GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity

The pharmacodynamic modulation effect of oxytocin on resting state functional connectivity network topology

Introduction

Neuroimaging studies have demonstrated that intranasal oxytocin has extensive effects on the resting state functional connectivity of social and emotional processing networks and may have therapeutic potential. However, the extent to which intranasal oxytocin modulates functional connectivity network topology remains less explored, with inconsistent findings in the existing literature. To address this gap, we conducted an exploratory data-driven study.

Methods

We recruited 142 healthy males and administered 24 IU of intranasal oxytocin or placebo in a randomized controlled double-blind design. Resting-state functional MRI data were acquired for each subject. Network-based statistical analysis and graph theoretical approaches were employed to evaluate oxytocin's effects on whole-brain functional connectivity and graph topological measures.

Results

Our results revealed that oxytocin altered connectivity patterns within brain networks involved in sensory and motor processing, attention, memory, emotion and reward functions as well as social cognition, including the default mode, limbic, frontoparietal, cerebellar, and visual networks. Furthermore, oxytocin increased local efficiency, clustering coefficients, and small-world propensity in specific brain regions including the cerebellum, left thalamus, posterior cingulate cortex, right orbitofrontal cortex, right superior frontal gyrus, left inferior frontal gyrus, and right middle orbitofrontal cortex, while decreasing nodal path topological measures in the left and right caudate.

Discussion

These findings suggest that intranasal oxytocin may produce its functional effects through influencing the integration and segregation of information flow within small-world brain networks, particularly in regions closely associated with social cognition and motivation.

ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI

Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data. Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. In this work, we develop an open-source toolbox, called Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION), offering comprehensive functions to streamline fMRI analysis. The ACTION is a Python-based and cross-platform toolbox with graphical user-friendly interfaces. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. Many popular methods for brain network construction and network feature extraction are included. In particular, it supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data (3,800+ resting-state fMRI scans) for model pretraining to enhance model performance for downstream tasks. To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. Furthermore, it enables users to design and test custom algorithms through scripting, greatly improving its utility and extensibility. We demonstrate the effectiveness and user-friendliness of ACTION on real fMRI data and present the experimental results. The software, along with its source code and manual, can be accessed online.

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

Reduced brain connectivity underlying value-based choices and outcomes in stimulant use disorder

BACKGROUND

Patients with stimulant use disorder (SUD) show impairments when making value-based choices that are associated with disruptions in neural processing across brain networks. Making optimal choices requires learning from outcomes to update knowledge and further optimise ongoing behaviour. The optimal functioning of neural systems that underpin the ability to make favourable choices is an essential component for life functioning, and successful recovery in patients with SUD. Therefore, we sought to investigate the neural processes that underpin value-based choices in SUD patients. We hypothesise that patients with SUD have reduced functional connectivity while making financial choices during a probabilistic reinforcement learning task.

METHODS

We investigated connectivity associated with loss and reward value-based choices and their outcomes in patients with SUD and healthy control participants during a pharmacological magnetic resonance imaging study. Participants received a single dose of a dopamine receptor agonist, pramipexole, and a dopamine receptor antagonist, amisulpride, in a randomised, double-blind, placebo-controlled, balanced, crossover design. Functional task-related connectivity was analysed taking a whole brain connectomics approach to identify networks that are differentially modulated by dopaminergic receptor functioning.

RESULTS

SUD patients showed widespread reductions in connectivity during both reward and loss value-based choices and outcomes, which were negatively correlated with the duration of stimulant drug use. Disturbances to functional brain connectivity in SUD patients during task performance were not modulated acutely by either amisulpride or pramipexole.

CONCLUSIONS

Reductions in brain connectivity, particularly when making value-based choices and processing outcomes, may underlie learning impairments in SUD patients. Given that acute dopaminergic modulation did not improve brain connectivity impairments in SUD patients, it is likely that alternative treatments are needed.

Automated brain segmentation and volumetry in dementia diagnostics: a narrative review with emphasis on FreeSurfer

Background

Dementia can be caused by numerous different diseases that present variable clinical courses and reveal multiple patterns of brain atrophy, making its accurate early diagnosis by conventional examinative means challenging. Although highly accurate and powerful, magnetic resonance imaging (MRI) currently plays only a supportive role in dementia diagnosis, largely due to the enormous volume and diversity of data it generates. AI-based software solutions/algorithms that can perform automated segmentation and volumetry analyses of MRI data are being increasingly used to address this issue. Numerous commercial and non-commercial software solutions for automated brain segmentation and volumetry exist, with FreeSurfer being the most frequently used.

Objectives

This Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis.

Methods

We performed a PubMed search for “FreeSurfer AND Dementia” and obtained 493 results. Based on these search results, we conducted an in-depth source analysis to identify additional publications, software tools, and methods. Studies were analyzed for design, patient collective, and for statistical evaluation (mathematical methods, correlations).

Results

In the studies identified, the main diseases and cohorts represented were Alzheimer’s disease (n = 276), mild cognitive impairment (n = 157), frontotemporal dementia (n = 34), Parkinson’s disease (n = 29), dementia with Lewy bodies (n = 20), and healthy controls (n = 356). The findings and methods of a selection of the studies identified were summarized and discussed.

Conclusion

Our evaluation showed that, while a large number of studies and software solutions are available, many diseases are underrepresented in terms of their incidence. There is therefore plenty of scope for targeted research.

Acute rapamycin treatment reveals novel mechanisms of behavioral, physiological, and functional dysfunction in a maternal inflammation mouse model of autism and sensory over-responsivity

Maternal inflammatory response (MIR) during early gestation in mice induces a cascade of physiological and behavioral changes that have been associated with autism spectrum disorder (ASD). In a prior study and the current one, we find that mild MIR results in chronic systemic and neuro-inflammation, mTOR pathway activation, mild brain overgrowth followed by regionally specific volumetric changes, sensory processing dysregulation, and social and repetitive behavior abnormalities. Prior studies of rapamycin treatment in autism models have focused on chronic treatments that might be expected to alter or prevent physical brain changes. Here, we have focused on the acute effects of rapamycin to uncover novel mechanisms of dysfunction and related to mTOR pathway signaling. We find that within 2 hours, rapamycin treatment could rapidly rescue neuronal hyper-excitability, seizure susceptibility, functional network connectivity and brain community structure, and repetitive behaviors and sensory over-responsivity in adult offspring with persistent brain overgrowth. These CNS-mediated effects are also associated with alteration of the expression of several ASD-,ion channel-, and epilepsy-associated genes, in the same time frame. Our findings suggest that mTOR dysregulation in MIR offspring is a key contributor to various levels of brain dysfunction, including neuronal excitability, altered gene expression in multiple cell types, sensory functional network connectivity, and modulation of information flow. However, we demonstrate that the adult MIR brain is also amenable to rapid normalization of these functional changes which results in the rescue of both core and comorbid ASD behaviors in adult animals without requiring long-term physical alterations to the brain. Thus, restoring excitatory/inhibitory imbalance and sensory functional network modularity may be important targets for therapeutically addressing both primary sensory and social behavior phenotypes, and compensatory repetitive behavior phenotypes.

