A Note on the Connection Between Trek Rules and Separable Nonlinear Least Squares in Linear Structural Equation Models

We show that separable nonlinear least squares (SNLLS) estimation is applicable to all linear structural equation models (SEMs) that can be specified in RAM notation. SNLLS is an estimation technique that has successfully been applied to a wide range of models, for example neural networks and dynamic systems, often leading to improvements in convergence and computation time. It is applicable to models of a special form, where a subset of parameters enters the objective linearly. Recently, Kreiberg et al. (Struct Equ Model Multidiscip J 28(5):725-739, 2021. https://doi.org/10.1080/10705511.2020.1835484 ) have shown that this is also the case for factor analysis models. We generalize this result to all linear SEMs. To that end, we show that undirected effects (variances and covariances) and mean parameters enter the objective linearly, and therefore, in the least squares estimation of structural equation models, only the directed effects have to be obtained iteratively. For model classes without unknown directed effects, SNLLS can be used to analytically compute least squares estimates. To provide deeper insight into the nature of this result, we employ trek rules that link graphical representations of structural equation models to their covariance parametrization. We further give an efficient expression for the gradient, which is crucial to make a fast implementation possible. Results from our simulation indicate that SNLLS leads to improved convergence rates and a reduced number of iterations.

Static and dynamic topological organization of brain functional connectome in acute mild traumatic brain injury

BACKGROUND

Prior studies have detected topological changes of brain functional networks in patients with acute mild traumatic brain injury (mTBI). However, the alterations of dynamic topological characteristics in mTBI have been scarcely elucidated.

PURPOSE

To evaluate static and dynamic functional connectivity topological networks in patients with acute mTBI using resting-state functional magnetic resonance imaging (fMRI).

MATERIAL AND METHODS

A total of 55 patients with acute mTBI and 55 age-, sex-, and education-matched healthy controls (HCs) were enrolled in this study. All participants underwent resting-state fMRI scans, and data were analyzed using graph-theory methods and a sliding window approach. Post-traumatic cognitive performance and resting-state fMRI data were collected within one week after injury. Static and dynamic functional connectivity patterns were determined by independent component analysis. Spearman's correlation analysis was further performed between fMRI changes and Montreal cognitive assessment (MoCA) scores.

RESULTS

Global efficiency was lower (P = 0.02), and local efficiency (P < 0.001) and mean Cp (P < 0.001) were higher in patients with acute mTBI than in HCs. Local efficiency was correlated with visuospatial/executive performance (r = -0.421; P = 0.002) in patients with acute mTBI. Significant differences in nodal efficiency and node degree centrality (P < 0.01) were found between the mTBI and HC groups. For dynamic properties, patients with mTBI showed higher variance (P = 0.016) in global efficiency than HCs.

CONCLUSIONS

The present study shows that patients with mTBI have abnormal brain functional connectome topology, especially the dynamic graph theory characteristics, which provide new insights into the role of topological network properties in patients with acute mTBI.

Locally common graphs

Goodman proved that the sum of the number of triangles in a graph on n nodes and its complement is at least n 3 ∕ 24 ; in other words, this sum is minimized, asymptotically, by a random graph with edge density 1/2. Erdős conjectured that a similar inequality will hold for K 4 in place of K 3 , but this was disproved by Thomason. But an analogous statement does hold for some other graphs, which are called common graphs. Characterization of common graphs seems, however, out of reach. Franek and Rödl proved that K 4 is common in a weaker, local sense. Using the language of graph limits, we study two versions of locally common graphs. We sharpen a result of Jagger, Štovíček and Thomason by showing that no graph containing K 4 can be locally common, but prove that all such graphs are weakly locally common. We also show that not all connected graphs are weakly locally common.

The Effect of dry Needling in Chronic Stroke with a complex Network Approach: A Case Study

Background: Dry Needling (DN) has been demonstrated to be effective in improving sensorimotor function and spasticity in patients with chronic stroke. Electroencephalogram (EEG) has been used to analyze if DN has effects on the central nervous system of patients with stroke. There are no studies on how DN works in patients with chronic stroke based on EEG analysis using complex networks. Objective: The aim of this study was to assess how DN works when it is applied in a patient with stroke, using the graph theory. Methods: One session of DN was applied to the spastic brachialis muscle of a 62-year-old man with right hemiplegia after stroke. EEG was used to analyze the effects of DN following metrics that measure the topological configuration: 1) network density, 2) clustering coefficient, 3) average shortest path length, 4) betweenness centrality, and 5) small-worldness. Measurements were taken before and during DN. Results: An improvement of the brain activity was observed in this patient with stroke after the application of DN, which led to variations of local parameters of the brain network in the delta, theta and alpha bands, and inclined towards those of the healthy control bands. Conclusions: This case study showed the positive effects of DN on brain network of a patient with chronic stroke.

Exploring structural connectomes in children with unilateral cerebral palsy using graph theory

We explored structural brain connectomes in children with spastic unilateral cerebral palsy (uCP) and its relation to sensory-motor function using graph theory. In 46 children with uCP (mean age = 10 years 7 months ± 2 years 9 months; Manual Ability Classification System I = 15, II = 16, III = 15) we assessed upper limb somatosensory and motor function. We collected multi-shell diffusion-weighted, T1-weighted and T2-FLAIR MRI and identified the corticospinal tract (CST) wiring pattern using transcranial magnetic stimulation. Structural connectomes were constructed using Virtual Brain Grafting-modified FreeSurfer parcellations and multi-shell multi-tissue constrained spherical deconvolution-based anatomically-constrained tractography. Graph metrics (characteristic path length, global/local efficiency and clustering coefficient) of the whole brain, the ipsilesional/contralesional hemisphere, and the full/ipsilesional/contralesional sensory-motor network were compared between lesion types (periventricular white matter (PWM) = 28, cortical and deep gray matter (CDGM) = 18) and CST-wiring patterns (ipsilateral = 14, bilateral = 14, contralateral = 12, unknown = 6) using ANCOVA with age as covariate. Using elastic-net regularized regression we investigated how graph metrics, lesion volume, lesion type, CST-wiring pattern and age predicted sensory-motor function. In both the whole brain and subnetworks, we observed a hyperconnectivity pattern in children with CDGM-lesions compared with PWM-lesions, with higher clustering coefficient (p = [<.001-.047], η p 2 $$ {\eta}_p^2 $$ =[0.09-0.27]), characteristic path length (p = .003, η p 2 $$ {\eta}_p^2 $$ =0.19) and local efficiency (p = [.001-.02], η p 2 $$ {\eta}_p^2 $$ =[0.11-0.21]), and a lower global efficiency with age (p = [.01-.04], η p 2 $$ {\eta}_p^2 $$ =[0.09-0.15]). No differences were found between CST-wiring groups. Overall, good predictions of sensory-motor function were obtained with elastic-net regression (R²  = .40-.87). CST-wiring pattern was the strongest predictor for motor function. For somatosensory function, all independent variables contributed equally to the model. In conclusion, we demonstrated the potential of structural connectomics in understanding disease severity and brain development in children with uCP.

