. At the end of the experiment, the participants performed aFST with both hands successively. For the EEG-data, connectivity was computed and compared between the phases before and after sFST and aFST and between the respective before-and the after-phases.

RESULTS

For the before-after comparison, brainwide distributed significant connectivity differences (p < .00079) were observed for sFST, but not for aFST. Additionally, comparing the before- and after-phases of sFST and aFST, respectively, revealed increased similarity between the after-phases than between the before-phases.

CONCLUSION

The results support the assumption that sFST and aFST are neurobiologically different phenomena. Furthermore, the aligned network properties of the after-phases compared to the before-phases indicate that sFST serve self-regulatory functions that aFST do not serve.

Functional connectivity changes during working memory in autism spectrum disorder: A two-year longitudinal MEG study

Working memory impairments have been reported in adults with autism spectrum disorder (ASD) and associated with functional outcomes and social difficulties. However, little is known about the developmental trajectory of working memory in youth with ASD. The current magnetoencephalography (MEG) study is the first to examine the longitudinal development over two years of working memory networks in youth with ASD. We analysed MEG data from 32 children and adolescents with and without ASD (64 datasets; 7-14 years), all tested twice at a two-year interval, during a visual n-back task, with two loads (1- and 2-back). We performed a whole-brain functional connectivity analysis to examine the networks during the successful recognition of visual stimuli. We demonstrate that youth with ASD show decreased connectivity in the theta frequency (4-7 Hz) in the higher memory load (2-back) condition compared to typically developing (TD) controls. This hypo-connected theta network was anchored in primary visual areas with connections to frontal, parietal and limbic regions. These network differences were found despite similar task performance between ASD and TD groups. Within the TD group, we found an increase in alpha (8-14 Hz) connectivity at Time 2 compared to Time 1 in both the 1- and 2-back conditions. These findings demonstrate the continued development of working memory mechanisms over middle childhood, which were not apparent in youth with ASD. Together, our findings support a network-based approach to understanding atypical neural functioning in ASD and the developmental trajectories of working memory processes over middle childhood.

Interhemispheric Connectivity Supports Load-Dependent Working Memory Maintenance for Complex Visual Stimuli

Abstract Introduction: One manipulation used to study the neural basis of working memory (WM) is to vary the information load at encoding, then measure activity and connectivity during maintenance in the delay period. A hallmark finding is increased delay activity and connectivity between frontoparietal brain regions with increased load. Most WM studies, however, employ simple stimuli during encoding and unfilled intervals during the delay. In this study, we asked how delay period activity and connectivity change during low and high load maintenance of complex stimuli. Methods: Twenty-two participants completed a modified Sternberg WM task with two or five naturalistic scenes as stimuli during scalp electroencephalography (EEG). On each trial, the delay was filled with phase-scrambled scenes to provide a visual perceptual control with similar color and spatial frequency as presented during encoding. Functional connectivity during the delay was assessed by the phase-locking value (PLV). Results: Results showed reduced theta/alpha delay activity amplitude during high compared with low WM load across frontal, central, and parietal sources. A network with higher connectivity during low load consisted of increased PLV between (1) left frontal and right posterior temporal sources in the theta/alpha bands, (2) right anterior temporal and left central sources in the alpha and lower beta bands, and (3) left anterior temporal and posterior temporal sources in the theta, alpha, and lower beta bands. Discussion: The findings suggest a role for interhemispheric connectivity during WM maintenance of complex stimuli with load modulation when limited attentional resources are essential for filtering. Impact statement The patterns of brain connectivity subserving working memory (WM) have largely been investigated to date using simple stimuli, including letters, digits, and shapes and during unfilled WM delay intervals. Fewer studies describe functional connectivity changes during the maintenance of more naturalistic stimuli in the presence of distractors. In the present study, we employed a scene-based WM task during electroencephalography in healthy humans and found that during low-load WM maintenance with distractors increased interhemispheric connectivity in frontotemporal networks. These findings suggest a role for increased interhemispheric connectivity during maintenance of complex stimuli when attentional resources are essential for filtering.

Positive hysteresis in emotion recognition: Face processing visual regions are involved in perceptual persistence, which mediates interactions between anterior insula and medial prefrontal cortex

Facial emotion perception can be studied from the point of view of dynamic systems whose output may depend not only on current input but also on prior history - a phenomenon known as hysteresis. In cognitive neuroscience, hysteresis has been described as positive (perceptual persistence) or negative (fatigue of current percept) depending on whether perceptual switching occurs later or earlier than actual physical stimulus changes. However, its neural correlates remain elusive. We used dynamic transitions between emotional expressions and combined behavioral assessment with functional magnetic resonance imaging (fMRI) to investigate the underlying circuitry of perceptual hysteresis in facial emotion recognition. Our findings revealed the involvement of face-selective visual areas - fusiform face area (FFA) and superior temporal sulcus (STS) - in perceptual persistence as well as the right anterior insula. Moreover, functional connectivity analyses revealed an interplay between the right anterior insula and medial prefrontal cortex, which showed to be dependent on the presence of positive hysteresis. Our results support the hypothesis that high-order regions are involved in perceptual stabilization and decision during perceptual persistence (positive hysteresis) and add evidence to the role of the anterior insula as a hub of sensory information in perceptual decision-making.