Different Trajectories of Functional Connectivity Captured with Gamma-Event Coupling and Broadband Measures of EEG in the Rat Fluid Percussion Injury Model

Functional connectivity (FC) after TBI is affected by an altered excitatory-inhibitory balance due to neuronal dysfunction, and the mechanistic changes observed could be reflected differently by contrasting methods. Local gamma event coupling FC (GEC-FC) is believed to represent multiunit fluctuations due to inhibitory dysfunction, and we hypothesized that FC derived from widespread, broadband amplitude signal (BBA-FC) would be different, reflecting broader mechanisms of functional disconnection. We tested this during sleep and active periods defined by high delta and theta EEG activity, respectively, at 1,7 and 28d after rat fluid-percussion-injury (FPI) or sham injury (n=6/group) using 10 indwelling, bilateral cortical and hippocampal electrodes. We also measured seizure and high-frequency oscillatory activity (HFOs) as markers of electrophysiological burden. BBA-FC analysis showed early hyperconnectivity constrained to ipsilateral sensory-cortex-to-CA1-hippocampus that transformed to mainly ipsilateral FC deficits by 28d compared to shams. These changes were conserved over active epochs, except at 28d when there were no differences to shams. In comparison, GEC-FC analysis showed large regions of hyperconnectivity early after injury within similar ipsilateral and intrahemispheric networks. GEC-FC weakened with time, but hyperconnectivity persisted at 28d compared to sham. Edge- and global connectivity measures revealed injury-related differences across time in GEC-FC as compared to BBA-FC, demonstrating greater sensitivity to FC changes post-injury. There was no significant association between sleep fragmentation, HFOs, or seizures with FC changes. The within-animal, spatial-temporal differences in BBA-FC and GEC-FC after injury may represent different mechanisms driving FC changes as a result of primary disconnection and interneuron loss.

Significance statement

The present study adds to the understanding of functional connectivity changes in preclinical models of traumatic brain injury. In previously reported literature, there is heterogeneity in the directionality of connectivity changes after injury, resulting from factors such as severity of injury, frequency band studied, and methodology used to calculate FC. This study aims to further clarify differential mechanisms that result in altered network topography after injury, by using Broadband Amplitude-Derived FC and Gamma Event Coupling-Derived FC in EEG. We found post-injury changes that differ in complexity and directionality between measures at and across timepoints. In conjunction with known results and future studies identifying different neural drivers underlying these changes, measures derived from this study could provide useful means from which to minimally-invasively study temporally-evolving pathology after TBI.

Unveiling the cognitive network organization through cognitive performance

The evaluation of cognitive functions interactions has become increasingly implemented in the cognition exploration. In the present study, we propose to examine the organization of the cognitive network in healthy participants through the analysis of behavioral performances in several cognitive domains. Specifically, we aim to explore cognitive interactions profiles, in terms of cognitive network, and as a function of participants' handedness. To this end, we proposed several behavioral tasks evaluating language, memory, executive functions, and social cognition performances in 175 young healthy right-handed and left-handed participants and we analyzed cognitive scores, from a network perspective, using graph theory. Our results highlight the existence of intricate interactions between cognitive functions both within and beyond the same cognitive domain. Language functions are interrelated with executive functions and memory in healthy cognitive functioning and assume a central role in the cognitive network. Interestingly, for similar high performance, our findings unveiled differential organizations within the cognitive network between right-handed and left-handed participants, with variations observed both at a global and nodal level. This original integrative network approach to the study of cognition provides new insights into cognitive interactions and modulations. It allows a more global understanding and consideration of cognitive functioning, from which complex behaviors emerge.

Spectrotemporal cues and attention jointly modulate fMRI network topology for sentence and melody perception

Speech and music are two fundamental modes of human communication. Lateralisation of key processes underlying their perception has been related both to the distinct sensitivity to low-level spectrotemporal acoustic features and to top-down attention. However, the interplay between bottom-up and top-down processes needs to be clarified. In the present study, we investigated the contribution of acoustics and attention to melodies or sentences to lateralisation in fMRI functional network topology. We used sung speech stimuli selectively filtered in temporal or spectral modulation domains with crossed and balanced verbal and melodic content. Perception of speech decreased with degradation of temporal information, whereas perception of melodies decreased with spectral degradation. Applying graph theoretical metrics on fMRI connectivity matrices, we found that local clustering, reflecting functional specialisation, linearly increased when spectral or temporal cues crucial for the task goal were incrementally degraded. These effects occurred in a bilateral fronto-temporo-parietal network for processing temporally degraded sentences and in right auditory regions for processing spectrally degraded melodies. In contrast, global topology remained stable across conditions. These findings suggest that lateralisation for speech and music partially depends on an interplay of acoustic cues and task goals under increased attentional demands.

The Modulatory Effects of Atomoxetine on Aberrant Connectivity During Attentional Processing in Cocaine Use Disorder

BACKGROUND

Cocaine use disorder is associated with cognitive deficits that reflect dysfunctional processing across neural systems. Because there are currently no approved medications, treatment centers provide behavioral interventions that have only short-term efficacy. This suggests that behavioral interventions are not sufficient by themselves to lead to the maintenance of abstinence in patients with cocaine use disorder. Self-control, which includes the regulation of attention, is critical for dealing with many daily challenges that would benefit from medication interventions that can ameliorate cognitive neural disturbances.

METHODS

To address this important clinical gap, we conducted a randomized, double-blind, placebo-controlled, crossover design study in patients with cocaine use disorder (n = 23) and healthy control participants (n = 28). We assessed the modulatory effects of acute atomoxetine (40 mg) on attention and conflict monitoring and their associated neural activation and connectivity correlates during performance on the Eriksen flanker task. The Eriksen flanker task examines basic attentional processing using congruent stimuli and the effects of conflict monitoring and response inhibition using incongruent stimuli, the latter of which necessitates the executive control of attention.

RESULTS

We found that atomoxetine improved task accuracy only in the cocaine group but modulated connectivity within distinct brain networks in both groups during congruent trials. During incongruent trials, the cocaine group showed increased task-related activation in the right inferior frontal and anterior cingulate gyri, as well as greater network connectivity than the control group across treatments.

CONCLUSIONS

The findings of the current study support a modulatory effect of acute atomoxetine on attention and associated connectivity in cocaine use disorder.

Distinct functional subnetworks of cognitive domains in older adults with minor cognitive deficits

Although past research has established a relationship between functional connectivity and cognitive function, less is known about which cognitive domains are associated with which specific functional networks. This study investigated associations between functional connectivity and global cognitive function and performance in the domains of memory, executive function and psychomotor speed in 166 older adults aged 75-91 years (mean = 80.3 ± 3.8) with minor cognitive deficits (Mini-Mental State Examination scores between 21 and 27). Functional connectivity was assessed within 10 standard large-scale resting-state networks and on a finer spatial resolution between 300 nodes in a functional connectivity matrix. No domain-specific associations with mean functional connectivity within large-scale resting-state networks were found. Node-level analysis revealed that associations between functional connectivity and cognitive performance differed across cognitive functions in strength, location and direction. Specific subnetworks of functional connections were found for each cognitive domain in which higher connectivity between some nodes but lower connectivity between other nodes were related to better cognitive performance. Our findings add to a growing body of literature showing differential sensitivity of functional connections to specific cognitive functions and may be a valuable resource for hypothesis generation of future studies aiming to investigate specific cognitive dysfunction with resting-state functional connectivity in people with beginning cognitive deficits.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease

Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.

