Differences In Brain Networks Research Articles

Measurement of Functional Brain Network Connectivity in People with Orthostatic Tremor.

Orthostatic tremor is a rare movement disorder characterized by a sensation of unsteadiness and leg tremor while standing. It has been hypothesized that the disorder is attributable to dysregulation of a central oscillatory network in the brain. This putative network includes primary motor cortex, supplementary motor area, cerebellum, thalamus, and pontine tegmentum. We studied this brain network by recording resting-state functional MRI data from individuals with orthostatic tremor. For each participant, we measured resting-state functional connectivity using a seed-based approach. Regions of interest included were components of the putative central oscillatory network and a primary motor thumb region (identified via transcranial magnetic stimulation). A non-central oscillatory network region of interest-posterior cingulate cortex-was included for comparative analysis of a well-characterized intrinsic network, the default mode network. Demographic information, medical history, and tremor characteristics were collected to test associations with functional connectivity. For normative context, data from the 1000 Functional Connectomes Project were analyzed using an identical approach. We observed that tremor and demographic variables were correlated with functional connectivity of central oscillatory network components. Furthermore, relative to healthy comparison participants, patients with orthostatic tremor exhibited qualitatively different patterns of cerebellar resting state functional connectivity. Our study enhances the current understanding of brain network differences related to orthostatic tremor and is consistent with a hypothesized selective decoupling of cerebellum. Additionally, associations observed between functional connectivity and factors including medical history and tremor features may suggest targets for treatment of orthostatic tremor.

Changes of structural functional connectivity coupling and its correlations with cognitive function in patients with major depressive disorder

BackgroundPrevious neuroimaging studies have reported structural and functional brain abnormalities in major depressive disorder (MDD). This study aimed to explore whether the coherence of structural-functional networks was affected by disease and investigate its correlation with clinical manifestations. MethodsThe severity of symptoms and cognitive function of 121 MDD patients and 139 healthy controls (HC) were assessed, and imaging data, including diffusion tensor imaging, T1 structural magnetic resonance imaging (MRI) and resting-state functional MRI, were collected. Spearman correlation coefficients of Kullback–Leibler similarity (KLS), fiber number (FN), fractional anisotropy (FA) and functional connectivity (FC) were calculated as coupling coefficients. Double-weight median correlation analysis was conducted to investigate the correlations between differences in brain networks and clinical assessments. ResultsThe percentage of total correct response of delayed matching to sample and the percentage of delayed correct response of pattern recognition memory was lower in MDD. Compared with the HC, KLS-FC coupling between the parietal lobe and subcortical area, FA-FC coupling between the temporal and parietal lobe, and FN-FC coupling in the frontal lobe was lower in MDD. Several correlations between structural-functional connectivity and clinical manifestations were identified. LimitationsFirst, our study lacks longitudinal follow-up data. Second, the sample size was relatively small. Moreover, we only used the Anatomical Automatic Labeling template to construct the brain network. Finally, the validation of the causal relationship of neuroimaging-behavior factors was still insufficient. ConclusionsThe alternation in structural-functional coupling were related to clinical characterization and might be involved in the neuropathology of depression.

Network-based statistics distinguish anomic and Broca's aphasia.

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI). We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM. NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest. We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.

Heart rate variability, interoceptive accuracy and functional connectivity in middle-aged and older patients with depression