Comparing resting-state connectivity of working memory networks in U.S. Service members with mild traumatic brain injury and posttraumatic stress disorder

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) are prevalent among military populations, and both have been associated with working memory (WM) impairments. Previous resting-state functional connectivity (rsFC) research conducted separately in PTSD and mTBI populations suggests that there may be similar and distinct abnormalities in WM-related networks. However, no studies have compared rsFC of WM brain regions in participants with mTBI versus PTSD. We used resting-state fMRI to investigate rsFC of WM networks in U.S. Service Members (n = 127; ages 18-59) with mTBI only (n = 46), PTSD only (n = 24), and an orthopedically injured (OI) control group (n = 57). We conducted voxelwise rsFC analyses with WM brain regions to test for differences in WM network connectivity in mTBI versus PTSD. Results revealed reduced rsFC between ventrolateral prefrontal cortex (vlPFC), lateral premotor cortex, and dorsolateral prefrontal cortex (dlPFC) WM regions and brain regions in the dorsal attention and somatomotor networks in both mTBI and PTSD groups versus controls. When compared to those with mTBI, individuals with PTSD had lower rsFC between both the lateral premotor WM seed region and middle occipital gyrus as well as between the dlPFC WM seed region and paracentral lobule. Interestingly, only vlPFC connectivity was significantly associated with WM performance across the samples. In conclusion, we found primarily overlapping patterns of reduced rsFC in WM brain regions in both mTBI and PTSD groups. Our finding of decreased vlPFC connectivity associated with WM is consistent with previous clinical and neuroimaging studies. Overall, these results provide support for shared neural substrates of WM in individuals with either mTBI or PTSD.

Load-dependent functional connectivity deficits during visual working memory in first-episode psychosis

INTRODUCTION

Aberrant network connectivity is a core deficit in schizophrenia and may underlie many of its associated cognitive deficits. Previous work in first-episode schizophrenia spectrum illness (FESz) suggests preservation of working memory network function during low-load conditions with dysfunction emerging as task complexity increases. This study assessed visual network connectivity and its contribution to load-dependent working memory impairments.

METHODS

Magnetoencephalography was recorded from 35 FESz and 28 matched controls (HC) during a lateralized change detection task. Impaired alpha desynchronization was previously identified within bilateral dorsal occipital (Occ) regions. Here, whole-brain alpha-band connectivity was examined using phase-locking (PLV) and bilateral Occ as connectivity seeds. Load effects on connectivity were assessed across participants, and PLV modulation within networks was compared between groups.

RESULTS

Occ exhibited significant load modulated connectivity with six regions (FDR-corrected). HC exhibited PLV enhancement with load in all connections. FESz failed to show PLV modulation between right Occ and left inferior frontal gyrus, lateral occipito-temporal sulcus, and anterior intermediate parietal sulcus. Smaller PLVs in all three network connections during both memory load conditions were associated with increased reality distortion in FESz (FDR-corrected.) CONCLUSION: Examination of functional connectivity across the visual working memory network in FESz revealed an inability to enhance communication between perceptual and executive networks in response to increasing cognitive demands. Furthermore, the degree of network communication impairment was associated with positive symptoms. These findings provide insights into the nature of brain dysconnectivity and its contribution to symptoms in early psychosis and identify potential targets for future interventions.

Decoding the temporal dynamics of affective scene processing

Natural images containing affective scenes are used extensively to investigate the neural mechanisms of visual emotion processing. Functional fMRI studies have shown that these images activate a large-scale distributed brain network that encompasses areas in visual, temporal, and frontal cortices. The underlying spatial and temporal dynamics, however, remain to be better characterized. We recorded simultaneous EEG-fMRI data while participants passively viewed affective images from the International Affective Picture System (IAPS). Applying multivariate pattern analysis to decode EEG data, and representational similarity analysis to fuse EEG data with simultaneously recorded fMRI data, we found that: (1) ∼80 ms after picture onset, perceptual processing of complex visual scenes began in early visual cortex, proceeding to ventral visual cortex at ∼100 ms, (2) between ∼200 and ∼300 ms (pleasant pictures: ∼200 ms; unpleasant pictures: ∼260 ms), affect-specific neural representations began to form, supported mainly by areas in occipital and temporal cortices, and (3) affect-specific neural representations were stable, lasting up to ∼2 s, and exhibited temporally generalizable activity patterns. These results suggest that affective scene representations in the brain are formed temporally in a valence-dependent manner and may be sustained by recurrent neural interactions among distributed brain areas.