A neural mechanism of cognitive reserve: The case of bilingualism

Cognitive Reserve (CR) refers to the preservation of cognitive function in the face of age- or disease-related neuroanatomical decline. While bilingualism has been shown to contribute to CR, the extent to which, and what particular aspect of, second language experience contributes to CR are debated, and the underlying neural mechanism(s) unknown. Intrinsic functional connectivity reflects experience-dependent neuroplasticity that occurs across timescales ranging from minutes to decades, and may be a neural mechanism underlying CR. To test this hypothesis, we used voxel-based morphometry and resting-state functional connectivity analyses of MRI data to compare structural and functional brain integrity between monolingual and bilingual older adults, matched on cognitive performance, and across levels of second language proficiency measured as a continuous variable. Bilingualism, and degree of second language proficiency specifically, were associated with lower gray matter integrity in a hub of the default mode network - a region that is particularly vulnerable to decline in aging and dementia - but preserved intrinsic functional network organization. Bilingualism moderated the association between neuroanatomical differences and cognitive decline, such that lower gray matter integrity was associated with lower executive function in monolinguals, but not bilinguals. Intrinsic functional network integrity predicted executive function when controlling for group differences in gray matter integrity and language status. Our findings confirm that lifelong bilingualism is a CR factor, as bilingual older adults performed just as well as their monolingual peers on tasks of executive function, despite showing signs of more advanced neuroanatomical aging, and that this is a consequence of preserved intrinsic functional network organization.

Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients

Alcohol dependence (AD) is a debilitating disease associated with high relapse rates even after long periods of abstinence. Thus, elucidating neurobiological substrates of relapse risk is fundamental for the development of novel targeted interventions that could promote long-lasting abstinence. In the present study, we analysed resting-state functional magnetic resonance imaging (rsfMRI) data from a sample of recently detoxified patients with AD (n = 93) who were followed up for 12 months after rsfMRI assessment. Specifically, we employed graph theoretic analyses to compare functional brain network topology and functional connectivity between future relapsers (REL, n = 59), future abstainers (ABS, n = 28) and age- and gender-matched controls (CON, n = 83). Our results suggest increased whole-brain network segregation, decreased global network integration and overall blunted connectivity strength in REL compared with CON. Conversely, we found evidence for a comparable network architecture in ABS relative to CON. At the nodal level, REL exhibited decreased integration and decoupling between multiple brain systems compared with CON, encompassing regions associated with higher-order executive functions, sensory and reward processing. Among patients with AD, increased coupling between nodes implicated in reward valuation and salience attribution constitutes a particular risk factor for future relapse. Importantly, aberrant network organization in REL was consistently associated with shorter abstinence duration during follow-up, portending to a putative neural signature of relapse risk in AD. Future research should further evaluate the potential diagnostic value of the identified changes in network topology and functional connectivity for relapse prediction at the individual subject level.

Reduced neural functional connectivity during working memory performance in methamphetamine use disorder

BACKGROUND

Methamphetamine misuse, a surging cause of morbidity and mortality worldwide, identifies Methamphetamine Use Disorder (MUD) as a critical public health problem. Treatment for MUD typically is sought during early abstinence when patients are experiencing cognitive difficulties that may hamper their engagement in treatment and recovery. Cognitive difficulties, particularly those that involve executive functions, likely reflect disruptions in neural functioning involving multiple brain areas and circuits.

METHODS

To extend knowledge in this area, we compared individuals with MUD (MUD group, n = 30) in early abstinence (3-11 days abstinent) with a healthy control group (HC, n = 33) on brain activation and network connectivity and topology, using functional magnetic resonance imaging (fMRI) during performance on an N-back working memory task. The N-back task involves the maintenance and manipulation of information in short-term memory and engages multiple neural processes related to executive functioning. The task was administered at two working-memory difficulty loads (1-back and 2-back).

RESULTS

Compared with the HC group, the MUD group had worse task performance but no differences in task-related brain activation. Network-based statistics analyses, however, revealed that the MUD group exhibited less functional network connectivity at both difficulty loads of the N-back task than the HC group. Additional graph theory analyses showed that path lengths were longer, and clustering was lower across these networks, which also exhibited disrupted small-world properties in the MUD group.

CONCLUSION

These results suggest a decoupling in network dynamics that may underlie deficits in cognition during early abstinence in MUD patients.

Functional connectivity in older adults-the effect of cerebral small vessel disease

Ageing is associated with functional reorganization that is mainly characterized by declining functional connectivity due to general neurodegeneration and increasing incidence of disease. Functional connectivity has been studied across the lifespan; however, there is a paucity of research within the older groups (≥75 years) where neurodegeneration and disease prevalence are at its highest. In this cross-sectional study, we investigated associations between age and functional connectivity and the influence of cerebral small vessel disease (CSVD)-a common age-related morbidity-in 167 community-dwelling older adults aged 75-91 years (mean = 80.3 ± 3.8). Resting-state functional MRI was used to determine functional connectivity within ten standard networks and calculate the whole-brain graph theoretical measures global efficiency and clustering coefficient. CSVD features included white matter hyperintensities, lacunar infarcts, cerebral microbleeds, and atrophy that were assessed in each individual and a composite score was calculated. Both main and interaction effects (age*CSVD features) on functional connectivity were studied. We found stable levels of functional connectivity across the age range. CSVD was not associated with functional connectivity measures. To conclude, our data show that the functional architecture of the brain is relatively unchanged after 75 years of age and not differentially affected by individual levels of vascular pathology.

Graph Theory-Based Approach in Brain Connectivity Modeling and Alzheimer's Disease Detection

There is strong evidence that the pathological findings of Alzheimer's disease (AD), consisting of accumulated amyloid plaques and neurofibrillary tangles, could spread around the brain through synapses and neural connections of neighboring brain sections. Graph theory is a helpful tool in depicting the complex human brain divided into various regions of interest (ROIs) and the connections among them. Thus, applying graph theory-based models in the study of brain connectivity comes natural in the study of AD propagation mechanisms. Moreover, graph theory-based computational approaches have been lately applied in order to boost data-driven analysis, extract model measures and robustness-effectiveness indexes, and provide insights on casual interactions between regions of interest (ROI), as imposed by the models' architecture.

Structural filtering of functional data offered discriminative features for autism spectrum disorder

This study attempted to answer the question, "Can filtering the functional data through the frequency bands of the structural graph provide data with valuable features which are not valuable in unfiltered data"?. The valuable features discriminate between autism spectrum disorder (ASD) and typically control (TC) groups. The resting-state fMRI data was passed through the structural graph's low, middle, and high-frequency band (LFB, MFB, and HFB) filters to answer the posed question. The structural graph was computed using the diffusion tensor imaging data. Then, the global metrics of functional graphs and metrics of functional triadic interactions were computed for filtered and unfiltered rfMRI data. Compared to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may indicate the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. There was no significant difference between ASDs and TCs when using the unfiltered data. All of these results demonstrated that significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. Also, these results demonstrated that frequency bands of the structural graph could offer significant findings which were not found in the unfiltered data. In conclusion, the results demonstrated the promising perspective of using structural graph frequency bands for attaining discriminative features and new knowledge, especially in the case of ASD.

Abnormal Brain Networks Related to Drug and Nondrug Reward Anticipation and Outcome Processing in Stimulant Use Disorder: A Functional Connectomics Approach

BACKGROUND

Drug addiction is associated with blunted neural responses to nondrug rewards, such as money, but heightened responses to drug cues that predict drug-reward outcomes. This dissociation underscores the role of incentive context in the attribution of motivational salience, which may reflect a narrowing toward drug-related goals. This hypothesis, however, has scarcely been investigated.