Background and objectiveMajor depressive disorder (MDD) is associated with increased cardiac morbidity. Reduced heart rate variability (HRV) as well as lower interoceptive accuracy (IAc) have been observed in MDD as possible sympathomimetic mechanisms related to insula activity. The salience network (SN) anchored by the insula has been posited as a crucial functional network for cardiac sensations and the default mode network (DMN) for MDD. This study aimed to investigate the relation between insula-centered and depression-related brain networks, IAc and HRV in patients with depression as a possible mechanism by which MDD increases cardiac morbidity. Methods30 depressed inpatients and 30 healthy subjects (derived from the population-based “Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression” cohort study, STAAB) all over 50 years were examined. HRV and IAc were assessed via electrocardiogram and a heartbeat perception task prior to a 3 T resting-state functional magnetic resonance imaging. Seed-to-voxel resting-state functional connectivity (FC) analysis was conducted with six seeds in the insula and two seeds in the DMN. ResultsDepressed patients on the one hand showed decreased FC between insula cortex and frontal as well occipital cortical brain regions compared to controls. Depressed patients on the other hand exhibited higher FC between the medial prefrontal cortex and the insula cortex compared to controls. However, depressed patients did not differ in HRV nor in IAc compared to controls. ConclusionThus, differences in insula-related brain networks in depression in our study were not mirrored by differences in HRV and IAc. Future research is needed to define the mechanism by which depression increases cardiac morbidity.

Resting‐state network connectivity changes in prodromal stage of Alzheimer’s disease

AbstractBackgroundAlzheimer Disease (AD), the most common type of dementia, is accompanied by a generalized loss of neurons and cause severe changes in brain’s structure and function. Mild Cognitive Impairment is associated with a higher risk of developing dementia. This clinical classification is particularly relevant because each subtype is linked to a presumed etiology, with the amnestic subtypes (aMCI) considered as a prodromal form of AD (Petersen et al., 2009, 2014). Resting state fMRI functional connectivity (FCrs‐fMRI) analysis is broadly used to detect brain network differences in neurodegenerative diseases such as AD.MethodForty‐five participants were evaluated: patients with aMCI (n = 17), patients with dementia due to AD (n = 14) and healthy controls (HC) (n = 14). All participants received an extensive neuropsychological battery and brain MRI scanning. Resting state functional scans consisted of 240 T2*‐weighted echo planar imaging volumes (repetition time 3000 ms; echo time 60 ms; ip angle 90°; 32 axial slices; matrix 64×64; voxel size 3.3×3.4×4 mm). Additionally, a high‐resolution T1‐weighted magnetization prepared rapid acquisition gradient echo image was acquired. Data analyses were performed using the Functional Connectivity Toolbox (CONN) for Matlab (https://www.nitrc.org/frs/) to identify large‐scale patterns of temporal signal‐intensity coherence, interpreted as functional connectivity (Beckmann et al., 2005). Regions of interests (ROIs) analysis was performed for each participants and among group. Spatial maps of the group independent component analysis were used in a linear model t against each individual fMRI data set.ResultThe Default Mode Network (DMN) showed two regions of lower functional connectivity (FC) in AD when comparing with HC within the precuneus (p<0.001 uncorrected). No regions of FC changes were found in the DMN when comparing aMCI with HC or AD.ConclusionThe changes in FC rsfMRI in adults with AD are specifically related to regions of the DMN.

6 Graph Analysis of Resting State Functional Brain Networks and Associations with Cognitive Outcomes in Survivors on Pediatric Brain Tumor

Objective:Adolescent and young adult survivors of pediatric brain tumors often live with long-term neuropsychological deficits, which have been found to be related to functional and structural brain changes related to the presence of the tumor itself as well as treatments such as radiation therapy. The importance of brain networks has become a central focus of research over recent decades across neurological populations. Graph theory is one way of analyzing network properties that can describe the integration, segregation, and other aspects of network organization. The existing literature using graph theory with survivors of brain tumor is small and inconsistent; therefore, more work is needed, particularly in survivors of pediatric brain tumors. The present study used graph theory to determine whether functional network properties in this population differ from healthy controls; whether graph metrics relate to core cognitive skills: attention, working memory, and processing speed; and whether they relate to a cumulative measure of neurological risk.Participants and Methods:31 survivors and 31 matched controls completed neuropsychological testing including measures of attention, working memory, and processing speed. They also underwent resting state functional magnetic resonance imaging. Resting state data were preprocessed and spatially constrained independent component analysis was completed to construct connectivity matrices. Finally, graph metrics were calculated utilizing an area under the curve method, including global efficiency, clustering coefficient, betweenness centrality, and small-worldness. Group differences and associations between graph metrics, cognitive outcomes, and neurological risk were analyzed using SPSS version 28.0.Results:Results revealed a significant difference such that brain tumor survivors exhibited less small-world properties in their functional brain networks. This was found to be related to working memory, such that less smallworldness in the network was related to poorer performance. There were no significant relationships with neurological risk, but there were nonsignificant correlations of small-moderate effect size such that lower global efficiency and clustering coefficient were associated with greater neurological risk. Comparisons to structural network analysis from a similar sample and additional post-hoc analyses are also discussed.Conclusions:These findings reveal that survivors of pediatric brain tumor indeed display significant differences in functional brain networks that are quantifiable by graph theory. It is also possible that, with further work, we might better understand how metrics such as smallworldness can be used to predict long-term cognitive outcomes and functional independence in adulthood.

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability.

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

EEG Spatial-temporal Dynamics of Resting-state Activity in Young Women with Anorexia Nervosa: Preliminary Evidence.

The aim of this study was to provide preliminary evidence on temporal dynamics of resting-state brain networks in youth with anorexia nervosa (AN) using electroencephalography (EEG). Resting-state EEG data were collected in 18 young women with AN and 18 healthy controls (HC). Between-group differences in brain networks were assessed using microstates analyses. Five microstates were identified across all subjects (A, B, C, D, E). Using a single set of maps representative of the whole dataset, group differences were identified for microstates A, C, and E. A common-for-all template revealed a relatively high degree of consistency in results for reduced time coverage of microstate C, but also an increased presence of microstate class E. AN and HC had different microstate transition probabilities, largely involving microstate A. Using LORETA, for microstate D, we found that those with AN had augmented activations in the left frontal inferior operculum, left insula, and bilateral paracentral lobule, compared with HC. For microstate E, AN had augmented activations in the para-hippocampal gyrus, caudate, pallidum, cerebellum, and cerebellar vermis. Our findings suggest altered microstates in young women with AN associated with integration of sensory and bodily signals, monitoring of internal/external mental states, and self-referential processes. Future research should examine how EEG-derived microstates could be applied to develop diagnostic and prognostic models of AN.

Differences in resting-state brain networks and gray matter between APOE ε2 and APOE ε4 carriers in non-dementia elderly.

Apolipoprotein E (APOE) ε2 and APOE ε4 are the most distinct alleles among the three APOE alleles, both structurally and functionally. However, differences in cognition, brain function, and brain structure between the two alleles have not been comprehensively reported in the literature, especially in non-demented elderly individuals. A neuropsychological test battery was used to evaluate the differences in cognitive performance in five cognitive domains. Independent component analysis (ICA) and voxel-based morphometry (VBM) were used separately to analyze resting-state functional magnetic resonance imaging (rs-fMRI) data and the structure MRI data between the two groups. Finally, correlations between differential brain regions and neuropsychological tests were calculated. APOE ε2 carriers had better cognitive performance in general cognitive, memory, attention, and executive function than APOE ε4 carriers (all p < 0.05). In ICA analyses of rs-fMRI data, the difference in the resting-state functional connectivity (rsFC) between two groups is shown in 7 brain networks. In addition, VBM analyses of the T1-weighted image revealed that APOE ε2 carriers had a larger thalamus and right postcentral gyrus volume and a smaller bilateral putamen volume than APOE ε4 carriers. Finally, differences in brain function and structure may be might be the reason that APOE ε2 carriers are better than APOE ε4 carriers in cognitive performance. These findings suggest that there are significant differences in brain function and structure between APOE ε2 carriers and APOE ε4 carriers, and these significant differences are closely related to their cognitive performance.

Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms.

Multimodal-based methods show great potential for neuroscience studies by integrating complementary information. There has been less multimodal work focussed on brain developmental changes. We propose an explainable multimodal deep dictionary learning method to uncover both the commonality and specificity of different modalities, which learns the shared dictionary and the modality-specific sparse representations based on the multimodal data and their encodings of a sparse deep autoencoder. By regarding three fMRI paradigms collected during two tasks and resting state as modalities, we apply the proposed method on multimodal data to identify the brain developmental differences. The results show that the proposed model can not only achieve better performance in reconstruction, but also yield age-related differences in reoccurring patterns. Specifically, both children and young adults prefer to switch among states during two tasks while staying within a particular state during rest, but the difference is that children possess more diffuse functional connectivity patterns while young adults have more focused functional connectivity patterns. To uncover the commonality and specificity of three fMRI paradigms to developmental differences, multimodal data and their encodings are used to train the shared dictionary and the modality-specific sparse representations. Identifying brain network differences helps to understand how the neural circuits and brain networks form and develop with age.

Brain Connectome Imaging Markers Research of Glucose Metabolism in the Early Stage of Alzheimer's Disease.

In recent years, increasing evidence had suggested that subjective cognitive decline (SCD) in unimpaired individuals may be the first symptom of Alzheimer's disease (AD). This study investigated the differences in the glucose metabolism network and the influence of the Apolipoprotein E (ApoE) gene between the SCD and normal control (NC) group by using graph theory. In this study, we included 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital in Beijing, China. 85 SCD subjects and 74 NC subjects were included. First, we calculated and compared network parameters between the two groups. We then identified the bilateral insula and bilateral parahippocampal gyrus as seed sites and studied the connections to the whole brain. The results showed that both the SCD and the NC showed small-world nature, but the metabolic network of SCD tended to be more regular. The clustering coefficient and local efficiency of SCD were significantly higher than those of NC (P<0.05). In addition, we found that carrying APOE resulted in enhanced metabolic connectivity, but with weaker aggregation and local information exchangeability. Our results suggested that there are differences in the glucose metabolic brain network between SCD and NC, suggesting that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.Clinical relevance- This study suggests that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.

Structural brain network analysis in occipital lobe epilepsy

BackgroundThis study aimed to analyze the structural brain network in patients with occipital lobe epilepsy (OLE) and investigate the differences in structural brain networks between patients with OLE and healthy controls.MethodsPatients with OLE and healthy controls with normal brain MRI findings were enrolled. They underwent diffusion tensor imaging using a 3.0T MRI scanner, and we computed the network measures of global and local structural networks in patients with OLE and healthy controls using the DSI studio program. We compared network measures between the groups.ResultsWe enrolled 23 patients with OLE and 42 healthy controls. There were significant differences in the global structural network between patients with OLE and healthy controls. The assortativity coefficient (-0.0864 vs. -0.0814, p = 0.0214), mean clustering coefficient (0.0061 vs. 0.0064, p = 0.0203), global efficiency (0.0315 vs. 0.0353, p = 0.0086), and small-worldness index (0.0001 vs. 0.0001, p = 0.0175) were lower, whereas the characteristic path length (59.2724 vs. 53.4684, p = 0.0120) was higher in patients with OLE than those in the healthy controls. There were several nodes beyond the occipital lobe that showed significant differences in the local structural network between the groups. In addition, the assortativity coefficient was negatively correlated with the duration of epilepsy (r=-0.676, p = 0.001).

Differential processing of intrinsic vs. extrinsic coordinates in wrist movement: connectivity and chronometry perspectives.

This study explores brain-network differences between the intrinsic and extrinsic motor coordinate frames. A connectivity model showing the coordinate frames difference was obtained using brain fMRI data of right wrist isometric flexions and extensions movements, performed in two forearm postures. The connectivity model was calculated by machine-learning-based neural representation and effective functional connectivity using psychophysiological interaction and dynamic causal modeling analyses. The model indicated the network difference wherein the inferior parietal lobule receives extrinsic information from the rostral lingual gyrus through the superior parietal lobule and transmits intrinsic information to the Handknob, whereas extrinsic information is transmitted to the Handknob directly from the rostral lingual gyrus. A behavioral experiment provided further evidence on the difference between motor coordinate frames showing onset timing delay of muscle activity of intrinsic coordinate-directed wrist movement compared to extrinsic one. These results suggest that, if the movement is externally directed, intrinsic coordinate system information is bypassed to reach the primary motor area.

Age differences in functional brain networks associated with loneliness and empathy.

Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality-loneliness and empathic responding-and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.

Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring

BackgroundThe connectome, constituting a unique fingerprint of a person’s brain, may be influenced by its prenatal environment, potentially affecting later-life resilience and mental health.MethodsWe conducted a prospective resting-state functional Magnetic Resonance Imaging study in 28-year-old offspring (N = 49) of mothers whose anxiety was monitored during pregnancy. Two offspring anxiety subgroups were defined: “High anxiety” (n = 13) group versus “low-to-medium anxiety” (n = 36) group, based on maternal self-reported state anxiety at 12–22 weeks of gestation. To predict resting-state functional connectivity of 32 by 32 ROIs, maternal state anxiety during pregnancy was included as a predictor in general linear models for both ROI-to-ROI and graph theoretical metrics. Sex, birth weight and postnatal anxiety were included as covariates.ResultsHigher maternal anxiety was associated with weaker functional connectivity of medial prefrontal cortex with left inferior frontal gyrus (t = 3.45, pFDR < 0.05). Moreover, network-based statistics (NBS) confirmed our finding and revealed an additional association of weaker connectivity between left lateral prefontal cortex with left somatosensory motor gyrus in the offspring. While our results showed a general pattern of lower functional connectivity in adults prenatally exposed to maternal anxiety, we did not observe significant differences in global brain networks between groups.ConclusionsWeaker (medial) prefrontal cortex functional connectivity in the high anxiety adult offspring group suggests a long-term negative impact of prenatal exposure to high maternal anxiety, extending into adulthood. To prevent mental health problems at population level, universal primary prevention strategies should aim at lowering maternal anxiety during pregnancy.

The Different Impact of Depressive or Manic First-episode on Pediatric Bipolar Disorder Patients: Evidence From Resting-state fMRI

Bipolar disorder may begin as depression or mania, which can affect the treatment and prognosis of bipolar disorder. However, the physiological and pathological differences of pediatric bipolar disorder (PBD) patients with different onset symptoms are not clear. The purpose of this study was to investigate the differences of clinical, cognitive function and intrinsic brain networks in PBD patients with first-episode depression and first-episode mania. A total of 63 participants, including 43 patients and 20 healthy controls, underwent resting-state fMRI scans. PBD patients were classified as first-episode depressive and first-episode manic based on their first-episode symptoms. Cognitive tests were used to measure attention and memory of all participants. Independent component analysis (ICA) was used to extract the salience network (SN), default-mode network (DMN), central executive network (ECN) and limbic network (LN) for each participant. Spearman rank correlation analysis was performed between abnormal activation and clinical and cognitive measures. The results showed that there were differences in cognitive functions such as attention and visual memory between first-episode depression and mania, as well as differences activation in anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), precuneus, inferior parietal cortex and parahippocampus. And significant associations of brain activity with clinical assessments or cognition were found in different patients. In conclusion, we found differential impairments in cognitive and brain network activation in first-episode depressive and first-episode manic PBD patients, and correlations were found between these impairments. These evidences may shed light on the different developmental paths of bipolar disorder.

Classification of human chronotype based on fMRI network-based statistics.

Chronotype-the relationship between the internal circadian physiology of an individual and the external 24-h light-dark cycle-is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9-5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health is not well-understood. To address this issue, we use rs-fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype over three scanning sessions. We develop a classification framework utilizing the Network Based-Statistic methodology, to understand if differentiable information about chronotype is embedded in functional brain networks and how this changes throughout the day. We find evidence of subnetworks throughout the day that differ between extreme chronotypes such that high accuracy can occur, describe rigorous threshold criteria for achieving 97.3% accuracy in the Evening and investigate how the same conditions hinder accuracy for other scanning sessions. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterize the relationship between internal physiology, external perturbations, brain networks, and disease.

Graph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumor

Survivors of pediatric brain tumors often live with long-term cognitive difficulties related to brain changes associated with the tumor itself as well as treatments such as radiation therapy. The present study used graph theory to examine functional network properties in this population and whether graph metrics relate to core cognitive skills: attention, working memory, and processing speed. 31 survivors and 31 matched controls completed neuropsychological testing and functional magnetic resonance imaging. Neuroimaging was preprocessed and spatially constrained ICA was completed, followed by calculation of area under the curve values of graph metrics. Results revealed a significant difference such that brain tumor survivors exhibited less small-world properties. This was found to be related to working memory, such that less small-worldness in the network was related to poorer performance. Furthermore, hub regions appear to be particularly vulnerable to disruption. Comparison to results of microstructural network analysis from a similar sample suggest functional connectivity graph metrics provide different and complementary information and additional post-hoc analyses are also discussed. These findings reveal that survivors of pediatric brain tumor indeed display significant differences in functional brain networks that are quantifiable by graph theory and build a foundation to better understand how metrics such as small-worldness can be used to predict long-term cognitive outcomes in adulthood. Ongoing neuroimaging research may play a part in precision medicine determining treatment protocols and interventions for pediatric brain tumor patients.

Child Developmental MRI (CDM) project: protocol for a multi-centre, cross-sectional study on elucidating the pathophysiology of attention-deficit/hyperactivity disorder and autism spectrum disorder through a multi-dimensional approach

IntroductionNeuroimaging studies on attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have demonstrated differences in extensive brain structure, activity and network. However, there remains heterogeneity and inconsistency across these findings,...

Age related changes and sex related differences of functional brain networks in childhood: A high-density EEG study

ObjectiveThe aim of this study was to explore functional network age-related changes and sex-related differences during the early lifespan with a high-density resting state electroencephalography (rs-EEG). MethodsWe analyzed two data sets of high-density rs-EEG in healthy children and adolescents. We recorded a 64-channel EEG and calculated functional connectomes in 27 participants aged 5–18 years. To validate our results, we used publicly available data and calculated functional connectomes in another 86 participants aged 6–18 years from a 128-channel rs-EEG. We were primarily interested in alpha frequency band, but we also analyzed theta and beta frequency bands. ResultsWe observed age-related increase of characteristic path, clustering coefficient and interhemispheric strength in the alpha frequency band of both data sets and in the beta frequency band of the larger validation data set. Age-related increase of global efficiency was seen in the theta band of the validation data set and in the alpha band of the test data set. Increase in small worldness was observed only in the alpha frequency band of the test data set. We also observed an increase of individual peak alpha frequency with age in both data sets. Sex-related differences were only observed in the beta frequency band of the larger validation data set, with females having higher values than same aged males. ConclusionsFunctional brain networks show indices of higher segregation, but also increasing global integration with maturation. Age-related changes are most prominent in the alpha frequency band. SignificanceTo the best of our knowledge, our study was the first to analyze maturation related changes and sex-related differences of functional brain networks with a high-density EEG and to compare functional connectomes generated from two diverse high-density EEG data sets. Understanding the age-related changes and sex-related differences of functional brain networks in healthy children and adolescents is crucial for identifying network abnormalities in different neurologic and psychiatric conditions, with the aim to identify possible markers for prognosis and treatment.