Employing Connectome-Based Models to Predict Working Memory in Multiple Sclerosis

Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample (n_internal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort (n_validation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks. Results: We successfully derived an MS-specific predictive model of WM in the internal sample (full: r_s = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort (r_s = -0.047; p = 0.78, mean squared error [MSE] = 0.006, R² = -2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: r_s = 0.33 p = 0.049, MSE = 0.009, R² = 13.4%; validation cohort: r_s = 0.46, p = 0.005, MSE = 0.005, R² = 16.9%), demonstrating their translational potential. Discussion: Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.

Auditory Target Detection Enhances Visual Processing and Hippocampal Functional Connectivity

Though dividing one's attention between two input streams typically impairs performance, detecting a behaviorally relevant stimulus can sometimes enhance the encoding of unrelated information presented at the same time. Previous research has shown that selection of this kind boosts visual cortical activity and memory for concurrent items. An important unanswered question is whether such effects are reflected in processing quality and functional connectivity in visual regions and in the hippocampus. In this fMRI study, participants were asked to memorize a stream of naturalistic images and press a button only when they heard a predefined target tone (400 or 1,200 Hz, counterbalanced). Images could be presented with a target tone, with a distractor tone, or without a tone. Auditory target detection increased activity throughout the ventral visual cortex but lowered it in the hippocampus. Enhancements in functional connectivity between the ventral visual cortex and the hippocampus were also observed following auditory targets. Multi-voxel pattern classification of image category was more accurate on target tone trials than on distractor and no tone trials in the fusiform gyrus and parahippocampal gyrus. This effect was stronger in visual cortical clusters whose activity was more correlated with the hippocampus on target tone than on distractor tone trials. In agreement with accounts suggesting that subcortical noradrenergic influences play a role in the attentional boost effect, auditory target detection also caused an increase in locus coeruleus activity and phasic pupil responses. These findings outline a network of cortical and subcortical regions that are involved in the selection and processing of information presented at behaviorally relevant moments.

Neural Evidence for Different Types of Position Coding Strategies in Spatial Working Memory

Sustained neural activity during the delay phase of spatial working memory tasks is compelling evidence for the neural correlate of active storage and maintenance of spatial information, however, it does not provide insight into specific mechanisms of spatial coding. This activity may reflect a range of processes, such as maintenance of a stimulus position or a prepared motor response plan. The aim of our study was to examine neural evidence for the use of different coding strategies, depending on the characteristics and demands of a spatial working memory task. Thirty-one (20 women, 23 ± 5 years) and 44 (23 women, 21 ± 2 years) participants performed a spatial working memory task while we measured their brain activity using fMRI in two separate experiments. Participants were asked to remember the position of a briefly presented target stimulus and, after a delay period, to use a joystick to indicate either the position of the remembered target or an indicated non-matching location. The task was designed so that the predictability of the response could be manipulated independently of task difficulty and memory retrieval process. We were particularly interested in contrasting conditions in which participants (i) could use prospective coding of the motor response or (ii) had to rely on retrospective sensory information. Prospective motor coding was associated with activity in somatomotor, premotor, and motor cortices and increased integration of brain activity with and within the somatomotor network. In contrast, retrospective sensory coding was associated with increased activity in parietal regions and increased functional connectivity with and within secondary visual and dorsal attentional networks. The observed differences in activation levels, dynamics of differences over trial duration, and integration of information within and between brain networks provide compelling evidence for the use of complementary spatial working memory strategies optimized to meet task demands.

Working memory and active sampling of the environment: Medial temporal contributions

Working memory (WM) refers to the ability to maintain and actively process information-either derived from perception or long-term memory (LTM)-for intelligent thought and action. This chapter focuses on the contributions of the temporal lobe, particularly medial temporal lobe (MTL) to WM. First, neuropsychological evidence for the involvement of MTL in WM maintenance is reviewed, arguing for a crucial role in the case of retaining complex relational bindings between memorized features. Next, MTL contributions at the level of neural mechanisms are covered-with a focus on WM encoding and maintenance, including interactions with ventral temporal cortex. Among WM use processes, we focus on active sampling of environmental information, a key input source to capacity-limited WM. MTL contributions to the bidirectional relationship between active sampling and memory are highlighted-WM control of active sampling and sampling as a way of selecting input to WM. Memory-based sampling studies relying on scene and object inspection, visual-based exploration behavior (e.g., vicarious behavior), and memory-guided visual search are reviewed. The conclusion is that MTL serves an important function in the selection of information from perception and transfer from LTM to capacity-limited WM.