METHODS

To address this important scientific gap, the current study performed an empirical assessment of differences in salience attribution by comparing patients with stimulant use disorder (SUD) (n = 41) with control participants (n = 48) on network connectivity related to anticipation and outcome processing using a modified monetary incentive delay task. We hypothesized increased task-related activation and connectivity to drug rewards in patients with SUD, and reduced task-related activation and connectivity to monetary rewards during incentive processing across brain networks.

RESULTS

In the presence of behavioral and regional brain activation similarities, we found that patients with SUD showed significantly less connectivity involving three separate distributed networks during monetary reward anticipation, and drug and monetary reward outcome processing. No group connectivity differences for drug reward anticipation were identified. Additional graph theory analyses revealed that patients with SUD had longer path lengths across these networks, all of which positively correlated with the duration of stimulant drug use.

CONCLUSIONS

Specific disruptions in connectivity in networks related to the anticipation of nondrug reward together with more general dysconnectivity in the processing of rewarding outcomes suggest an insensitivity to consequences. These observations support the notion of a predominance of habitual control in patients with SUD.

Altered resting state brain metabolic connectivity in dementia with Lewy bodies

Although dementia with Lewy bodies (DLB) have Parkinsonism in common with Parkinson's disease (PD) or PD dementia (PDD), they have different neuropathologies that underlie Parkinsonism. Altered brain functional connectivity that may correspond to neuropathology has been reported in PD while never been studied in DLB. To identify the characteristic brain connectivity of Parkinsonism in DLB, we compared the resting state metabolic connectivity in striato-thalamo-cortical (STC) circuit, nigrostriatal pathway, and cerebello-thalamo-cortical motor (CTC) circuit in 27 patients with drug-naïve DLB and 27 age- and sex-matched normal controls using 18F-fluoro-2-deoxyglucose PET. We derived 118 regions of interest using the Automated Anatomical Labeling templates and the Wake Forest University Pick-Atlas. We applied the sparse inverse covariance estimation method to construct the metabolic connectivity matrix. Patients with DLB, with or without Parkinsonism, showed lower inter-regional connectivity between the areas included in the STC circuit (motor cortex–striatum, midbrain–striatum, striatum–globus pallidus, and globus pallidus–thalamus) than the controls. DLB patients with Parkinsonism showed less reduced inter-regional connectivity between the midbrain and the striatum than those without Parkinsonism, and higher inter-regional connectivity between the areas included in the CTC circuit (motor cortex–pons, pons–cerebellum, and cerebellum–thalamus) than those without Parkinsonism and the controls. The resting state metabolic connectivity in the STC circuit may be reduced in DLB. In DLB with Parkinsonism, the CTC circuit and the nigrostriatal pathway may be activated to mitigate Parkinsonism. This difference in the brain connectivity may be a candidate biomarker for differentiating DLB from PD or PDD.

Regional Disruption of White Matter Integrity and Network Connectivity Are Related to Cognition

BACKGROUND

Cognitive impairment is common in the elderly population. Exploring patterns of white matter damage at the microstructural level would give important indications for the underlying mechanisms.

OBJECTIVE

To investigate the spatial patterns of white matter microstructure and structural network alternations in relation to different cognition domainsMethods:Participants from the community-based Shunyi Study were included to investigate the association between white matter measurements and cognition cross-sectionally, via both global and local analysis. Cognitive functions were assessed using digit span, trail making test (TMT)-A/B, Fuld object Memory, and 12-Word Philadelphia Verbal Learning Test (PVLT). White matter measurements including fractional anisotropy (FA), mean diffusivity (MD), and structural network parameters were calculated based on diffusion tensor imaging.

RESULTS

Of the 943 participants included, the mean (SD) age was 55.8 (9.1) years, and the mean (SD) education level was 6.7 (3.2) years. We found the whole set of cognitive measurements was related to diffused white matter microstructural integrity damage and lower global efficiency. Poor executive functions (TMTA/B complete time) were related to lower FA and higher MD predominantly on the anterior white matter skeleton, while verbal memory loss (PVLT test scores) was related to sub-network dysconnectivity in the midline and the right temporal lobe.

CONCLUSION

The anterior brain is dominantly involved in executive dysfunction, while midline and right temporal brain disconnection are more prominent in verbal memory loss. Global and regional disruption of white matter integrity and network connectivity is the anatomical basis of the cognitive impairment in the aging population.

Local efficiency analysis of restingstate functional brain network in methamphetamine users

This study set out to assess restingstate functional connectivity (rs-FN) and graph theorybased local efficiency within the left and right hemispheres of methamphetamine (MA) abusers. Functional brain networks of 19 MA abusers and 21 control participants were analyzed using restingstate fMRI. Graph edges in functional networks of the brain were defined and recurrence plot was used. We found that MA abuse may be accompanied by alterations of rs-FN within the defaultmode network (DMN), executive control network (ECN), and the salience network (SN) in both hemispheres of the brain. We also observed that such effects of MA may be correlated with duration of MA abuse and abstinence in many components of the DMN and SN. The results would seem to suggest that MAinduced alterations of local efficiency may, in part, account for maladaptive decision making, deficits in executive function and control over drug seeking/taking, and relapse.

Partial resistance to citalopram in a Wistar-Kyoto rat model of depression: An evaluation using resting-state functional MRI and graph analysis

Wistar-Kyoto (WKY) rats as an endogenous depression model partially lack a response to classic selective serotonin reuptake inhibitors (SSRIs). Thus, this strain has the potential to be established as a model of treatment-resistant depression (TRD). However, the SSRI resistance in WKY rats is still not fully understood. In this study, WKY and control rats were subjected to a series of tests, namely, a forced swim test (FST), a sucrose preference test (SPT), and an open field test (OFT), and were scanned in a 7.0-T MRI scanner before and after three-week citalopram or saline administration. Behavioral results demonstrated that WKY rats had increased immobility in the FST and decreased sucrose preference in the SPT and central time spent in the OFT. However, citalopram did not improve immobility in the FST. The amplitude of low-frequency fluctuation (ALFF) analysis showed regional changes in the striatum and hippocampus of WKY rats. However, citalopram partially reversed the ALFF value in the dorsal part of the two regions. Functional connectivity (FC) analysis showed that FC strengths were decreased in WKY rats compared with controls. Nevertheless, citalopram partially increased FC strengths in WKY rats. Based on FC, global graph analysis demonstrated decreased network efficiency in WKY + saline group compared with control + saline group, but citalopram showed weak network efficiency improvement. In conclusion, resting-state fMRI results implied widely affected brain function at both regional and global levels in WKY rats. Citalopram had only partial effects on these functional changes, indicating a potential treatment resistance mechanism.

PyMVPD: A Toolbox for Multivariate Pattern Dependence

Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate pattern dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for multivariate pattern dependence. The toolbox includes linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models.

Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics

BACKGROUND

Suicidal ideation (SI) typically emerges during adolescence but is challenging to predict. Given the potentially lethal consequences of SI, it is important to identify neurobiological and psychosocial variables explaining the severity of SI in adolescents.

METHODS

In 106 participants (59 female) recruited from the community, we assessed psychosocial characteristics and obtained resting-state fMRI data in early adolescence (baseline: aged 9-13 years). Across 250 brain regions, we assessed local graph theory-based properties of interconnectedness: local efficiency, eigenvector centrality, nodal degree, within-module z-score, and participation coefficient. Four years later (follow-up: ages 13-19 years), participants self-reported their SI severity. We used least absolute shrinkage and selection operator (LASSO) regressions to identify a linear combination of psychosocial and brain-based variables that best explain the severity of SI symptoms at follow-up. Nested-cross-validation yielded model performance statistics for all LASSO models.

RESULTS

A combination of psychosocial and brain-based variables explained subsequent severity of SI (R2  = .55); the strongest was internalizing and externalizing symptom severity at follow-up. Follow-up LASSO regressions of psychosocial-only and brain-based-only variables indicated that psychosocial-only variables explained 55% of the variance in SI severity; in contrast, brain-based-only variables performed worse than the null model.

CONCLUSIONS

A linear combination of baseline and follow-up psychosocial variables best explained the severity of SI. Follow-up analyses indicated that graph theory resting-state metrics did not increase the prediction of the severity of SI in adolescents. Attending to internalizing and externalizing symptoms is important in early adolescence; resting-state connectivity properties other than local graph theory metrics might yield a stronger prediction of the severity of SI.

Brain Under Fatigue – Can Perceived Fatigability in Multiple Sclerosis Be Seen on the Level of Functional Brain Network Architecture?

Background

Fatigue is one of the most common symptoms of multiple sclerosis (MS), significantly affecting the functioning of the patients. However, the neural underpinnings of physical and mental fatigue in MS are still vague. The aim of our study was to investigate the functional architecture of resting-state networks associated with fatigue in patients with MS.

Methods

The sum of 107 high-functioning patients underwent a resting-state scanning session and filled out the 9-item Fatigue Severity Scale (FSS). Based on the FSS score, we identified patients with different levels of fatigue using the cluster analysis. The low-fatigue group consisted of n = 53 subjects, while the high-fatigue group n = 48. The neuroimaging data were analyzed in terms of functional connectivity (FC) between various resting-state networks as well as amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF).

Results

Two-sample t-test revealed between-group differences in FC of posterior salience network (SN). No differences occurred in default mode network (DMN) and sensorimotor network (SMN). Moreover, differences in fALFF were shown in the right middle frontal gyrus and right superior frontal gyrus, however, no ALFF differences took place.

Conclusion

Current study revealed significant functional network (FN) architecture between-group differences associated with fatigue. Present results suggest the higher level of fatigue is related to deficits in awareness as well as higher interoceptive awareness and nociception.

Functional network connectivity and topology during naturalistic stimulus is altered in first-episode psychosis

BACKGROUND

Psychotic disorders have been suggested to derive from dysfunctional integration of signaling between brain regions. Earlier studies have found several changes in functional network synchronization as well as altered network topology in patients with psychotic disorders. However, studies have used mainly resting-state that makes it more difficult to link functional alterations to any specific stimulus or experience. We set out to examine functional connectivity as well as graph (topological) measures and their association to symptoms in first-episode psychosis patients during movie viewing. Our goal was to understand whole-brain functional dynamics of complex naturalistic information processing in psychosis and changes in brain functional organization related to symptoms.

METHODS

71 first-episode psychosis patients and 57 control subjects watched scenes from the movie Alice in Wonderland during 3 T fMRI. We compared functional connectivity and graph measures indicating integration, segregation and centrality between groups, and examined the association between topology and symptom scores in the patient group.

RESULTS

We identified a subnetwork with predominantly decreased links of functional connectivity in first-episode psychosis patients. The subnetwork was mainly comprised of nodes of and links between the cingulo-opercular, sensorimotor and default-mode networks. In topological measures, we observed between-group differences in properties of centrality.

CONCLUSIONS

Functional brain networks are affected during naturalistic information processing already in the early stages of psychosis, concentrated in salience- and cognitive control-related hubs and subnetworks. Understanding these aberrant dynamics could add to better targeted cognitive and behavioral interventions in the early stages of psychotic disorders.

Disruption of large-scale electrophysiological networks in stroke patients with visuospatial neglect

Stroke frequently produces attentional dysfunctions including symptoms of hemispatial neglect, which is characterized by a breakdown of awareness for the contralesional hemispace. Recent studies with functional MRI (fMRI) suggest that hemineglect patients display abnormal intra- and interhemispheric functional connectivity. However, since stroke is a vascular disorder and fMRI signals remain sensitive to nonneuronal (i.e., vascular) coupling, more direct demonstrations of neural network dysfunction in hemispatial neglect are warranted. Here, we utilize electroencephalogram (EEG) source imaging to uncover differences in resting-state network organization between patients with right hemispheric stroke (N = 15) and age-matched, healthy controls (N = 27), and determine the relationship between hemineglect symptoms and brain network organization. We estimated intra- and interregional differences in cortical communication by calculating the spectral power and amplitude envelope correlations of narrow-band EEG oscillations. We first observed focal frequency-slowing within the right posterior cortical regions, reflected in relative delta/theta power increases and alpha/beta/gamma decreases. Secondly, nodes within the right temporal and parietal cortex consistently displayed anomalous intra- and interhemispheric coupling, stronger in delta and gamma bands, and weaker in theta, alpha, and beta bands. Finally, a significant association was observed between the severity of left-hemispace search deficits (e.g., cancellation test omissions) and reduced functional connectivity within the alpha and beta bands. In sum, our novel results validate the hypothesis of large-scale cortical network disruption following stroke and reinforce the proposal that abnormal brain oscillations may be intimately involved in the pathophysiology of visuospatial neglect.

Stroke patients often exhibit a disabling deficit of visual awareness in the hemifield opposite to their brain lesion, known as hemineglect. Recent studies with functional MRI (fMRI) suggest that hemineglect patients display abnormal functional coupling (i.e., connectivity) within and between brain hemispheres. However, since stroke is a vascular disorder and fMRI measures nonneuronal (i.e., vascular) coupling, we here provide direct evidence of neural network dysfunction in hemineglect by using electroencephalogram (EEG) source imaging, which measures the electrical fluctuations of large neuronal populations. Overall, we observed a breakdown of interhemispheric network connectivity within alpha/beta rhythms, which specifically correlated with the degree of patients’ hemispatial errors. The high temporal resolution and frequency content of EEG signals could lead to more sensitive markers and targeted rehabilitation approaches of hemineglect.

Electrophysiological Activity and Brain Network During Recovery of Propofol Anesthesia: A Stereoelectroencephalography-Based Analysis in Patients With Intractable Epilepsy—An Exploratory Research

Background: The oscillations and interactions between different brain areas during recovery of consciousness (ROC) from anesthesia in humans are poorly understood. Reliable stereoelectroencephalography (SEEG) signatures for transitions between unconsciousness and consciousness under anesthesia have not yet been fully identified.

Objective: This study was designed to observe the change of electrophysiological activity during ROC and construct a ROC network based on SEEG data to describe the network property of cortical and deep areas during ROC from propofol-induced anesthetic epileptic patients.

