Brain Modularity: A Biomarker of Intervention-related Plasticity

Resting state brain network segregation is associated with walking speed and working memory in older adults

Older adults exhibit larger individual differences in walking ability and cognitive function than young adults. Characterizing intrinsic brain connectivity differences in older adults across a wide walking performance spectrum may provide insight into the mechanisms of functional decline in some older adults and resilience in others. Thus, the objectives of this study were to: (1) determine whether young adults and high- and low-functioning older adults show group differences in brain network segregation, and (2) determine whether network segregation is associated with working memory and walking function in these groups. The analysis included 21 young adults and 81 older adults. Older adults were further categorized according to their physical function using a standardized assessment; 54 older adults had low physical function while 27 were considered high functioning. Structural and functional resting state magnetic resonance images were collected using a Siemens Prisma 3T scanner. Working memory was assessed with the NIH Toolbox list sorting test. Walking speed was assessed with a 400 m walk test at participants' self-selected speed. We found that network segregation in mobility-related networks (sensorimotor, vestibular) was higher in older adults with higher physical function compared to older adults with lower physical function. There were no group differences in laterality effects on network segregation. We found multivariate associations between working memory and walking speed with network segregation scores. The interaction of left sensorimotor network segregation and age groups was associated with higher working memory function. Higher left sensorimotor, left vestibular, right anterior cingulate cortex, and interaction of left anterior cingulate cortex network segregation and age groups were associated with faster walking speed. These results are unique and significant because they demonstrate higher network segregation is largely related to higher physical function and not age alone.

Impaired brain ability of older adults to transit and persist to latent states with well-organized structures at wakeful rest

The intrinsic brain functional network organization continuously changes with aging. By integrating spatial and temporal information, the process of how brain networks temporally reconfigure and remain well-organized spatial structure largely reflects the brain function, thereby holds the potential to capture its age-related declines. In this study, we examined the spatiotemporal brain dynamics from resting-state functional Magnetic Resonance Imaging (fMRI) data of healthy young and older adults using a Hidden Markov Model (HMM). Six brain states were generated by HMM, with the young group showing higher fractional occupancy and mean dwell time in states 1, 3, and 4 (SY1, SY2 and SY3), and the older group in states 2, 5, and 6 (SO1, SO2 and SO3). Importantly, comparisons of transition probabilities revealed that the older group showed a reduced brain ability to transition into states dominated by the younger group, as well as a diminished capacity to persist in them. Moreover, graph analysis revealed that these young-specific states exhibited higher modularity and k-coreness. Collectively, these findings suggested that the older group showed impaired brain ability of both transition into and sustain well spatially organized states. This emphasized that the temporal changes in brain state organization, rather than its static mode, could be a key biomarker for detecting age-related functional decline. These insights may pave the way for targeted interventions aimed at mitigating cognitive decline in the aging population.

Longitudinal changes in brain connectivity correlate with neuropsychological testing in brain tumor resection patients

Background

Patients undergoing brain tumor resection experience neurological and cognitive (i.e., neurocognitive) changes reflected in altered performance on neuropsychological tests. These changes can be difficult to explain or predict. Brain connectivity, measured with neuroimaging, offers one potential model for examining these changes. In this study, we evaluated whether longitudinal changes in brain connectivity correlated with changes in neurocognitive abilities in patients before and after brain tumor resection.

Methods

Patients underwent functional and diffusion MR scanning and neuropsychological evaluation before tumor resection followed by repeat scanning and evaluation 2 weeks post-resection. Using this functional and diffusion imaging data, we measured changes in the topology of the functional and structural networks. From the neuropsychological testing scores, we derived a composite score that described a patient's overall level of neurocognitive functioning. We then used a multiple linear regression model to test whether structural and functional connectivity measures were correlated with changes in composite scores.

Results

Multiple linear regression on 21 subjects showed that functional connectivity changes were highly correlated with changes in neuropsychological evaluation scores (R2 adjusted = 0.79, p < 0.001). Changes in functional local efficiency (p < 0.001) and global efficiency (p < 0.05) were inversely correlated with changes in composite score, while changes in modularity (p < 0.01) as well as the patient's age (p < 0.05) were directly correlated with changes in composite score.

Conclusion

Short interval changes in brain functional connectivity markers were strongly correlated with changes in the composite neuropsychological test scores in brain tumor resection patients. Our findings support the need for further exploration of brain connectivity as a biomarker relevant to brain tumor patients.

Community modularity structure promotes the evolution of phase clusters and chimeralike states

Community modularity structure is widely observed across various brain scales, reflecting a balance between information processing efficiency and neural wiring metabolic efficiency. Revealing the relationship between community structure and brain function facilitates our further understanding of the brain. Here, we construct an adaptive neural network (ANN) consisting of leaky integrate-and-fire neurons with adaptivity governed by spike-time-dependent plasticity rules. The ANN demonstrates diverse dynamic collective behaviors, including traveling waves dominated by initial states, phase-cluster formations, and chimeralike states. In addition to functional clustering, ANN spontaneously organizes into community structures characterized by densely interconnected modules with sparse interconnections. Neurons within modules synchronize, while those across modules remain asynchronous, forming phase-cluster states. By encoding neural rhythms, the ANN segments into asynchronous and synchronous structural modules, leading to chimeralike states. These findings provide further evidence supporting the perspective that function emerges from structure and that structure is influenced by function in complex dynamic processes.

