Brain Network Connectivity Research Articles

3.49 Functional Brain Network Connectivity With Executive Functions in Children and Adolescents: A Community-Based Study

3.49 Functional Brain Network Connectivity With Executive Functions in Children and Adolescents: A Community-Based Study

Abnormalities in modular connectivity of functional brain networks and cognitive changes in patients with anti -N-methyl-D-aspartate receptor encephalitis

ObjectiveTo explore potential mechanisms of cognitive changes in patients with anti-NMDAR encephalitis (ANMDARE) from intramodule and intermodule effects of brain functional networks. MethodsResting-state functional MRI(rs-fMRI) imaging data was collected from 30 ANMDARE and 30 healthy controls (HCs). A brain functional matrix was constructed, and sparsity was established by module similarity. For both groups, changes in functional connectivity (FC) within and between modules was calculated, and whole-brain functional topology was analyzed. Finally, the association of brain functional with cognitive function in ANMDARE was further analyzed. ResultsCompared to HCs, ANMDARE had enhanced connectivity within the modules that included the occipito-parietal-temporal and parahippocampal gyri. ANMDARE had significantly higher participation coefficients (PC) in the right inferior frontal gyrus than HCs and significantly lower PC in the left superior parietal lobule, left caudate nucleus, and right putamen. No statistically significant differences in global topological properties were found between the two groups. No correlations were found between functional and structural brain indicators and the Cognitive Assessment Scale and the Emotional Deficit Scale. ConclusionsPatients with ANMDARE are manifested by enhanced intramodular FC and intermodular connectivity changes in the brain. This may help to understand the pathophysiological mechanisms of the disease from a global perspective.

Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks.

Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.

Conectividad cerebral, empatía, insensibilidad emocional y toma de decisiones en el trastorno de la conducta: Una revisión sistemática.

The deficits in empathy, emotional insensitivity, and decision-making constitute the core of symptoms defining conduct disorder. Although several studies have established some correlates between brain connectivity and symptoms separately, there has been little effort to systematize such information. The current systematic review aimed to describe the findings obtained through analyzing brain connectivity networks by functional magnetic resonance imaging associated with alterations in empathy, emotional insensitivity, and decision-making in subjects with conduct disorder. A systematic search was carried out for original studies published in Medline (PubMed), Scopus, Scielo, Google Scholar, Scopus, and Nature. Articles published between 2000 and 2022 were included; a qualitative synthesis was obtained with the six selected articles at the end of the process. The findings show altered brain connectivity patterns in brain regions associated with empathy and insensitivity in subjects with conduct disorder.

Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study.

Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine. We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results. The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86. Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.

Noninvasive Brain Imaging and Stimulation in Post-Stroke Motor Rehabilitation: A Review

This paper provides a comprehensive review of current state of non-invasive brain imaging and brain stimulation in motor rehabilitation after stroke. The functional organization of the brain is determined by brain network connections. Brain imaging enables us to map the network organization of the brain, so as to assess the motor function in stroke state. The non-invasive functional imaging techniques include Electroencephalography (EEG) and magnetoencephalography (MEG) with better temporal resolution, and functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) with better spatial resolution. Based on single-mode or multi-mode imaging methods, the changes of brain networks caused by stroke and during motor recovery progress can be observed. Brain stimulation is an effective way to reorganize brain networks. Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are the most widely studied non-invasive neuromodulation techniques. Combined with imaging, brain stimulation can alter brain networks in controlled ways and making neuromodulation a closed loop. This review also outlines the methods of facilitate motor rehabilitation: 1) multi-mode imaging for motor assessment; 2) brain stimulation combined with rehabilitation training; 3) close-loop motor rehabilitation; 4) influencing factors motor rehabilitation.

Functional Connectivity Mechanisms Underlying Symptom Reduction Following Lisdexamfetamine Treatment in Binge-Eating Disorder: A Clinical Trial

BackgroundSpeculation exists as to whether lisdexamfetamine dimesylate (LDX) acts on the functional connectivity (FC) of brain networks that modulate appetite, reward, or inhibitory control in binge-eating disorder (BED). Better insights into its action may help guide the development of more targeted therapeutics and identify who will benefit most from this medication. Here, we use a comprehensive data-driven approach to investigate the brain FC changes that underlie the therapeutic action of LDX in patients with BED. MethodsForty-six participants with moderate to severe BED received LDX titrated to 50 or 70 mg for an 8-week period. Twenty age-matched healthy control participants were also recruited. Resting-state functional magnetic resonance imaging was used to probe changes in brain FC pre- and post treatment and correlated with change in clinical measures. ResultsNinety-seven percent of trial completers (n = 31) experienced remission or a reduction to mild BED during the 8-week LDX trial. Widespread neural FC changes occurred, with changes in default mode to limbic, executive control to subcortical, and default mode to executive control networks associated with improvements in clinical outcomes. These connections were not distinct from control participants at pretreatment but were different from control participants following LDX treatment. Pretreatment connectivity did not predict treatment response. ConclusionsFC between networks associated with self-referential processing, executive function, and reward seem to underlie the therapeutic effect of LDX in BED. This suggests that LDX activates change via multiple systems, with most changes in compensatory networks rather than in those characterizing the BED diagnosis.

