Connectivity Measures Research Articles

Graph Theory Analysis of Structural Connectivity in Limbic‐predominant Age‐related TDP‐43 Encephalopathy Neuropathological Change (LATE‐NC)

AbstractBackgroundThe anatomical distribution of limbic‐predominant age‐related TDP‐43 encephalopathy neuropathological change (LATE‐NC) typically involves the amygdala, hippocampus, and other subcortical, temporal and frontal lobe regions. But its impact on the structural integrity of this network of regions remains unknown. In this study, we tested the hypothesis that LATE‐NC is associated with abnormalities in graph theoretic measures of structural connectivity in the network of regions known to be involved in LATE‐NC.MethodThis study included 148 deceased older adults participating in Rush Memory and Aging Project and Religious Orders Study (Fig.1A). Ex‐vivo diffusion tensor imaging (DTI) scans were acquired on 3T scanners, followed by detailed neuropathologic examination by a board‐certified neuropathologist (Fig.1B). Fractional anisotropy (FA) maps derived from DTI scans were used to define region‐to‐region structural connectivity in a network of 19 regions known to be involved in LATE‐NC using graph theory‐based network measures (Fig.2).General linear models were used to investigate the association of global and local graph measures with LATE‐NC stages, controlling for other neuropathologies (Alzheimer’s disease, Lewy bodies, arteriolosclerosis, atherosclerosis, cerebral amyloid angiopathy, gross and microscopic infarcts), demographics (age at death, sex, education), total white matter hyperintensity volume, postmortem intervals and scanner variables. Statistical significance was set at p<0.05.ResultLinear regression revealed that greater LATE‐NC stage was associated with increased characteristic path length and with lower values of the remaining global network measures (p<0.05) (Fig.3A). LATE‐NC was also associated with lower local efficiency, increased path length, lower nodal strength, and lower clustering coefficient in 74%, 79%, 32%, and 63% of the nodes, respectively (p<0.05) (Fig.3B). Finally, LATE‐NC was associated with both greater (in 21% of nodes) and lower (in 16% of nodes) eigenvector centrality, and with both greater and lower betweenness centrality in 21% of nodes, respectively (p<0.05) (Fig.3B).ConclusionUsing graph theory and a hypothesis‐driven approach, we found significant degradation of structural integrity in white matter of the network of regions affected by LATE‐NC. These findings may shed light on the effects of LATE‐NC on brain connectivity, and in combination with other imaging and clinical information, may aid towards the development of tools for in‐vivo detection of this devastating neuropathology.

Social connection in long-term care homes

Background:Social connection is a basic human need and is important for quality of life and care for residents in long-term care (LTC) homes. Research has established associations between aspects of social connection (e.g., social engagement, social support and loneliness) and mental health outcomes (e.g., depression). Yet, despite living in a congregate setting, those in LTC homes often experience poor social connection. Social connection has unique considerations for LTC homes, including that most residents are living with cognitive impairment or dementia, which requires a customized measurement approach.Research Objective:The social connection in long-term care home residents (SONNET) study aims to improve measurement of social connection in LTC homes by addressing three specific questions: (1) What existing measures assess social connection in LTC homes and what are their psychometric properties? (2) What do residents, families, staff and clinicians consider to be the important elements of social connection in LTC homes? (3) Can a new measure accurately assess social connection in LTC home residents?Method:The three study questions will be addressed through: (1) A systematic review of existing measures, where measures will be characterized using content analysis and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methods; (2) Qualitative interviews with residents, families, staff and clinicians to understand important aspects of social connection, and, (3) Development and testing of a new measure of social connection in Canada and the UK.Preliminary results of the ongoing study:Previous research will be discussed, including a scoping review summarizing research evidence linking social connection to mental health outcomes as well as strategies to build and maintain social connection during the COVID-19 pandemic. The SONNET study update will include preliminary findings from the systematic review and qualitative interviews, as well as development of a conceptual model and key considerations for a new measure.Conclusion:Social connection is an important concept in LTC homes. A robust measure of social connection, developed specifically for this setting, will enable researchers and care settings to test the effects of interventions and to report outcomes at the individual-, home- and system-level.