Resting state brain network segregation is associated with walking speed and working memory in older adults

Older adults exhibit larger individual differences in walking ability and cognitive function than young adults. Characterizing intrinsic brain connectivity differences in older adults across a wide walking performance spectrum may provide insight into the mechanisms of functional decline in some older adults and resilience in others. Thus, the objectives of this study were to: (1) determine whether young adults and high- and low-functioning older adults show group differences in brain network segregation, and (2) determine whether network segregation is associated with working memory and walking function in these groups. The analysis included 21 young adults and 81 older adults. Older adults were further categorized according to their physical function using a standardized assessment; 54 older adults had low physical function while 27 were considered high functioning. Structural and functional resting state magnetic resonance images were collected using a Siemens Prisma 3T scanner. Working memory was assessed with the NIH Toolbox list sorting test. Walking speed was assessed with a 400 m-walk test at participants' self-selected speed. We found that network segregation in mobility-related networks (sensorimotor, vestibular) was higher in older adults with higher physical function compared to older adults with lower physical function. There were no group differences in laterality effects on network segregation. We found multivariate associations between working memory and walking speed with network segregation scores. The interaction of left sensorimotor network segregation and age groups was associated with higher working memory function. Higher left sensorimotor, left vestibular, right anterior cingulate cortex, and interaction of left anterior cingulate cortex network segregation and age groups were associated with faster walking speed. These results are unique and significant because they demonstrate higher network segregation is largely related to higher physical function and not age alone.

Frequency-specific and state-dependent neural responses to brain stimulation

Non-invasive brain stimulation is promising for treating many neuropsychiatric and neurological conditions. It could be optimized by understanding its intracranial responses in different brain regions. We implanted multi-site intracranial electrodes and systematically assessed the acute responses in these regions to transcranial alternating current stimulation (tACS) at different frequencies. We observed robust neural oscillation changes in the hippocampus and amygdala in response to non-invasive tACS procedures, and these effects were frequency-specific and state-dependent. Notably, the hippocampus responded most strongly and stably to 10 Hz stimulation, with pronounced changes across a wide frequency range, suggesting the potential of 10 Hz oscillatory stimulation to modulate a broad range of neural activity related to cognitive functions. Future work with increased sample sizes is required to determine the clinical implications of these findings for therapeutic efficiency.

Stable construction and analysis of MDD modular networks based on multi-center EEG data

BACKGROUND

The modular structure can reflect the activity pattern of the brain, and exploring it may help us understand the pathogenesis of major depressive disorder (MDD). However, little is known about how to build a stable modular structure in MDD patients and how modules are separated and integrated.

METHOD

We used four independent resting state Electroencephalography (EEG) datasets. Different coupling methods, window lengths, and optimized community detection algorithms were used to find a reliable and robust modular structure, and the module differences of MDD were analyzed from the perspectives of global module attributes and local topology in multiple frequency bands.

RESULTS

The combination of the Phase Lag Index (PLI) and the Louvain algorithm can achieve better results and can achieve stability at smaller window lengths. Compared with Healthy Controls (HC), MDD had higher Modularity (Q) values and the number of modules in low-frequency bands. In addition, MDD showed significant structural changes in the frontal and parietal-occipital lobes, which were confirmed by further correlation analysis.

CONCLUSION

Our results provided a reliable validation of the modular structure construction method in MDD patients and contributed strong evidence for the changes in emotional cognition and visual system function in MDD patients from a new perspective. These results would afford valuable insights for further exploration of the pathogenesis of MDD.

Slowing Cognitive Decline in Major Depressive Disorder and Mild Cognitive Impairment: A Randomized Clinical Trial

Importance

Older adults with major depressive disorder (MDD) or mild cognitive impairment (MCI) are at high risk for cognitive decline.

Objective

To assess the efficacy of cognitive remediation (CR) plus transcranial direct current stimulation (tDCS) targeting the prefrontal cortex in slowing cognitive decline, acutely improving cognition, and reducing progression to MCI or dementia in older adults with remitted MDD (rMDD), MCI, or both.

Design, Setting, and Participants

This randomized clinical trial was conducted at 5 academic hospitals in Toronto, Ontario, Canada. Participants were older adults who had rMDD (with or without MCI, age ≥65 y) or MCI without rMDD (age ≥60 y). Assessments were made at baseline, month 2, and yearly from baseline for 3 to 7 years.

Interventions

CR plus tDCS (hereafter, active) or sham plus sham 5 days a week for 8 weeks followed by twice-a-year 5-day boosters and daily at-home CR or sham CR.

Main Outcomes and Measures

The primary outcome was change in global composite cognitive score. Secondary outcomes included changes in 6 cognitive domains, moderating effect of the diagnosis, moderating effect of APOE ε4 status, change in composite score at month 2, and progression to MCI or dementia over time.

Results

Of 486 older adults who provided consent, 375 (with rMDD, MCI, or both) received at least 1 intervention session (mean [SD] age, 72.2 [6.4] years; 232 women [62%] and 143 men [38%]). Over a median follow-up of 48.3 months (range, 2.1-85.9), CR and tDCS slowed cognitive decline in older adults with rMDD or MCI (adjusted z score difference [active - sham] at month 60, 0.21; 95% CI, 0.07 to 0.35; likelihood ratio test [LRT] P = .006). In the preplanned primary analysis, CR and tDCS did not improve cognition acutely (adjusted z score difference [active - sham] at month 2, 0.06, 95% CI, -0.006 to 0.12). Similarly, the effect of CR and tDCS on delaying progression from normal cognition to MCI or MCI to dementia was weak and not significant (hazard ratio, 0.66; 95% CI, 0.40 to 1.08; P = .10). Preplanned analyses showed treatment effects for executive function (LRT P = .04) and verbal memory (LRT P = .02) and interactions with diagnosis (P = .01) and APOE ε4 (P < .001) demonstrating a larger effect among those with rMDD and in noncarriers of APOE ε4.

Conclusions and Relevance

The study showed that CR and tDCS, both targeting the prefrontal cortex, is efficacious in slowing cognitive decline in older adults at risk of cognitive decline, particularly those with rMDD (with or without MCI) and in those at low genetic risk for Alzheimer disease.

Trial Registration

ClinicalTrials.gov Identifier: NCT02386670.