Altered Functional Connectivity of Large-Scale Brain Networks in Psychogenic Erectile Dysfunction Associated with Cognitive Impairments.

Several studies have demonstrated that psychogenic erectile dysfunction (pED) patients potentially suffer from cognitive dysfunction. Despite that previous neuroimaging studies have reported abnormal functional connections of brain areas associated with cognitive function in pED, the underlying mechanisms of cognitive dysfunction in pED remain elusive. This study aims to investigate the underlying mechanisms of cognitive dysfunction by analyzing large-scale brain networks. A total of 30 patients with pED and 30 matched healthy controls (HCs) were recruited in this study and scanned by resting-state functional magnetic resonance imaging. The Dosenbach Atlas was used to define large-scale networks across the brain. The resting-state functional connectivity (FC) within and between large-scale brain networks was calculated to compare pED patients with HCs. The relationship among cognitive performances and altered FC of large-scale brain networks was further explored in pED patients. Our results showed that the decreased FC within visual network, and between visual network and default mode network, visual network and frontoparietal network, and ventral attention and default mode network were found in pED patients. Furthermore, there was a positive correlation between immediate memory score and FC within visual network. The visuospatial score was negatively correlated with decreased FC between ventral attention network and default mode network. Taken together, our findings revealed the relationship between cognitive impairments and altered FC between large-scale brain networks in pED patients, providing the new evidence about the neural mechanisms of cognitive dysfunction in pED patients.

Music-emotion EEG coupling effects based on representational similarity

BackgroundMusic can evoke intense emotions and music emotion is a complex cognitive process. However, we know little about the cognitive mechanisms underlying these processes, and there are significant individual differences in the emotional responses to the same musical stimuli. New MethodWe used the inter-subject representational similarity analysis (IS-RSA) method to investigate the shared music emotion responses across multiple participants. In addition, we extended IS-RSA to estimate the group cross-frequency coupling effects of music emotion. Based on the cross-frequency coupling IS-RSA, we analyzed the differences in cross-frequency coupling patterns under different music emotions using MI. Comparison of existing methods: most current IS-RSA analyses focus on within-frequency band analysis. However, the cognitive processing of music emotion involves not only activation and brain network connections differences within frequency bands but also information communication between frequency bands. ResultsThe results of the within-frequency band IS-RSA analysis showed that the theta and gamma frequency bands play important roles in the inter-participant consistency of music emotion. The inter-frequency band IS-RSA analysis showed that the theta-beta coupling pattern exhibited stronger inter-participant consistency compared to the theta-gamma coupling pattern, and the theta-beta coupling had significant consistent representation across various music conditions. Through the significant regions of cross-frequency coupling representation similarity analysis, we performed phase-amplitude coupling analysis on FC4-C6 and FC4-Pz connections. For the theta-beta coupling pattern, we found that the MI of these two connections exhibited different coupling patterns under different music conditions, and they showed a significant decrease compared to the baseline period.

Functional connectomics in depression: insights into therapies.

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

Graph-aware modeling of brain connectivity networks

Graph-aware modeling of brain connectivity networks

Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data

IntroductionFunctional connectivity in the brain is often studied with blood oxygenation level dependent (BOLD) resting state functional magnetic resonance imaging (rsfMRI), but the BOLD signal is several steps removed from neuronal activity. Arterial spin labeling (ASL), particularly pulsed ASL (PASL), has also the capacity to measure the blood-flow changes in response to activity. In this paper, we investigated the feasibility of extracting major brain networks from PASL data, in contrast with rsfMRI analsyis. Materials and methodsIn this retrospective study, we analyzed a cohort dataset that consists of 21 mild traumatic brain injury (mTBI) patients and 29 healthy controls, which was collected in a previous study. By extracting 10 major brain networks from the data of both PASL and rsfMRI, we contrasted their similarities and differences in the 10 networks extracted from both modalities. ResultsOur data demonstrated that PASL could be used to extract all 10 major brain networks. Eight out of 10 networks demonstrated over 60 ​% similarity to rsfMRI data. Meanwhile, there are similar but not identical changes in networks detected between mTBI patients and healthy controls with both modalities. Notably, the PASL-extracted default mode network (DMN), other than the rsfMRI-extracted DMN, includes some regions known to be associated with the DMN in other studies. It demonstrated that PASL data can be analyzed to identify resting state networks with reasonable reliability, even without rsfMRI data. ConclusionOur analysis provides an opportunity to extract functional connectivity information in heritage datasets in which ASL but not BOLD was collected.

