Core networks and their reconfiguration patterns across cognitive loads

Task-Rest Reconfiguration Efficiency of the Reward Network Across Adolescence and Its Association With Early Life Stress and Depressive Symptoms

OBJECTIVE

Adolescents face significant changes in many domains of their daily lives that require them to flexibly adapt to changing environmental demands. To shift efficiently among various goals, adolescents must reconfigure their brains, disengaging from previous tasks and engaging in new activities.

METHOD

To examine this reconfiguration, we obtained resting-state and task-based functional magnetic resonance imaging (fMRI) scans in a community sample of 164 youths. We assessed the similarity of functional connectivity (FC) of the reward network between resting state and a reward-processing state, indexing the degree of reward network reconfiguration required to meet task demands. Given research documenting relations among reward network function, early life stress (ELS), and adolescent depression, we examined the association of reconfiguration efficiency with age across adolescence, the moderating effect of ELS on this association, and the relation between reconfiguration efficiency and depressive symptoms.

RESULTS

We found that older adolescents showed greater reconfiguration efficiency than younger adolescents and, furthermore, that this age-related association was moderated by the experience of ELS.

CONCLUSION

These findings suggest that reconfiguration efficiency of the reward network increases over adolescence, a developmental pattern that is attenuated in adolescents exposed to severe ELS. In addition, even after controlling for the effects of age and exposure to ELS, adolescents with higher levels of depressive symptoms exhibited greater reconfiguration efficiency, suggesting that they have brain states at rest that are more strongly optimized for reward processing than do asymptomatic youth.

PLAIN LANGUAGE SUMMARY

Adolescents face significant changes in many domains of their lives which requires them to flexibly adapt and reconfigure their brains to disengage from previous tasks and engage in new activities. In this study of a sample of 164 youth aged 9 to 20, the authors found an age-related increase in the reconfiguration efficiency of the reward network, which was pronounced in older adolescents exposed to severe early life stress. In addition, the study findings indicate that adolescents with higher levels of depressive symptoms showed greater reconfiguration efficiency, suggesting that their brains may be more optimized for processing rewards even at rest compared to their peers without any symptoms.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science.

Brain structural damage networks at different stages of schizophrenia

BACKGROUND

Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia.

METHODS

We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks.

RESULTS

Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time.

CONCLUSIONS

Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.

Reconfiguration of Functional Brain Network Organization and Dynamics With Changing Cognitive Demands in Children With Attention-Deficit/Hyperactivity Disorder

BACKGROUND

The pathophysiology of attention-deficit/hyperactivity disorder (ADHD) is characterized by atypical brain network organization and dynamics. Although functional brain networks adaptively reconfigure across cognitive contexts, previous studies have largely focused on network dysfunction during the resting state. In this preliminary study, we examined how functional brain network organization and dynamics flexibly reconfigure across rest and 2 cognitive control tasks with different cognitive demands in 30 children with ADHD and 36 typically developing children (ages 8-12 years).

METHODS

We leveraged graph theoretical analyses to interrogate the segregation (modularity, within-module degree) and integration (global efficiency, node dissociation index) of frontoparietal, cingulo-opercular/salience, default mode, somatomotor, and visual networks. We also conducted edge time series analyses to quantify connectivity dynamics within and between these networks.

RESULTS

Across resting and task-based states, children with ADHD demonstrated significantly lower whole-graph modularity and a greater node dissociation index between default mode and visual networks. Furthermore, a significant task-by-diagnosis interaction was observed for frontoparietal network within-module degree, which decreased from rest to task in children with ADHD but increased in typically developing children. Finally, children with ADHD displayed significantly more dynamic connectivity within and across cingulo-opercular/salience, default mode, and somatomotor networks, especially during task performance. Exploratory analyses revealed associations between network dynamics, cognitive performance, and ADHD symptoms.

CONCLUSIONS

By integrating static and dynamic network analyses across changing cognitive demands, this study provides novel insight into how context-specific, context-general, and timescale-dependent network connectivity is altered in children with ADHD. Our findings highlight the involvement and clinical relevance of both association and sensory/motor systems in ADHD.

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.

MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification

Traditional individual identification methods, such as face and fingerprint recognition, carry the risk of personal information leakage. The uniqueness and privacy of electroencephalograms (EEG) and the popularization of EEG acquisition devices have intensified research on EEG-based individual identification in recent years. However, most existing work uses EEG signals from a single session or emotion, ignoring large differences between domains. As EEG signals do not satisfy the traditional deep learning assumption that training and test sets are independently and identically distributed, it is difficult for trained models to maintain good classification performance for new sessions or new emotions. In this article, an individual identification method, called Multi-Loss Domain Adaptor (MLDA), is proposed to deal with the differences between marginal and conditional distributions elicited by different domains. The proposed method consists of four parts: a) Feature extractor, which uses deep neural networks to extract deep features from EEG data; b) Label predictor, which uses full-layer networks to predict subject labels; c) Marginal distribution adaptation, which uses maximum mean discrepancy (MMD) to reduce marginal distribution differences; d) Associative domain adaptation, which adapts to conditional distribution differences. Using the MLDA method, the cross-session and cross-emotion EEG-based individual identification problem is addressed by reducing the influence of time and emotion. Experimental results confirmed that the method outperforms other state-of-the-art approaches.

Mindfulness-based therapy improves brain functional network reconfiguration efficiency

Mindfulness-based interventions are showing increasing promise as a treatment for psychological disorders, with improvements in cognition and emotion regulation after intervention. Understanding the changes in functional brain activity and neural plasticity that underlie these benefits from mindfulness interventions is thus of interest in current neuroimaging research. Previous studies have found functional brain changes during resting and task states to be associated with mindfulness both cross-sectionally and longitudinally, particularly in the executive control, default mode and salience networks. However, limited research has combined information from rest and task to study mindfulness-related functional changes in the brain, particularly in the context of intervention studies with active controls. Recent work has found that the reconfiguration efficiency of brain activity patterns between rest and task states is behaviorally relevant in healthy young adults. Thus, we applied this measure to investigate how mindfulness intervention changed functional reconfiguration between rest and a breath-counting task in elderly participants with self-reported sleep difficulties. Improving on previous longitudinal designs, we compared the intervention effects of a mindfulness-based therapy to an active control (sleep hygiene) intervention. We found that mindfulness intervention improved self-reported mindfulness measures and brain functional reconfiguration efficiency in the executive control, default mode and salience networks, though the brain and behavioral changes were not associated with each other. Our findings suggest that neuroplasticity may be induced through regular mindfulness practice, thus bringing the intrinsic functional configuration in participants' brains closer to a state required for mindful awareness.

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.

Interaction Between Memory Load and Experimental Design on Brain Connectivity and Network Topology

The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation. While providing many insights into brain functions, the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal. Recent studies utilized one single long run for task trials of the same condition, the so-called continuous design, to investigate functional connectivity based on task functional magnetic resonance imaging. Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment, which has been examined for working memory, sensory, motor, and semantic task experiments in previous research. But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks. This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks, by using repeated-measures analysis of variance. Across 50 young healthy adults, behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load. Imaging results revealed that the cingulo-opercular, fronto-parietal, and default model networks were associated with not only task activation, but significant main effects of design and load as well as their interaction on intra- and inter-network functional connectivity and global network topology. Moreover, a significant behavior-brain association was identified for the continuous design. This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.

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.

Inflammation Disrupts Cognitive Integrity via Plasma Neurofilament Light Chain Coupling Brain Networks in Alzheimer’s Disease

Objective: Background: Plasma neurofilament light chain (NFL) is a recognized biomarker for Alzheimer’s disease (AD) and inflammation. Intrinsically organized default mode network core subsystem and frontoparietal network (FPN) and their interactions support complex cognitive function. The present study investigated the inflammatory effect on cognitive integrity via plasma NFL coupling internetwork interactions in AD. Objective: Objective: This study investigates the hypothesis that inflammation-related plasma NFL could affect the interactions of the core subsystem and FPN, which leads to the aggravation of the clinical symptoms of AD-spectrum patients. Objective: Methods: A total of 112 AD-spectrum participants underwent complete resting-state fMRI, neuropsychological tests, and plasma NFL at baseline (n = 112) and after approximately 17 months of follow-up (n = 112). The specific intersystem changes in the core subsystem and FPN were calculated and compared across groups. Then, the classifications of different AD-spectrum groups were analyzed using the association of plasma NFL and the changed intersystem interacting regions. Finally, mediation analysis was applied to investigate the significance of plasma NFL coupling networks on cognitive impairments in these subjects. Objective: Results: Discrimination of disease-related interactions of the core subsystem and FPN was found in AD-spectrum patients, which was the neural circuit fundamental to plasma NFL disrupting cognitive integrity. Furthermore, the clinical significance of plasma NFL coupling networks on AD identification and monitoring cognitive impairments were revealed in these subjects. Conclusion: The characteristic change in inflammation-related plasma NFL coupled with brain internetwork interactions could be used as a potential observation indicator in the progression of AD patients.

Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites

BACKGROUND

The dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity.

RESULTS

In this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N = 809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC = 81%, SEN = 83.4%, SPE = 80.6%, and F1-score = 79.4%) than that only using FC (ACC = 78.2%, SEN = 76.2%, SPE = 76.5%, and F1-score = 77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R = -0.38, P < 0.001; three classes classification: R = -0.404, P < 0.001). More importantly, several commonly used machine learning models confirmed that the tdNCD would provide additional information for classifying AD from normal controls.

CONCLUSIONS

The present study demonstrated dynamic reconfiguration of nodal FC abnormities in AD. The tdNCD highlights the potential for further understanding core mechanisms of brain dysfunction in AD. Evaluating the tdNCD FC provides a promising way to understand AD processes better and investigate novel diagnostic brain imaging biomarkers for AD.

Physical activity is associated with increased resting-state functional connectivity in networks predictive of cognitive decline in clinically unimpaired older adults

Introduction

Physical activity (PA) promotes resilience with respect to cognitive decline, although the underlying mechanisms are not well understood. We examined the associations between objectively measured PA and resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) across seven anatomically distributed neural networks.

Methods

rs-fcMRI, amyloid beta (Aβ) positron emission tomography (PET), PA (steps/day × 1 week), and longitudinal cognitive (Preclinical Alzheimer's Cognitive Composite) data from 167 cognitively unimpaired adults (ages 63 to 90) were used. We used linear and linear mixed-effects regression models to examine the associations between baseline PA and baseline network connectivity and between PA, network connectivity, and longitudinal cognitive performance.

Results

Higher PA was associated selectively with greater connectivity in three networks previously associated with cognitive decline (default, salience, left control). This association with network connectivity accounted for a modest portion of PA's effects on Aβ-related cognitive decline.

Discussion

Although other mechanisms are likely present, PA may promote resilience with respect to Aß-related cognitive decline, partly by increasing connectivity in a subset of cognitive networks.

Impaired body-centered sensorimotor transformations in congenitally deaf people

Congenital deafness modifies an individual’s daily interaction with the environment and alters the fundamental perception of the external world. How congenital deafness shapes the interface between the internal and external worlds remains poorly understood. To interact efficiently with the external world, visuospatial representations of external target objects need to be effectively transformed into sensorimotor representations with reference to the body. Here, we tested the hypothesis that egocentric body-centred sensorimotor transformation is impaired in congenital deafness. Consistent with this hypothesis, we found that congenital deafness induced impairments in egocentric judgements, associating the external objects with the internal body. These impairments were due to deficient body-centred sensorimotor transformation per se, rather than the reduced fidelity of the visuospatial representations of the egocentric positions. At the neural level, we first replicated the previously well-documented critical involvement of the frontoparietal network in egocentric processing, in both congenitally deaf participants and hearing controls. However, both the strength of neural activity and the intra-network connectivity within the frontoparietal network alone could not account for egocentric performance variance. Instead, the inter-network connectivity between the task-positive frontoparietal network and the task-negative default-mode network was significantly correlated with egocentric performance: the more cross-talking between them, the worse the egocentric judgement. Accordingly, the impaired egocentric performance in the deaf group was related to increased inter-network connectivity between the frontoparietal network and the default-mode network and decreased intra-network connectivity within the default-mode network. The altered neural network dynamics in congenital deafness were observed for both evoked neural activity during egocentric processing and intrinsic neural activity during rest. Our findings thus not only demonstrate the optimal network configurations between the task-positive and -negative neural networks underlying coherent body-centred sensorimotor transformations but also unravel a critical cause (i.e. impaired body-centred sensorimotor transformation) of a variety of hitherto unexplained difficulties in sensory-guided movements the deaf population experiences in their daily life.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

Abnormal Large-Scale Neuronal Network in High Myopia

Aim

Resting state functional magnetic resonance imaging (rs-fMRI) was used to analyze changes in functional connectivity (FC) within various brain networks and functional network connectivity (FNC) among various brain regions in patients with high myopia (HM).

Methods

rs-fMRI was used to scan 82 patients with HM (HM group) and 59 healthy control volunteers (HC group) matched for age, sex, and education level. Fourteen resting state networks (RSNs) were extracted, of which 11 were positive. Then, the FCs and FNCs of RSNs in HM patients were examined by independent component analysis (ICA).