Cross‐scale functional connectivity patterns of the aging brain learned from the multi‐cohort iSTAGing study

AbstractBackgroundBrain functional connectivity (FC) measures derived from resting‐state fMRI (rsfMRI) data have advanced our understanding of the brain organization. However, most existing studies investigate the brain functional networks (FNs) at a specific scale, ignoring potentially complementary FC information at different levels of spatial granularity as well as the cross‐scale information. Given that the brain is a multi‐scale, hierarchical system, FCs between FNs at different spatial scales may provide complementary information to those measured within‐scales for characterizing the aging brain.MethodWe computed multiscale FNs to capture their FC measures across multiple scales and investigated the associations between cross‐scale FCs and the brain aging based on rsfMRI data of a diverse cohort (n = 4186, aged 22 to 97 years) from seven sites of the iSTAGING study [1]. For each subject, we computed the FNs at seven scales, yielding seven sets of k (k = 17, 25, 50, 75, 100, 125 and 150) FNs, same as our previous study [2‐3]. We estimated FC measures between the 542 FNs using Pearson’s correlation, yielding the cross‐scale FC matrices that were harmonized across sites in the tangent space using ComBat‐GAM [4] with sex and age as covariates. The harmonized FC measures were used as features to build an age prediction model using Ridge regression as illustrated in Figure 1. The brain age prediction model was compared with that built on harmonized FC measures between FNs of the same scales, with the performance estimated with a nested five‐fold cross‐validation.ResultThe cross‐scale FC measures performed significantly better than the within‐scale FC measures for the brain age prediction, as summarized in Table 1. Figure 2 shows the most informative FC measures for the brain age prediction. Specifically, FC measures computed both cross‐ and within‐scales between the visual, somatomotor, and frontoparietal networks provided relatively more useful information for the brain age prediction than FC measures between other FNs.ConclusionOur study demonstrated that the cross‐scale FC measures provided complementary information to the within‐scale FC measures for predicting the brain age, facilitating enhanced understanding of aging related changes of the brain functional organization.

Visuospatial Dysfunction in Alzheimer’s Disease Maps to a Network Centered on the Inferior Parietal Lobule

AbstractBackgroundVisuospatial dysfunction is the predominant symptom in ∼5% of patients with Alzheimer’s disease (AD) and common in amnestic patients. If we can predict which patients are more likely to have trouble with visuospatial functioning, we can intervene earlier to prevent falls and car accidents and improve quality of life.MethodThere were 184 subjects with AD in the Alzheimer’s Disease Neuroimaging Initiative database with MRI scans and clock‐draw and clock‐copy scores ranging from 1‐5. Atrophy was defined as thinner than expected compared to age and sex matched controls. Subject network maps were generated averaging connectivity to each atrophied vertex using the normative connectome. Maps were compared to their behavioral scores generating behavior specific network maps. Atrophy networks were directly compared to a similar network generated from clock‐draw and stroke subjects.ResultAtrophy did not correlate with clock‐draw and ‐copy scores but atrophy networks identified distinct distributed brain networks. Clock‐copy scores directly correlate with atrophy connected to the bilateral angular and visuomotor gyri, which were also hubs of the network derived from stroke and clock‐draw scores.ConclusionThese results suggest that visuospatial dysfunction in AD does not occur due to atrophy in a single brain region but a distributed brain network. The concordance between atrophy in AD and stroke suggest that atrophy network mapping is a valid measure of connectivity underlying visuospatial dysfunction in AD.