Brain network deficits in breast cancer patients after early neoadjuvant chemotherapy: A longitudinal MRI study

Breast cancer (BC) patients who undergo chemotherapy are likely to develop chemotherapy-related cognitive impairment (CRCI). Recent studies of BC patients after chemotherapy have used graph theory to investigate the topological properties of the brain functional connectome. However, little is known about structural morphological networks in BC patients after early neoadjuvant chemotherapy (NAC). Brain morphological network organization in 47 female participants with BC was investigated before and after NAC. Topological properties of brain networks were ascertained based on morphological similarities in regional gray matter using a graph theory approach based on 3D T1-weighted MRI data. Nonparametric permutation testing was used to assess longitudinal-group differences in topological metrics. Compared with BC patients before NAC, BC patients after early NAC showed significantly increased global efficiency (p = .048), decreased path length (p = .033), and abnormal nodal properties and connectivity, mainly located in the central executive network (CEN). The change in the network efficiency of the right caudate was negatively correlated with the change in the Self-Rating Anxiety Scale score (r = -.435, p = .008), and the change in the nodal degree of the left superior frontal gyrus (dorsolateral part) was positively correlated with the change in the Functional Assessment of Cancer Therapy score (r = .547, p = .002). BC participants showed randomization in global properties and dysconnectivity in the CEN after early NAC. NAC may disrupt the cognitive balance of the brain morphological network in individuals with BC.

Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS

Background

Patients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients.

Methods

We collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups.

Results

Compared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores.

Conclusion

This study reveals that MCS patients' frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.

Cognitive performance and brain structural connectome alterations in major depressive disorder

BACKGROUND

Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks.

METHODS

Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength.

RESULTS

All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course.

CONCLUSIONS

Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.

Different computational relations in language are captured by distinct brain systems

A critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.

Adaptive and pathological connectivity responses in Parkinson's disease brain networks

Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.

Topological network properties of resting-state functional connectivity patterns are associated with metal mixture exposure in adolescents

Introduction

Adolescent exposure to neurotoxic metals adversely impacts cognitive, motor, and behavioral development. Few studies have addressed the underlying brain mechanisms of these metal-associated developmental outcomes. Furthermore, metal exposure occurs as a mixture, yet previous studies most often consider impacts of each metal individually. In this cross-sectional study, we investigated the relationship between exposure to neurotoxic metals and topological brain metrics in adolescents.

Methods

In 193 participants (53% females, ages: 15-25 years) enrolled in the Public Health Impact of Metals Exposure (PHIME) study, we measured concentrations of four metals (manganese, lead, copper, and chromium) in multiple biological media (blood, urine, hair, and saliva) and acquired resting-state functional magnetic resonance imaging scans. Using graph theory metrics, we computed global and local efficiency (global:GE; local:LE) in 111 brain areas (Harvard Oxford Atlas). We used weighted quantile sum (WQS) regression models to examine association between metal mixtures and each graph metric (GE or LE), adjusted for sex and age.

Results

We observed significant negative associations between the metal mixture and GE and LE [βGE = -0.076, 95% CI (-0.122, -0.031); βLE= -0.051, 95% CI (-0.095, -0.006)]. Lead and chromium measured in blood contributed most to this association for GE, while chromium measured in hair contributed the most for LE.

Discussion

Our results suggest that exposure to this metal mixture during adolescence reduces the efficiency of integrating information in brain networks at both local and global levels, informing potential neural mechanisms underlying the developmental toxicity of metals. Results further suggest these associations are due to combined joint effects to different metals, rather than to a single metal.

MEG Node Degree for Focus Localization: Comparison with Invasive EEG

Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may add information to the workup of epilepsy surgery. Here, we investigate node degree (ND), a graph-theoretical parameter of functional connectivity, in relation to the seizure onset zone (SOZ) determined by invasive EEG (iEEG) in a consecutive series of 50 adult patients. Resting state data were subjected to whole brain, all-to-all connectivity analysis using the imaginary part of coherence. Graphs were described using parcellated ND. SOZ localization was investigated on a lobar and sublobar level. On a lobar level, all frequency bands except alpha showed significantly higher maximal ND (mND) values inside the SOZ compared to outside (ratios 1.11-1.20, alpha 1.02). Area-under-the-curve (AUC) was 0.67-0.78 for all expected alpha (0.44, ns). On a sublobar level, mND inside the SOZ was higher for all frequency bands (1.13-1.38, AUC 0.58-0.78) except gamma (1.02). MEG ND is significantly related to SOZ in delta, theta and beta bands. ND may provide new localization tools for presurgical evaluation of epilepsy surgery.

Cerebellar functional disruption and compensation in mesial temporal lobe epilepsy

Background

Cerebellar functional alterations are common in patients with mesial temporal lobe epilepsy (MTLE), which contribute to cognitive decline. This study aimed to deepen our knowledge of cerebellar functional alterations in patients with MTLE.