Results

Compared with the HC group, FC in visual network 1 (VN1), dorsal attention network (DAN), auditory network 2 (AN2), visual network 3 (VN3), and sensorimotor network (SMN) significantly increased in the HM group. FC in default mode network 1 (DMN1) significantly decreased. Furthermore, some brain regions in default mode network 2 (DMN2), default mode network 3 (DMN3), auditory network 1 (AN1), executive control network (ECN), and significance network (SN) increased while others decreased. FNC analysis also showed that the network connection between the default mode network (DMN) and cerebellar network (CER) was enhanced in the HM group.

Conclusion

Compared with HCs, HM patients showed neural activity dysfunction within and between specific brain networks, particularly in the DMN and CER. Thus, HM patients may have deficits in visual, cognitive, and motor balance functions.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Efficient Brain Connectivity Reconfiguration Predicts Higher Marital Quality and Lower Depression

Social-information processing is important for successful romantic relationships and protecting against depression, and depends on functional connectivity (FC) within and between large-scale networks. Functional architecture evident at rest is adaptively reconfigured during task and there were two possible associations between brain reconfiguration and behavioral performance during neurocognitive tasks (efficiency effect and distraction-based effect). This study examined relationships between brain reconfiguration during social-information processing and relationship-specific and more general social outcomes in marriage. Resting-state FC was compared with FC during social-information processing (watching relationship-specific and general emotional stimuli) of 29 heterosexual couples, and the FC similarity (reconfiguration efficiency) was examined in relation to marital quality and depression 13 months later. The results indicated wives' reconfiguration efficiency (globally and in visual association network) during relationship-specific stimuli processing was related to their own marital quality. Higher reconfiguration efficiency (globally and in medial frontal, frontal-parietal, default mode, motor/sensory and salience networks) in wives during general emotional stimuli processing was related to their lower depression. These findings suggest efficiency effects on social outcomes during social cognition, especially among married women. The efficiency effects on relationship-specific and more general outcome are respectively higher during relationship-specific stimuli or general emotional stimuli processing.

Development of Individual Variability in Brain Functional Connectivity and Capability across the Adult Lifespan

Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18-88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.

Task-related brain functional network reconfigurations relate to motor recovery in chronic subcortical stroke

Stroke leads to both regional brain functional disruptions and network reorganization. However, how brain functional networks reconfigure as task demand increases in stroke patients and whether such reorganization at baseline would facilitate post-stroke motor recovery are largely unknown. To address this gap, brain functional connectivity (FC) were examined at rest and motor tasks in eighteen chronic subcortical stroke patients and eleven age-matched healthy controls. Stroke patients underwent a 2-week intervention using a motor imagery-assisted brain computer interface-based (MI-BCI) training with or without transcranial direct current stimulation (tDCS). Motor recovery was determined by calculating the changes of the upper extremity component of the Fugl-Meyer Assessment (FMA) score between pre- and post-intervention divided by the pre-intervention FMA score. The results suggested that as task demand increased (i.e., from resting to passive unaffected hand gripping and to active affected hand gripping), patients showed greater FC disruptions in cognitive networks including the default and dorsal attention networks. Compared to controls, patients had lower task-related spatial similarity in the somatomotor-subcortical, default-somatomotor, salience/ventral attention-subcortical and subcortical-subcortical connections, suggesting greater inefficiency in motor execution. Importantly, higher baseline network-specific FC strength (e.g., dorsal attention and somatomotor) and more efficient brain network reconfigurations (e.g., somatomotor and subcortical) from rest to active affected hand gripping at baseline were related to better future motor recovery. Our findings underscore the importance of studying functional network reorganization during task-free and task conditions for motor recovery prediction in stroke.

Age differences in functional network reconfiguration with working memory training

Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.

