Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles.

Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine-adenine-guanine repeat number. Moreover, the transcriptome-connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.

Cortical activation and brain network efficiency during dual tasks: An fNIRS study.

OBJECTIVE: Dual task (DT) is a commonly used paradigm indicative of executive functions. Brain activities during DT walking is usually measured by portable functional near infrared spectroscopy (fNIRS). Previous studies focused on cortical activation in prefrontal cortex and overlooked other brain regions such as sensorimotor cortices. This study is aimed at investigating the modulations of cortical activation and brain network efficiency in multiple brain regions from single to dual tasks with different complexities and their relationships with DT performance. METHODS: Forty-two healthy adults [12 males; mean age: 27.7 (SD=6.5) years] participated in this study. Participants performed behavioral tasks with portable fNIRS simultaneous recording. There were three parts of behavioral tasks: cognitive tasks while standing (serial subtraction of 3's and 7's), walking alone and DT (walk while subtraction, including serial subtraction of 3's and 7's). Cognitive cost, walking cost and cost sum (i.e., sum of cognitive and walking costs) were calculated for DT. Cortical activation, local and global network efficiency were calculated for each task. RESULTS: The cognitive cost was greater and the walking cost was less during DT with subtraction 3's compared with 7's (P's = 0.032 and 0.019, respectively). Cortical activation and network efficiency were differentially modulated among single and dual tasks (P's < 0.05). Prefrontal activation during DT was positively correlated with DT costs, while network efficiency was negatively correlated with DT costs (P's < 0.05). CONCLUSIONS: Our results revealed prefrontal over-activation and reduced network efficiency in individuals with poor DT performance. Our findings suggest that reduced network efficiency could be a possible mechanism contributing to poor DT performance, which is accompanied by compensatory prefrontal over-activation.

Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis.

BACKGROUND: Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). OBJECTIVE: This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. METHODS: Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. RESULTS: In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). CONCLUSION: Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.

Well-being is associated with cortical thickness network topology of human brain.

BACKGROUND: Living a happy and meaningful life is an eternal topic in positive psychology, which is crucial for individuals' physical and mental health as well as social functioning. Well-being can be subdivided into pleasure attainment related hedonic well-being or emotional well-being, and self-actualization related eudaimonic well-being or psychological well-being plus social well-being. Previous studies have mostly focused on human brain morphological and functional mechanisms underlying different dimensions of well-being, but no study explored brain network mechanisms of well-being, especially in terms of topological properties of human brain morphological similarity network. METHODS: Therefore, in the study, we collected 65 datasets including magnetic resonance imaging (MRI) and well-being data, and constructed human brain morphological network based on morphological distribution similarity of cortical thickness to explore the correlations between topological properties including network efficiency and centrality and different dimensions of well-being. RESULTS: We found emotional well-being was negatively correlated with betweenness centrality in the visual network but positively correlated with eigenvector centrality in the precentral sulcus, while the total score of well-being was positively correlated with local efficiency in the posterior cingulate cortex of cortical thickness network. CONCLUSIONS: Our findings demonstrated that different dimensions of well-being corresponded to different cortical hierarchies: hedonic well-being was involved in more preliminary cognitive processing stages including perceptual and attentional information processing, while hedonic and eudaimonic well-being might share common morphological similarity network mechanisms in the subsequent advanced cognitive processing stages.

Assessment of the current performance of grazing infrastructure across Irish dairy farms.

The increased average Irish dairy herd size in a post-quota environment has put heightened pressure on grazing infrastructure. In a rotational grazing system, grazing infrastructure consists of the paddock system, which delineates the grazing areas into appropriately sized grazing parcels, and the roadway network, which connects these paddocks to the milking parlor. Where herd size has increased without corresponding adaptations to the infrastructure, farm management and roadway network performance has been affected. The links between suboptimal grazing infrastructure and roadway network efficiency are poorly understood and not widely documented. The aims of this study were to (1) analyze the effect of herd expansion and paddock size on pasture allocations per paddock, (2) identify the factors that affect the total distance walked per year, and (3) create a metric to compare the efficiency of roadway networks across farms of varying grazing platforms. A sample population of 135 Irish dairy farms with a median herd size of 150 cows was used for this analysis. Herds were split into the following 5 categories: <100 cows, 100 to 149 cows, 150 to 199 cows, 200 to 249 cows, and ≥250 cows. Herds with ≥250 cows had a greater number of paddocks per farm and rotated around the grazing paddocks more frequently, with 46% of paddocks only suitable for 12 h allocations relative to herd size, compared with just 10% to 27% of paddocks for herds with <100 cows to herds with 200-249 cows. When predicting the total distance walked per year on each study farm, the mean distance from a paddock to the milking parlor was the strongest indicator (R2 = 0.8247). Other metrics, such as herd size, have failed to account for the location of the milking parlor relative to the grazing platform. The creation of the relative mean distance from a paddock to milking parlor (RMDMP) metric allowed the calculation of a farm's roadway network efficiency for moving the herd between paddocks and the milking parlor. The analyzed farms increased their efficiency in terms of RMDMP (0.34-40.74%) as they increased herd size post quota. However, the position of new additional paddocks relative to the milking parlor substantially affected their RMDMP.