Methods

In this study, participants were recruited from an ongoing prospective cohort of 13 patients with left TLE (LTLE), 17 patients with right TLE (RTLE), and 30 healthy controls (HCs). Functional magnetic resonance imaging data were collected during a Chinese verbal fluency task. Group independent component (IC) analysis (group ICA) was applied to segment the cerebellum into six functionally separated networks. Functional connectivity was compared among cerebellar networks, cerebellar activation maps, and the centrality parameters of cerebellar regions. For cerebellar functional profiles with significant differences, we calculated their correlation with clinical features and neuropsychological scores.

Result

Compared to HCs and patients with LTLE, patients with RTLE had higher cerebellar functional connectivity between the default mode network (DMN) and the oculomotor network and lower cerebellar functional connectivity from the frontoparietal network (FPN) to the dorsal attention network (DAN) (p < 0.05, false discovery rate- (FDR-) corrected). Cerebellar degree centrality (DC) of the right lobule III was significantly higher in patients with LTLE compared to HC and patients with RTLE (p < 0.05, FDR-corrected). Higher cerebellar functional connectivity between the DMN and the oculomotor network, as well as lower cerebellar degree centrality of the right lobule III, was correlated with worse information test performance.

Conclusion

Cerebellar functional profiles were altered in MTLE and correlated with long-term memory in patients.

Touchscreen-Based Cognitive Training Alters Functional Connectivity Patterns in Aged But Not Young Male Rats

Age-related cognitive decline is related to cellular and systems-level disruptions across multiple brain regions. Because age-related cellular changes within different structures do not show the same patterns of dysfunction, interventions aimed at optimizing function of large-scale brain networks may show greater efficacy at improving cognitive outcomes in older adults than traditional pharmacotherapies. The current study aimed to leverage a preclinical rat model of aging to determine whether cognitive training in young and aged male rats with a computerized paired-associates learning (PAL) task resulted in changes in global resting-state functional connectivity. Moreover, seed-based functional connectivity was used to examine resting state connectivity of cortical areas involved in object-location associative memory and vulnerable in old age, namely the medial temporal lobe (MTL; hippocampal cortex and perirhinal cortex), retrosplenial cortex (RSC), and frontal cortical areas (prelimbic and infralimbic cortices). There was an age-related increase in global functional connectivity between baseline and post-training resting state scans in aged, cognitively trained rats. This change in connectivity following cognitive training was not observed in young animals, or rats that traversed a track for a reward between scan sessions. Relatedly, an increase in connectivity between perirhinal and prelimbic cortices, as well as reduced reciprocal connectivity within the RSC, was found in aged rats that underwent cognitive training, but not the other groups. Subnetwork activation was associated with task performance across age groups. Greater global functional connectivity and connectivity between task-relevant brain regions may elucidate compensatory mechanisms that can be engaged by cognitive training.

Immediate Effects of Vagal Nerve Stimulation in Drug-Resistant Epilepsy Revealed by Magnetoencephalographic Recordings

Objective: Vagus nerve stimulation (VNS) has been a neuromodulatory option for treating drug-resistant epilepsy (DRE), but its mechanism remains unclear. To obtain insight into the mechanism by which VNS reduces epileptic seizures, the immediate effects of VNS in brain networks of DRE patients were investigated when the patients' vagal nerve stimulators were turned on. Methods: The brain network properties of 14 DRE patients with a vagal nerve stimulator and 14 healthy controls were evaluated using magnetoencephalography recordings for 6 main frequency bands. Results: Compared with healthy controls, DRE patients exhibited significant increases in functional connectivity in the theta, alpha, beta, and gamma bands and significant reductions in the small-world measure in the theta and beta bands. During periods when patients' vagal nerve stimulators were turned on, DRE patients showed significant reductions in functional connectivity in the theta and alpha bands and a significant increase in the small-world measure in the theta band when compared with periods when patients' vagal nerve stimulators were turned off. Conclusions: Our results indicate that the brain networks of DRE patients were pathologically hypersynchronous and instantaneous VNS can decrease the synchronization of brain networks of epileptic patients, which might play a key role in the mechanism by which VNS reduces epileptic seizures. In the theta band, instantaneous VNS can increase the network efficiency of DRE patients, and the increment in network efficiency may be helpful for improving brain cognitive function in epileptic patients. Impact statement For the first time, we investigated the immediate effects of vagus nerve stimulation (VNS) in the brain networks of drug-resistant epilepsy patients using magnetoencephalography. Our results show that instantaneous VNS can decrease the hypersynchronization of epileptic networks and increase the network efficiency of epileptic patients. Our results are helpful in understanding the mechanism of action by which VNS reduces epileptic seizures and improves the cognitive function in epileptic patients and the brain network reorganization caused by long-term VNS.

Linking and unlinking the paediatric brain: age-invariant neural correlates of general anaesthesia

There is no single electroencephalographic metric for general anaesthesia that is validated for both children and adults. This is, in part, because of the changing electroencephalographic features associated with development. Here, we discuss how alterations in correlated brain activity during general anaesthesia advance our understanding of anaesthetic monitoring and the neurobiology of consciousness.

Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review

(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.

Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms

Introduction

Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections).

Methods

Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used.

Results

Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants.

Discussion

In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.

Rehabilitation assisted by Space technology-A SAHC approach in immobilized patients-A case of stroke

Introduction: The idea behind the presentation of this case relates to utilizing space technology in earth applications with mutual benefit for both patients confined to bed and astronauts. Deconditioning and the progressiveness of skeletal muscle loss in the absence of adequate gravity stimulus have been of physiological concern. A robust countermeasure to muscle disuse is still a challenge for both immobilized patients and astronauts in long duration space missions. Researchers in the space medicine field concluded that artificial gravity (AG) produced by short-radius centrifugation on a passive movement therapy device, combined with exercise, has been a robust multi-system countermeasure as it re-introduces an acceleration field and gravity load. Methods: A short-arm human centrifuge (SAHC) alone or combined with exercise was evaluated as a novel, artificial gravity device for an effective rehabilitation strategy in the case of a stroke patient with disability. The results reveal valuable information on an individualized rehabilitation strategy against physiological deconditioning. A 73-year-old woman was suddenly unable to speak, follow directions or move her left arm and leg. She could not walk, and self-care tasks required maximal assistance. Her condition was getting worse over the years, also she was receiving conventional rehabilitation treatment. Intermittent short-arm human centrifuge individualized protocols were applied for 5 months, three times a week, 60 treatments in total. Results: It resulted in significant improvement in her gait, decreased atrophy with less spasticity on the left body side, and ability to walk at least 100 m with a cane. Balance and muscle strength were improved significantly. Cardiovascular parameters improved responding to adaptations to aerobic exercise. Electroencephalography (EEG) showed brain reorganization/plasticity evidenced through functional connectivity alterations and activation in the cortical regions, especially of the precentral and postcentral gyrus. Stroke immobility-related disability was also improved. Discussion: These alterations were attributed to the short-arm human centrifuge intervention. This case study provides novel evidence supporting the use of the short-arm human centrifuge as a promising therapeutic strategy in patients with restricted mobility, with application to astronauts with long-term muscle disuse in space.