Overlapping and dissociable brain activations for fluid intelligence and executive functions

Cognitive enhancement interventions aimed at boosting human fluid intelligence (gf) have targeted executive functions (EFs), such as updating, inhibition, and switching, in the context of transfer-inducing cognitive training. However, even though the link between EFs and gf has been demonstrated at the psychometric level, their neurofunctional overlap has not been quantitatively investigated. Identifying whether and how EFs and gf might share neural activation patterns could provide important insights into the overall hierarchical organization of human higher-order cognition, as well as suggest specific targets for interventions aimed at maximizing cognitive transfer. We present the results of a quantitative meta-analysis of the available fMRI and PET literature on EFs and gf in humans, showing the similarity between gf and (i) the overall global EF network, as well as (ii) specific maps for updating, switching, and inhibition. Results highlight a higher degree of similarity between gf and updating (80% overlap) compared with gf and inhibition (34%), and gf and switching (17%). Moreover, three brain regions activated for both gf and each of the three EFs also were identified, located in the left middle frontal gyrus, left inferior parietal lobule, and anterior cingulate cortex. Finally, resting-state functional connectivity analysis on two independent fMRI datasets showed the preferential behavioural correlation and anatomical overlap between updating and gf. These findings confirm a close link between gf and EFs, with implications for brain stimulation and cognitive training interventions.

Task-evoked reconfiguration of the fronto-parietal network is associated with cognitive performance in brain tumor patients

In healthy participants, the strength of task-evoked network reconfigurations is associated with cognitive performance across several cognitive domains. It is, however, unclear whether the capacity for network reconfiguration also plays a role in cognitive deficits in brain tumor patients. In the current study, we examined whether the level of reconfiguration of the fronto-parietal (‘FPN’) and default mode network (‘DMN’) during task execution is correlated with cognitive performance in patients with different types of brain tumors. For this purpose, we combined data from a resting state and task-fMRI paradigm in patients with a glioma or meningioma. Cognitive performance was measured using the in-scanner working memory task, as well as an out-of-scanner cognitive flexibility task. Task-evoked changes in functional connectivity strength (defined as the mean of the absolute values of all connections) and in functional connectivity patterns within and between the FPN and DMN did not differ significantly across meningioma and fast (HGG) and slowly growing glioma (LGG) patients. Across these brain tumor patients, a significant and positive correlation was found between the level of task-evoked reconfiguration of the FPN and cognitive performance. This suggests that the capacity for FPN reconfiguration also plays a role in cognitive deficits in brain tumor patients, as was previously found for normal cognitive performance in healthy controls.

Altered intrinsic brain activity and regional cerebral blood flow in patients with chronic neck and shoulder pain

Purpose

To identify the changes of intrinsic brain activity and regional cerebral blood flow in patients with chronic neck and shoulder pain (CNSP) by using amplitude of low-frequency fluctuation (ALFF) analysis and arterial spin labelling study.

Material and methods

In total, 28 CNSP patients and 25 age-matched and sex-matched healthy controls (HCs) participated in the study. Resting-state functional magnetic resonance imaging (rs-fMRI) and arterial spin labelling (ASL) MRI were acquired. Correlations between ALFF and cerebral blood flow (CBF) were analysed. Subsequently, the differences in ALFF and CBF were compared in the two groups. Finally, the visual analogue scale (VAS) was also assessed in the CNSP group.

Results

Compared with HCs, CNSP patients showed significantly abnormal ALFF and CBF in several brain regions, including the cerebellum posterior lobe, middle orbitofrontal gyrus, medial superior frontal gyrus, middle temporal gyrus, precuneus, cingulate gyrus, middle occipital gyrus, middle frontal gyrus, postcentral gyrus, precentral gyrus, and superior parietal gyrus. Correlation analysis showed that the ALFF value of the medial superior frontal gyrus positively correlated with the VAS score. However, no correlation was found between the CBF values and the VAS score.

Conclusions

The altered ALFF and CBF values in CNSP patients were observed in different pain-related brain regions that were involved in pain modulation and perception. The combination of rs-fMRI and ASL MRI might provide complementary information for increasing our understanding of the neuropathology in CNSP.

Connectivity differences between Gulf War Illness (GWI) phenotypes during a test of attention