Effectiveness of Emergent Ad Hoc Coordination Groups in Public Health Emergencies.

Whether emergent groups positively or negatively influence a disaster response remains inconclusive in the literature. We analyzed the effect of an emergent group on two interorganizational networks for information communication and resource coordination during a public health emergency response. Using the 2015 Middle East Respiratory Syndrome (MERS) Coronavirus in Korea as a study case, we identified an ad hoc entity that appeared in both networks. This emergent group, which consists of government officials and public health specialists, directed and coordinated organizations at the center of the response networks. We found that the emergent group positively contributed to efficient information communication but had no effect on the resource network's efficiency. Our interpretation is that the ad hoc entity was filling relational gaps in the information network, but was redundant in the resource network.

Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia.

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.

Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise.

PURPOSE: Exhaustive cardiovascular load can affect neural processing and is associated with decreases in sensorimotor performance. The purpose of this study was to explore intensity-dependent modulations in brain network efficiency in response to treadmill running assessed from resting-state electroencephalography (EEG) measures. METHODS: Sixteen trained participants were tested for individual peak oxygen uptake (VO2 peak) and performed an incremental treadmill exercise at 50% (10 min), 70% (10 min) and 90% speed VO2 peak (all-out) followed by cool-down running and active recovery. Before the experiment and after each stage, borg scale (BS), blood lactate concentration (BLa), resting heartrate (HRrest) and 64-channel EEG resting state were assessed. To analyze network efficiency, graph theory was applied to derive small world index (SWI) from EEG data in theta, alpha-1 and alpha-2 frequency bands. RESULTS: Analysis of variance for repeated measures revealed significant main effects for intensity on BS, BLa, HRrest and SWI. While BS, BLa and HRrest indicated maxima after all-out, SWI showed a reduction in the theta network after all-out. CONCLUSION: Our explorative approach suggests intensity-dependent modulations of resting-state brain networks, since exhaustive exercise temporarily reduces brain network efficiency. Resting-state network assessment may prospectively play a role in training monitoring by displaying the readiness and efficiency of the central nervous system in different training situations.

Structural covariance networks in children and their associations with maternal behaviors.

There is a substantial body of research documenting the influence of early adverse experience on brain development. In contrast, relatively little attention has been directed toward the influence of 'normative' variation in parenting behaviors. This study investigated associations between parenting behaviors and structural brain networks, as measured by structural covariance, in a community sample of children. One hundred and forty-five typically developing 8-year-olds and their mothers completed questionnaire measures and two observed parent-child interaction tasks. Structural MRI scans were also obtained from the children. Structural covariance networks based on partial correlation between cortical thickness estimates were constructed, and estimates of efficiency were obtained using graph theoretical analysis. Associations between affective and communicative maternal behaviors and these network metrics were investigated. High levels of observed negative affective and communicative maternal behaviors were associated with decreased local efficiency, whereas high levels of positive affective maternal behaviors were associated with increased local efficiency. The regions implicated (including the cingulate cortex, temporal pole, and temporo-parietal junction) are thought to be involved in the processing of social information. Minimal support was found for an association between global efficiency and maternal behaviors. Our findings suggest that variations in parenting behaviors are associated with structural organization of socio-emotional brain networks in children.

Association between seizure freedom and default mode network reorganization in patients with unilateral temporal lobe epilepsy.

RATIONALE: The spontaneous synchronized activity and intrinsic organization of the Default Mode Network (DMN) has been found to be altered because of epileptic activity of temporal lobe origin. Thus, the aim of the present study was to compare DMN's topological properties in patients with seizure-free (SF) and not seizure-free (NSF) temporal lobe epilepsy (TLE). METHODS: Functional connectivity within the DMN was determined from an 8-minute resting state functional magnetic resonance imaging (fMRI) in 27 patients with TLE (12 SF, 15 NSF) and 15 healthy controls (HC). The DMN regions of interest were extracted according to the automated anatomical labeling (AAL) atlas. Network properties were assessed using standard graph-theoretical measures. RESULTS: Analyses revealed, irrespectively of focus lateralization, borderline significance for longer paths (p = 0.049) and in trend reduced local efficiency within the DMN of SF when compared with that of NSF (p = 0.075). The SF and NSF patients did not differ in global network topology from HC (p > 0.05). At the nodal network level, the degree of central hubs was significantly reduced in SF when compared with that in NSF (0.002 ≤ p ≤ 0.080) and HC (0.001 ≤ p ≤ 0.066) while simultaneously, right anterior superior temporal gyrus revealed significantly higher degree in SF than in NSF (p = 0.005) and HC (p = 0.016). CONCLUSION: Seizure freedom seems to be associated with hub redistributions that may underlie longer paths and (in trend) reduced local efficiency of the network. An associated slower system response might reduce the probability of a rapid spread of epileptic discharges over the whole network and may help to prevent hypersynchronous neuronal activity in brain networks that may result in epileptic seizures.

Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis.

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing.

Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data.

Acquiring resting-state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi-site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi-site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi-site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI-derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi-site fMRI neuroimaging studies.

Time-resolved resting-state brain networks.

Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.

Connectome sensitivity or specificity: which is more important?

Connectomes with high sensitivity and high specificity are unattainable with current axonal fiber reconstruction methods, particularly at the macro-scale afforded by magnetic resonance imaging. Tensor-guided deterministic tractography yields sparse connectomes that are incomplete and contain false negatives (FNs), whereas probabilistic methods steered by crossing-fiber models yield dense connectomes, often with low specificity due to false positives (FPs). Densely reconstructed probabilistic connectomes are typically thresholded to improve specificity at the cost of a reduction in sensitivity. What is the optimal tradeoff between connectome sensitivity and specificity? We show empirically and theoretically that specificity is paramount. Our evaluations of the impact of FPs and FNs on empirical connectomes indicate that specificity is at least twice as important as sensitivity when estimating key properties of brain networks, including topological measures of network clustering, network efficiency and network modularity. Our asymptotic analysis of small-world networks with idealized modular structure reveals that as the number of nodes grows, specificity becomes exactly twice as important as sensitivity to the estimation of the clustering coefficient. For the estimation of network efficiency, the relative importance of specificity grows linearly with the number of nodes. The greater importance of specificity is due to FPs occurring more prevalently between network modules rather than within them. These spurious inter-modular connections have a dramatic impact on network topology. We argue that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity.

Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes.

Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc.

Weak network efficiency in young children with Autism Spectrum Disorder: Evidence from a functional near-infrared spectroscopy study.

Functional near infrared spectroscopy (fNIRS) is particularly suited for the young population and ecological measurement. However, thus far, not enough effort has been given to the clinical diagnosis of young children with Autism Spectrum Disorder (ASD) by using fNIRS. The current study provided some insights into the quantitative analysis of functional networks in young children (ages 4.8-8.0years old) with and without ASD and, in particular, investigated the network efficiency and lobe-level connectivity of their functional networks while watching a cartoon. The main results included that: (i) Weak network efficiency was observed in young children with ASD, even for a wide range of threshold for the binarization of functional networks; (ii) A maximum classification accuracy rate of 83.3% was obtained for all participants by using the k-means clustering method with network efficiencies as the feature parameters; and (iii) Weak lobe-level inter-region connections were uncovered in the right prefrontal cortex, including its linkages with the left prefrontal cortex and the bilateral temporal cortex. Such results indicate that the right prefrontal cortex might make a major contribution to the psychopathology of young children with ASD at the functional network architecture level, and at the functional lobe-connectivity level, respectively.

Altered network efficiency in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is associated with dysfunction between cognitive control and affective processing system. However, little is known about alterations of the nodal and edge efficiency in abnormal systems of MDD patients. We used two independent datasets and two different structural templates to investigate the alterations of the nodal and edge efficiency of whole-brain functional networks of MDD. METHOD: Forty-two MDD and forty-two age, education-matched controls were selected to investigate network efficiency abnormalities of the MDD patients' cortical and subcortical regions, as well as the disrupted functional connectivity between these regions, from the perspective of network topological architectures. In addition, another dataset, which included thirty MDD patients and thirty controls, was also investigated using the same method. RESULTS: Results showed that MDD group demonstrated significant increase in the local efficiency, although not change of global efficiency. In addition, nodal efficiency was found to increase in affective processing regions (i.e., amygdale, thalamus, hippocampus), but decrease in cognitive control related regions, which included dorsolateral prefrontal cortex and anterior cingulate cortex. The edge efficiency was found to increase, involving both connectivity between thalamus and limbic system regions and connectivity between hippocampus and regions (i.e., amygdala, thalamus). More important, result was replicated within independent datasets for the first and different structural templates for another. CONCLUSIONS: Our results indicated that MDD was associated with disrupted functional connectivity networks between cognitive control and affective processing systems. The findings might shed light on the pathological mechanism of depression and provide potential biomarkers for clinic treatment of depression.

