Structural constraints of functional connectivity drive cognitive impairment in the early stages of multiple sclerosis

2020 ◽  
pp. 135245852097180
Author(s):  
Ismail Koubiyr ◽  
Mathilde Deloire ◽  
Bruno Brochet ◽  
Pierre Besson ◽  
Julie Charré-Morin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Structure Function ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Coupling ◽  
Structural Constraints ◽  
Characteristic Path Length ◽  
Functional Connections

Background: The relationship between structural and functional deficits in multiple sclerosis (MS) is unclear. Objective: This study explored structure-function relationships during the 5 years following a clinically isolated syndrome and their role in cognitive performance. Methods: Thirty-two patients were enrolled after their first neurological episode suggestive of MS and followed for 5 years, along with 10 matched healthy controls. We assessed structural (using diffusion tensor imaging) and functional (using resting-state functional magnetic resonance imaging (fMRI)) brain network metrics, clinical and cognitive scores at each follow-up visit. Structural–functional coupling, calculated as the correlation coefficient between strengths of structural and functional networks, was used to assess structure–function relationships. Results: Structural clustering coefficient was significantly increased after 5 years, whereas characteristic path length decreased. Structural connections decreased after 1 year and increased after 5 years. Functional connections and related path lengths were decreased after 5 years. Structural–functional coupling had increased significantly after 5 years. This structural–functional coupling was associated with cognitive and clinical evolution, with stronger coupling associated with a decline in both domains. Conclusion: Our findings provide novel biological evidence that MS leads to a more constrained anatomical-dependant functional connectivity. The collapse of this network seems to lead to both cognitive worsening and clinical disability.

scholarly journals Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yi Liang ◽  
Chunli Chen ◽  
Fali Li ◽  
Dezhong Yao ◽  
Peng Xu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Local Area ◽  
Brain Network ◽  
Fuzzy Entropy ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Functional Brain Network ◽  
Before And After

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.

scholarly journals A Dissociation in Effects of Risperidone Monotherapy on Functional and Anatomical Connectivity Within the Default Mode Network

2018 ◽  
Vol 45 (6) ◽  
pp. 1309-1318 ◽  
Author(s):  
Xiaofen Zong ◽  
Maolin Hu ◽  
Spiro P Pantazatos ◽  
J John Mann ◽  
Gaohua Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Healthy Volunteers ◽  
Default Mode Network ◽  
Diffusion Tensor ◽  
Control Group ◽  
Functional Coupling ◽  
Antipsychotic Treatment ◽  
Positive Symptoms ◽  
Default Mode

Abstract Respective changes in functional and anatomical connectivities of default mode network (DMN) after antipsychotic treatment have been reported. However, alterations in structure–function coupling after treatment remain unknown. We performed diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging in 42 drug-naive first-episode schizophrenia patients (FESP) both at baseline and after 8-weeks risperidone monotherapy, and in 38 healthy volunteers. Independent component analysis was used to assess voxel-wise DMN synchrony. A 3-step procedure was used to trace fiber paths between DMN components. Structure–function couplings were assessed by Pearson’s correlations between mean fractional anisotropy and temporal correlation coefficients in major tracts of DMN. Pretreatment, FESP showed impaired functional connectivity in posterior cingulate cortex/precuneus (PCC/PCUN) and medial prefrontal cortex (mPFC), but no abnormalities in fibers connecting DMN components. After treatment, there were significant increases in functional connectivities of PCC/PCUN. Increases in functional connectivity between PCC/PCUN and mPFC correlated with improvement in positive symptoms. The structure–function coupling in tracts connecting PCC/PCUN and bilateral medial temporal lobes decreased after treatment. No alterations in DMN fiber integrity were detected. This combination of functional and anatomical findings in FESP contributes novel evidence related to neurobehavioral treatment effects. Increased functional connectivities between PCC/PCUN and mPFC may be treatment response biomarkers for positive symptoms. Increases in functional connectivities, no alterations in fiber integrity, combined with decreases in structural–functional coupling, suggest that DMN connectivities may be dissociated by modality after 8-week treatment. Major limitations of this study, however, include lack of repeat scans in healthy volunteers and control group of patients taking placebo or comparator antipsychotics.

scholarly journals Changes in Structural Connectivity Following a Cognitive Intervention in Children With Traumatic Brain Injury

2016 ◽  
Vol 31 (2) ◽  
pp. 190-201 ◽  
Author(s):  
Weihong Yuan ◽  
Amery Treble-Barna ◽  
McKay M. Sohlberg ◽  
Beth Harn ◽  
Shari L. Wade
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mixed Model ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Cognitive Intervention ◽  
Preliminary Evidence ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Characteristic Path Length