Proteins involved in synapse organization are associated with longitudinal loss in gray matter network connectivity in prodromal AD

AbstractBackgroundLower gray matter connectivity measures are related to faster cognitive decline in prodromal Alzheimer’s Disease (AD). We investigated the biological underpinnings responsible for this loss of brain connections using cerebrospinal fluid (CSF) proteomics.MethodData from ADNI was used to select 82 individuals with MCI and abnormal CSF amyloid (mean age±SD 74±7) and 37 controls with normal amyloid levels (mean age±SD 75±5), for whom repeated MRI‐scans (median of 6 scans over 1.5 follow‐up years) and baseline CSF proteomic data (in total 306 proteins) were available. We used an automated pipeline (Tijms, Series et al. 2012) to construct gray matter networks from 3D‐T1 sequences and calculate connectivity density and gamma – measures previously found to be most strongly related to cognitive decline (Dicks, Vermunt et al. 2020). Linear mixed models were applied to test associations between baseline CSF protein levels and repeated connectivity metrics, controlling for intracranial volume, age and sex. Proteins that demonstrated significant associations (p<0.05) were identified and examined for their biological relevance according to Gene Ontology.ResultConnectivity density declined over time with ‐0.8% per year (pMCI = 0.001; figure 1), which did not differ from controls (pdif = 0.15). Gamma declined faster in MCI than in controls (bMCI = ‐0.016 vs. bCN = ‐0.007, pdif <0.001; figure 1). Higher levels of 99/108 proteins were associated with faster decline in connectivity density (a.o. tau and neuroprentaxins). These proteins were involved in regulation of synapse organization (GO:0050807; pFDR = 7.00E‐07; figure 2). Higher levels of 9/108 proteins associated with less decline and were involved in chemotaxis (GO:0048247; pFDR = 1.40E‐02). Similarly, 25 out 30 proteins (83%) associated with alterations in gamma over time, showed associations of higher levels with less gamma decline and were also involved in chemotaxis (GO:0050926; pFDR = 9.42E‐05; figure 3). There were no protein‐connectivity relationships in controls.ConclusionProteins involved in synapse organization were associated with faster decline in connectivity density over time in patients, while chemotaxis was associated with less decline. This suggests that synaptic degeneration may predict disease progression while immune responses may serve as a resilience mechanism in AD.

Forecasting the spread of an invasive forest‐defoliating insect

AbstractAimBiological invasions are an escalating environmental challenge due to their substantial ecological and socio‐economic consequences. Accurate near‐term forecasts of future areas occupied by an invasive species could enhance the efficiency and efficacy of invasion monitoring and management but spread forecasting models have been developed and tested for few invasive species thus far.LocationNortheastern USA.MethodsWe developed a quantitative model to forecast 1‐year‐ahead occupancy of Lymantria dispar dispar, an expanding invasive forest insect introduced into the eastern United States in 1869 that causes large‐scale defoliation of forests. We validated and tested the model using historical distribution and density data from a large‐scale network of pheromone‐baited traps. We first assessed how forecast accuracy depended on trap catch density thresholds for determining occupancy and on the spatial scale of local connectivity measures. Next, we tested how increasing the computational complexity and biological detail encoded in the models affected prediction accuracy.ResultsModels using lower occupancy thresholds and measuring connectivity over shorter distances tended to perform best. A simple model using only coarse and generic representations of habitat suitability and local diffusive spread potential performed at least as well as more complex models. Our best models achieved total accuracy and true‐positive rates exceeding 95%.ConclusionsOur baseline model illustrates the utility of a mechanistic model to forecast year‐to‐year occupancy dynamics of L. dispar. Near‐term spread forecasting can be a valuable tool for invasion management, even for species without detailed a priori ecological knowledge.

Thalamic Contributions to Functional Circuitry in Preclinical Alzheimer’s Disease