One quarter of veterans returning from the 1990–1991 Persian Gulf War have developed Gulf War Illness (GWI) with chronic pain, fatigue, cognitive and gastrointestinal dysfunction. Exertion leads to characteristic, delayed onset exacerbations that are not relieved by sleep. We have modeled exertional exhaustion by comparing magnetic resonance images from before and after submaximal exercise. One third of the 27 GWI participants had brain stem atrophy and developed postural tachycardia after exercise (START: Stress Test Activated Reversible Tachycardia). The remainder activated basal ganglia and anterior insulae during a cognitive task (STOPP: Stress Test Originated Phantom Perception). Here, the role of attention in cognitive dysfunction was assessed by seed region correlations during a simple 0-back stimulus matching task (“see a letter, push a button”) performed before exercise. Analysis was analogous to resting state, but different from psychophysiological interactions (PPI). The patterns of correlations between nodes in task and default networks were significantly different for START (n = 9), STOPP (n = 18) and control (n = 8) subjects. Edges shared by the 3 groups may represent co-activation caused by the 0-back task. Controls had a task network of right dorsolateral and left ventrolateral prefrontal cortex, dorsal anterior cingulate cortex, posterior insulae and frontal eye fields (dorsal attention network). START had a large task module centered on the dorsal anterior cingulate cortex with direct links to basal ganglia, anterior insulae, and right dorsolateral prefrontal cortex nodes, and through dorsal attention network (intraparietal sulci and frontal eye fields) nodes to a default module. STOPP had 2 task submodules of basal ganglia–anterior insulae, and dorsolateral prefrontal executive control regions. Dorsal attention and posterior insulae nodes were embedded in the default module and were distant from the task networks. These three unique connectivity patterns during an attention task support the concept of Gulf War Disease with recognizable, objective patterns of cognitive dysfunction.

Genetic Taster Status as a Mediator of Neural Activity and Swallowing Mechanics in Healthy Adults

As part of a larger study examining relationships between taste properties and swallowing, we assessed the influence of genetic taster status (GTS) on measures of brain activity and swallowing physiology during taste stimulation in healthy men and women. Twenty-one participants underwent videofluoroscopic swallowing study (VFSS) and functional magnetic resonance imaging (fMRI) during trials of high-intensity taste stimuli. The precisely formulated mixtures included sour, sweet-sour, lemon, and orange taste profiles and unflavored controls. Swallowing physiology was characterized via computational analysis of swallowing mechanics plus other kinematic and temporal measures, all extracted from VFSS recordings. Whole-brain analysis of fMRI data assessed blood oxygen responses to neural activity associated with taste stimulation. Swallowing morphometry, kinematics, temporal measures, and neuroimaging analysis revealed differential responses by GTS. Supertasters exhibited increased amplitude of most pharyngeal movements, and decreased activity in the primary somatosensory cortex compared to nontasters and midtasters. These preliminary findings suggest baseline differences in swallowing physiology and the associated neural underpinnings associated with GTS. Given the potential implications for dysphagia risk and recovery patterns, GTS should be included as a relevant variable in future research regarding swallowing function and dysfunction.

Activation-based association profiles differentiate network roles across cognitive loads

Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level-dependent (BOLD) response across several large-scale brain networks, the unique and shared contributions of each large-scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared-association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared-association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2-back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared-associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared-associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

Abnormal amplitude of low frequency fluctuation and functional connectivity in non-neuropsychiatric systemic lupus erythematosus: a resting-state fMRI study

PURPOSE

To explore the amplitude of low frequency fluctuation (ALFF) and functional connectivity (FC) disorders in non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) patients by resting-state functional magnetic resonance imaging (rs-fMRI) and to study whether there are some clinical biomarkers that can be used to monitor the brain dysfunction.

METHODS

Based on the rs-fMRI data of 36 non-NPSLE patients and 30 normal controls, we first obtained the regions with abnormal ALFF signals in non-NPSLE patients. Then, by taking these areas as seed regions of interest (ROIs), we calculated the FC between ROIs and the whole brain to assess the network-level alterations. Finally, we correlated the altered values of ALFF and FC in non-NPSLE patients to some clinical data.

RESULTS

Compared with the controls, non-NPSLE patients showed decreased ALFF in bilateral precuneus and increased ALFF in right cuneus and right calcarine fissure surrounding cortex (CAL). At network level, non-NPSLE patients exhibited higher FC between left precuneus and left middle occipital gyrus (MOG)/left superior occipital gyrus (SOG)/right middle frontal gyrus (MFG)/right dorsolateral superior frontal gyrus (SFGdor), and higher FC between right cuneus and bilateral precuneus/left posterior cingulate gyrus (PCG). The abnormal ALFF in right CAL and abnormal FC in right cuneus-left precuneus, right cuneus-right precuneus, and right cuneus-left PCG were correlated with the patients' certain clinical data (p < 0.05).

CONCLUSION

Rs-fMRI is a promising tool for detecting the brain function disorders in non-NPSLE patients and to help understand the neurophysiological mechanisms. C4 and Systemic Lupus Erythematosus Disease Activity Index may be biomarkers of brain dysfunction in non-NPSLE patients.

Core networks and their reconfiguration patterns across cognitive loads

Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks.