Global Functional Connectivity Research Articles

Global functional connectivity reorganization reflects cognitive processing speed deficits and fatigue in multiple sclerosis.

Cognitive impairment (CI) in multiple sclerosis (MS) is associated with bidirectional changes in resting-state centrality measures. However, practicable functional magnetic resonance imaging (fMRI) biomarkers of CI are still lacking. The aim of this study was to assess the graph-theory-based degree rank order disruption index (kD) and its association with cognitive processing speed as a marker of CI in patients with MS (PwMS) in a secondary cross-sectional fMRI analysis. Differentiation between PwMS and healthy controls (HCs) using kD and its correlation with CI (Symbol Digit Modalities Test) was compared to established imaging biomarkers (regional degree, volumetry, diffusion-weighted imaging, lesion mapping). Additional associations were assessed for fatigue (Fatigue Scale for Motor and Cognitive Functions), gait and global disability. Analysis in 56 PwMS and 58 HCs (35/27 women, median age 45.1/40.5 years) showed lower kD in PwMS than in HCs (median -0.30/-0.06, interquartile range 0.55/0.54; p = 0.009, Mann-Whitney U test), yielding acceptable yet non-superior differentiation (area under curve 0.64). kD and degree in medial prefrontal cortex (MPFC) correlated with CI (kD/MPFC Spearman's ρ = 0.32/-0.45, p = 0.019/0.001, n = 55). kD also explained fatigue (ρ = -0.34, p = 0.010, n = 56) but neither gait nor disability. kD is a potential biomarker of CI and fatigue warranting further validation.

Reliability of variability and complexity measures for task and task-free BOLD fMRI.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Sensory over-responsivity and orbitofrontal cortex connectivity in obsessive-compulsive disorder

BackgroundSensory over-responsivity (SOR) in obsessive-compulsive disorder (OCD) is associated with illness severity and functional impairment. However, the neural substrates of SOR in OCD have not yet been directly probed. MethodsWe examined resting-state global functional connectivity markers of SOR in 119 adults with OCD utilizing the CONN-fMRI Functional Connectivity Toolbox for SPM (v21a). We quantified SOR with the sensory sensitivity and sensory avoiding subscales of the Adult and Adolescent Sensory Profile (AASP). We also measured: OCD severity, with the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and Obsessive-Compulsive Inventory-Revised (OCI-R); sensory phenomena with the Sensory Phenomena Scale (SPS); general anxiety, with the Beck Anxiety Inventory (BAI); and depressive symptomatology, with Quick Inventory of Depressive Symptoms, Self-Report (QIDS-SR). ResultsThere was a significant positive relationship of SOR with global connectivity in anterior and medial OFC (Brodmann's area 11, k = 154, x = 14, y = 62, z = −18, whole-brain corrected at FWE p < 0.05). LimitationsFuture investigations should explore neural responses to sensory stimulation tasks in OCD and compare findings with those obtained in other conditions also characterized by high SOR, such as autism spectrum disorder. ConclusionsThis study implicates OFC functional connectivity as a neurobiological mechanism of SOR in OCD and suggests that the substrates of SOR in OCD may be dissociable from both that of other symptoms in OCD, and SOR in other disorders. With replication and extension, the finding may be leveraged to develop and refine treatments for OCD and investigate the pathophysiology of SOR in other conditions.

A Local-Ascending-Global Learning Strategy for Brain-Computer Interface

Neuroscience research indicates that the interaction among different functional regions of the brain plays a crucial role in driving various cognitive tasks. Existing studies have primarily focused on constructing either local or global functional connectivity maps within the brain, often lacking an adaptive approach to fuse functional brain regions and explore latent relationships between localization during different cognitive tasks. This paper introduces a novel approach called the Local-Ascending-Global Learning Strategy (LAG) to uncover higher-level latent topological patterns among functional brain regions. The strategy initiates from the local connectivity of individual brain functional regions and develops a K-Level Self-Adaptive Ascending Network (SALK) to dynamically capture strong connectivity patterns among brain regions during different cognitive tasks. Through the step-by-step fusion of brain regions, this approach captures higher-level latent patterns, shedding light on the progressively adaptive fusion of various brain functional regions under different cognitive tasks. Notably, this study represents the first exploration of higher-level latent patterns through progressively adaptive fusion of diverse brain functional regions under different cognitive tasks. The proposed LAG strategy is validated using datasets related to fatigue (SEED-VIG), emotion (SEED-IV), and motor imagery (BCI_C_IV_2a). The results demonstrate the generalizability of LAG, achieving satisfactory outcomes in independent-subject experiments across all three datasets. This suggests that LAG effectively characterizes higher-level latent patterns associated with different cognitive tasks, presenting a novel approach to understanding brain patterns in varying cognitive contexts.

EEG rhythmic and arrhythmic spectral components and functional connectivity at resting state may predict the development of synucleinopathies in idiopathic REM sleep behavior disorder.

Idiopathic/isolated REM-sleep behavior disorder (iRBD) often precedes the onset of synucleinopathies. Here, we investigated whether baseline resting-state EEG advanced spectral power and functional connectivity differ between iRBD patients who converted towards a synucleinopathy at follow-up and those who did not. Eighty-one participants with iRBD (66.89±6.91 years) underwent a baseline resting-state EEG recording, a neuropsychological assessment and a neurological examination. We estimated EEG power spectral density using standard analyses and derived spectral estimates of rhythmic and arrhythmic components. Global and pairwise EEG functional connectivity analyses were computed using the weighted phase-lag index (wPLI). Pixel-based permutation tests were used to compare groups. After a mean follow-up of 5.01±2.76 years, 34 patients were diagnosed with a synucleinopathy (67.81±7.34 years) and 47 remained disease-free (65.53±7.09 years). Among patients who converted, 22 were diagnosed with Parkinson's disease and 12 with dementia with Lewy bodies. As compared to patients who did not convert, patients who converted exhibited at baseline higher relative theta standard power, steeper slopes of the arrhythmic component and higher theta rhythmic power mostly in occipital regions. Furthermore, patients who converted showed higher beta global wPLI but lower alpha wPLI between left temporal and occipital regions. Analyses of resting-state EEG rhythmic and arrhythmic components and functional connectivity suggest an imbalanced excitatory-to-inhibitory activity within large-scale networks, which is associated with later development of a synucleinopathy in iRBD patients.

Associations between handedness and brain functional connectivity patterns in children

Handedness develops early in life, but the structural and functional brain connectivity patterns associated with it remains unknown. Here we investigate associations between handedness and the asymmetry of brain connectivity in 9- to 10-years old children from the Adolescent Brain Cognitive Development (ABCD) study. Compared to right-handers, left-handers had increased global functional connectivity density in the left-hand motor area and decreased it in the right-hand motor area. A connectivity-based index of handedness provided a sharper differentiation between right- and left-handers. The laterality of hand-motor connectivity varied as a function of handedness in unimodal sensorimotor cortices, heteromodal areas, and cerebellum (P < 0.001) and reproduced across all regions of interest in Discovery and Replication subsamples. Here we show a strong association between handedness and the laterality of the functional connectivity patterns in the absence of differences in structural connectivity, brain morphometrics, and cortical myelin between left, right, and mixed handed children.

Global Functional Connectivity is Associated with Mind Wandering Domain of Comfort.

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. To assess the relationship between brain activity and subjective experiences, various questionnaires are used. Previous studies using Amsterdam Resting State Questionnaire were focusing on fMRI functional connectivity or EEG microstates and spectral aspect. Here, we utilized Global Field Synchronization as the parameter to estimate global functional connectivity. By re-analyzing the resting-state data from 226 young healthy participants we showed a strong evidence of relationship between ARSQ domain of Comfort and GFS values in the alpha range (r = 0.210, BF10 = 12.338) and substantial evidence for positive relationship between ARSQ domain of Comfort and GFS in the beta frequency range (r = 196, BF10 = 6.307). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

Acute Stress Increases Striatal Connectivity With Cortical Regions Enriched for μ and κ Opioid Receptors

BackgroundUnderstanding the neurobiological effects of stress is critical for addressing the etiology of major depressive disorder (MDD). Using a dimensional approach involving individuals with differing degree of MDD risk, we investigated 1) the effects of acute stress on cortico-cortical and subcortical-cortical functional connectivity (FC) and 2) how such effects are related to gene expression and receptor maps. MethodsAcross 115 participants (37 control, 39 remitted MDD, 39 current MDD), we evaluated the effects of stress on FC during the Montreal Imaging Stress Task. Using partial least squares regression, we investigated genes whose expression in the Allen Human Brain Atlas was associated with anatomical patterns of stress-related FC change. Finally, we correlated stress-related FC change maps with opioid and GABAA (gamma-aminobutyric acid A) receptor distribution maps derived from positron emission tomography. ResultsResults revealed robust effects of stress on global cortical connectivity, with increased global FC in frontoparietal and attentional networks and decreased global FC in the medial default mode network. Moreover, robust increases emerged in FC of the caudate, putamen, and amygdala with regions from the ventral attention/salience network, frontoparietal network, and motor networks. Such regions showed preferential expression of genes involved in cell-to-cell signaling (OPRM1, OPRK1, SST, GABRA3, GABRA5), similar to previous genetic MDD studies. ConclusionsAcute stress altered global cortical connectivity and increased striatal connectivity with cortical regions that express genes that have previously been associated with imaging abnormalities in MDD and are rich in μ and κ opioid receptors. These findings point to overlapping circuitry underlying stress response, reward, and MDD.

Early prediction of dementia using fMRI data with a graph convolutional network approach

Objective. Alzheimer’s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs). Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI. Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification. Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at: https://github.com/Shuning-Han/FC-based-GCN.

Examining the role of dopamine in methylphenidate's effects on resting brain function.

The amplitude of low-frequency fluctuations (ALFF) and global functional connectivity density (gFCD) are fMRI (Functional MRI) metrics widely used to assess resting brain function. However, their differential sensitivity to stimulant-induced dopamine (DA) increases, including the rate of DA rise and the relationship between them, have not been investigated. Here we used, simultaneous PET-fMRI to examine the association between dynamic changes in striatal DA and brain activity as assessed by ALFF and gFCD, following placebo, intravenous (IV), or oral methylphenidate (MP) administration, using a within-subject double-blind placebo-controlled design. In putamen, MP significantly reduced D2/3 receptor availability and strongly reduced ALFF and increased gFCD in the brain for IV-MP (Cohen's d > 1.6) but less so for oral-MP (Cohen's d < 0.6). Enhanced gFCD was associated with both the level and the rate of striatal DA increases, whereas decreased ALFF was only associated with the level of DA increases. These findings suggest distinct representations of neurovascular activation with ALFF and gFCD by stimulant-induced DA increases with differential sensitivity to the rate and the level of DA increases. We also observed an inverse association between gFCD and ALFF that was markedly enhanced during IV-MP, which could reflect an increased contribution from MP's vasoactive properties.

Localization of Silent Cerebral Infarcts in Children with Sickle Cell Disease Impacts Structural Disconnection

Silent cerebral infarcts (SCIs) occur in 40% of children with sickle cell disease (SCD) by 18 years of age and are associated with cognitive dysfunction. SCIs localize to the border zone of the brain, but the significance of this localization is unclear. In adults with stroke, an infarct's disruption of white matter (WM) tracts, or structural disconnection, is more strongly associated with the functional impact of the infarct than the size of the lesion. We aim to define the WM tracts disconnected by SCIs and the relationship between structural disconnection and functional connectivity (FC) in SCD. Simulated and real-world data were used to test the hypothesis that specific WM tracts are vulnerable to disconnection if an infarct occurs in the region at highest risk for SCIs, and that FC will decrease with increased structural disconnection in SCD. Utilizing data from the Silent Infarct Transfusion (SITT) trial, the regions at greatest risk for SCIs were defined (risk region, PMID 30061156). A simulated dataset comprised of 1000 infarcts within the risk region and 1000 infarcts within the white matter outside of the risk region was created. The real-world dataset was comprised of 44 participants with SCD and SCIs (median age 13.0 [11.0, 15.8] years, 29 female/15 male). The Human Connectome Project (HCP) 842 atlas contains thousands of streamlines comprising 28 WM tracts (excluding brainstem and cerebellar tracts). The Lesion Connectivity Toolbox defined streamlines within the HCP 842 atlas that intersected with each individual lesion, permitting quantification of two measures of structural disconnection: tract-wise disconnection (the percentage of streamlines within each tract that intersect a lesion) and global disconnection (the average percentage of streamlines connecting each pair of gray matter (GM) parcels in the FC parcellation (PMID 25316338)). Tract density images (TDi) illustrate the number of streamlines impacted by a lesion within each voxel of the brain permitting computation of total lesion impact as the volume of the TDi image associated with each lesion. Resting state functional connectivity MRI was obtained in 27 of the real-world participants for computation of whole brain modularity as a metric of global FC. The WM tracts with the highest mean tract-wise disconnection due to lesions within the risk region were the posterior and mid-anterior corpus callosum, superior longitudinal fasciculus, frontal aslant tract, and middle longitudinal fasciculus while the inferior frontal occipital fasciclus, anterior commissure, inferior longitudinal fasciculus, posterior corpus callosum, and arculate fasciculus were most impacted by lesions outside of the risk region. Numerous tracts showed a significant difference in tract-wise disconnection resulting from lesions in the risk region vs. lesions outside the risk region. The tracts impacted due to real-world lesions were similar to tracts impacted from lesions within the risk region (Table 1). Despite lesioned volume being equivalent (p = 0.31) for simulated data within and outside the risk region, the average volume of lesion impact as measured by volume of the TDi image was significantly larger for lesions occurring inside the risk region (p &amp;lt; 0.001; Fig 1). In the real-world dataset, whole brain modularity, a metric of global FC, decreased as global structural disconnection increased (r = -0.49, p =0.009) Using simulated data that was validated with a real-world dataset, specific WM tracts vulnerable to SCIs were identified. Previous work on cerebral hemodynamics in SCD elucidated the mechanism of injury in the border zone, and these results demonstrate the significance of the localization of this injury to the border zone. SCIs have a far-reaching effect as lesions in the risk region within the border zone were associated with a greater volume of structural disconnection compared to lesions of the same size distributed outside the risk region. These results suggest that the regions in which the SCIs localize represent “high impact” areas of the brain, where a lesion of a given volume may intersect a large number of streamlines. Further, given the correlation between global structural disconnection and whole brain modularity, these data link structural disconnection with function of the brain, suggesting these “high impact” regions may also play a key role in mediating communication between functionally connected GM regions.

Emergence of cortical network motifs for short-term memory during learning

Learning of adaptive behaviors requires the refinement of coordinated activity across multiple brain regions. However, how neural communications develop during learning remains poorly understood. Here, using two-photon calcium imaging, we simultaneously recorded the activity of layer 2/3 excitatory neurons in eight regions of the mouse dorsal cortex during learning of a delayed-response task. Across learning, while global functional connectivity became sparser, there emerged a subnetwork comprising of neurons in the anterior lateral motor cortex (ALM) and posterior parietal cortex (PPC). Neurons in this subnetwork shared a similar choice code during action preparation and formed recurrent functional connectivity across learning. Suppression of PPC activity disrupted choice selectivity in ALM and impaired task performance. Recurrent neural networks reconstructed from ALM activity revealed that PPC-ALM interactions rendered choice-related attractor dynamics more stable. Thus, learning constructs cortical network motifs by recruiting specific inter-areal communication channels to promote efficient and robust sensorimotor transformation.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient.

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Comparing the efficacy of data-driven denoising methods for a multi-echo fMRI acquisition at 7T

In functional magnetic resonance imaging (fMRI) of the brain the measured signal is corrupted by several (e.g. physiological, motion, and thermal) noise sources and depends on the image acquisition. Imaging at ultrahigh field strength is becoming increasingly popular as it offers increased spatial accuracy. The latter is of particular benefit in brainstem neuroimaging given the small cross-sectional area of most nuclei. However, physiological noise scales with field strength in fMRI acquisitions. Although this problem is in part solved by decreasing voxel size, it is clear that adequate physiological denoising is of utmost importance in brainstem-focused fMRI experiments. Multi-echo sequences have been reported to facilitate highly effective denoising through TE-dependence of Blood Oxygen Level Dependent (BOLD) signals, in a denoising method referred to as multi-echo independent component analysis (ME-ICA). It has not been explored previously how ME-ICA compares to other data-driven denoising approaches at ultrahigh field strength. In the current study, we compared the efficacy of several denoising methods, including anatomical component based correction (aCompCor), Automatic Removal of Motion Artifacts (ICA-AROMA) aggressive and non-aggressive options, ME-ICA, and a combination of ME-ICA and aCompCor. We assessed several data quality metrics, including temporal signal-to-noise ratio (tSNR), delta variation signal (DVARS), spectral density of the global signal, functional connectivity and Shannon spectral entropy. Moreover, we looked at the ability of each method to uncouple the global signal and respiration. In line with previous reports at lower field strengths, we demonstrate that after applying ME-ICA, the data is best post-processed in order to remove spatially diffuse noise with a method such as aCompCor. Our findings indicate that ME-ICA combined with aCompCor and the aggressive option of ICA-AROMA are highly effective denoising approaches for multi-echo data acquired at 7T. ME-ICA combined with aCompCor potentially preserves more signal-of-interest as compared to the aggressive option of ICA-AROMA.

Network biomarkers in recovered psychosis patients who discontinued antipsychotics

There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.

Secondary neurodegeneration following Stroke: what can blood biomarkers tell us?

Stroke is one of the leading causes of death and the primary source of disability in adults, resulting in neuronal necrosis of ischemic areas, and in possible secondary degeneration of regions surrounding or distant to the initial damaged area. Secondary neurodegeneration (SNDG) following stroke has been shown to have different pathogenetic origins including inflammation, neurovascular response and cytotoxicity, but can be associated also to regenerative processes. Aside from focal neuronal loss, ipsilateral and contralateral effects distal to the lesion site, disruptions of global functional connectivity and a transcallosal diaschisis have been reported in the chronic stages after stroke. Furthermore, SNDG can be observed in different areas not directly connected to the primary lesion, such as thalamus, hippocampus, amygdala, substantia nigra, corpus callosum, bilateral inferior fronto-occipital fasciculus and superior longitudinal fasciculus, which can be highlighted by neuroimaging techniques. Although the clinical relevance of SNDG following stroke has not been well understood, the identification of specific biomarkers that reflect the brain response to the damage, is of paramount importance to investigate in vivo the different phases of stroke. Actually, brain-derived markers, particularly neurofilament light chain, tau protein, S100b, in post-stroke patients have yielded promising results. This review focuses on cerebral morphological modifications occurring after a stroke, on associated cellular and molecular changes and on state-of-the-art of biomarkers in acute and chronic phase. Finally, we discuss new perspectives regarding the implementation of blood-based biomarkers in clinical practice to improve the rehabilitation approaches and post stroke recovery.

Effects of family income on brain functional connectivity in US children: associations with cognition.

Higher family income (FI) is associated with larger cortical gray matter volume and improved cognitive performance in children. However, little is known about the effects of FI on brain functional and structural connectivity. This cross-sectional study investigates the effects of FI on brain connectivity and cognitive performance in 9- to 11-years old children (n = 8739) from the Adolescent Brain Cognitive Development (ABCD) study. Lower FI was associated with decreased global functional connectivity density (gFCD) in the default-mode network (DMN), inferior and superior parietal cortices and in posterior cerebellum, and increased gFCD in motor, auditory, and extrastriate visual areas, and in subcortical regions both for girls and boys. Findings demonstrated high reproducibility in Discovery and Reproducibility samples. Cognitive performance partially mediated the association between FI and DMN connectivity, whereas DMN connectivity did not mediate the association between FI and cognitive performance. In contrast, there was no significant association between FI and structural connectivity. Findings suggest that poor cognitive performance, which likely reflects multiplefactors (genetic, nutritional, the level and quality of parental interactions, and educational exposure [1]), contributes to reduced DMN functional connectivity in children from low-income families. Follow-up studies are needed to help clarify if this leads to reductions in structural connectivity as these children age.

Diffuse glioma-induced structural reorganization in close association with preexisting syntax-related networks

Glioma in the left frontal cortex has been reported to cause agrammatic comprehension and induce global functional connectivity alterations within the syntax-related networks. However, it remains unclear to what extent the structural reorganization is affected by preexisting syntax-related networks. We examined 28 patients with a diffuse glioma in the left hemisphere and 23 healthy participants. Syntactic abilities were assessed by a picture-sentence matching task with various sentence types. The lesion responsible for agrammatic comprehension was identified by region-of-interest-based lesion-symptom mapping (RLSM). Cortical structural alterations were examined by surface-based morphometry (SBM), in which the cortical thickness and fractal dimension were measured with three-dimensional magnetic resonance imaging (MRI). Fiber tracking on the human population-averaged diffusion MRI template was performed to examine whether the cortical structural alterations were associated with the syntax-related networks. The RLSM revealed associations between agrammatic comprehension and a glioma in the posterior limb of the left internal capsule. The SBM demonstrated that decreased cortical thickness and/or increased complexity of the right posterior insula were associated not only with agrammatic comprehension of the patients but also with the syntactic abilities of healthy participants. The fiber tracking revealed that the route between these two regions was anatomically integrated into the preexisting syntax-related networks previously identified. These results suggest a potential association between agrammatic comprehension in patients with diffuse glioma and structural variations in specific tracts and cortical regions, which may be closely related to the syntax-related networks.

Structural-and-dynamical similarity predicts compensatory brain areas driving the post-lesion functional recovery mechanism.

The focal lesion alters the excitation-inhibition (E-I) balance and healthy functional connectivity patterns, which may recover over time. One possible mechanism for the brain to counter the insult is global reshaping functional connectivity alterations. However, the operational principles by which this can be achieved remain unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict whether specific compensatory areas initiate lost E-I regulation after lesion. We hypothesize that similar structural areas (SSAs) and dynamically similar areas (DSAs) corresponding to a lesioned site are the crucial dynamical units to restore lost homeostatic balance within the surviving cortical brain regions. SSAs and DSAs are independent measures, one based on structural similarity properties measured by Jaccard Index and the other based on post-lesion recovery time. We unravel the relationship between SSA and DSA by simulating a whole brain mean field model deployed on top of a virtually lesioned structural connectome from human neuroimaging data to characterize global brain dynamics and functional connectivity at the level of individual subjects. Our results suggest that wiring proximity and similarity are the 2 major guiding principles of compensation-related utilization of hemisphere in the post-lesion functional connectivity re-organization process.

Perifocal Edema in Patients with Meningioma is Associated with Impaired Whole-Brain Connectivity as Detected by Resting-State fMRI.

Meningiomas are intracranial tumors that usually carry a benign prognosis. Some meningiomas cause perifocal edema. Resting-state fMRI can be used to assess whole-brain functional connectivity, which can serve as a marker for disease severity. Here, we investigated whether the presence of perifocal edema in preoperative patients with meningiomas leads to impaired functional connectivity and if these changes are associated with cognitive function. Patients with suspected meningiomas were prospectively included, and resting-state fMRI scans were obtained. Impairment of functional connectivity was quantified on a whole-brain level using our recently published resting-state fMRI-based marker, called the dysconnectivity index. Using uni- and multivariate regression models, we investigated the association of the dysconnectivity index with edema and tumor volume as well as cognitive test scores. Twenty-nine patients were included. In a multivariate regression analysis, there was a highly significant association of dysconnectivity index values and edema volume in the total sample and in a subsample of 14 patients with edema, when accounting for potential confounders like age and temporal SNR. There was no statistically significant association with tumor volume. Better neurocognitive performance was strongly associated with lower dysconnectivity index values. Resting-state fMRI showed a significant association between impaired functional connectivity and perifocal edema, but not tumor volume, in patients with meningiomas. We demonstrated that better neurocognitive function was associated with less impairment of functional connectivity. This result shows that our resting-state fMRI marker indicates a detrimental influence of peritumoral brain edema on global functional connectivity in patients with meningiomas.