AbstractBackgroundThalamic nuclei are heterogeneously impacted by Alzheimer’s disease (AD) pathology. The anterior thalamus shows tau deposition early on in AD, while other nuclei, such as the mediodorsal (MD) thalamus are spared by tau until much later in the disease process (Braak and Braak, 1991). Furthermore, anterior and MD thalamus have dissociable roles in memory performance – the anterior has a greater role in recollective memory, while the MD plays a role in familiarity. Additionally, the anterior thalamus has direct anatomical links to brain regions implicated in poor short‐term memory performance in AD, while MD thalamus connectivity is primarily to regions spared of AD pathology until late. We sought to identify differences in functional connectivity between the anterior and MD thalamus in amyloid negative and positive individuals.MethodWe examined resting‐state functional MRI data from the A4 study (n = 569), including 254 healthy controls and 315 individuals with preclinical AD based on amyloid SUVR>1.14 (Table 1). We assessed functional connectivity of anterior and MD thalamus to the whole brain using 2mm radius spherical seeds at the center of mass of Morel atlas nuclei. All results were covaried for age, sex, handedness, and years of education.ResultsAnterior thalamic activity was positively correlated with parahippocampal gyrus, fusiform, and insula, and negatively with the striatum and thalamus. MD thalamic activity was positively correlated with the amygdala, ventral striatum, and anterior cingulate, and negatively correlated to the superior temporal gyrus and regions of the parieto‐occipital lobe. In preclinical AD, compared with HCs, the positive correlations in either thalamic nuclei were lost.ConclusionThe thalamic functional connectivity observed agrees with preexisting literature dissociating structures related to the anterior and MD thalamus. Our results provide a basis for further exploration of thalamic nuclei to AD‐involved functional networks, as well as future analysis of the observed connectivity measures to cognition.

Thalamic Contributions to Functional Circuitry in Preclinical Alzheimer’s Disease

AbstractBackgroundThalamic nuclei are heterogeneously impacted by Alzheimer’s disease (AD) pathology. The anterior thalamus shows tau deposition early on in AD, while other nuclei, such as the mediodorsal (MD) thalamus are spared by tau until much later in the disease process (Braak and Braak, 1991). Furthermore, anterior and MD thalamus have dissociable roles in memory performance – the anterior has a greater role in recollective memory, while the MD plays a role in familiarity. Additionally, the anterior thalamus has direct anatomical links to brain regions implicated in poor short‐term memory performance in AD, while MD thalamus connectivity is primarily to regions spared of AD pathology until late. We sought to identify differences in functional connectivity between the anterior and MD thalamus in amyloid negative and positive individuals.MethodWe examined resting‐state functional MRI data from the A4 study (n = 569), including 254 healthy controls and 315 individuals with preclinical AD based on amyloid SUVR>1.14 (Table 1). We assessed functional connectivity of anterior and MD thalamus to the whole brain using 2mm radius spherical seeds at the center of mass of Morel atlas nuclei. All results were covaried for age, sex, handedness, and years of education.ResultAnterior thalamic activity was positively correlated with parahippocampal gyrus, fusiform, and insula, and negatively with the striatum and thalamus. MD thalamic activity was positively correlated with the amygdala, ventral striatum, and anterior cingulate, and negatively correlated to the superior temporal gyrus and regions of the parieto‐occipital lobe. In preclinical AD, compared with HCs, the positive correlations in either thalamic nuclei were lost.ConclusionThe thalamic functional connectivity observed agrees with preexisting literature dissociating structures related to the anterior and MD thalamus. Our results provide a basis for further exploration of thalamic nuclei to AD‐involved functional networks, as well as future analysis of the observed connectivity measures to cognition.

Clinical appications of EEG/MEG in AD: diagnosis, prognosis and treatment monitoring

