scholarly journals Individual Variation in Brain Network Topography is Linked to Schizophrenia Symptomatology

2019 ◽  
Author(s):  
Uzma Nawaz ◽  
Ivy Lee ◽  
Adam Beermann ◽  
Shaun Eack ◽  
Matcheri Keshavan ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Negative Symptom ◽  
Individual Variation ◽  
Symptom Severity ◽  
Spatial Organization ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Network ◽  
Brain Organization

AbstractBackgroundResting state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia negative symptom severity and network connectivity are actually due to individual differences in network spatial organization.Methods44 participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole brain functional connectivity correlates with negative symptom severity at the individual voxel level.ResultsBrain connectivity to a region of the right dorso-lateral pre-frontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of two networks: the default mode network (DMN) and the task positive network (TPN). Both networks demonstrate strong (r∼0.49) and significant (p<0.001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN.ConclusionPreviously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (e.g. TMS).

scholarly journals Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

2020 ◽  
Author(s):  
Uzma Nawaz ◽  
Ivy Lee ◽  
Adam Beermann ◽  
Shaun Eack ◽  
Matcheri Keshavan ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Negative Symptom ◽  
Individual Variation ◽  
Symptom Severity ◽  
Spatial Organization ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Organization ◽  
Functional Brain Network

Abstract Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P &lt; .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).

scholarly journals Resting-State Functional Connectivity of the Punishment Network Associated With Conformity

2020 ◽  
Vol 14 ◽  
Author(s):  
Yin Du ◽  
Yinan Wang ◽  
Mengxia Yu ◽  
Xue Tian ◽  
Jia Liu
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Three Phase ◽  
The Right ◽  
Global Brain

Fear of punishment prompts individuals to conform. However, why some people are more inclined than others to conform despite being unaware of any obvious punishment remains unclear, which means the dispositional determinants of individual differences in conformity propensity are poorly understood. Here, we explored whether such individual differences might be explained by individuals’ stable neural markers to potential punishment. To do this, we first defined the punishment network (PN) by combining all potential brain regions involved in punishment processing. We subsequently used a voxel-based global brain connectivity (GBC) method based on resting-state functional connectivity (FC) to characterize the hubs in the PN, which reflected an ongoing readiness state (i.e., sensitivity) for potential punishment. Then, we used the within-network connectivity (WNC) of each voxel in the PN of 264 participants to explain their tendency to conform by using a conformity scale. We found that a stronger WNC in the right thalamus, left insula, postcentral gyrus, and dACC was associated with a stronger tendency to conform. Furthermore, the FC among the four hubs seemed to form a three-phase ascending pathway, contributing to conformity propensity at every phase. Thus, our results suggest that task-independent spontaneous connectivity in the PN could predispose individuals to conform.

scholarly journals Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Alkinoos Athanasiou ◽  
Nikos Terzopoulos ◽  
Niki Pandria ◽  
Ioannis Xygonakis ◽  
Nicolas Foroglou ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Functional Connectivity ◽  
Motor Imagery ◽  
Healthy Subjects ◽  
Brain Connectivity ◽  
Descending Pathways ◽  
Brain Organization ◽  
Sensorimotor Network ◽  
Cord Injury

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

scholarly journals Resting-state brain network topological properties and the correlation with neuropsychological assessment in adolescent narcolepsy

SLEEP ◽  
2020 ◽  
Vol 43 (8) ◽  
Author(s):  
Xiao Fulong ◽  
Spruyt Karen ◽  
Lu Chao ◽  
Zhao Dianjiang ◽  
Zhang Jun ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Neuropsychological Assessment ◽  
Resting State ◽  
Brain Connectivity ◽  
Small World ◽  
Theoretical Method ◽  
Brain Network ◽  
Topological Properties ◽  
Depressive Feelings ◽  
Network Properties