Graph Theory Measures and Their Application to Neurosurgical Eloquence

Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.

Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach

Age-associated changes in brain function play an important role in the development of neurodegenerative diseases. Although previous work has examined age-related changes in static functional connectivity, accumulating evidence suggests that advancing age is especially associated with alterations in the dynamic interactions and transitions between different brain states, which hitherto have received less attention. Conclusions of previous studies in this domain are moreover limited by suboptimal replicability of resting-state functional magnetic resonance imaging (fMRI) and culturally homogenous cohorts. Here, we investigate the robustness of age-associated changes in dynamic functional connectivity (dFC) by capitalizing on the availability of fMRI cohorts from two cultures (Western European and Chinese). In both the LEMON (Western European) and SALD (Chinese) cohorts, we consistently identify two distinct states: a more frequent segregated within-network connectivity state (state I) and a less frequent integrated between-network connectivity state (state II). Moreover, in both these cohorts, older (55-80 years) compared to younger participants (20-35 years) exhibited lower occurrence of and spent less time in state I. Older participants also tended to exhibit more transitions between networks and greater variance in global efficiency. Overall, our cross-cultural replication of age-associated changes in dFC metrics implies that advancing age is robustly associated with a reorganization of dynamic brain activation that favors the use of less functionally specific networks.

Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks

Background and purpose

Patients with asymptomatic carotid stenosis, even without stroke, are at high risk for cognitive impairment, and the neuroanatomical basis remains unclear. Using a novel edge-centric structural connectivity (eSC) analysis from individualized single-subject cortical thickness networks, we aimed to examine eSC and network measures in severe (> 70%) asymptomatic carotid stenosis (SACS).

Methods

Twenty-four SACS patients and 24 demographically- and comorbidities-matched controls were included, and structural MRI and multidomain cognitive data were acquired. Individual eSC was estimated via the Manhattan distances of pairwise cortical thickness histograms.

Results

In the eSC analysis, SACS patients showed longer interhemispheric but shorter intrahemispheric Manhattan distances seeding from left lateral temporal regions; in network analysis the SACS patients had a decreased system segregation paralleling with white matter hyperintensity burden and recall memory. Further network-based statistic analysis identified several eSC and subgraph features centred around the Perisylvian regions that predicted silent lesion load and cognitive tests.

Conclusion

We conclude that SACS exhibits abnormal eSC and a less-optimized trade-off between physical cost and network segregation, providing a reference and perspective for identifying high-risk individuals.

Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales

Background

Brain assessment shows great values in prognosis, treatment, resource allocation, and decision-making for patients with disorders of consciousness (DOC). However, less research focused on cortical conditions of patients with unresponsive wakefulness syndrome (UWS).

Methods

We recorded resting-state EEG and TMS-EEG from post-anoxic patients with UWS, diagnosed by repeated Coma Recovery Scale-Revised (CRS-R). Measurements of functional connectivity and networks were performed by phase lock value (PLV) and network parameters of graph theory (average path length, clustering coefficient, and small-world). Global cortical reactivity values (GCRV) were used to assess cortical excitability.

Results

The coefficient of variation (CV) presented marked inter-individual variations of PLV (CV = 0.285), network parameters (CV > 0.2), and GCRV (CV = 0.929) within these patients. The patients' PLV and network parameters at theta and alpha bands significantly correlated with their GCRV values. Patients with higher PLV (r = 0.560, 0.406), as well as better preserved network (lower average path length (r = -0.522, -0.483), higher clustering coefficient (r = 0.522, 0.445), and small-world (r = 0.522, 0.445) at theta and alpha bands, presented higher GCRV. The functional connectivity, which is significantly correlated with frontal GCRV, is also mainly located in the frontal region. These correlations were not significant at other frequency bands: Delta, beta, and gamma bands.

Conclusion

These findings suggested that the CRS-R-diagnosed post-anoxic patients with UWS had very different cortical conditions. Functional networks and cortical excitability measured by TMS-EEG could complement behavioral assessment to assess these patients' cortical conditions.

Significance

It provides a deeper understanding of neurophysiological dysfunction in patients with UWS and hints to the clinics that neural-electrophysiological assessment for such patients may be necessary to acquire their brain conditions, which may benefit stratified management for them.

Graph identification of proteins in tomograms (GRIP-Tomo)

In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three-dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo-multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single-domain protein native topologies as well as distinct single-domain proteins from multimeric complexes within noisy volumes.

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.

Abnormal structural and functional network topological properties associated with left prefrontal, parietal, and occipital cortices significantly predict childhood TBI-related attention deficits: A semi-supervised deep learning study

Introduction

Traumatic brain injury (TBI) is a major public health concern in children. Children with TBI have elevated risk in developing attention deficits. Existing studies have found that structural and functional alterations in multiple brain regions were linked to TBI-related attention deficits in children. Most of these existing studies have utilized conventional parametric models for group comparisons, which have limited capacity in dealing with large-scale and high dimensional neuroimaging measures that have unknown nonlinear relationships. Nevertheless, none of these existing findings have been successfully implemented to clinical practice for guiding diagnoses and interventions of TBI-related attention problems. Machine learning techniques, especially deep learning techniques, are able to handle the multi-dimensional and nonlinear information to generate more robust predictions. Therefore, the current research proposed to construct a deep learning model, semi-supervised autoencoder, to investigate the topological alterations in both structural and functional brain networks in children with TBI and their predictive power for post-TBI attention deficits.