AbstractBackgroundElectroencephalography (EEG) and magnetoencephalography (MEG) capture neuronal functioning directly, thereby providing an objective intermediate marker between pathological changes and cognitive symptoms in neurodegenerative diseases. Both techniques are highly suitable as tools in the context of memory clinics and their use for treatment monitoring is increasingly recognized.MethodWe will highlight recent developments important for the use of EEG / MEG as diagnostic and prognostic markers in dementia and as outcome measures in Alzheimer’s disease (AD) trials.ResultIn memory clinics, both visual and quantitative EEG / MEG are being used as supportive evidence in diagnostic challenges, especially when other pathology specific –likely more invasive‐ biomarkers are not available. Increasing numbers of papers using artificial intelligence to differentiate a specific dementia type from cognitively healthy subjects report fair to excellent accuracies that are especially high for dementia with Lewy Bodies (DLB). Multi‐class classification between six memory clinic diagnoses resulted in MEG‐based diagnostic profiles describing the probability estimates of each class. These diagnostic profiles provide an informative and intuitive tool for clinical use. In pre‐clinical stages of AD spectral changes are predictive of future cognitive decline, and in the mild cognitive impairment stage it differentiates between progression into dementia due to AD or DLB. As EEG / MEG are found to be sensitive to longitudinal changes, there is increasing interest in the use of EEG as outcome measures in clinical trials. Both spectral and functional connectivity measures showed a treatment effect in phase 2 pharmacological studies in early AD, giving objective support for (subtle) clinical effects of the drug on trial. The pursuit of discovering even more sensitive, robust and complementary measures lead to novel neurophysiological markers reflecting functional connectivity, signal variability and neuronal hyperexcitability.ConclusionEEG / MEG provide diagnostic and prognostic information applicable in memory clinics. The use of sensitive conventional and novel markers in (pharmacological) trials can support clinical treatment effects and have potential to reduce the size of trials.

Comparing feature selection methods to improve the classification of Mild Cognitive Impairment patients based on Magnetoencephalography data

AbstractBackgroundMachine learning (ML) algorithms are generating great interest for early diagnose of Alzheimer’s disease (AD). Despite several approaches have been developed, limited research has been conducted on the impact of feature selection in classification performance. In this study we compare two feature selection methods, LASSO and Elastic Nets (EN), applied to ML algorithms based on magnetoencephalography (MEG) data from mild cognitive impairment (MCI) patients.MethodAn eyes‐closed resting‐state MEG recording was performed by 262 participants (117 healthy controls and 145 MCI). Functional connectivity measures were estimated for all classical frequency bands using the Phase‐Locking Value. To develop the ML algorithms, specific regions of interest from the default mode network, based on the Automatic Anatomical Labelling atlas, were selected, resulting in a total of 325 features. After dividing the dataset into training (80%) and test (20%) sets, feature selection with 10000 iterations and five‐fold cross validation was performed independently for each band using LASSO and EN algorithms. Afterwards, several classification models were trained with a five‐fold cross validation and then validated on the test set, over 500 iterations.ResultThe classifier showing the best performance was the linear Support Vector Machine for the beta frequency band (12‐30 Hz). Given the similarity of the training and test performance values, the results reveal that overfitting was avoided when using the 45 features selected by LASSO instead of the 47 features selected by EN, reaching a validation accuracy of 77%. Additionally, LASSO consistently identified three functional connectivity links as relevant in all frequency bands but alpha: right superior frontal gyrus & right superior frontal gyrus orbital, left posterior cingulate cortex & left hippocampus, right posterior cingulate cortex & right precuneus.ConclusionML algorithms applied to MEG data can successfully discriminate MCI patients from healthy controls. In addition, LASSO algorithm applied for feature selection leads to better classification performance. Moreover, the resulting relevant functional connectivity links involve areas affected by AD pathology even in early stages of its development (López‐Sanz et al., 2019). Further exploration of this methodology is needed, combining this data with structural neuroimaging or complementary biomarkers.

Virtual brain model‐guided optimization of transcranial direct current stimulation in Alzheimer’s disease

AbstractBackgroundNon‐invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) can potentially counteract disrupted local and distributed brain network activity in Alzheimer’s disease (AD) with a beneficial effect on cognition. However, results of tDCS studies in AD up till now are inconsistent, probably partly due to different methodological choices such as electrode placement. We therefore explored tDCS optimization using a virtual brain network model of AD. Our aim was to compare a large, representative set of virtual tDCS intervention setups, to identify and potentially explain the optimal theoretical tDCS electrode positions for reducing or restoring functional network disruption.MethodWe simulated 20 tDCS setups in a computational macroscopic dynamic network model of 78 neural masses, coupled according to human structural topology. AD network damage was simulated using an activity‐dependent degeneration algorithm, which produces typical gradual oscillatory slowing and loss of connectivity. Current flow modeling was used to estimate tDCS‐targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density in the lower (8‐10 Hz) and upper (8‐13 Hz) alpha bands, total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC).ResultThe performance of different tDCS strategies varied considerably, with best setups temporarily improving all spectral and connectivity outcome measures, while other setups had little to no effect, or even caused network deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of a region (e.g. hub) and its involvement in successful tDCS was not observed.ConclusionModeling tDCS produces specific, falsifiable predictions on how to counter disrupted brain dynamics in AD. In follow‐up studies, our general predictions will be compared to those of personalized virtual models, and then validated with tDCS‐magnetoencephalography (MEG) in a clinical AD patient cohort. This modeling‐informed approach can guide and perhaps accelerate tDCS therapy development, as well as enhance our understanding of tDCS effects.