Increased functional interaction within frontoparietal network during working memory task in major depressive disorder

Abnormal fronto-parietal activation has been suggested as a neural underpinning of the working memory (WM) deficits in major depressive disorder (MDD). However, the potential interaction within the frontoparietal network during WM processing in MDD remains unclear. This study aimed to examine the role of abnormal functional interactions within frontoparietal network in the neuropathological mechanisms of WM deficits in MDD. A total of 40 MDD patients and 47 demographic matched healthy controls (HCs) were included. Functional magnetic resonance imaging and behavioral data were collected during numeric n-back tasks. The psychophysiological interaction and dynamic causal modelling methods were applied to investigate the connectivity within the frontoparietal network in MDD during n-back tasks. The psychophysiological interaction analysis revealed that MDD patients showed increased functional connectivity between the right inferior parietal lobule (IPL) and the right dorsolateral prefrontal cortex (dlPFC) compared with HCs during the 2-back task. The dynamic causal modelling analysis revealed that MDD patients had significantly increased forward modulation connectivity from the right IPL to the right dlPFC than HCs during the 2-back task. Partial correlation was used to calculate the relationship between connective parameters and psychological variables in the MDD group, which showed that the effective connectivity from right IPL to right dlPFC was correlated negatively with the sensitivity index d' of WM performances and positively with the depressive severity in MDD group. In conclusion, the abnormal functional and effective connectivity between frontal and parietal regions might contribute to explain the neuropathological mechanism of working memory deficits in major depressive disorder.

Amyloid-Beta Influences Memory via Functional Connectivity During Memory Retrieval in Alzheimer's Disease

Objective: Amnesia in Alzheimer's disease (AD) appears early and could be caused by encoding deficiency, consolidation dysfunction, and/or impairment in the retrieval of stored memory information. The relationship between AD pathology biomarker β-amyloid and memory dysfunction is unclear. Method: The memory task functional MRI and amyloid PET were simultaneously performed to investigate the relationship between memory performance, memory phase-related functional connectivity, and cortical β-amyloid deposition. We clustered functional networks during memory maintenance and compared network connectivity between groups in each memory phase. Mediation analysis was performed to investigate the mediator between β-amyloid and related cognitive performance. Results: Alzheimer's disease was primarily characterized by decreased functional connectivity in a data-driven network composed of an a priori default mode network, limbic network, and frontoparietal network during the memory maintenance (0.205 vs. 0.236, p = 0.04) and retrieval phase (0.159 vs. 0.183, p = 0.017). Within the network, AD had more regions with reduced connectivity during the retrieval than the maintenance and encoding phases (chi-square p = 0.01 and < 0.001). Furthermore, the global cortical β-amyloid negatively correlated with network connectivity during the memory retrieval phase (R = - 0.247, p = 0.032), with this relationship mediating the effect of cortical β-amyloid on memory performance (average causal mediation effect = - 0.05, p = 0.035). Conclusion: We demonstrated that AD had decreased connectivity in specific networks during the memory retrieval phase. Impaired functional connectivity during memory retrieval mediated the adverse effect of β-amyloid on memory. These findings help to elucidate the involvement of cortical β-amyloid (Aβ) in the memory performance in the early stages of AD.

Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex

The prefrontal cortex (PFC) plays a pivotal role in goal-directed cognition, yet its representational code remains an open problem with decoding techniques ineffective in disentangling task-relevant variables from PFC. Here we applied regularized linear discriminant analysis to human scalp EEG data and were able to distinguish a mental-rotation task versus a color-perception task with 87% decoding accuracy. Dorsal and ventral areas in lateral PFC provided the dominant features dissociating the two tasks. Our findings show that EEG can reliably decode two independent task states from PFC and emphasize the PFC dorsal/ventral functional specificity in processing the where rotation task versus the what color task.

Different patterns of functional and structural alterations of hippocampal sub-regions in subcortical vascular mild cognitive impairment with and without depression symptoms

In addition to cognitive impairments, depression symptoms were reported in subcortical vascular mild cognitive impairment. Although hippocampal alterations were associated with cognitive decline in subcortical vascular mild cognitive impairment, the neural mechanism underlying depression symptoms remains unclear. Thus, a cohort of 18 patients with depression symptoms, 17 patients without depression symptoms, and 23 normal controls was used. Functionally, significantly altered resting-state functional connectivity between hippocampal emotional sub-region and right posterior cingulate cortex, between hippocampal cognitive sub-region and right inferior parietal gyrus and between hippocampal perceptual sub-region and left inferior temporal gyrus were identified among three groups. Structurally, significantly altered structural associations between hippocampal emotional sub-region and 6 frontal regions/right pole part of superior temporal gyrus/right inferior occipital gyrus, between hippocampal cognitive sub-region and right orbital part of inferior frontal gyrus /right anterior cingulate cortex, and between hippocampal perceptual and right orbital part of inferior frontal gyrus / left inferior temporal gyrus / left thalamus were identified among the three groups. Further analyses also showed correlations between functional connectivity and depression symptoms and/or cognitive impairments of patients. Together, these results showed different patterns of functional and structural alterations of the hippocampal sub-regions in the subcortical vascular mild cognitive impairment with and without depression, which might be specially associated with the depression symptoms and cognitive impairments in these patients.