Methods: We analyzed SEEG data recorded from sixteen right-handed epileptic patients during ROC from propofol anesthesia from March 1, 2019, to December 31, 2019. Power spectrum density (PSD), correlation, and coherence were used to describe different brain areas' electrophysiological activity. The clustering coefficient, characteristic path length, modularity, network efficiency, degrees, and betweenness centrality were used to describe the network changes during ROC from propofol anesthesia. Statistical analysis was performed using MATLAB 2016b. The power spectral data from different contacts were analyzed using a one-way analysis of variance (ANOVA) test with Tukey's post-hoc correction. One sample t-test was used for the analysis of network property. Kolmogorov-Smirnov test was used to judge data distribution. Non-normal distribution was analyzed using the signed rank-sum test.

Result: From the data of these 16 patients, 10 cortical, and 22 deep positions were observed. In this network, we observed that bilateral occipital areas are essential parts that have strong links with many regions. The recovery process is different in the bilateral cerebral cortex. Stage B (propofol 3.0-2.5 μg/ml) and E (propofol 1.5 μg/ml-ROC) play important roles during ROC exhibiting significant changes. The clustering coefficient gradually decreases with the recovery from anesthesia, and the changes mainly come from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient are similar. Deep areas tend to form functional modules. The left occipital lobe, the left temporal lobe, bilateral amygdala are essential nodes in the network. Some specific cortical regions (i.e., left angular gyrus, right angular gyrus, right temporal lobe, left temporal lobe, and right angular gyrus) and deep regions (i.e., right amygdala, left cingulate gyrus, right insular lobe, right amygdala) have more significant constraints on other regions.

Conclusion: We verified that the bilateral cortex's recovery process is the opposite, which is not found in the deep regions. Significant PSD changes were observed in many areas at the beginning of stop infusion and near recovery. Our study found that during the ROC process, the modularity and clustering coefficient of the deep area network is significantly improved. However, the changes of the bilateral cerebral cortex were different. Power spectrum analysis shows that low-frequency EEG in anesthesia recovery accounts for a large proportion. The changes of the bilateral brain in the process of anesthesia recovery are different. The clustering coefficient gradually decreased with the recovery from anesthesia, and the changes mainly came from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient were similar. During ROC, the left occipital lobe, the left temporal lobe, bilateral amygdala were essential nodes in the network. The findings of the current study suggest SEEG as an effective tool for providing direct evidence of the anesthesia recovery mechanism.

The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics

BACKGROUND

From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies.

METHODS

Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis.

RESULTS

Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively).

CONCLUSION

Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration.

LIMITATIONS

Included number of trials was low and heterogeneity should be noted.

Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study

Objective: Our aim was to investigate the dynamics of functional connectivity (FC) networks during seizure termination in patients with childhood absence epilepsy (CAE) using magnetoencephalography (MEG) and graph theory (GT) analysis.

Methods: MEG data were recorded from 22 drug-naïve patients diagnosed with CAE. FC analysis was performed to evaluate the FC networks in seven frequency bands of the MEG data. GT analysis was used to assess the topological properties of FC networks in different frequency bands.

Results: The patterns of FC networks involving the frontal cortex were altered significantly during seizure termination compared with those during the ictal period. Changes in the topological parameters of FC networks were observed in specific frequency bands during seizure termination compared with those in the ictal period. In addition, the connectivity strength at 250–500 Hz during the ictal period was negatively correlated with seizure frequency.

Conclusions: FC networks associated with the frontal cortex were involved in the termination of absence seizures. The topological properties of FC networks in different frequency bands could be used as new biomarkers to characterize the dynamics of FC networks related to seizure termination.

Cortical network modularity changes along the course of frontotemporal and Alzheimer's dementing diseases

Cortical network modularity underpins cognitive functions, so we hypothesized its progressive derangement along the course of frontotemporal (FTD) and Alzheimer's (AD) dementing diseases. EEG was recorded in 18 FTD, 18 AD, and 20 healthy controls (HC). In the FTD and AD patients, the EEG recordings were performed at the prodromal stage of dementia, at the onset of dementia, and three years after the onset of dementia. HC underwent three EEG recordings at 2-3-year time interval. Information flows underlying EEG activity recorded at electrode pairs were estimated by means of Mutual Information (MI) analysis. The functional organization of the cortical network was modelled by means of the Graph theory analysis on MI adjacency matrices. Graph theory analysis showed that the main hub of HC (Parietal area) was lost in FTD patients at onset of dementia, substituted by provincial hubs in frontal leads. No changes in global network organization were found in AD. Despite a progressive cognitive impairment during the FTD and AD progression, only the FTD patients showed a derangement in the cortical network modularity, possibly due to dysfunctions in frontal functional connectivity.

Cortical autonomic network connectivity predicts symptoms in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) represents a significant public health challenge given the presence of many unexplained patient symptoms. Research has shown that many features in ME/CFS may result from a dysfunctional autonomic nervous system (ANS). We explored the role of the cortical autonomic network (CAN) involved in higher-order control of ANS functioning in 34 patients with ME/CFS and 34 healthy controls under task-free conditions. All participants underwent resting-state quantitative electroencephalographic (qEEG) scalp recordings during an eyes-closed condition. Source analysis was performed using exact low-resolution electromagnetic tomography (eLORETA), and lagged coherence was used to estimate intrinsic functional connectivity between each node across 7 frequency bands: delta (1-3 Hz), theta (4-7 Hz), alpha-1 (8-10 Hz), alpha-2 (10-12 Hz), beta-1 (13-18 Hz), beta-2 (19-21 Hz), and beta-3 (22-30 Hz). Symptom ratings were measured using the DePaul Symptom Questionnaire and the Short Form (SF-36) health survey. Graph theoretical analysis of weighted, undirected connections revealed significant group differences in baseline CAN organization. Regression results showed that cognitive, affective, and somatomotor symptom cluster ratings were associated with alteration to CAN topology in patients, depending on the frequency band. These findings provide evidence for reduced higher-order homeostatic regulation and adaptability in ME/CFS. If confirmed, these findings address the CAN as a potential therapeutic target for managing patient symptoms.

Brain Functional Network Architecture Reorganization and Alterations of Positive and Negative Affect, Experiencing Pleasure and Daytime Sleepiness in Cataract Patients after Intraocular Lenses Implantation

Background: Cataracts are associated with progressive blindness, and despite the decline in prevalence in recent years, it remains a major global health problem. Cataract extraction is reported to influence not only perception, attention and memory but also daytime sleepiness, ability to experience pleasure and positive and negative affect. However, when it comes to the latter, the magnitude and prevalence of this effect still remains uncertain. The current study aims to evaluate the hemodynamic basis of daytime sleepiness, ability to experience pleasure and positive and negative affect in cataract patients after the intraocular lens (IOL) implantation. Methods: Thirty-four cataract patients underwent resting-state functional magnetic resonance imaging evaluation before and after cataract extraction and intraocular lens implantation. Both global and local graph metrics were calculated in order to investigate the hemodynamic basis of excessive sleepiness (ESS), experiencing pleasure (SHAPS) as well as positive and negative affect (PANAS) in cataract patients. Results: Eigenvector centrality and clustering coefficient alterations associated with cataract extraction are significantly correlated with excessive sleepiness, experiencing pleasure as well as positive and negative affect. Conclusions: The current study reveals the hemodynamic basis of sleepiness, pleasure and affect in patients after cataract extraction and intraocular lens implantation. The aforementioned mechanism constitutes a proof for changes in functional network activity associated with postoperative vision improvement.