Regression and alignment for functional data and network topology

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

KETAMINE: Neural- and network-level changes

Ketamine is a widely used clinical drug that has several functional and clinical applications, including its use as an anaesthetic, analgesic, anti-depressive, anti-suicidal agent, among others. Among its diverse behavioral effects, it influences short-term memory and induces psychedelic effects. At the neural level across different brain areas, it modulates neural firing rates, neural tuning, brain oscillations, and modularity, while promoting hypersynchrony and random connectivity between neurons. In our recent studies we demonstrated that topical application of ketamine on the visual cortex alters neural tuning and promotes vigorous connectivity between neurons by decreasing their firing variability. Here, we begin with a brief review of the literature, followed by results from our lab, where we synthesize a dendritic model of neural tuning and network changes following ketamine application. This model has potential implications for focused modulation of cortical networks in clinical settings. Finally, we identify current gaps in research and suggest directions for future studies, particularly emphasizing the need for more animal experiments to establish a platform for effective translation and synergistic therapies combining ketamine with other protocols such as training and adaptation. In summary, investigating ketamine's broader systemic effects, not only provides deeper insight into cognitive functions and consciousness but also paves the way to advance therapies for neuropsychiatric disorders.

Brain network correlates of affective symptoms in aMCI

Affective symptoms (i.e., depression, anxiety, and apathy) are the most prevalent subsyndrome of neuropsychiatric symptoms (NPS) in preclinical dementia, such as amnestic mild cognitive impairment (aMCI), and remain a challenge to understand and treat. The distressing nature of these symptoms and complexity of their concurrence and interaction necessitates improved understanding of their underlying neural correlates. We analyzed the relationships between functional brain topology (i.e., the way the brain's functional network is organized to allow efficient communication between regions) and affective symptoms in aMCI using cross-sectional and longitudinal methods. The analyses demonstrated that increased clustering coefficient (CC) was related to lower baseline and greater decreases in affective symptoms, while higher participation coefficient (PC) was correlated with more severe baseline affective symptoms. These findings suggest that the brain losing the capacity to form segregated functional units may be related to prevalence and severity of affective symptoms seen in aMCI.

Enhanced efficiency in the bilingual brain through the inter-hemispheric cortico-cerebellar pathway in early second language acquisition

Bilingualism has a profound impact on the structure and function of the brain, but it is not yet well understood how this experience influences brain functional organization. We examine a large sample (151 participants) of monolinguals and bilinguals with varied age of second language acquisition, who underwent resting-state functional magnetic brain imaging. Whole-brain network analyses reveal higher global efficiency in bilingual individuals than monolinguals, indicating enhanced functional integration in the bilingual brain. Moreover, the age at which the second language was acquired correlated with this increased efficiency, suggesting that earlier exposure to a second language has lasting positive effects on brain functional organization. Further investigation using the network-based statistics approach indicates that this effect is primarily driven by heightened functional connectivity between association networks and the cerebellum. These findings show that the timing of bilingual learning experience alters the brain functional organization at both global and local levels.

Sex differences in neural networks recruited by frontloaded binge alcohol drinking

Frontloading is an alcohol drinking pattern where intake is skewed towards the onset of access. This study aimed to identify brain regions involved in frontloading. Whole brain imaging was performed in 63 C57Bl/6J (32 female, 31 male) mice that underwent 8 days of binge drinking using drinking-in-the-dark (DID). On Days 1-7 mice received 20% (v/v) alcohol or water for 2 h. Intake was measured in 1-min bins using volumetric sippers. On Day 8 mice were perfused 80 min into the DID session and brains were extracted. Brains were processed to stain for Fos protein using iDISCO+. Following light sheet imaging, ClearMap2.1 was used to register brains to the Allen Brain Atlas and detect Fos+ cells. For network analyses, Day 8 drinking patterns were used to characterize mice as frontloaders or non-frontloaders using a change-point analysis. Functional correlation matrices were calculated for each group from log10 Fos values. Euclidean distances were calculated from these R values and clustering was used to determine modules (highly connected groups of brain regions). In males, alcohol access decreased modularity (three modules in both frontloaders and non-frontloaders) as compared to water (seven modules). In females, an opposite effect was observed. Alcohol access (nine modules for frontloaders) increased modularity as compared to water (five modules). Further, different brain regions served as hubs in frontloaders as compared to control groups. In conclusion, alcohol consumption led to fewer, but more densely connected, groups of brain regions in males but not females and we identify several brain-wide signatures of frontloading.

Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.

A measure of reliability convergence to select and optimize cognitive tasks for individual differences research

Surging interest in individual differences has faced setbacks in light of recent replication crises in psychology, for example in brain-wide association studies exploring brain-behavior correlations. A crucial component of replicability for individual differences studies, which is often assumed but not directly tested, is the reliability of the measures we use. Here, we evaluate the reliability of different cognitive tasks on a dataset with over 250 participants, who each completed a multi-day task battery. We show how reliability improves as a function of number of trials, and describe the convergence of the reliability curves for the different tasks, allowing us to score tasks according to their suitability for studies of individual differences. We further show the effect on reliability of measuring over multiple time points, with tasks assessing different cognitive domains being differentially affected. Data collected over more than one session may be required to achieve trait-like stability.

Edge-Community Entropy Is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition

Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogs of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old, 55.7% female), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. At the whole-brain level, global entropy moderated declines in fluid cognition across the lifespan and uniquely covaried with age when controlling for the network segregation metric modularity. Importantly, relationships between both metrics (entropy and modularity) and fluid cognition were age dependent, although entropy's relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.

Dynamic Reconfiguration of Brain Functional Network in Stroke

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer temporal network method, a dynamic modular structure was recognized based on a time-resolved function network. The dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, revealing the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It's also noted that previous studies using static methods could not reveal this severity-dependent alteration in network interaction. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain neuronal images could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The present method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction.

Resting-State Functional Connectivity Change in Frontoparietal and Default Mode Networks After Acute Exercise in Youth

BACKGROUND

A single bout of aerobic exercise can provide acute benefits to cognition and emotion in children. Yet, little is known about how acute exercise may impact children's underlying brain networks' resting-state functional connectivity (rsFC).

OBJECTIVE

Using a data-driven multivariate pattern analysis, we investigated the effects of a single dose of exercise on acute rsFC changes in 9-to-13-year-olds.

METHODS

On separate days in a crossover design, participants (N = 21) completed 20-mins of acute treadmill walking at 65-75% heart rate maximum (exercise condition) and seated reading (control condition), with pre- and post-fMRI scans. Multivariate pattern analysis was used to investigate rsFC change between conditions.