Objective. Structural connectivity analysis based on graph theory and diffusion tensor imaging tractography is a novel method that quantifies the topological characteristics in the brain network. This study aimed to examine structural connectivity changes following the Attention Intervention and Management (AIM) program designed to improve attention and executive function (EF) in children with traumatic brain injury (TBI). Methods. Seventeen children with complicated mild to severe TBI (13.66 ± 2.68 years; >12 months postinjury) completed magnetic resonance imaging (MRI) and neurobehavioral measures at time 1, 10 of whom completed AIM and assessment at time 2. Eleven matched healthy comparison (HC) children (13.37 ± 2.08 years) completed MRI and neurobehavioral assessment at both time points, but did not complete AIM. Network characteristics were analyzed to quantify the structural connectivity before and after the intervention. Results. Mixed model analyses showed that small-worldness was significantly higher in the TBI group than the HC group at time 1, and both small-worldness and normalized clustering coefficient decreased significantly at time 2 in the TBI group whereas the HC group remained relatively unchanged. Reductions in mean local efficiency were significantly correlated with improvements in verbal inhibition and both parent- and child-reported EF. Increased normalized characteristic path length was significantly correlated with improved sustained attention. Conclusion. The results provide preliminary evidence suggesting that graph theoretical analysis may be a sensitive tool in pediatric TBI for detecting ( a) abnormalities of structural connectivity in brain network and ( b) structural neuroplasticity associated with neurobehavioral improvement following a short-term intervention for attention and EF.

scholarly journals Multiple sclerosis reduces synchrony of the magnocellular pathway

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255324
Author(s):  
Masoud Seraji ◽  
Maryam Mohebbi ◽  
Amirhossein Safari ◽  
Bart Krekelberg
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Disease Progression ◽  
Information Transfer ◽  
Demyelinating Disease ◽  
Clinical Symptoms ◽  
Visual Stimuli ◽  
Clustering Coefficient ◽  
Magnocellular Pathway ◽  
Characteristic Path Length

Multiple Sclerosis (MS) is an autoimmune demyelinating disease that damages the insulation of nerve cell fibers in the brain and spinal cord. In the visual system, this demyelination results in a robust delay of visually evoked potentials (VEPs), even in the absence of overt clinical symptoms such as blurred vision. VEPs, therefore, offer an avenue for early diagnosis, monitoring disease progression, and, potentially, insight into the differential impairment of specific pathways. A primary hypothesis has been that visual stimuli driving the magno-, parvo-, and konio-cellular pathways should lead to differential effects because these pathways differ considerably in terms of myelination. Experimental tests of this hypothesis, however, have led to conflicting results. Some groups reported larger latency effects for chromatic stimuli, while others found equivalent effects across stimulus types. We reasoned that this lack of pathway specificity could, at least in part, be attributed to the relatively coarse measure of pathway impairment afforded by the latency of a VEP. We hypothesized that network synchrony could offer a more sensitive test of pathway impairments. To test this hypothesis, we analyzed the synchrony of occipital electroencephalography (EEG) signals during the presentation of visual stimuli designed to bias activity to one of the three pathways. Specifically, we quantified synchrony in the occipital EEG using two graph-theoretic measures of functional connectivity: the characteristic path length (L; a measure of long-range connectivity) and the clustering coefficient (CC; a measure of short-range connectivity). Our main finding was that L and CC were both smaller in the MS group than in controls. Notably, this change in functional connectivity was limited to the magnocellular pathway. The effect sizes (Hedge’s g) were 0.89 (L) and 1.26 (CC) measured with magno stimuli. Together, L and CC define the small-world nature of a network, and our finding can be summarized as a reduction in the small-worldness of the magnocellular network. We speculate that the reduced efficiency of information transfer associated with a reduction in small-worldness could underlie visual deficits in MS. Relating these measures to differential diagnoses and disease progression is an important avenue for future work.

scholarly journals Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Zhang ◽  
Rosa Cortese ◽  
Nicola De Stefano ◽  
Antonio Giorgio
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Impairment ◽  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Diffusion Kurtosis Imaging ◽  
High Angular Resolution ◽  
Diffusion Spectrum Imaging ◽  
Analysis Methods ◽  
Scan Time

Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC–FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.

scholarly journals Disrupted functional connectivity in PD with probable RBD and its cognitive correlates

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Javier Oltra ◽  
Anna Campabadal ◽  
Barbara Segura ◽  
Carme Uribe ◽  
Maria Jose Marti ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Verbal Memory ◽  
Path Length ◽  
Behavior Disorder ◽  
Brain Network ◽  
Characteristic Path Length ◽  
Whole Brain ◽  
Sleep Behavior ◽  
Cognitive Correlates ◽  
Theoretical Approaches