Schizophrenia-like topological changes in the structural connectome of individuals with subclinical psychotic experiences.

Schizophrenia is often regarded as a "dysconnectivity" disorder and recent work using graph theory has been used to better characterize dysconnectivity of the structural connectome in schizophrenia. However, there are still little data on the topology of connectomes in less severe forms of the condition. Such analysis will identify topological markers of less severe disease states and provide potential predictors of further disease development. Individuals with psychotic experiences (PEs) were identified from a population-based cohort without relying on participants presenting to clinical services. Such individuals have an increased risk of developing clinically significant psychosis. 123 individuals with PEs and 125 controls were scanned with diffusion-weighted MRI. Whole-brain structural connectomes were derived and a range of global and local GT-metrics were computed. Global efficiency and density were significantly reduced in individuals with PEs. Local efficiency was reduced in a number of regions, including critical network hubs. Further analysis of functional subnetworks showed differential impairment of the default mode network. An additional analysis of pair-wise connections showed no evidence of differences in individuals with PEs. These results are consistent with previous findings in schizophrenia. Reduced efficiency in critical core hubs suggests the brains of individuals with PEs may be particularly predisposed to dysfunction. The absence of any detectable effects in pair-wise connections illustrates that, at less severe stages of psychosis, white-matter alterations are subtle and only manifest when examining network topology. This study indicates that topology could be a sensitive biomarker for early stages of psychotic illness.

Altered brain functional networks in heavy smokers.

Recent neuroimaging studies have demonstrated that cigarette smoking is associated with changed brain structure and function. However, little is known about alterations of the topological organization of brain functional networks in heavy smokers. Thirty-one heavy smokers and 33 non-smokers underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding the correlation matrices of 90 brain regions and their topological properties were analyzed using graph network analysis. Non-parametric permutation tests were performed to investigate group differences in network topological measures and multiple regression analysis was conducted to determine the relationships between the network metrics and smoking-related variables. Both heavy smokers and non-smokers exhibited small-world architecture in their brain functional networks. Compared with non-smokers, however, heavy smokers showed altered topological measurements characterized by lower global efficiency, higher local efficiency and clustering coefficients and greater path length. Furthermore, heavy smokers demonstrated decreased nodal global efficiency mainly in brain regions within the default mode network, whereas increased nodal local efficiency predominated in the visual-related regions. In addition, heavy smokers exhibited an association between the altered network metrics and the duration of cigarette use or the severity of nicotine dependence. Our results suggest that heavy smokers may have less efficient network architecture in the brain, and chronic cigarette smoking is associated with disruptions in the topological organization of brain networks. Our findings may further the understanding of the effects of chronic cigarette smoking on the brain and the pathophysiological mechanisms underlying nicotine dependence.

Structural Network Efficiency Predicts Conversion to Incident Parkinsonism in Patients With Cerebral Small Vessel Disease.

BACKGROUND: To investigate whether structural network disconnectivity is associated with parkinsonian signs and their progression, as well as with an increased risk of incident parkinsonism. METHODS: In a prospective cohort (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort study) consisting of 293 participants with small vessel disease (SVD), we assessed parkinsonian signs and incident parkinsonism over an 8-year follow-up. In addition, we reconstructed the white matter network followed by graph-theoretical analyses to compute the network metrics. Conventional magnetic resonance imaging markers for SVD were assessed. RESULTS: We included 293 patients free of parkinsonism at baseline (2011), with a mean age 68.8 (standard deviation [SD] 8.4) years, and 130 (44.4%) were men. Nineteen participants (6.5%) developed parkinsonism during a median (SD) follow-up time of 8.3 years. Compared with participants without parkinsonism, those with all-cause parkinsonism had higher Unified Parkinson's Disease Rating scale (UPDRS) scores and lower global efficiency at baseline. Baseline global efficiency was associated with UPDRS motor scores in 2011 (ß = -0.047, p < .001) and 2015 (ß = -0.84, p < .001), as well as with the changes in UPDRS scores during the 4-year follow-up (ß = -0.63, p = .004). In addition, at the regional level, we identified an inter-hemispheric disconnected network associated with an increased UPDRS motor score. Besides, lower global efficiency was associated with an increased risk of all-cause and vascular parkinsonism independent of SVD markers. CONCLUSIONS: Our findings suggest that global network efficiency is associated with a gradual decline in motor performance, ultimately leading to incident parkinsonism in the elderly with SVD. Global network efficiency may have the added value to serve as a useful marker to capture changes in motor signs.