RESULTS

Three clusters in the left lateral prefrontal cortex (lPFC) of the frontoparietal network (FPN) had significantly different rsFC after the exercise condition compared to the control condition. Post-hoc analyses revealed that from before to after acute exercise, activity of these FPN clusters became more correlated with bilateral lPFC and the left basal ganglia. Additionally, the left lPFC became more anti-correlated with the precuneus of the default mode network (DMN). An opposite pattern was observed from before to after seated reading.

CONCLUSIONS

The findings suggest that a single dose of exercise increases connectivity within the FPN, FPN integration with subcortical regions involved in movement and cognition, and segregation of FPN and DMN. Such patterns, often associated with healthier cognitive and emotional control, may underlie the transient mental benefits observed following acute exercise in youth.

A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease

Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity

This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across three distinct task modes (eyes open, eyes closed, and SSVEP stimulation) and two neuromodulation patterns (sham-tDCS and anodal-tDCS). Brain arousal and brain functional connectivity were measured by extracting features of fractal EEG and information flow gain, respectively. Anodal-tDCS led to diminished offsets and enhanced information flow gains, indicating improvements in both brain arousal and brain information transmission capacity. Additionally, anodal-tDCS markedly enhanced SSVEP-BCIs performance as evidenced by increased amplitudes and accuracies, whereas sham-tDCS exhibited lesser efficacy. This study proffers invaluable insights into the application of neuromodulation methods for bolstering BCI performance, and concurrently authenticates two potent electrophysiological markers for multifaceted characterization of brain states.

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.

Community detection in the human connectome: Method types, differences and their impact on inference

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Hierarchical consciousness: the Nested Observer Windows model

Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.

The Impact of Exercise Prescription Variables on Intervention Outcomes in Musculoskeletal Pain: An Umbrella Review of Systematic Reviews

BACKGROUND

Musculoskeletal pain conditions are the largest contributors to disability and healthcare burden globally. Exercise interventions improve physical function and quality of life in individuals with musculoskeletal pain, yet optimal exercise prescription variables (e.g. duration, frequency, intensity) are unclear.

OBJECTIVE

We aimed to examine evidence gaps, methodological quality and exercise prescription recommendations in systematic reviews of exercise for musculoskeletal pain.

METHODS

In our prospectively registered umbrella review, PubMed, SPORTDiscus, Cochrane Database of Systematic Reviews, EMBASE, and CINAHL were searched from inception to 14 February 2023. Backward citation tracking was performed. We included peer-reviewed, English language, systematic reviews and meta-analyses of randomized controlled trials (RCTs) and controlled clinical trials (CCTs) that compared exercise with conservative treatment, placebo or other exercise interventions in adults with musculoskeletal pain. Data were extracted from the following groups of reviews based on their reporting of exercise prescription data and analysis of the relationship between prescription variables and outcomes: (1) those that did not report any exercise prescription data, (2) those that reported exercise prescription data but did not perform a quantitative analysis and (3) those that performed a quantitative analysis of the relationship between exercise prescription variables and outcomes. Outcome measures were physical function, pain, mental health, adverse effects and adherence to treatment. AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews) was used to assess methodological quality.

RESULTS

From 6757 records, 274 systematic reviews were included. 6.6% of reviews did not report any exercise prescription data, and only 10.9% quantitatively analyzed the relationship between prescription variables and the outcome(s). The overall methodological quality was critically low in 85% of reviews.

CONCLUSION

High methodological quality evidence is lacking for optimal exercise training prescription variables in individuals with musculoskeletal pain. To better inform practice and evidence gaps, future systematic reviews should (1) identify optimum exercise prescription variables, for example, via dose-response (network) meta-analysis, (2) perform high-quality reviews per AMSTAR-2 criteria and (3) include outcomes of mental health, adverse events and exercise adherence.

PROSPERO REGISTRATION NUMBER

CRD42021287440 ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021287440 ).

Regression and Alignment for Functional Data and Network Topology

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Sex Differences in Neural Networks Recruited by Frontloaded Binge Alcohol Drinking

Frontloading is an alcohol drinking pattern where intake is skewed toward the onset of access. The goal of the current study was to identify brain regions involved in frontloading. Whole brain imaging was performed in 63 C57Bl/6J (32 female and 31 male) mice that underwent 8 days of binge drinking using the drinking-in-the-dark (DID) model. On days 1-7, three hours into the dark cycle, mice received 20% (v/v) alcohol or water for two hours. Intake was measured in 1-minute bins using volumetric sippers, which facilitated analyses of drinking patterns. On day 8 mice were perfused 80 minutes into the DID session and brains were extracted. Brains were then processed to stain for Fos protein using iDISCO+. Following light sheet imaging, ClearMap2.1 was used to register brains to the Allen Brain Atlas and detect Fos+ cells. For brain network analyses, day 8 drinking patterns were used to characterize mice as frontloaders or non-frontloaders using a recently developed change-point analysis. Based on this analysis the groups were female frontloaders (n = 20), female non-frontloaders (n = 2), male frontloaders (n = 13) and male non-frontloaders (n = 8). There were no differences in total alcohol intake in animals that frontloaded versus those that did not. Only two female mice were characterized as non-frontloaders, thus preventing brain network analysis of this group. Functional correlation matrices were calculated for each group from log10 Fos values. Euclidean distances were calculated from these R values and hierarchical clustering was used to determine modules (highly connected groups of brain regions). In males, alcohol access decreased modularity (3 modules in both frontloaders and non-frontloaders) as compared to water drinkers (7 modules). In females, an opposite effect was observed. Alcohol access (9 modules for frontloaders) increased modularity as compared to water drinkers (5 modules). These results suggest sex differences in how alcohol consumption reorganizes the functional architecture of neural networks. Next, key brain regions in each network were identified. Connector hubs, which primarily facilitate communication between modules, and provincial hubs, which facilitate communication within modules, were of specific interest for their important and differing roles. In males, 4 connector hubs and 17 provincial hubs were uniquely identified in frontloaders (i.e., were brain regions that did not have this status in male non-frontloaders or water drinkers). These represented a group of hindbrain regions (e.g., locus coeruleus and the pontine gray) functionally connected to striatal/cortical regions (e.g., cortical amygdalar area) by the paraventricular nucleus of the thalamus. In females, 16 connector and 17 provincial hubs were uniquely identified which were distributed across 8 of the 9 modules in the female frontloader alcohol drinker network. Only one brain region (the nucleus raphe pontis) was a connector hub in both sexes, suggesting that frontloading in males and females may be driven by different brain regions. In conclusion, alcohol consumption led to fewer, but more densely connected, groups of brain regions in males but not females, and recruited different hub brain regions between the sexes. These results suggest that alcohol frontloading leads to a reduction in network efficiency in male mice.