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.

A human colliculus-pulvinar-amygdala pathway encodes negative emotion

Animals must rapidly respond to threats to survive. In rodents, threat-related signals are processed through a subcortical pathway from the superior colliculus to the amygdala, a putative "low road" to affective behavior. This pathway has not been well characterized in humans. We developed a novel pathway identification framework that uses pattern recognition to identify connected neural populations and optimize measurement of inter-region connectivity. We first verified that the model identifies known thalamocortical pathways with high sensitivity and specificity in 7 T (n = 56) and 3 T (n = 48) fMRI experiments. Then we identified a human functional superior colliculus-pulvinar-amygdala pathway. Activity in this pathway encodes the intensity of normative emotional responses to negative images and sounds but not pleasant images or painful stimuli. These results provide a functional description of a human "low road" pathway selective for negative exteroceptive events and demonstrate a promising method for characterizing human functional brain pathways.

Balancing Flexibility and Interference in Working Memory

Working memory is central to cognition, flexibly holding the variety of thoughts needed for complex behavior. Yet, despite its importance, working memory has a severely limited capacity, holding only three to four items at once. In this article, I review experimental and computational evidence that the flexibility and limited capacity of working memory reflect the same underlying neural mechanism. I argue that working memory relies on interactions between high-dimensional, integrative representations in the prefrontal cortex and structured representations in the sensory cortex. Together, these interactions allow working memory to flexibly maintain arbitrary representations. However, the distributed nature of working memory comes at the cost of causing interference between items in memory, resulting in a limited capacity. Finally, I discuss several mechanisms used by the brain to reduce interference and maximize the effective capacity of working memory. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Resting-state functional connectivity of the human hippocampus in periadolescent children: Associations with age and memory performance

The hippocampus is necessary for declarative (relational) memory, and the ability to form hippocampal‐dependent memories develops through late adolescence. This developmental trajectory of hippocampal‐dependent memory could reflect maturation of intrinsic functional brain networks, but resting‐state functional connectivity (rs‐FC) of the human hippocampus is not well‐characterized for periadolescent children. Measuring hippocampal rs‐FC in periadolescence would thus fill a gap, and testing covariance of hippocampal rs‐FC with age and memory could inform theories of cognitive development. Here, we studied hippocampal rs‐FC in a cross‐sectional sample of healthy children (N = 96; 59 F; age 9–15 years) using a seed‐based approach, and linked these data with NIH Toolbox measures, the Picture‐Sequence Memory Test (PSMT) and the List Sorting Working Memory Test (LSWMT). The PSMT was expected to rely more on hippocampal‐dependent memory than the LSWMT. We observed hippocampal rs‐FC with an extensive brain network including temporal, parietal, and frontal regions. This pattern was consistent with prior work measuring hippocampal rs‐FC in younger and older samples. We also observed novel, regionally specific variation in hippocampal rs‐FC with age and hippocampal‐dependent memory but not working memory. Evidence consistent with these findings was observed in a second, validation dataset of similar‐age healthy children drawn from the Philadelphia Neurodevelopment Cohort. Further, a cross‐dataset analysis suggested generalizable properties of hippocampal rs‐FC and covariance with age and memory. Our findings connect prior work by describing hippocampal rs‐FC and covariance with age and memory in typically developing periadolescent children, and our observations suggest a developmental trajectory for brain networks that support hippocampal‐dependent memory.

Thoughts on neurophysiological signal analysis and classification

Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.

Functional neural network configuration in late childhood varies by age and cognitive state

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fMRI data from 60 children aged 9–12 during resting and tasks involving decision making, visual perception, and working memory were examined.

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At rest, the child brain exhibited network organization similar to adults though the degree of similarity was age- and network-dependent.

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During tasks, brain network configurations showed task-induced and age-related changes in integration.

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Frontoparietal network showed flexible connectivity pattern across states while networks for sensory and motor processing remained stable.

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Findings demonstrate that network connectivity characteristics may serve as markers for neural and cognitive maturation.

Late childhood and early adolescence is characterized by substantial brain maturation which contributes to both adult-like and age-dependent resting-state network connectivity patterns. However, it remains unclear whether these functional network characteristics in children are subject to differential modulation by distinct cognitive demands as previously found in adults. We conducted network analyses on fMRI data from 60 children (aged 9–12) during resting and during three distinct tasks involving decision making, visual perception, and spatial working memory. Graph measures of network architecture, functional integration, and flexibility were calculated for each of the four states. During resting state, the children’s network architecture was similar to that in young adults (N = 60, aged 20–23) but the degree of similarity was age- and network-dependent. During the task states, the children's whole-brain network exhibited enhanced integration in response to increased cognitive demand. Additionally, the frontoparietal network showed flexibility in connectivity patterns across states while networks implicated in motor and visual processing remained relatively stable. Exploratory analyses suggest different relationships between behavioral performance and connectivity profiles for the working memory and perceptual tasks. Together, our findings demonstrate state- and age-dependent features in functional network connectivity during late childhood, potentially providing markers for brain and cognitive development.

Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults

Degrading face stimuli reduces face discrimination in both young and older adults, but the brain correlates of this decline in performance are not fully understood. We used functional magnetic resonance imaging to examine the effects of degraded face stimuli on face and nonface brain networks and tested whether these changes would predict the linear declines seen in performance. We found decreased activity in the face network (FN) and a decrease in the similarity of functional connectivity (FC) in the FN across conditions as degradation increased but no effect of age. FC in whole-brain networks also changed with increasing degradation, including increasing FC between the visual network and cognitive control networks. Older adults showed reduced modulation of this whole-brain FC pattern. The strongest predictors of within-participant decline in accuracy were changes in whole-brain network FC and FC similarity of the FN. There was no influence of age on these brain-behavior relations. These results suggest that a systems-level approach beyond the FN is required to understand the brain correlates of performance decline when faces are obscured with noise. In addition, the association between brain and behavior changes was maintained into older age, despite the dampened FC response to face degradation seen in older adults.

Spatiotemporal dynamics of working memory under the influence of emotions based on EEG

OBJECTIVE

Previous studies have reported that working memory (WM) may be affected by emotions and that the effect may exist in different stages of WM. However, at present it remains controversial whether emotions inhibit or facilitate WM, and how the mechanism of dynamic information transmission in the brain during WM is affected by emotions.

APPROACH

In this study, we used a video database to induce three emotions (negative, neutral, and positive) and adopted a change detection paradigm based on electroencephalography. Event-related potential (ERP) analysis, event-related spectral perturbation analysis, source location analysis based on the dipole localization method and the distributed source localization method, and effective connectivity analysis were performed.

MAIN RESULTS

Both behavioral and ERP results suggest that positive emotions have no significant effect on WM capacity, while negative emotions could facilitate WM capacity. Furthermore, the effective connectivity results based on two source location methods suggest that the long-range connectivity between the frontal and posterior areas can reflect the influence of positive and negative emotions on the WM network, in which the connectivity under the positive emotion condition occurs in the earlier period of WM maintenance, while the connectivity under the negative emotion condition occurs in the later period of WM maintenance.

SIGNIFICANCE

The consistency of the behavioral, ERP, and effective connectivity results suggests that under the negative emotion condition, the top-down attention modulation between the frontoparietal area and posterior area could promote the most relevant information storage during WM maintenance.

The what, where and how of delay activity

Working memory is characterized by neural activity that persists during the retention interval of delay tasks. Despite the ubiquity of this delay activity across tasks, species and experimental techniques, our understanding of this phenomenon remains incomplete. Although initially there was a narrow focus on sustained activation in a small number of brain regions, methodological and analytical advances have allowed researchers to uncover previously unobserved forms of delay activity various parts of the brain. In light of these new findings, this Review reconsiders what delay activity is, where in the brain it is found, what roles it serves and how it may be generated.

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.

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

Use of Virtual Reality Working Memory Task and Functional Near-Infrared Spectroscopy to Assess Brain Hemodynamic Responses to Methylphenidate in ADHD Children

Virtual reality (VR) neuropsychological tests have emerged as a method to explore drug effects in real-life contexts in attention deficit hyperactivity disorder (ADHD) children. Functional near-infrared spectroscopy (fNIRS) is a useful tool to measure brain activity during VR tasks in ADHD children with motor restlessness. The present study aimed to explore the acute effects of methylphenidate (MPH) on behavioral performance and brain activity during a VR-based working memory task simulating real-life classroom settings in ADHD children. In total, 23 children with ADHD performed a VR n-back task before and 2 h after MPH administration concurrent with measurements of oxygenated hemoglobin signal changes with fNIRS. Altogether, 12 healthy control (HC) subjects participated in the same task but did not receive MPH treatment. Reaction time (RT) was shortened after MPH treatment in the 1-back condition, but changes in brain activation were not observed. In the 2-back condition, activation of the left dorsolateral prefrontal cortex (DLPFC) and bilateral medial prefrontal cortex (mPFC) was decreased alongside behavioral changes such as shorter RT, lower RT variability, and higher accuracy after MPH administration. Bilateral mPFC activation in the 2-back condition inversely correlated with task accuracy in the pre-MPH condition; this inverse correlation was not observed after MPH administration. In ADHD children, deactivation of the default mode network mediated by mPFC reduced during high working memory load, which was restored through MPH treatment. Our results suggest that the combination of VR classroom tasks and fNIRS examination makes it easy to assess drug effects on brain activity in ADHD children in settings simulating real-life.