Sustained connectivity correlates of motor function in Alzheimer’s disease

AbstractBackgroundIn addition to cognitive deficits, issues with motor control are present across the dementia spectrum. Traditional functional connectivity measures synchrony of intrinsic activity between brain regions and is altered in patients with Alzheimer’s disease in regions supporting motor function; however, less is known regarding the temporal stability of that synchrony. Sustained connectivity uses the width of cross correlation curves of BOLD time series to reflect the relative duration of synchronous activity between brain regions. This study investigates sustained connectivity across the dementia spectrum in motor regions.Method109 participants were included in this analysis and included 32 individuals with mild cognitive impairment (MCI) (18 F, 73.84 ± 5.14), 30 with Alzheimer’s disease (AD) (19 F, 77.37 ± 6.45), and 47 control individuals (29 F, mean age 72.13 ± 4.68). All study participants completed MP2RAGE structural imaging and two 15‐minute resting fMRI scans. Preprocessing steps included segmentation, motion realignment, registration to MNI space, and regression of motion parameters. Sustained connectivity values were averaged across both scans and only participants with more than 50% volumes remaining after censoring were included in analysis. A 17 resting‐state network parcellation was used to evaluate sustained connectivity across the brain with each network treated as a single ROI. A linear regression analysis was used to compare sustained connectivity across groups controlling for mean head motion, age, and sex. T statistics and p‐values are reported for across group sustained connectivity values.ResultAcross all three groups, sustained connectivity with the dorsal and ventral somatomotor networks was significantly increased across a number of resting‐state networks including the central visual, anterior ventral attention, and lateral temporal default mode network (DMN; all dorsal) and the somatomotor association, anterior ventral attention, medial frontoparietal, lateral temporal and lateral DMN (all ventral; Figure 1; pFDR < .05).ConclusionThese data suggest sustained connectivity may be a potential metric for assessing progressive cognitive decline in dementia. Our results indicate that sustained connectivity may be used to track changes in the temporal duration of functional connectivity in motor networks in individuals with intact cognition, mild cognitive impairment, and Alzheimer’s disease.

Novel EEG tools to predict early cognitive impairments in Parkinson’s disease

AbstractBackgroundParkinson’s disease (PD) is a neurodegenerative disorder mainly characterised by motor symptoms but often also associated with dementia, increasing in likelihood with duration of disease. Electroencephalography (EEG) can aid in the early diagnosis of those at risk of developing dementia. Therefore, we analysed EEG recordings across conscious states from PD patients with and without cognitive symptoms (Latreille et al in 2016, https://doi.org/10.1093/brain/aww018).MethodBaseline data (REM sleep, NREM sleep, WAKE) from 38 PD patients (15 with and 23 without mild cognitive impairment) and 27 healthy subjects provided power spectra (qEEG), relative band power, as well as dominant frequency and its standard deviation to investigate markers for cognitive impairment. At follow‐up (an average 4.5 years after baseline), 11 patients developed dementia (PDD). Connectivity measures for left and right occipital and central electrodes were obtained as described in Crouch et al, 2018, https://doi.org/10.1038/s41598‐018‐19707‐1.ResultqEGG analyses confirmed previous reports of enhanced theta power during REM, NREM and WAKE for patients who developed PDD. A reduction in alpha power during WAKE was observed for MCI and PDD groups when compared to controls. Alpha rhythms generally decrease with age, alongside enhanced theta power, and this outcome may contribute to the global cognitive status of patients. A reduced and less variable dominant frequency was evident for MCI and PDD patients during WAKE, indicative of reduced flexibility of the network. Finally, rPDC detected disease‐specific changes, such as enhanced cross‐hemispheric connectivity in PDD in the alpha band during NREM sleep that indicate a lack of dynamism in the network. Also noted was the diminished interhemispheric communication in delta power between both central and occipital channels in PDD during WAKE.ConclusionTogether, we here confirm that changes of theta and alpha power are optimal discriminatory biomarkers for PD with cognitive decline (with MCI and at risk of dementia). Also, spectral and rPDC analyses indicate that dementia in PD is preceded by a loss of dynamism in EEG activity during MCI stages, both at the electrode and network level.