Toward precision brain health: accurate prediction of a cognitive index trajectory using neuroimaging metrics

The goal of precision brain health is to accurately predict individuals' longitudinal patterns of brain change. We trained a machine learning model to predict changes in a cognitive index of brain health from neurophysiologic metrics. A total of 48 participants (ages 21-65) completed a sensorimotor task during 2 functional magnetic resonance imaging sessions 6 mo apart. Hemodynamic response functions (HRFs) were parameterized using traditional (amplitude, dispersion, latency) and novel (curvature, canonicality) metrics, serving as inputs to a neural network model that predicted gain on indices of brain health (cognitive factor scores) for each participant. The optimal neural network model successfully predicted substantial gain on the cognitive index of brain health with 90% accuracy (determined by 5-fold cross-validation) from 3 HRF parameters: amplitude change, dispersion change, and similarity to a canonical HRF shape at baseline. For individuals with canonical baseline HRFs, substantial gain in the index is overwhelmingly predicted by decreases in HRF amplitude. For individuals with non-canonical baseline HRFs, substantial gain in the index is predicted by congruent changes in both HRF amplitude and dispersion. Our results illustrate that neuroimaging measures can track cognitive indices in healthy states, and that machine learning approaches using novel metrics take important steps toward precision brain health.

Modular brain network in volitional eyes closing: enhanced integration with a marked impact on hubs

Volitional eyes closing would shift brain's information processing modes from the "exteroceptive" to "interoceptive" state. This transition induced by the eyes closing is underpinned by a large-scale reconfiguration of brain network, which is still not fully comprehended. Here, we investigated the eyes-closing-relevant network reconfiguration by examining the functional integration among intrinsic modules. Our investigation utilized a publicly available dataset with 48 subjects being scanned in both eyes closed and eyes open conditions. It was found that the modular integration was significantly enhanced during the eyes closing, including lower modularity index, higher participation coefficient, less provincial hubs, and more connector hubs. Moreover, the eyes-closing-enhanced integration was particularly noticeable in the hubs of network, mainly located in the default-mode network. Finally, the hub-dominant modular enhancement was positively correlated to the eyes-closing-reduced entropy of BOLD signal, suggesting a close connection to the diminished consciousness of individuals. Collectively, our findings strongly suggested that the enhanced modular integration with substantially reorganized hubs characterized the large-scale cortical underpinning of the volitional eyes closing.

[A comparative assessment of neurochemical and neurophysiological parameters of cardiac surgery patients who underwent different versions of multitasking cognitive training]

OBJECTIVE

To compare biomarkers of neurovascular unit (NVU) - S100β, NSE, BDNF and indicators of the brain electrical activity in patients who underwent coronary artery bypass grafting (CABG) depending on the use of different versions of multi-tasking cognitive training (CT).

MATERIAL AND METHODS

The study included 89 people, of whom 47 completed the CTI (postural and three cognitive tasks (counting backwards, verbal fluency and the open-ended task «Unusual use of an ordinary object») and 42 patients, who underwent CTII (visuomotor reaction and the same cognitive tasks) in the early postoperative CABG period. The patients of both groups underwent complex testing of psychomotor, executive functions, attention, short-term memory and EEG study in the perioperative period of CABG. Concentrations of NVU markers in peripheral blood serum were also analyzed.

RESULTS

The highest values of S100β protein concentration in both patients with CTI and CTII were observed on the 1st-2nd days after CABG, followed by a significant decrease on the 10th-12th days to preoperative values only in the CTII group. Also, during CTI, low concentrations of S100β and NSE protein were associated with higher indicators of cognitive status, such as short-term memory and general integral index. In patients with CTI, the concentration of BDNF on days 10-12 of CABG was significantly higher compared to patients with CTII, and its higher levels were associated with higher levels of attention. Only if the training was successful, the patients with CTI had greater preoperative levels of EEG alpha-1 and alpha-2 activity compared to patients with CTII.

CONCLUSION

The version of multitasking CT using the postural motor component more actively triggered the processes of neuroplasticity due to the expression of BDNF and its success was due to the greater presence of alpha-activity in the preoperative period of CABG. Further research is needed to study the neurophysiological mechanisms of recovery of cognitive functions after cardiac surgery.

Benefits of a targeted rehabilitation of number transcoding in secondary acalculia: A single-case experimental design

BACKGROUND

Despite its potentially significant functional and emotional impact, acalculia is still too rarely assessed and managed by speech and language therapists. Research on the rehabilitation of numerical transcoding remains scarce in the literature and, despite positive results, presents a low level of evidence.

AIMS

The present study aims to evaluate the effectiveness of a targeted rehabilitation of numerical transcoding in two patients suffering from a chronic secondary acalculia.

METHODS & PROCEDURES

Two post-brain injury females with secondary acalculia took part in a single-case experimental design with multiple baseline across subjects according to a three-phase experimental protocol: baseline involving global cognitive rehabilitation (5-7 measurements with randomized sequential introduction); targeted intervention (10 measurements); follow-up (2 immediate measurements and 1 month after the end of the intervention). Repeated outcome measures consisted of six lists composed of numbers of equivalent difficulty that were used alternately to assess numerical transcoding. We used a reverse digit span as a control measure to assess the specificity of the intervention. Rehabilitation lasted 5 weeks and consisted of errorless learning with colour cues, tables and number-words cards.