Frontoparietal Network Connectivity During an N-Back Task in Adults With Autism Spectrum Disorder

BACKGROUND

Short-term and working memory (STM and WM) deficits have been demonstrated in individuals with autism spectrum disorder (ASD) and may emerge through atypical functional activity and connectivity of the frontoparietal network, which exerts top-down control necessary for successful STM and WM processes. Little is known regarding the spectral properties of the frontoparietal network during STM or WM processes in ASD, although certain neural frequencies have been linked to specific neural mechanisms.

METHODS

We analysed magnetoencephalographic data from 39 control adults (26 males; 27.15 ± 5.91 years old) and 40 adults with ASD (26 males; 27.17 ± 6.27 years old) during a 1-back condition (STM) of an n-back task, and from a subset of this sample during a 2-back condition (WM). We performed seed-based connectivity analyses using regions of the frontoparietal network. Interregional synchrony in theta, alpha, and beta bands was assessed with the phase difference derivative and compared between groups during periods of maintenance and recognition.

RESULTS

During maintenance of newly presented vs. repeated stimuli, the two groups did not differ significantly in theta, alpha, or beta phase synchrony for either condition. Adults with ASD showed alpha-band synchrony in a network containing the right dorsolateral prefrontal cortex, bilateral inferior parietal lobules (IPL), and precuneus in both 1- and 2-back tasks, whereas controls demonstrated alpha-band synchrony in a sparser set of regions, including the left insula and IPL, in only the 1-back task. During recognition of repeated vs. newly presented stimuli, adults with ASD exhibited decreased theta-band connectivity compared to controls in a network with hubs in the right inferior frontal gyrus and left IPL in the 1-back condition. Whilst there were no group differences in connectivity in the 2-back condition, adults with ASD showed no frontoparietal network recruitment during recognition, whilst controls activated networks in the theta and beta bands.

CONCLUSIONS

Our findings suggest that since adults with ASD performed well on the n-back task, their appropriate, but effortful recruitment of alpha-band mechanisms in the frontoparietal network to maintain items in STM and WM may compensate for atypical modulation of this network in the theta band to recognise previously presented items in STM.

Assessing Neural Compensation With Visuospatial Working Memory Load Using Near-Infrared Imaging

Alzheimer's disease is characterized by the progressive deterioration of cognitive abilities particularly working memory while mild cognitive impairment (MCI) represents its prodrome. It is generally believed that neural compensation is intact in MCI but absent in Alzheimer's disease. This study investigated the effects of increasing task load as a means to induce neural compensation through a novel visual working memory (VSWM) task using functional near-infrared spectroscopy (fNIRS). The bilateral prefrontal cortex (PFC) was explored due to its relevance in VSWM and neural compensation. A total of 31 healthy controls (HC), 12 patients with MCI and 18 patients with mild Alzheimer's disease (mAD) were recruited. Although all groups showed sensitivity in terms of behavioral performance (i.e. score) towards increasing task load (level 1 to 3), only in MCI load effect on cortical response (as measured by fNIRS) was significant. At lower task load, bilateral PFC activation did not differ between MCI and HC. Neural compensation in the form of hyperactivation was only noticeable in MCI with a moderate task load. Lack of hyperactivation in mAD, coupled with significantly poorer task performance across task loads, suggested the inability to compensate due to a greater degree of neurodegeneration. Our findings provided an insight into the interaction of cognitive load theory and neural compensatory mechanisms. The experiment results demonstrated the feasibility of inducing neural compensation with the proposed VSWM task at the right amount of cognitive load. This may provide a promising avenue to develop an effective cognitive training and rehabilitation for dementia population.

Computer-Based Cognitive Training Improves Brain Functional Connectivity in the Attentional Networks: A Study With Primary School-Aged Children

We have shown that a computer-based program that trains schoolchildren in cognitive tasks that mainly tap working memory (WM), implemented by teachers and integrated into school routine, improved cognitive and academic skills compared with an active control group. Concretely, improvements were observed in inhibition skills, non-verbal IQ, mathematics and reading skills. Here, we focus on a subsample from the overarching study who volunteered to be scanned using a resting state fMRI protocol before and 6-month after training. This sample reproduced the aforementioned behavioral effects, and brain functional connectivity changes were observed within the attentional networks (ATN), linked to improvements in inhibitory control. Findings showed stronger relationships between inhibitory control scores and functional connectivity in a right middle frontal gyrus (MFG) cluster in trained children compared to children from the control group. Seed-based analyses revealed that connectivity between the r-MFG and homolateral parietal and superior temporal areas were more strongly related to inhibitory control in trained children compared to the control group. These findings highlight the relevance of computer-based cognitive training, integrated in real-life school environments, in boosting cognitive/academic performance and brain functional connectivity.

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.

Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation

Differentiation of real interactions between different brain regions from spurious ones has been a challenge in neuroimaging researches. While using electroencephalographic data, those spurious interactions are mostly caused by the volume conduction (VC) effect between the recording sites. In this study, we address the problem by jointly modeling the causal relationships among brain regions and the mixing effects of volume conduction. The VC effect is formulated with a time-invariant linear equation, and the causal relationships between the brain regions are modeled with a nonlinear multivariate autoregressive process. These two models are simultaneously implemented by a multilayer neural network. The internal hidden layers represent the interactions among the regions, while the external layers are devoted for the relationship between the source activities and observed EEG measurements at the scalp. The causal interactions are estimated by the causality coefficient measure, which is based on the information (weights and parameters) embedded in the network. The proposed method is verified using various simulated data. It is then applied to the real EEG signals collected from a memory retrieval test. The results showed that the method is able to eliminate the volume conduction interferences and consequently leads to higher accuracy in identification of true causal interactions. Graphical abstract The proposed network structure used to simultaneously model the volume conduction and source interactions. By this special structure, we first move from the sensor space to the source space at the first layer. Then, within internal hidden layers, the interactions between the sources are represented in the form of a general (nonlinear) multivariate autoregressive (nMVAR) model. Finally, we return from the source space to the sensor space at the last layer of the network. The proposed method bypasses the effect of volume conduction and causes more accurate connectivity estimation.

The anterior insula channels prefrontal expectancy signals during affective processing

Expectancy shapes our perception of impending events. Although such an interplay between cognitive and affective processes is often impaired in mental disorders, it is not well understood how top-down expectancy signals modulate future affect. We therefore track the information flow in the brain during cognitive and affective processing segregated in time using task-specific cross-correlations. Participants in two independent fMRI studies (N₁ = 37 & N₂ = 55) were instructed to imagine a situation with affective content as indicated by a cue, which was then followed by an emotional picture congruent with expectancy. To correct for intrinsic covariance of brain function, we calculate resting-state cross-correlations analogous to the task. First, using factorial modeling of delta cross-correlations (task-rest) of the first study, we find that the magnitude of expectancy signals in the anterior insula cortex (AIC) modulates the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. Second, using hierarchical linear modeling of lagged connectivity, we demonstrate that expectancy signals in the AIC indeed foreshadow this opposing pattern in the prefrontal cortex. Third, we replicate the results in the second study using a higher temporal resolution, showing that our task-specific cross-correlation approach robustly uncovers the dynamics of information flow. We conclude that the AIC arbitrates the recruitment of distinct prefrontal networks during cued picture processing according to triggered expectations. Taken together, our study provides new insights into neuronal pathways channeling cognition and affect within well-defined brain networks. Better understanding of such dynamics could lead to new applications tracking aberrant information processing in mental disorders.

Increased flanker task and forward digit span performance in caudate-nucleus-dependent response strategies

One of two memory systems can be used to navigate in a new environment. Hippocampus-dependent spatial strategy consists of creating a cognitive map of an environment and caudate nucleus-dependent response strategy consists of memorizing a rigid sequence of turns. Spontaneous use of the response strategy is associated with greater activity and grey matter within the caudate nucleus while the spatial strategy is associated with greater activity and grey matter in the hippocampus. The caudate nucleus is involved in executive functions such as working memory, cognitive control and certain aspects of attention such as attentional disengaging. This study therefore aimed to investigate whether response learners would display better performance on tests of executive and attention functioning compared to spatial learners. Fifty participants completed the 4/8 virtual maze to assess navigational strategy, the forward and backward visual digit span and the Attention Network Test - Revised to assess both attention disengagement and cognitive control. Results revealed that response learners showed significantly higher working memory capacity, more efficient attention disengagement and better cognitive control. Results suggest that response learners, who putatively display more grey matter and activity in the caudate nucleus, are associated with better working memory span, cognitive control and attentional disengagement.

Impaired prefrontal functional connectivity associated with working memory task performance and disorganization despite intact activations in schizophrenia

Working memory (WM) deficits are key features of schizophrenia and are associated with significant functional impairment. The precise mechanisms of WM and their relationship between WM deficits with other clinical symptoms of schizophrenia remain unclear. Contemporary models propose that WM requires synchronous activity across brain regions within a distributed network, including lateral prefrontal cortex (PFC) and task-relevant posterior sensory cortical regions. This suggests that WM deficits in patients may be due to PFC functional connectivity (FC) impairments rather than activation impairments per se. We tested this hypothesis by measuring the magnitude of FC between lateral PFC and visual cortex and univariate activations within these regions during visual WM. We found decreased FC in patients compared to healthy subjects in the context of similar levels of univariate activity. Furthermore, this decreased FC was associated with task performance and clinical symptomatology in patients. The magnitude of FC, particularly during the delay period, was positively correlated with WM task accuracy, while FC during cue was inversely correlated with severity of disorganization. Taken together, these results suggest that impairment in lateral PFC FC is a key aspect of information processing impairment in patients with schizophrenia, and may be a sensitive index of altered neurophysiology.

Dynamic network coding of working-memory domains and working-memory processes

The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machine-learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes (encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity that they evoke. This is the case even when focusing on 'multiple demands' brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to brain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.