Volatility spillovers across Russian oil and gas sector. Evidence of the impact of global markets and extraordinary events

This paper provides empirical evidence on the evolution of the impact of external shocks on the volatility connectedness of Russian oil and gas companies’ stock prices. We examine how the spillover structure has changed following the outbreak of COVID-19, as well as under the impact of the 2022–2023 events.We have extended the Diebold–Yilmaz approach to calculate conditional connectivity measures adjusted for the influence of global factors and unexpected shocks caused by extraordinary events.We use an augmented vector autoregressive model (VARX) to evaluate the adjusted conditional connectivity measures. The dynamics of connectivity indices are analyzed using the moving window method for VAR and VARX models, as well as using a dynamic model with time-varying coefficients (TVP-VAR). The comparison of adjusted and unadjusted connectivity measures allows us to draw conclusions about the impact of changes in foreign financial markets as well as shocks caused by extraordinary events on connectedness structure.We found that measures of conditional connectivity change much more slowly than unconditional ones. Our results support the hypothesis that essential part of the fluctuation of Russian oil and gas connectedness indices can be explained by exposure to unexpected shocks and global economic and political factors. Accordingly, the transformation of the network structure of risk transfer occurs quite smoothly, without sharp jumps and takes a considerable amount of time.

Sleep loss, caffeine, sleep aids and sedation modify brain abnormalities of mild traumatic brain injury

Little evidence exists about how mild traumatic brain injury (mTBI) is affected by commonly encountered exposures of sleep loss, sleep aids, and caffeine that might be potential therapeutic opportunities. In addition, while propofol sedation is administered in severe TBI, its potential utility in mild TBI is unclear. Each of these exposures is known to have pronounced effects on cerebral metabolism and blood flow and neurochemistry. We hypothesized that they each interact with cerebral metabolic dynamics post-injury and change the subclinical characteristics of mTBI. MTBI in rats was produced by head rotational acceleration injury that mimics the biomechanics of human mTBI. Three mTBIs spaced 48 h apart were used to increase the likelihood that vulnerabilities induced by repeated mTBI would be manifested without clinically relevant structural damage. After the third mTBI, rats were immediately sleep deprived or administered caffeine or suvorexant (an orexin antagonist and sleep aid) for the next 24 h or administered propofol for 5 h. Resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were performed 24 h after the third mTBI and again after 30 days to determine changes to the brain mTBI phenotype. Multi-modal analyses on brain regions of interest included measures of functional connectivity and regional homogeneity from rs-fMRI, and mean diffusivity (MD) and fractional anisotropy (FA) from DTI. Each intervention changed the mTBI profile of subclinical effects that presumably underlie healing, compensation, damage, and plasticity. Sleep loss during the acute post-injury period resulted in dramatic changes to functional connectivity. Caffeine, propofol sedation and suvorexant were especially noteworthy for differential effects on microstructure in gray and white matter regions after mTBI. The present results indicate that commonplace exposures and short-term sedation alter the subclinical manifestations of repeated mTBI and therefore likely play roles in symptomatology and vulnerability to damage by repeated mTBI.

Structural connectivity as a predictive factor for responsiveness to levetiracetam treatment in epilepsy.