Tipping the scales: how clinical assessment shapes the neural correlates of Parkinson's disease mild cognitive impairment

Mild cognitive impairment in Parkinson's disease (PD-MCI) is associated with consistent structural and functional brain changes. Whether different approaches for diagnosing PD-MCI are equivalent in their neural correlates is presently unknown. We aimed to profile the neuroimaging changes associated with the two endorsed methods of diagnosing PD-MCI. We recruited 53 consecutive non-demented PD patients and classified them as PD-MCI according to comprehensive neuropsychological examination as operationalized by the Movement Disorders Task Force. Voxel-based morphometry, cortical thickness, functional connectivity and graph theoretical measures were obtained on a 3-Tesla MRI scanner. 18 patients (32%) were classified as PD-MCI with Level-II criteria, 19 (33%) with the Parkinson's disease Cognitive Rating Scale (PD-CRS) and 32 (60%) with the Montreal Cognitive Assessment (MoCA) scale. Though regions of atrophy differed across classifications, reduced gray matter in the precuneus was found using both Level-II and PD-CRS classifications in PD-MCI patients. Patients diagnosed with the PD-CRS also showed extensive changes in cortical thickness, concurring with the MoCA in regions of the cingulate cortex, and again with Level-II regarding cortical thinning in the precuneus. Functional connectivity analysis found higher coherence within salience network regions of interest, and decreased anticorrelations between salience/central executive and default-mode networks in the PD-CRS classification for PD-MCI patients. Graph theoretical metrics showed a widespread decrease in node degree for the three classifications in PD-MCI, whereas betweenness centrality was increased in select nodes of the default mode network (DMN). Clinical and neuroimaging commonalities between the endorsed methods of cognitive assessment suggest a corresponding set of neural correlates in PD-MCI: loss of structural integrity in DMN structures, mainly the precuneus, and a loss of weighted connections in the salience network that might be counterbalanced by increased centrality in the DMN. Furthermore, the similarity of the results between exhaustive Level-II and screening Level-I tools might have practical implications in the search for neuroimaging biomarkers of cognitive impairment in Parkinson's disease.

Functional connectivity pre-post exercise intervention in individuals with relapsing-remitting multiple sclerosis

OBJECTIVE

Exercise interventions have emerged as a promising approach for managing symptoms associated with multiple sclerosis (MS). However, changes in brain function underlying exercise-related improvements in symptoms of MS have not been fully investigated, and in no instances have they been investigated using a graph theory approach. For the first time, the effects of an exercise intervention on functional brain network connectivity were examined using graph theory analyses of resting-state functional MRI (fMRI) data among individuals with relapsing-remitting MS (RRMS).

METHODS

Resting-state fMRI data were obtained from 10 participants before and after 12 weeks of a speeded walking intervention. Functional connectivity data were preprocessed in Data Processing Assistant for Resting-State fMRI Advanced (DPARSF A version 4.2) and analyzed in GraphVar2.02 to compute global and local graph theory metrics. To examine differences in graph metrics before and after the intervention, one-sample permutation tests were performed.

RESULTS

There were no significant pre to post exercise intervention changes in global metrics. Changes in local metrics (i.e. clustering coefficient, local efficiency, degree centrality and betweenness centrality) were mixed, with both increases and decreases observed.

CONCLUSION

Following a 12-week speeded walking exercise intervention, there were no significant increases or decreases in global graph metrics and results at the level of local metrics were equivocal in individuals with RRMS. Further research with experimental designs that include baseline and longitudinal follow-up, as well as larger sample sizes, is needed to understand the underlying mechanisms of symptom improvement following exercise in RRMS.

Spinal Cord Resting State Activity in Individuals With Fibromyalgia Who Take Opioids

Chronic pain coincides with myriad functional alterations throughout the brain and spinal cord. While spinal cord mechanisms of chronic pain have been extensively characterized in animal models and in vitro, to date, research in patients with chronic pain has focused only very minimally on the spinal cord. Previously, spinal cord functional magnetic resonance imaging (fMRI) identified regional alterations in spinal cord activity in patients (who were not taking opioids) with fibromyalgia, a chronic pain condition. Here, in patients with fibromyalgia who take opioids (N = 15), we compared spinal cord resting-state fMRI data vs. patients with fibromyalgia not taking opioids (N = 15) and healthy controls (N = 14). We hypothesized that the opioid (vs. non-opioid) patient group would show greater regional alterations in spinal cord activity (i.e., the amplitude of low frequency fluctuations or ALFF, a measure of regional spinal cord activity). However, we found that regional spinal cord activity in the opioid group was more similar to healthy controls, while regional spinal cord activity in the non-opioid group showed more pronounced differences (i.e., ventral increases and dorsal decreases in regional ALFF) vs. healthy controls. Across patient groups, self-reported fatigue correlated with regional differences in spinal cord activity. Additionally, spinal cord functional connectivity and graph metrics did not differ among groups. Our findings suggest that, contrary to our main hypothesis, patients with fibromyalgia who take opioids do not have greater alterations in regional spinal cord activity. Thus, regional spinal cord activity may be less imbalanced in patients taking opioids compared to patients not taking opioids.

Dyconnmap: Dynamic connectome mapping-A neuroimaging python module

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.

Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes

Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of "global efficiency" and decrease of the "clustering coefficient" in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes.

Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning

This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.

Reconfiguration dynamics of a language-and-memory network in healthy participants and patients with temporal lobe epilepsy

Current theoretical frameworks suggest that human behaviors are based on strong and complex interactions between cognitive processes such as those underlying language and memory functions in normal and neurological populations. We were interested in assessing the dynamic cerebral substrate of such interaction between language and declarative memory, as the composite function, in healthy controls (HC, N = 19) and patients with temporal lobe epilepsy (TLE, N = 16). Our assumption was that the language and declarative memory integration is based on a language-and-memory network (LMN) that is dynamic and reconfigures according to task demands and brain status. Therefore, we explored two types of LMN dynamics, a state reconfiguration (intrinsic resting-state compared to extrinsic state assessed with a sentence recall task) and a reorganization of state reconfiguration (TLE compared to HC). The dynamics was evaluated in terms of segregation (community or module detection) and integration (connector hubs). In HC, the level of segregation was the same in both states and the mechanism of LMN state reconfiguration was shown through module change of key language and declarative memory regions with integrative roles. In TLE patients, the reorganization of LMN state reconfiguration was reflected in segregation increase and extrinsic modules that were based on shorter-distance connections. While lateral and mesial temporal regions enabled state reconfiguration in HC, these regions showed reduced flexibility in TLE. We discuss our results in a connectomic perspective and propose a dynamic model of language and declarative memory functioning. We claim that complex and interactive cognitive functions, such as language and declarative memory, should be investigated dynamically, considering the interaction between cognitive networks.

Childhood exercise predicts response inhibition in later life via changes in brain connectivity and structure

Participation in exercise during early life (i.e., childhood through adolescence) enhances response inhibition; however, it is unclear whether participation in exercise during early life positively predicts response inhibition in later life. This historical cohort study was designed to clarify whether participation in exercise (e.g., structured sports participation) during early life predicts response inhibition in adulthood and if so, to reveal the brain connectivity and cortical structures contributing to this association. We analyzed data derived from 214 participants (women = 104, men = 110; age: 26‒69 years). Results indicated that participation in exercise during childhood (before entering junior high school; ≤ 12 years old) significantly predicted better response inhibition. No such association was found if exercise participation took place in early adolescence or later (junior high school or high school; ≥ 12 years old). The positive association of exercise participation during childhood with response inhibition was moderated by decreased structural and functional connectivity in the frontoparietal (FPN), cingulo-opercular (CON), and default mode networks (DMN), and increased inter-hemispheric structural networks. Greater cortical thickness and lower levels of dendritic arborization and density in the FPN, CON, and DMN also moderated this positive association. Our results suggest that participation in exercise during childhood positively predicts response inhibition later in life and that this association can be moderated by changes in neuronal circuitry, such as increased cortical thickness and efficiency, and strengthened inter-hemispheric connectivity.