OUTCOMES & RESULTS

During baseline period involving global cognitive rehabilitation, transcoding scores remained unchanged. In contrast, there was a significant improvement in scores for both patients during the intervention phase targeting transcoding and maintenance of benefits 1-month post-intervention.

CONCLUSIONS & IMPLICATIONS

This study demonstrates that a specific rehabilitation targeting numerical transcoding following chronic secondary acalculia can be effective in improving transcoding skills.

WHAT THIS PAPER ADDS

What is already known on the subject Transcoding difficulties in patients with acalculia can cause a significant disability in everyday life activities. In secondary acalculia, rehabilitation of cognitive functions associated with number processing (attention, working memory, language) is not sufficient for improvement of transcoding. What this paper adds to existing knowledge An intervention specifically targeting numerical transcoding significantly and durably improves the skills of patients with chronic secondary acalculia. What are the potential or actual clinical implications of this work? Procedural error-free intervention using colour cueing, tables, cards with number-words, copy and repetition seems effective to improve transcoding skills in chronic acalculia.

Cognition's dependence on functional network integrity with age is conditional on structural network integrity

Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain's structural connectivity.

Functional network structure supports resilience to memory deficits in cognitively normal older adults with amyloid-β pathology

Older adults may harbor large amounts of amyloid-β (Aβ) pathology, yet still perform at age-normal levels on memory assessments. We tested whether functional brain networks confer resilience or compensatory mechanisms to support memory in the face of Aβ pathology. Sixty-five cognitively normal older adults received high-resolution resting state fMRI to assess functional networks, 18F-florbetapir-PET to measure Aβ, and a memory assessment. We characterized functional networks with graph metrics of local efficiency (information transfer), modularity (specialization of functional modules), and small worldness (balance of integration and segregation). There was no difference in functional network measures between older adults with high Aβ (Aβ+) compared to those with no/low Aβ (Aβ-). However, in Aβ+ older adults, increased local efficiency, modularity, and small worldness were associated with better memory performance, while this relationship did not occur Aβ- older adults. Further, the association between increased local efficiency and better memory performance in Aβ+ older adults was localized to local efficiency of the default mode network and hippocampus, regions vulnerable to Aβ and involved in memory processing. Our results suggest functional networks with modular and efficient structures are associated with resilience to Aβ pathology, providing a functional target for intervention.

Improving comparability across cognitive training trials for brain aging: A focus on interoperability

Cognitive training may promote healthy brain aging and prevent dementia, but results from individual studies are inconsistent. There are disagreements on how to evaluate cognitive training interventions between clinical and basic scientists. Individual labs typically create their own assessment and training materials, leading to difficulties reproducing methods. Here, we advocate for improved interoperability: the exchange and cooperative development of a consensus for cognitive training design, analysis, and result interpretation. We outline five guiding principles for improving interoperability: (i) design interoperability, developing standard design and analysis models; (ii) material interoperability, promoting sharing of materials; (iii) interoperability incentives; (iv) privacy and security norms, ensuring adherence to accepted ethical standards; and (v) interpretability prioritization, encouraging a shared focus on neurobiological mechanisms to improve clinical relevance. Improving interoperability will allow us to develop scientifically optimized, clinically useful cognitive training programs to slow/prevent brain aging.

HIGHLIGHTS

Interoperability facilitates progress via resource sharing and comparability.Better interoperability is needed in cognitive training for brain aging research.We adapt an interoperability framework to cognitive training research.We suggest five guiding principles for improved interoperability.We propose an open-source pipeline to facilitate interoperability.

Exploring neural heterogeneity in inattention and hyperactivity

Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability-a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge through multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification

The idea that brain networks have a hierarchical modular organization is pervasive. Increasing evidence suggests that brain modules overlap. However, little is known about the hierarchical overlapping modular structure in the brain. In this study, we developed a framework to uncover brain hierarchical overlapping modular structures based on a nested-spectral partition algorithm and an edge-centric network model. Overlap degree between brain modules is symmetrical across hemispheres, with highest overlap observed in the control and salience/ventral attention networks. Furthermore, brain edges are clustered into two groups: intrasystem and intersystem edges, to form hierarchical overlapping modules. At different levels, modules are self-similar in the degree of overlap. Additionally, the brain's hierarchical structure contains more individual identifiable information than a single-level structure, particularly in the control and salience/ventral attention networks. Our results offer pathways for future studies aimed at relating the organization of hierarchical overlapping modules to brain cognitive behavior and disorders.

Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation

In the last decade, the exclusive role of the hippocampus in human declarative learning has been challenged. Recently, we have shown that gains in performance observed in motor sequence learning (MSL) during the quiet rest periods interleaved with practice are associated with increased hippocampal activity, suggesting a role of this structure in motor memory reactivation. Yet, skill also develops offline as memory stabilizes after training and overnight. To examine whether the hippocampus contributes to motor sequence memory consolidation, here we used a network neuroscience strategy to track its functional connectivity offline 30 min and 24 h post learning using resting-state functional magnetic resonance imaging. Using a graph-analytical approach we found that MSL transiently increased network modularity, reflected in an increment in local information processing at 30 min that returned to baseline at 24 h. Within the same time window, MSL decreased the connectivity of a hippocampal-sensorimotor network, and increased the connectivity of a striatal-premotor network in an antagonistic manner. Finally, a supervised classification identified a low-dimensional pattern of hippocampal connectivity that discriminated between control and MSL data with high accuracy. The fact that changes in hippocampal connectivity were detected shortly after training supports a relevant role of the hippocampus in early stages of motor memory consolidation.

Evaluating brain modularity benefits of an acting intervention: a discriminant-analysis framework

Purpose

Aging is associated with a reduction in brain modularity as well as aspects of executive function, namely, updating, shifting, and inhibition. Previous research has suggested that the aging brain exhibits plasticity. Further, it has been hypothesized that broad-based intervention models may be more effective in eliciting overall gains in executive function than interventions targeted at specific executive skills (e.g., computer-based training). To this end, we designed a 4-week theater-based acting intervention in older adults within an RCT framework. We hypothesized that older adults would show improvements in brain modularity and aspects of executive function, ascribed to the acting intervention.

Materials and methods

The participants were 179 adults from the community, aged 60-89 years and on average, college educated. They completed a battery of executive function tasks and resting state functional MRI scans to measure brain network modularity pre- and post-intervention. Participants in the active intervention group (n = 93) enacted scenes with a partner that involved executive function, whereas the active control group (n = 86) learned about the history and styles of acting. Both groups met two times/week for 75-min for 4 weeks. A mixed model was used to evaluate intervention effects related to brain modularity. Discriminant-analysis was used to determine the role of seven executive functioning tasks in discriminating the two groups. These tasks indexed subdomains of updating, switching, and inhibition. Discriminant tasks were subject to a logistic regression analysis to determine how post-intervention executive function performance interacted with changes in modularity to predict group membership.

Results

We noted an increase in brain modularity in the acting group, relative to pre-intervention and controls. Performance on updating tasks were representative of the intervention group. However, post-intervention performance on updating did not interact with the observed increase in brain modularity to distinguish groups.

Conclusion

An acting intervention can facilitate improvements in modularity and updating, both of which are sensitive to aging and may confer benefits to daily functioning and the ability to learn.

Canalization and plasticity in psychopathology

This theoretical article revives a classical bridging construct, canalization, to describe a new model of a general factor of psychopathology. To achieve this, we have distinguished between two types of plasticity, an early one that we call 'TEMP' for 'Temperature or Entropy Mediated Plasticity', and another, we call 'canalization', which is close to Hebbian plasticity. These two forms of plasticity can be most easily distinguished by their relationship to 'precision' or inverse variance; TEMP relates to increased model variance or decreased precision, whereas the opposite is true for canalization. TEMP also subsumes increased learning rate, (Ising) temperature and entropy. Dictionary definitions of 'plasticity' describe it as the property of being easily shaped or molded; TEMP is the better match for this. Importantly, we propose that 'pathological' phenotypes develop via mechanisms of canalization or increased model precision, as a defensive response to adversity and associated distress or dysphoria. Our model states that canalization entrenches in psychopathology, narrowing the phenotypic state-space as the agent develops expertise in their pathology. We suggest that TEMP - combined with gently guiding psychological support - can counter canalization. We address questions of whether and when canalization is adaptive versus maladaptive, furnish our model with references to basic and human neuroscience, and offer concrete experiments and measures to test its main hypotheses and implications. This article is part of the Special Issue on "National Institutes of Health Psilocybin Research Speaker Series".

Global brain c-Fos profiling reveals major functional brain networks rearrangements after alcohol reexposure

Many fundamental questions on alcohol use disorder (AUD) are frequently difficult to address by examining a single brain structure, but should be viewed from the whole brain perspective. c-Fos is a marker of neuronal activation. Global brain c-Fos profiling in rodents represents a promising platform to study brain functional networks rearrangements in AUD. We used a mouse model of alcohol drinking in IntelliCage. We trained mice to voluntarily drink alcohol, next subjected them to withdrawal and alcohol reexposure. We have developed a dedicated image computational workflow to identify c-Fos-positive cells in three-dimensional images obtained after whole-brain optical clearing and imaging in the light-sheet microscope. We provide a complete list of 169 brain structures with annotated c-Fos expression. We analyzed functional networks, brain modularity and engram index. Brain c-Fos levels in animals reexposed to alcohol were different from both control and binge drinking animals. Structures involved in reward processing, decision making and characteristic for addictive behaviors, such as precommissural nucleus, nucleus Raphe, parts of colliculus and tecta stood out particularly. Alcohol reexposure leads to a massive change of brain modularity including a formation of numerous smaller functional modules grouping structures involved in addiction development. Binge drinking can lead to substantial functional remodeling in the brain. We provide a list of structures that can be used as a target in pharmacotherapy but also point to the networks and modules that can hold therapeutic potential demonstrated by a clinical trial in patients.

Whole-brain tracking of cocaine and sugar rewards processing

Natural rewards, such as food, and sex are appetitive stimuli available for animals in their natural environment. Similarly, addictive rewards such as drugs of abuse possess strong, positive valence, but their action relies on their pharmacological properties. Nevertheless, it is believed that both of these kinds of rewards activate similar brain circuitry. The present study aimed to discover which parts of the brain process the experience of natural and addictive rewards. To holistically address this question, we used a single-cell whole-brain imaging approach to find patterns of activation for acute and prolonged sucrose and cocaine exposure. We analyzed almost 400 brain structures and created a brain-wide map of specific, c-Fos-positive neurons engaged by these rewards. Acute but not prolonged sucrose exposure triggered a massive c-Fos expression throughout the brain. Cocaine exposure on the other hand potentiated c-Fos expression with prolonged use, engaging more structures than sucrose treatment. The functional connectivity analysis unraveled an increase in brain modularity after the initial exposure to both types of rewards. This modularity was increased after repeated cocaine, but not sucrose, intake. To check whether discrepancies between the processing of both types of rewards can be found on a cellular level, we further studied the nucleus accumbens, one of the most strongly activated brain structures by both sucrose and cocaine experience. We found a high overlap between natural and addictive rewards on the level of c-Fos expression. Electrophysiological measurements of cellular correlates of synaptic plasticity revealed that natural and addictive rewards alike induce the accumulation of silent synapses. These results strengthen the hypothesis that in the nucleus accumbens drugs of abuse cause maladaptive neuronal plasticity in the circuitry that typically processes natural rewards.