To investigate whether structural connectivity or glymphatic system function is a potential predictive factor for levetiracetam (LEV) response in patients with newly diagnosed epilepsy. We enrolled patients with newly diagnosed epilepsy who were administered LEV as initial monotherapy and underwent diffusion tensor imaging (DTI) at diagnosis. We categorized the patients into drug response. We used graph theory to calculate the network measures for structural connectivity based on the DTI scans in patients with epilepsy. Additionally, we evaluated glymphatic system function by calculating the DTI analysis along the perivascular space (DTI-ALPS) index based on DTI scans. We enrolled 84 patients with epilepsy. The clinical factors and DTI-ALPS index did not differ between the groups. However, some of the structural connectivity measures significantly differ between the groups. The poor responders exhibited a higher mean clustering coefficient, global efficiency, and small-worldness index than the good responders (p = 0.003, p = 0.048, and p = 0.038, respectively). In the receiver operating characteristic curve analysis, the mean clustering coefficient exhibited the highest performance in predicting the responsiveness to LEV (area under the curve of 0.677). In the multiple logistic regression analysis, the mean clustering coefficient of the structural connectivity measures was the only significant predictor of LEV response (p = 0.014). Furthermore, in the survival analysis, the mean clustering coefficient was the only significant predictor of LEV response (p = 0.026). We demonstrated that structural connectivity is a potential predictive factor for responsiveness to LEV treatment in patients with newly diagnosed epilepsy.

Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury

Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.

The convenient method and application for monitoring the health of traffic sign poles based on mobile phone

Traffic sign poles are crucial components of the highway system, and their maintenance mainly relies on the subjective judgment of staff, which is low-efficiency and will lead to mistaken inspections. This paper proposes a convenient and effective method to monitor the health of sign poles by using a mobile phone. It is unknown whether a mobile phone can be used as a conventional acceleration sensor. Therefore, the performance of the mobile phone was initially tested to ensure its suitability for acquiring the acceleration data of the traffic sign pole. The results show that the acceleration sensor of mobile phones is high-performance and can be used as a traditional acceleration sensor under the similar sampling frequency. The mobile phone can measure the 1st, 2nd and 5th natural frequencies of the traffic sign pole. Although the 1st and 5th natural frequencies have a large error with simulation, the 2nd natural frequency is accurate and stable. The looseness of the base connection greatly impacts on the 2nd natural frequency, which can be used as a measure of the loose base connection. The 2nd natural frequency was measured for 21 times and found to conform to a normal distribution. The relationship between the 2nd natural frequency and base connection loosening was investigated, which fitted to the equation. The fitting result is good and can be used to predict the degree of sign pole base connection loosening. Therefore, the mobile phone based convenient health monitoring method for traffic sign poles is feasible.

Optimal approaches to analyzing functional MRI data in glioma patients

BackgroundResting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results. New methodWe first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163). ResultsOur results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density. Comparison with existing methods and conclusionsOur current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.

Electroencephalogram measured functional connectivity for delirium detection: a systematic review.

Delirium is an acute alteration of consciousness marked by confusion, inattention, and changes in cognition. Some speculate that delirium may be a disorder of functional connectivity, but the requirement to lay still may limit measurement with existing functional imaging modalities in this population. Electroencephalography (EEG) may allow for a more feasible approach to the study of potential connectivity disturbances in delirium. We conducted a systematic review to investigate whether there are EEG-measurable differences in brain functional connectivity in the resting state associated with delirium. Medline, PubMed, PsychInfo, Embase and CINAHL were searched for relevant articles containing original data studying EEG functional connectivity measures in delirium. The search yielded 1,516 records. Following strict inclusion criteria, four studies were included in the review. The studies used a variety of EEG measures including phase lag index, coherence, entropy, shortest path length, minimum spanning tree, and network clustering coefficients to study functional connectivity between scalp electrodes. Across connectivity measures, delirium was associated with decreased brain functional connectivity. All four studies found decreased alpha band connectivity for patients with delirium. None of the studies directly compared the different motor subtypes of delirium. This systematic review provides converging evidence for disturbances in oscillatory-based functional connectivity in delirium.