Small-World Networks and Their Relationship With Hippocampal Glutamine/Glutamate Concentration in Healthy Adults With Varying Genetic Risk for Alzheimer's Disease

Background

Apolipoprotein E ɛ4 allele (ApoE4) is the most common gene polymorphism related to Alzheimer's disease (AD). Impaired synaptic dysfunction occurs in ApoE4 carriers before any clinical symptoms. It remains unknown whether ApoE4 status affects the hippocampal neuromodulation, which further influences brain network topology.

Purpose

To study the relationship of regional and global network properties by using graph theory analysis and glutamatergic (Glx) neuromodulation in the ApoE isoforms.

Study Type

Prospective.

Subjects

Eighty‐four cognitively normal adults (26 ApoE4 and 58 non‐ApoE4 carriers).

Field Strength/Sequence

Gradient‐echo echo‐planar and point resolved spectroscopy sequence at 3 T.

Assessment

Glx concentration in bilateral hippocampi were processed with jMRUI (4.0), and graph theory metrics (global: γ, λ, small‐worldness in whole brain; regional: nodal clustering coefficient (C_i) and nodal characteristic path length (L_i)) in top 20% highly connected hubs of subgroups (low‐risk: non‐ApoE4; high‐risk: APOE4) were calculated and compared.

Statistical Tests

Two‐sample t test was used to compare metrics between subgroups. Correlations between regional properties and Glx by Pearson's partial correlation with false discovery rate correction.

Results

Significant differences (P < 0.05) in C_i between subgroups were found in hubs of left inferior frontal, bilateral inferior temporal, and bilateral precentral gyri, right parahippocampus, and bilateral precuneus. In addition, there was a significant correlation between Glx in the left hippocampus and C_i in inferior frontal gyrus (r = −0.537, P = 0.024), right inferior temporal (r = −0.478, P = 0.043), right parahippocampus (r = −0.629, P = 0.016), left precentral (r = −0.581, P = 0.022), right precentral (r = −0.651, P = 0.003), left precuneus (r = −0.545, P = 0.024), and right precuneus (r = −0.567, P = 0.022); and L_i in left precuneus (r = 0.575, P = 0.032) and right precuneus (r = 0.586, P = 0.032) in the high‐risk group, but not in the low‐risk group.

Data Conclusion

Our results suggested that healthy ApoE4 carriers exhibit poorer local interconnectivity. Moreover, the close relationship between glutamate and small‐world network properties in ApoE4 carriers might reflect a compensatory response to the impaired network efficiency.

Evidence Level

2

Technical Efficacy

Stage 3

The dissociative subtype of posttraumatic stress disorder is associated with subcortical white matter network alterations

Posttraumatic stress disorder (PTSD) is characterized by intrusions, avoidance, and hyperarousal while patients of the dissociative subtype (PTSD-D) experience additional dissociative symptoms. A neurobiological model proposes hyper-inhibition of limbic structures mediated by prefrontal cortices to underlie dissociation in PTSD. Here, we tested whether functional alterations in fronto-limbic circuits are underpinned by white matter network abnormalities on a network level. 23 women with PTSD-D and 19 women with classic PTSD participated. We employed deterministic diffusion tractography and graph theoretical analyses. Mean fractional anisotropy (FA) was chosen as a network weight and group differences assessed using network-based statistics. No significant white matter network alterations comprising both frontal and limbic structures in PTSD-D relative to classic PTSD were found. A subsequent whole brain exploratory analysis revealed relative FA alterations in PTSD-D in two subcortical networks, comprising connections between the left amygdala, hippocampus, and thalamus as well as links between the left ventral diencephalon, putamen, and pallidum, respectively. Dissociative symptom severity in the PTSD-D group correlated with FA values within both networks. Our findings suggest fronto-limbic inhibition in PTSD-D may present a dynamic neural process, which is not hard-wired via white matter tracts. Our exploratory results point towards altered fiber tract communication in a limbic-thalamic circuit, which may underlie (a) an initial strong emotional reaction to trauma reminders before conscious regulatory processes are enabled and (b) deficits in early sensory processing. In addition, aberrant structural connectivity in low-level motor regions may present neural correlates for dissociation as a passive threat-response.

The structure dilemma in biological and artificial neural networks

Brain research up to date has revealed that structure and function are highly related. Thus, for example, studies have repeatedly shown that the brains of patients suffering from schizophrenia or other diseases have a different connectome compared to healthy people. Apart from stochastic processes, however, an inherent logic describing how neurons connect to each other has not yet been identified. We revisited this structural dilemma by comparing and analyzing artificial and biological-based neural networks. Namely, we used feed-forward and recurrent artificial neural networks as well as networks based on the structure of the micro-connectome of C. elegans and of the human macro-connectome. We trained these diverse networks, which markedly differ in their architecture, initialization and pruning technique, and we found remarkable parallels between biological-based and artificial neural networks, as we were additionally able to show that the dilemma is also present in artificial neural networks. Our findings show that structure contains all the information, but that this structure is not exclusive. Indeed, the same structure was able to solve completely different problems with only minimal adjustments. We particularly put interest on the influence of weights and the neuron offset value, as they show a different adaption behaviour. Our findings open up new questions in the fields of artificial and biological information processing research.

Aberrant basal ganglia-thalamo-cortical network topology in juvenile absence epilepsy: A resting-state EEG-fMRI study

PURPOSE

The underlying pathophysiology of juvenile absence epilepsy (JAE) is unclear. Since cortical and subcortical brain regions are thought to be altered in genetic generalized epilepsy, the present study examined the resting-state functional network topology of the same regions in JAE.

METHODS

Electroencephalography and functional magnetic resonance imaging (EEG-fMRI) were performed on 18 JAE patients and 28 healthy controls (HCs). The topology of functional networks was analyzed using the graph-theoretic method. Both global and nodal network parameters were calculated, and parameters differing significantly between the two groups were correlated with clinical variables.

RESULTS

Both JAE patients and HCs had small-world functional network topological architectures. However, JAE patients showed higher values for the global parameters of clustering coefficient (Cp) and normalized characteristic path length (Lambda). At the nodal level, patients exhibited greater centrality at widespread cortices, including the left superior parietal gyrus, right superior temporal gyrus, right orbital part of middle frontal gyrus and bilateral supplementary motor area. Conversely, patients showed decreased nodal centrality predominantly in the limbic network, left thalamus and right caudate nucleus. Degree centrality in the right hippocampus and betweenness centrality in the right caudate nucleus positively correlated with epilepsy duration.

CONCLUSION

The global functional network of JAE shows small-world properties, but tends to be regular with higher segregation and lower integration. Regions in the basal ganglia-thalamo-cortical network have aberrant nodal centrality. The hippocampus and caudate nucleus may reorganize as epilepsy progresses. Our findings indicate the pathogenesis and compensatory mechanisms to seizure attacks and cognitive deficits of JAE.