Intrinsic prefrontal functional connectivity according to cognitive impairment in patients with end-stage renal disease.

This study aimed to investigate differences in intrinsic prefrontal functional connectivity according to the presence of cognitive impairment in patients with end-stage renal disease (ESRD) using functional near-infrared spectroscopy (fNIRS). We prospectively enrolled 37 patients with ESRD who had been undergoing hemodialysis for more than 6 months and had no history of neurological or psychiatric disorders. All patients with ESRD underwent the Korean version of the Montreal Cognitive Assessment (MoCA-K) to assess cognitive function. The NIRSIT Lite device (OBELAB Inc.) was used to acquire fNIRS data, and the NIRSIT Lite Analysis Tool program was used to process the data and generate a functional connectivity matrix. We obtained functional connectivity measures by applying graph theory to the connectivity matrix using the BRAPH (brain analysis using graph theory) program. Of the 37 patients with ESRD, 23 had cognitive impairment, whereas 14 patients showed no cognitive impairment. Intrinsic prefrontal functional connectivity was significantly different between groups. Network measures of strength, global efficiency, and mean clustering coefficient were lower in ESRD patients with cognitive impairment than in those without cognitive impairment (4.458 vs. 5.129, p = 0.02; 0.397 vs. 0.437, p = 0.03; and 0.316 vs. 0.421, p = 0.003; respectively). There were no significant correlations between MoCA-K scores and clinical characteristics. We demonstrated a significant association between cognitive function and intrinsic prefrontal functional connectivity in patients with ESRD. ESRD patients with cognitive impairment have reduced connectivity and segregation in the prefrontal brain network compared to those without cognitive impairment.

Disruptions of salience network during uncertain anticipation of conflict control in anxiety

BackgroundAnxiety has been characterized by disrupted processing of conflict control, while little is known about anticipatory processing of conflicts in anxiety. Anticipation is the key factor in both anxiety and cognitive control, especially under uncertain conditions. The current study therefore examined neurocomputational mechanisms of uncertain anticipation of conflict control in anxiety. MethodsTwenty-six participants with high-trait anxiety and twenty-nine low-trait anxiety participants completed a cue-flanker task with functional magnetic resonance imaging. The hierarchical drift diffusion model (HDDM) was used to measure the cognitive computations during the task. To identify the neurocomputational mechanism of anticipatory control in anxiety, mediation analysis and dynamic causal modelling (DCM) analysis were conducted to examine the relationship between functional connectivity of brain networks and the parameters of HDDM. ResultsWe found influences of regulatory signals from the dorsolateral prefrontal cortex to dorsal anterior cingulate cortex on decision threshold in low-trait anxiety (LTA), but not in high-trait anxiety (HTA), especially for the condition with uncertain cues. The results indicate deficient top-down anticipatory control of upcoming conflicts in anxious individuals. DCM and HDDM analyses revealed that lower decision threshold was associated with higher intrinsic connectivity of salience network (SN) in anxious individuals, suggesting that dysfunctional SN disrupts anticipation of conflict control under uncertainty in anxiety. ConclusionsOur results suggest hyperfunction of the SN underlies the deficient information accumulation during uncertain anticipation of upcoming conflicts in anxiety. Our findings shed new light on the mechanisms of anticipation processing and the psychopathology of anxiety.

Whole-brain resting-state connectivity underlying impaired inhibitory control during early versus longer-term abstinence in cocaine addiction.

Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD.

The Relationship between Retained Primitive Reflexes and Hemispheric Connectivity in Autism Spectrum Disorders.