Evaluating the treatment outcomes of repetitive transcranial magnetic stimulation in patients with moderate-to-severe Alzheimer's disease

The repetitive transcranial magnetic stimulation (rTMS) shows great potential in the treatment of Alzheimer's disease (AD). However, its treatment efficacy for AD patients in moderate to severe stage is relatively evaluated. Here, we proposed a randomized, sham-controlled, clinical trial of rTMS among 35 moderate-to-severe AD patients. A high frequency (10 Hz) stimulation of the left dorsal lateral prefrontal cortex (DLPFC), 60-session long treatment lasting for 3 months procedure was adopted in the trial. Each participant completed a battery of neuropsychological tests at baseline and post-treatment for evaluation of the rTMS therapeutic effect. Twelve of them completed baseline resting-state functional magnetic resonance imaging (fMRI) for exploration of the underlying neural contribution to individual difference in treatment outcomes. The result showed that the rTMS treatment significantly improved cognitive performance on the severe impairment battery (SIB), reduced psychiatric symptoms on the neuropsychiatric inventory (NPI), and improved the clinician's global impression of change (CIBIC-Plus). Furthermore, the result preliminarily proposed resting-state multivariate functional connectivity in the (para) hippocampal region as well as two clusters in the frontal and occipital cortices as a pre-treatment neuroimaging marker for predicting individual differences in treatment outcomes. The finding could brought some enlightenment and reference for the rTMS treatment of moderate and severe AD patients.

Taking Sides: Asymmetries in the Evolution of Human Brain Development in Better Understanding Autism Spectrum Disorder

Confirmation from structural, functional, and behavioral studies agree and suggest a configuration of atypical lateralization in individuals with autistic spectrum disorders (ASD). It is suggested that patterns of cortical and behavioral atypicality are evident in individuals with ASDs with atypical lateralization being common in individuals with ASDs. The paper endeavors to better understand the relationship between alterations in typical cortical asymmetries and functional lateralization in ASD in evolutionary terms. We have proposed that both early genetic and/or environmental influences can alter the developmental process of cortical lateralization. There invariably is a “chicken or egg” issue that arises whether atypical cortical anatomy associated with abnormal function, or alternatively whether functional atypicality generates abnormal structure.

Reorganization of the functional connectome from rest to a visual perception task in schizophrenia and bipolar disorder

Functional connectome organization is altered in schizophrenia (SZ) and bipolar disorder (BD). However, it remains unclear whether network reorganization during a task relative to rest is also altered in these disorders. This study examined connectome organization in patients with SZ (N = 43) and BD (N = 42) versus healthy controls (HC; N = 39) using fMRI data during a visual object-perception task and at rest. Graph analyses were conducted for the whole-brain network using indices selected a priori: three reflecting network segregation (clustering coefficient, local efficiency, modularity), two reflecting integration (characteristic path length, global efficiency). Group differences were limited to network segregation and were more evident in SZ (clustering coefficient, modularity) than in BD (clustering coefficient) compared to HC. State differences were found across groups for segregation (local efficiency) and integration (characteristic path length). There was no group-by-state interaction for any graph index. In summary, aberrant network organization compared to HC was confirmed, and was more evident in SZ than in BD. Yet, reorganization was largely intact in both disorders. These findings help to constrain models of dysconnection in SZ and BD, suggesting that the extent of functional dysconnectivity in these disorders tends to persist across changes in mental state.

Plasticity impairment alters community structure but permits successful pattern separation in a hippocampal network model

Patients who suffer from traumatic brain injury (TBI) often complain of learning and memory problems. Their symptoms are principally mediated by the hippocampus and the ability to adapt to stimulus, also known as neural plasticity. Therefore, one plausible injury mechanism is plasticity impairment, which currently lacks comprehensive investigation across TBI research. For these studies, we used a computational network model of the hippocampus that includes the dentate gyrus, CA3, and CA1 with neuron-scale resolution. We simulated mild injury through weakened spike-timing-dependent plasticity (STDP), which modulates synaptic weights according to causal spike timing. In preliminary work, we found functional deficits consisting of decreased firing rate and broadband power in areas CA3 and CA1 after STDP impairment. To address structural changes with these studies, we applied modularity analysis to evaluate how STDP impairment modifies community structure in the hippocampal network. We also studied the emergent function of network-based learning and found that impaired networks could acquire conditioned responses after training, but the magnitude of the response was significantly lower. Furthermore, we examined pattern separation, a prerequisite of learning, by entraining two overlapping patterns. Contrary to our initial hypothesis, impaired networks did not exhibit deficits in pattern separation with either population- or rate-based coding. Collectively, these results demonstrate how a mechanism of injury that operates at the synapse regulates circuit function.

Unifying framework for cognitive training interventions in brain aging

Cognitive training is a promising tool for slowing or preventing cognitive decline in older adults at-risk for dementia. Its success, however, has been limited by a lack of evidence showing that it reliably causes broad training effects: improvements in cognition across a range of domains that lead to real-world benefits. Here, we propose a framework for enhancing the effect of cognitive training interventions in brain aging. The focus is on (A) developing cognitive training task paradigms that are informed by population-level cognitive characteristics and pathophysiology, and (B) personalizing how these sets are presented to participants during training via feedback loops that aim to optimize "mismatch" between participant capacity and training demands using both adaptation and random variability. In this way, cognitive training can better alter whole-brain topology in a manner that supports broad training effects in the context of brain aging.

Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study

Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities ("modules") during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients' recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.

Reduced Modular Segregation of White Matter Brain Networks in Attention Deficit Hyperactivity Disorder

OBJECTIVE

Despite studies reporting alterations in the brain networks of patients with ADHD, alterations in the modularity of white matter (WM) networks are still unclear.

METHOD

Based on the results of module division by generalized Louvain algorithm, the modularity of ADHD was evaluated. The correlation between the modular changes of ADHD and its clinical characteristics was analyzed.

RESULTS

The participation coefficient and the connectivity between modules of ADHD increased, and the modularity coefficient decreased. Provincial hubs of ADHD did not change, and the number of connector hubs increased. All results showed that the modular segregation of WM networks of ADHD decreased. Modules with reduced modular segregation are mainly responsible for language and motor functions. Moreover, modularity showed evident correlation with the symptoms of ADHD.

CONCLUSION

The modularity changes in WM network provided a novel insight into the understanding of brain cognitive alterations in ADHD.