Methods

Functional magnetic resonance imaging data during sustained attention processing task and diffusion tensor imaging data from 110 subjects (55 children with TBI and 55 group-matched controls) were used to construct the functional and structural brain networks, respectively. A total of 60 topological properties were selected as brain features for building the model.

Results

The model was able to differentiate children with TBI and controls with an average accuracy of 82.86%. Functional and structural nodal topological properties associated with left frontal, inferior temporal, postcentral, and medial occipitotemporal regions served as the most important brain features for accurate classification of the two subject groups. Post hoc regression-based machine learning analyses in the whole study sample showed that among these most important neuroimaging features, those associated with left postcentral area, superior frontal region, and medial occipitotemporal regions had significant value for predicting the elevated inattentive and hyperactive/impulsive symptoms.

Discussion

Findings of this study suggested that deep learning techniques may have the potential to help identifying robust neurobiological markers for post-TBI attention deficits; and the left superior frontal, postcentral, and medial occipitotemporal regions may serve as reliable targets for diagnosis and interventions of TBI-related attention problems in children.

Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy

Objective

This study aims to investigate the impact of vagus nerve stimulation (VNS) on the connectivity and small-world metrics of brain functional networks during seizure periods.

Methods

Ten refractory epilepsy patients underwent video encephalographic monitoring before and after VNS treatment. The 2-min electroencephalogram segment containing the ictal was selected for each participant, resulting in a total of 20 min of seizure data. The weighted phase lag index (wPLI) and small-world metrics were calculated for the whole frequency band and different frequency bands (delta, theta, alpha, beta, and gamma). Finally, the relevant metrics were statistically analyzed, and the false discovery rate was used to correct for differences after multiple comparisons.

Results

In the whole band, the wPLI was notably enhanced, and the network metrics, including degree (D), clustering coefficient (CC), and global efficiency (GE), increased, while characteristic path length (CPL) decreased (P < 0.01). In different frequency bands, the wPLI between the parieto-occipital and frontal regions was significantly strengthened in the delta and beta bands, while the wPLI within the frontal region and between the frontal and parieto-occipital regions were significantly reduced in the beta and gamma bands (P < 0.01). In the low-frequency band (<13 Hz), the small-world metrics demonstrated significantly increased CC, D, and GE, with a significantly decreased CPL, indicating a more efficient network organization. In contrast, in the gamma band, the GE decreased, and the CPL increased, suggesting a shift toward less efficient network organization.

Conclusion

VNS treatment can significantly change the wPLI and small-world metrics. These findings contribute to a deeper understanding of the impact of VNS therapy on brain networks and provide objective indicators for evaluating the efficacy of VNS.

Tracking functional network connectivity dynamics in the elderly

Introduction

Functional magnetic resonance imaging (fMRI) has shown that aging disturbs healthy brain organization and functional connectivity. However, how this age-induced alteration impacts dynamic brain function interaction has not yet been fully investigated. Dynamic function network connectivity (DFNC) analysis can produce a brain representation based on the time-varying network connectivity changes, which can be further used to study the brain aging mechanism for people at different age stages.

Method

This presented investigation examined the dynamic functional connectivity representation and its relationship with brain age for people at an elderly stage as well as in early adulthood. Specifically, the resting-state fMRI data from the University of North Carolina cohort of 34 young adults and 28 elderly participants were fed into a DFNC analysis pipeline. This DFNC pipeline forms an integrated dynamic functional connectivity (FC) analysis framework, which consists of brain functional network parcellation, dynamic FC feature extraction, and FC dynamics examination.

Results

The statistical analysis demonstrates that extensive dynamic connection changes in the elderly concerning the transient brain state and the method of functional interaction in the brain. In addition, various machine learning algorithms have been developed to verify the ability of dynamic FC features to distinguish the age stage. The fraction time of DFNC states has the highest performance, which can achieve a classification accuracy of over 88% by a decision tree.

Discussion

The results proved there are dynamic FC alterations in the elderly, and the alteration was found to be correlated with mnemonic discrimination ability and could have an impact on the balance of functional integration and segregation.

Functional contextual implementation of an evolutionary, entropy-based, and embodied free energy framework: Utilizing Lagrangian mechanics and evolutionary game theory's truth vs. fitness test of the veridicality of phenomenological experience

The Bayesian approach of cognitive science largely takes the position that evolution drives perception to produce precepts that are veridical. However, some efforts utilizing evolutionary game theory simulations have shown that perception is more likely based on a fitness function, which promotes survival rather than promoting perceptual truth about the environment. Although these findings do not correspond well with the standard Bayesian approach to cognition, they may correspond with a behavioral functional contextual approach that is ontologically neutral (a-ontological). This approach, formalized through a post-Skinnerian account of behaviorism called relational frame theory (RFT), can, in fact, be shown to correspond well with an evolutionary fitness function, whereby contextual functions form that corresponds to a fitness function interface of the world. This fitness interface approach therefore may help provide a mathematical description for a functional contextual interface of phenomenological experience. Furthermore, this more broadly fits with a neurological active inference approach based on the free-energy principle (FEP) and more broadly with Lagrangian mechanics. These assumptions of how fitness beats truth (FBT) and FEP correspond to RFT are then discussed within a broader multidimensional and evolutionary framework called the extended evolutionary meta-model (EEMM) that has emerged out of the functional contextual behavioral science literature to incorporate principles of cognition, neurobiology, behaviorism, and evolution and are discussed in the context of a novel RFT framework called "Neurobiological and Natural Selection Relational Frame Theory" (N-frame). This framework mathematically connects RFT to FBT, FEP, and EEMM within a single framework that expands into dynamic graph networking. This is then discussed for its implications of empirical work at the non-ergodic process-based idiographic level as applied to individual and societal level dynamic modeling and clinical work. This discussion is framed within the context of individuals that are described as evolutionary adaptive and conscious (observer-self) agents that minimize entropy and can promote a prosocial society through group-level values and psychological flexibility.

Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment

Objective

Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data.

Methods

The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed.

Results

Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI.

Conclusion

Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.

Brain diffusion tensor imaging reveals altered connections and networks in epilepsy patients

Introduction

Accumulating evidence shows that epilepsy is a disease caused by brain network dysfunction. This study explored changes in brain network structure in epilepsy patients based on graph analysis of diffusion tensor imaging data.

Methods

The brain structure networks of 42 healthy control individuals and 26 epilepsy patients were constructed. Using graph theory analysis, global and local network topology parameters of the brain structure network were calculated, and changes in global and local characteristics of the brain network in epilepsy patients were quantitatively analyzed.

Results

Compared with the healthy control group, the epilepsy patient group showed lower global efficiency, local efficiency, clustering coefficient, and a longer shortest path length. Both healthy control individuals and epilepsy patients showed small-world attributes, with no significant difference between groups. The epilepsy patient group showed lower nodal local efficiency and nodal clustering coefficient in the right olfactory cortex and right rectus and lower nodal degree centrality in the right olfactory cortex and the left paracentral lobular compared with the healthy control group. In addition, the epilepsy patient group showed a smaller fiber number of edges in specific regions of the frontal lobe, temporal lobe, and default mode network, indicating reduced connection strength.

Discussion

Epilepsy patients exhibited lower global and local brain network properties as well as reduced white matter fiber connectivity in key brain regions. These findings further support the idea that epilepsy is a brain network disorder.

Problem-solving training modifies cognitive functioning and related functional connectivity in healthy adults

Cognitive functioning evolves throughout life. Regular practice of stimulating activities maintains or even strengthens cognitive skills. This study investigated the effects of a cognitive training programme based on complex closed-ended problem solving on innovative thinking. To this end, using partial least squares variance-based structural equation modeling, we first evaluated in 83 healthy adults how inhibition, cognitive flexibility, and reasoning were related to the distinct dimensions of innovative thinking. Second, we assessed how these interactions were modified after cognitive training based on problem solving in a subgroup of 16 subjects compared to leisure activity based on crossword solving in another subgroup of 15 subjects. Third, in a pilot fMRI study, we evaluated changes in brain connectivity at rest as a result of training in the problem solving group. Data on cognitive measures showed that innovative thinking was influenced by reasoning in control subjects, whereas it was influenced by cognitive flexibility following problem-solving training. These findings highlight that a cognitive intervention based on complex closed-ended problem solving promotes innovative thinking by changing the way subjects recruit and use relevant cognitive processes. Modifications in the resting-state connectivity of attention, default mode and visual networks were observed in the problem solving group.

Low-dissipation optimization of the prefrontal cortex in the -12° head-down tilt position: A functional near-infrared spectroscopy study

Introduction

Our present study set out to investigate the instant state of the prefrontal cortex (PFC) in healthy subjects before and after placement in the -12°head-down tilt (HDT) position in order to explore the mechanism behind the low-dissipation optimization state of the PFC.

Methods

40 young, right-handed healthy subjects (male: female = 20: 20) were enrolled in this study. Three resting state positions, 0°initial position, -12°HDT position, and 0°rest position were sequentially tested, each for 10 minutes. A continuous-wave functional near-infrared spectroscopy (fNIRS) instrument was used to assess the resting state hemodynamic data of the PFC. After preprocessing the hemodynamics data, we evaluated changes in resting-state functional connectivity (rsFC) level and beta values of PFC. The subjective visual analogue scale (VAS) was applied before and after the experiment. The presence of sleep changes or adverse reactions were also recorded.

Results

Pairwise comparisons of the concentrations of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and hemoglobin (HbT) revealed significant differences in the aforementioned positions. Specifically, the average rsFC of PFC showed a gradual increase throughout the whole process. In addition, based on graph theory, the topological properties of brain network, such as small-world network and nodal degree centrality were analyzed. The results show that global efficiency and small-world sigma (σ) value were differences between 0°initial and 0°rest.

Discussion

In this study, placement in the -12°HDT had a significant effect on PFC function, mainly manifested as self-inhibition, decreased concentration of HbO in the PFC, and improved rsFC, which may provide ideas to the understanding and explanation of neurological diseases.

Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame

BACKGROUND

A concise visualization framework of related reports would increase readability and improve patient management. To this end, temporal referrals to prior comparative exams are an essential connection to previous exams in written reports. Due to unstructured narrative texts' variable structure and content, their extraction is hampered by poor computer readability. Natural language processing (NLP) permits the extraction of structured information from unstructured texts automatically and can serve as an essential input for such a novel visualization framework.

OBJECTIVE

This study proposes and evaluates an NLP-based algorithm capable of extracting the temporal referrals in written radiology reports, applies it to all the radiology reports generated for 10 years, introduces a graphical representation of imaging reports, and investigates its benefits for clinical and research purposes.

METHODS

In this single-center, university hospital, retrospective study, we developed a convolutional neural network capable of extracting the date of referrals from imaging reports. The model's performance was assessed by calculating precision, recall, and F1-score using an independent test set of 149 reports. Next, the algorithm was applied to our department's radiology reports generated from 2011 to 2021. Finally, the reports and their metadata were represented in a modulable graph.

RESULTS

For extracting the date of referrals, the named-entity recognition (NER) model had a high precision of 0.93, a recall of 0.95, and an F1-score of 0.94. A total of 1,684,635 reports were included in the analysis. Temporal reference was mentioned in 53.3% (656,852/1,684,635), explicitly stated as not available in 21.0% (258,386/1,684,635), and omitted in 25.7% (317,059/1,684,635) of the reports. Imaging records can be visualized in a directed and modulable graph, in which the referring links represent the connecting arrows.

CONCLUSIONS

Automatically extracting the date of referrals from unstructured radiology reports using deep learning NLP algorithms is feasible. Graphs refined the selection of distinct pathology pathways, facilitated the revelation of missing comparisons, and enabled the query of specific referring exam sequences. Further work is needed to evaluate its benefits in clinics, research, and resource planning.