Autism Spectrum Disorder (ASD) can be identified by a general tendency toward a reduction in the expression of low-band, widely dispersed integrative activities, which is made up for by an increase in localized, high-frequency, regionally dispersed activity. The study assessed ASD children and adults all possessing retained primitive reflexes (RPRs) compared with a control group that did not attempt to reduce or remove those RPRs and then examined the effects on qEEG and brain network connectivity. Analysis of qEEG spectral and functional connectivity was performed, to identify associations with the presence or absence of retained primitive reflexes (RPRs), before and after an intervention based on TENS unilateral stimulation. The results point to abnormal lateralization in ASD, including long-range underconnectivity, a greater left-over-right qEEG functional connectivity ratio, and short-range overconnectivity in ASD. Clinical improvement and the absence of RPRs may be linked to variations in qEEG frequency bands and more optimized brain networks, resulting in more developmentally appropriate long-range connectivity links, primarily in the right hemisphere.

Statistical Approaches to Characterize Functional Connectivity in Brain and Physiologic Networks on a Single-Subject Basis.

The trend toward personalized medicine necessitates drawing conclusions from descriptive indexes of physiopathological states estimated from individual recordings of biomedical signals, using statistical analyses that focus on subject-specific differences between experimental conditions. In this context, the present work introduces an approach to assess functional connectivity in brain and physiologic networks by pairwise information-theoretic measures of coupling between signals, whose significance and variations between conditions are statistically validated on a single-subject basis through the use of surrogate and bootstrap data analyses. The approach is illustrated on single-subject recordings of (i) resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired in a pediatric patient with hepatic encephalography associated to a portosystemic shunt and undergoing liver vascular shunt correction, and of (ii) cardiovascular and cerebrovascular time series acquired at rest and during head-up tilt in a subject suffering from orthostatic intolerance.

Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea.

Topological data analysis (TDA) is an emerging technique for biological signal processing. TDA leverages the invariant topological features of signals in a metric space for robust analysis of signals even in the presence of noise. In this paper, we leverage TDA on brain connectivity networks derived from electroencephalogram (EEG) signals to identify statistical differences between pediatric patients with obstructive sleep apnea (OSA) and pediatric patients without OSA. We leverage a large corpus of data, and show that TDA enables us to see a statistical difference between the brain dynamics of the two groups.Clinical relevance- This establishes the potential of topological data analysis as a tool to identify obstructive sleep apnea without requiring a full polysomnogram study, and provides an initial investigation towards easier and more scalable obstructive sleep apnea diagnosis.

Pharmacological treatment-associated brain structural and functional alterations in major depressive disorder: A narrative review

Major depressive disorder (MDD) is a complicated mental disorder with an unclear etiology and a relatively limited response to treatment. Magnetic resonance imaging (MRI) can help identify the underlying neuropathophysiological mechanisms of MDD. A better understanding of structural and functional alterations in the brain before and after treatment is necessary to improve treatment targets at the brain level. This narrative review summarizes the literature on longitudinal MRI studies in MDD, which identify brain regions with changes in neural activity after antidepressant treatment, providing an objective imaging basis for early diagnosis and assessment of the efficacy of antidepressant therapy. It was found that the neural activity in the prefrontal cortex, cingulate cortex, precuneus, and hippocampus was reduced in patients with untreated MDD. In addition, functional connectivity of the default mode network (DMN), central executive, and salience networks was enhanced, gray matter volume was reduced, and white matter structure was damaged. After antidepressant treatment, neural activity in some brain regions increased and functional connectivity of brain networks and gray and white matter structures returned to normal states. Although the results have been inconsistent, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli are promising predictors of successful treatment, and hyperconnectivity within and between the DMN and executive control network exhibit similar results.

Functional connectivity of the left inferior parietal lobule mediates the impact of anxiety and depression symptoms on sleep quality in healthy adults.

Individuals with anxiety and depression symptoms are vulnerable to sleep disturbances. The current study aimed to explore the shared neuro-mechanisms underlying the effect of anxiety and depression symptoms on sleep quality. We recruited a cohort of 92 healthy adults who underwent functional magnetic resonance imaging scanning. We measured anxiety and depression symptoms using the Zung Self-rating Anxiety/Depression Scales and sleep quality using the Pittsburgh Sleep Quality Index. Independent component analysis was used to explore the functional connectivity (FC) of brain networks. Whole-brain linear regression analysis showed that poor sleep quality was associated with increased FC in the left inferior parietal lobule (IPL) within the anterior default mode network. Next, we extracted the covariance of anxiety and depression symptoms using principal component analysis to represent participants' emotional features. Mediation analysis revealed that the intra-network FC of the left IPL mediated the association between the covariance of anxiety and depression symptoms and sleep quality. To conclude, the FC of the left IPL may be a potential neural substrate in the association between the covariance of anxiety and depression symptoms and poor sleep quality, and may serve as a potential intervention target for the treatment of sleep disturbance in the future.