AbstractRecent studies associated rapid eye movement sleep behavior disorder (RBD) in Parkinson’s disease (PD) with severe cognitive impairment and brain atrophy. However, whole-brain functional connectivity has never been explored in this group of PD patients. In this study, whole-brain network-based statistics and graph-theoretical approaches were used to characterize resting-state interregional functional connectivity in PD with probable RBD (PD-pRBD) and its relationship with cognition. Our sample consisted of 30 healthy controls, 32 PD without probable RBD (PD-non pRBD), and 27 PD-pRBD. The PD-pRBD group showed reduced functional connectivity compared with controls mainly involving cingulate areas with temporal, frontal, insular, and thalamic regions (p < 0.001). Also, the PD-pRBD group showed reduced functional connectivity between right ventral posterior cingulate and left medial precuneus compared with PD-non pRBD (p < 0.05). We found increased normalized characteristic path length in PD-pRBD compared with PD-non pRBD. In the PD-pRBD group, mean connectivity strength from reduced connections correlated with visuoperceptual task and normalized characteristic path length correlated with processing speed and verbal memory tasks. This work demonstrates the existence of disrupted functional connectivity in PD-pRBD, together with abnormal network integrity, that supports its consideration as a severe PD subtype.

scholarly journals Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhibao Li ◽  
Chong Liu ◽  
Qiao Wang ◽  
Kun Liang ◽  
Chunlei Han ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Functional Connectivity ◽  
Frequency Band ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Frequency Bands ◽  
The Brain ◽  
Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p &lt; 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p &lt; 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p &lt; 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

scholarly journals Hemispheric Asymmetry of Functional Brain Networks under Different Emotions Using EEG Data

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 939
Author(s):  
Rui Cao ◽  
Huiyu Shi ◽  
Xin Wang ◽  
Shoujun Huo ◽  
Yan Hao ◽  
...  
Keyword(s):  
Hemispheric Asymmetry ◽  
Right Hemisphere ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Phase Lag ◽  
Characteristic Path Length ◽  
High Arousal ◽  
Eeg Data ◽  
The Right

Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants watched different emotional videos. According to the videos’ emotional categories, the data were divided into four categories: high arousal high valence (HAHV), low arousal high valence (LAHV), low arousal low valence (LALV) and high arousal low valence (HALV). The phase lag index as a connectivity index was calculated in theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz) and gamma (31–45 Hz) bands. Hemispheric networks were constructed for each trial, and graph theory was applied to quantify the hemispheric networks’ topological properties. Statistical analyses showed significant topological differences in the gamma band. The left hemispheric network showed significantly higher clustering coefficient (Cp), global efficiency (Eg) and local efficiency (Eloc) and lower characteristic path length (Lp) under HAHV emotion. The right hemispheric network showed significantly higher Cp and Eloc and lower Lp under HALV emotion. The results showed that the left hemisphere was dominant for HAHV emotion, while the right hemisphere was dominant for HALV emotion. The research revealed the relationship between emotion and hemispheric asymmetry from the perspective of brain networks.

Disruption of Cortical Connectivity during Remifentanil Administration Is Associated with Cognitive Impairment but Not with Analgesia

2015 ◽  
Vol 122 (1) ◽  
pp. 140-149 ◽  
Author(s):  
Ahmad Khodayari-Rostamabad ◽  
Søren S. Olesen ◽  
Carina Graversen ◽  
Lasse P. Malver ◽  
Geana P. Kurita ◽  
...  
Keyword(s):  
Reaction Time ◽  
Functional Connectivity ◽  
Path Length ◽  
Experimental Pain ◽  
Clustering Coefficient ◽  
Cognitive Test ◽  
Characteristic Path Length ◽  
Network Properties ◽  
Continuous Reaction ◽  
Continuous Reaction Time

Abstract Background: The authors investigated the effect of remifentanil administration on resting electroencephalography functional connectivity and its relationship to cognitive function and analgesia in healthy volunteers. Methods: Twenty-one healthy male adult subjects were enrolled in this placebo-controlled double-blind cross-over study. For each subject, 2.5 min of multichannel electroencephalography recording, a cognitive test of sustained attention (continuous reaction time), and experimental pain scores to bone-pressure and heat stimuli were collected before and after infusion of remifentanil or placebo. A coherence matrix was calculated from the electroencephalogram, and three graph-theoretical measures (characteristic path-length, mean clustering coefficient, and relative small-worldness) were extracted to characterize the overall cortical network properties. Results: Compared to placebo, most graph-theoretical measures were significantly altered by remifentanil at the alpha and low beta range (8 to 18 Hz; all P &lt; 0.001). Taken together, these alterations were characterized by an increase in the characteristic path-length (alpha 17% and low beta range 24%) and corresponding decrements in mean clustering coefficient (low beta range −25%) and relative small-worldness (alpha −17% and low beta range −42%). Changes in characteristic path-lengths after remifentanil infusion were correlated to the continuous reaction time index (r = −0.57; P = 0.009), while no significant correlations between graph-theoretical measures and experimental pain tests were seen. Conclusions: Remifentanil disrupts the functional connectivity network properties of the electroencephalogram. The findings give new insight into how opioids interfere with the normal brain functions and have the potential to be biomarkers for the sedative effects of opioids in different clinical settings.

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