Longitudinal changes in functional connectivity and pain-induced brain activations in patients with migraine: a functional MRI study pre- and post- treatment with Erenumab

Abstract

Background

Migraine involves central and peripheral nervous system mechanisms. Erenumab, an anti-calcitonin gene-related peptide (CGRP) receptor monoclonal antibody with little central nervous system penetrance, is effective for migraine prevention. The objective of this study was to determine if response to erenumab is associated with alterations in brain functional connectivity and pain-induced brain activations.

Methods

Adults with 6–25 migraine days per month during a 4-week headache diary run-in phase underwent pre-treatment brain functional MRI (fMRI) that included resting-state functional connectivity and BOLD measurements in response to moderately painful heat stimulation to the forearm. This was followed by two treatments with 140 mg erenumab, at baseline and 4 weeks later. Post-treatment fMRI was performed 2 weeks and 8 weeks following the first erenumab treatment. A longitudinal Sandwich estimator analysis was used to identify pre- to post-treatment changes in resting-state functional connectivity and brain activations in response to thermal pain. fMRI findings were compared between erenumab treatment-responders vs. erenumab non-responders.

Results

Pre- and post-treatment longitudinal imaging data were available from 32 participants. Average age was 40.3 (+/− 13) years and 29 were female. Pre-treatment average migraine day frequency was 13.8 (+/− 4.7) / 28 days and average headache day frequency was 15.8 (+/− 4.4) / 28 days. Eighteen of 32 (56%) were erenumab responders. Compared to erenumab non-responders, erenumab responders had post-treatment differences in 1) network functional connectivity amongst pain-processing regions, including higher global efficiency, clustering coefficient, node degree, regional efficiency, and modularity, 2) region-to-region functional connectivity between several regions including temporal pole, supramarginal gyrus, and hypothalamus, and 3) pain-induced activations in the middle cingulate, posterior cingulate, and periaqueductal gray matter.

Conclusions

Reductions in migraine day frequency accompanying erenumab treatment are associated with changes in resting state functional connectivity and central processing of extracranial painful stimuli that differ from erenumab non-responders.

Trial registration

clinicaltrials.gov (NCT03773562).

Investigating the Diversity of Tuberculosis Spoligotypes with Dimensionality Reduction and Graph Theory

The spoligotype is a graphical description of the CRISPR locus present in Mycobacterium tuberculosis, which has the particularity of having only 68 possible spacers. This spoligotype, which can be easily obtained either in vitro or in silico, allows to have a summary information of lineage or even antibiotic resistance (when known to be associated to a particular cluster) at a lower cost. The objective of this article is to show that this representation is richer than it seems, and that it is under-exploited until now. We first recall an original way to represent these spoligotypes as points in the plane, allowing to highlight possible sub-lineages, particularities in the animal strains, etc. This graphical representation shows clusters and a skeleton in the form of a graph, which led us to see these spoligotypes as vertices of an unconnected directed graph. In this paper, we therefore propose to exploit in detail the description of the variety of spoligotypes using a graph, and we show to what extent such a description can be informative.

Integration of structural brain networks is related to openness to experience: A diffusion MRI study with CSD-based tractography

Openness to experience is one of the big five traits of personality which recently has been the subject of several studies in neuroscience due to its importance in understanding various cognitive functions. However, the neural basis of openness to experience is still unclear. Previous studies have found largely heterogeneous results, suggesting that various brain regions may be involved in openness to experience. Here we suggested that performing structural connectome analysis may shed light on the neural underpinnings of openness to experience as it provides a more comprehensive look at the brain regions that are involved in this trait. Hence, we investigated the involvement of brain network structural features in openness to experience which has not yet been explored to date. The magnetic resonance imaging (MRI) data along with the openness to experience trait score from the self-reported NEO Five-Factor Inventory of 100 healthy subjects were evaluated from Human Connectome Project (HCP). CSD-based whole-brain probabilistic tractography was performed using diffusion-weighted images as well as segmented T1-weighted images to create an adjacency matrix for each subject. Using graph theoretical analysis, we computed global efficiency (GE) and clustering coefficient (CC) which are measures of two important aspects of network organization in the brain: functional integration and functional segregation respectively. Results revealed a significant negative correlation between GE and openness to experience which means that the higher capacity of the brain in combining information from different regions may be related to lower openness to experience.

Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease

AIMS

This study aimed to characterize the topological alterations and classification performance of high-order functional connectivity (HOFC) networks in cognitively preserved patients with Parkinson's disease (PD), relative to low-order FC (LOFC) networks.

METHODS

The topological metrics of the constructed networks (LOFC and HOFC) obtained from fifty-one cognitively normal patients with PD and 60 matched healthy control subjects were analyzed. The discriminative abilities were evaluated using machine learning approach.

RESULTS

The HOFC networks in the PD group showed decreased segregation and integration. The normalized clustering coefficient and small-worldness in the HOFC networks were correlated to motor performance. The altered nodal centralities (distributed in the precuneus, putamen, lingual gyrus, supramarginal gyrus, motor area, postcentral gyrus and inferior occipital gyrus) and intermodular FC (frontoparietal and visual networks, sensorimotor and subcortical networks) were specific to HOFC networks. Several highly connected nodes (thalamus, paracentral lobule, calcarine fissure and precuneus) and improved classification performance were found based on HOFC profiles.

CONCLUSION

This study identified disrupted topology of functional interactions at a high level with extensive alterations in topological properties and improved differentiation ability in patients with PD prior to clinical symptoms of cognitive impairment, providing complementary insights into complex neurodegeneration in PD.

Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

INTRODUCTION

Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence.

METHODS

A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods.

RESULTS

Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers.

DISCUSSION

On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.

Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales

Graph theoretical measures have frequently been used to study disrupted connectivity in Alzheimer's disease human brain connectomes. However, prior studies have noted that differences in graph creation methods are confounding factors that may alter the topological observations found in these measures. In this study, we conduct a novel investigation regarding the effect of parcellation scale on graph theoretical measures computed for fiber density networks derived from diffusion tensor imaging. We computed 4 network-wide graph theoretical measures of average clustering coefficient, transitivity, characteristic path length, and global efficiency, and we tested whether these measures are able to consistently identify group differences among healthy control (HC), mild cognitive impairment (MCI), and AD groups in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort across 5 scales of the Lausanne parcellation. We found that the segregative measure of transtivity offered the greatest consistency across scales in distinguishing between healthy and diseased groups, while the other measures were impacted by the selection of scale to varying degrees. Global efficiency was the second most consistent measure that we tested, where the measure could distinguish between HC and MCI in all 5 scales and between HC and AD in 3 out of 5 scales. Characteristic path length was highly sensitive to the variation in scale, corroborating previous findings, and could not identify group differences in many of the scales. Average clustering coefficient was also greatly impacted by scale, as it consistently failed to identify group differences in the higher resolution parcellations. From these results, we conclude that many graph theoretical measures are sensitive to the selection of parcellation scale, and further development in methodology is needed to offer a more robust characterization of AD's relationship with disrupted connectivity.

Dimensionality reduction and recurrence analysis reveal hidden structures of striatal pathological states

A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.

Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography

Virtual reality (VR), a rapidly evolving technology that simulates three-dimensional virtual environments for users, has been proven to activate brain functions. However, the continuous alteration pattern of the functional small-world network in response to comprehensive three-dimensional stimulation rather than realistic two-dimensional media stimuli requires further exploration. Here, we aimed to validate the effect of VR on the pathways and network parameters of a small-world organization and interpret its mechanism of action. Fourteen healthy volunteers were selected to complete missions in an immersive VR game. The changes in the functional network in six different frequency categories were analyzed using graph theory with electroencephalography data measured during the pre-, VR, and post-VR stages. The mutual information matrix revealed that interactions between the frontal and posterior areas and those within the frontal and occipital lobes were strengthened. Subsequently, the betweenness centrality (BC) analysis indicated more robust and extensive pathways among hubs. Furthermore, a specific lateralized channel (O1 or O2) increment in the BC was observed. Moreover, the network parameters improved simultaneously in local segregation, global segregation, and global integration. The overall topological improvements of small-world organizations were in high-frequency bands and exhibited some degree of sustainability.

Cognitive control training enhances the integration of intrinsic functional networks in adolescents

Introduction

We have demonstrated that intensive cognitive training can produce sustained improvements in cognitive performance in adolescents. Few studies, however, have investigated the neural basis of these training effects, leaving the underlying mechanism of cognitive plasticity during this period unexplained.

Methods

In this study, we trained 51 typically developing adolescents on cognitive control tasks and examined how their intrinsic brain networks changed by applying graph theoretical analysis. We hypothesized that the training would accelerate the process of network integration, which is a key feature of network development throughout adolescence.

Results

We found that the cognitive control training enhanced the integration of functional networks, particularly the cross-network integration of the cingulo-opercular network. Moreover, the analysis of additional data from older adolescents revealed that the cingulo-opercular network was more integrated with other networks in older adolescents than in young adolescents.

Discussion

These findings are consistent with the hypothesis that cognitive control training may speed up network development, such that brain networks exhibit more mature patterns after training.

A Multigraph-Based Representation of Hi-C Data

Chromatin-chromatin interactions and three-dimensional (3D) spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts to environmental stimuli, the linear sequence information needs to be interpreted in three-dimensional space, which is still a challenging task. Here, we propose a novel, heuristic approach to represent Hi-C datasets by a whole-genomic pseudo-structure in 3D space. The baseline of our approach is the construction of a multigraph from genomic-sequence data and Hi-C interaction data, then applying a modified force-directed layout algorithm. The resulting layout is a pseudo-structure. While pseudo-structures are not based on direct observation and their details are inherent to settings, surprisingly, they demonstrate interesting, overall similarities of known genome structures of both barley and rice, namely, the Rabl and Rosette-like conformation. It has an exciting potential to be extended by additional omics data (RNA-seq, Chip-seq, etc.), allowing to visualize the dynamics of the pseudo-structures across various tissues or developmental stages. Furthermore, this novel method would make it possible to revisit most Hi-C data accumulated in the public domain in the last decade.

Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures

Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis. We describe a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone. We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices, and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore, we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach, we developed a deep learning model for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.6% overall accuracy while the trained model with extracted networks attains 96.5% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

Neuromodulation of brain power topography and network topology by prefrontal transcranial photobiomodulation

Objective.Transcranial photobiomodulation (tPBM) has shown promising benefits, including cognitive improvement, in healthy humans and in patients with Alzheimer's disease. In this study, we aimed to identify key cortical regions that present significant changes caused by tPBM in the electroencephalogram (EEG) oscillation powers and functional connectivity in the healthy human brain.Approach. A 64-channel EEG was recorded from 45 healthy participants during a 13 min period consisting of a 2 min baseline, 8 min tPBM/sham intervention, and 3 min recovery. After pre-processing and normalizing the EEG data at the five EEG rhythms, cluster-based permutation tests were performed for multiple comparisons of spectral power topographies, followed by graph-theory analysis as a topological approach for quantification of brain connectivity metrics at global and nodal/cluster levels.Main results. EEG power enhancement was observed in clusters of channels over the frontoparietal regions in the alpha band and the centroparietal regions in the beta band. The global measures of the network revealed a reduction in synchronization, global efficiency, and small-worldness of beta band connectivity, implying an enhancement of brain network complexity. In addition, in the beta band, nodal graphical analysis demonstrated significant increases in local information integration and centrality over the frontal clusters, accompanied by a decrease in segregation over the bilateral frontal, left parietal, and left occipital regions.Significance.Frontal tPBM increased EEG alpha and beta powers in the frontal-central-parietal regions, enhanced the complexity of the global beta-wave brain network, and augmented local information flow and integration of beta oscillations across prefrontal cortical regions. This study sheds light on the potential link between electrophysiological effects and human cognitive improvement induced by tPBM.

The Age of Reason: Functional Brain Network Development during Childhood

Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it.SIGNIFICANCE STATEMENT Anthropologists have called the transition from early to middle childhood the "age of reason", when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor

BACKGROUND AND OBJECTIVE

Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics.

RESULTS

All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity.

CONCLUSION

These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.