Abstract Study Objectives To evaluate functional connectivity and topological properties of brain networks, and to investigate the association between brain topological properties and neuropsychiatric behaviors in adolescent narcolepsy. Methods Resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessment were applied in 26 adolescent narcolepsy patients and 30 healthy controls. fMRI data were analyzed in three ways: group independent component analysis and a graph theoretical method were applied to evaluate topological properties within the whole brain. Lastly, network-based statistics was utilized for group comparisons in region-to-region connectivity. The relationship between topological properties and neuropsychiatric behaviors was analyzed with correlation analyses. Results In addition to sleepiness, depressive symptoms and impulsivity were detected in adolescent narcolepsy. In adolescent narcolepsy, functional connectivity was decreased between regions of the limbic system and the default mode network (DMN), and increased in the visual network. Adolescent narcolepsy patients exhibited disrupted small-world network properties. Regional alterations in the caudate nucleus (CAU) and posterior cingulate gyrus were associated with subjective sleepiness and regional alterations in the CAU and inferior occipital gyrus were associated with impulsiveness. Remodeling within the salience network and the DMN was associated with sleepiness, depressive feelings, and impulsive behaviors in narcolepsy. Conclusions Alterations in brain connectivity and regional topological properties in narcoleptic adolescents were associated with their sleepiness, depressive feelings, and impulsive behaviors.

scholarly journals Childhood trauma history is linked to abnormal brain connectivity in major depression

2019 ◽  
Vol 116 (17) ◽  
pp. 8582-8590 ◽  
Author(s):  
Meichen Yu ◽  
Kristin A. Linn ◽  
Russell T. Shinohara ◽  
Desmond J. Oathes ◽  
Philip A. Cook ◽  
...  
Keyword(s):  
Childhood Trauma ◽  
Network Architecture ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Network ◽  
Childhood Experiences ◽  
Attention Network ◽  
Sensorimotor Network ◽  
Frontoparietal Network ◽  
Subcortical Regions

Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n= 189 patients with MDD,n= 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

scholarly journals Functional Connectivity Network Disruption Underlies Domain-Specific Impairments in Attention for Children Born Very Preterm

2020 ◽  
Author(s):  
M D Wheelock ◽  
R E Lean ◽  
S Bora ◽  
T R Melzer ◽  
A T Eggebrecht ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Attention Problems ◽  
School Age Children ◽  
Prospective Longitudinal Study ◽  
Domain Specific ◽  
Very Preterm ◽  
Prospective Longitudinal

Abstract Attention problems are common in school-age children born very preterm (VPT; &lt; 32 weeks gestational age), but the contribution of aberrant functional brain connectivity to these problems is not known. As part of a prospective longitudinal study, brain functional connectivity (fc) was assessed alongside behavioral measures of selective, sustained, and executive attention in 58 VPT and 65 full-term (FT) born children at corrected-age 12 years. VPT children had poorer sustained, shifting, and divided attention than FT children. Within the VPT group, poorer attention scores were associated with between-network connectivity in ventral attention, visual, and subcortical networks, whereas between-network connectivity in the frontoparietal, cingulo-opercular, dorsal attention, salience and motor networks was associated with attention functioning in FT children. Network-level differences were also evident between VPT and FT children in specific attention domains. Findings contribute to our understanding of fc networks that potentially underlie typical attention development and suggest an alternative network architecture may help support attention in VPT children.

scholarly journals Longitudinal Brain Functional Connectivity Changes of the Cortical Motor-Related Network in Subcortical Stroke Patients with Acupuncture Treatment

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yongxin Li ◽  
Ya Wang ◽  
Chenxi Liao ◽  
Wenhua Huang ◽  
Ping Wu
Keyword(s):  
Functional Connectivity ◽  
Western Medicine ◽  
Primary Motor Cortex ◽  
Brain Connectivity ◽  
Effective Means ◽  
Brain Network ◽  
Stroke Patients ◽  
Rehabilitation Therapy ◽  
Cortical Motor ◽  
Before And After

In clinical practice, the effectiveness of the rehabilitation therapy such as acupuncture combining conventional Western medicine (AG) on stroke people’s motor-related brain network and their behaviors has not been systematically studied. In the present study, seventeen adult ischemic patients were collected and divided into two groups: the conventional Western medicine treatment group (CG) and the AG. The neurological deficit scores (NDS) and resting-state functional MRI data were collected before and after treatment. Compared with the CG patients, AG patients exhibited a significant enhancement of the percent changes of NDS from pre- to posttreatment intervention. All patients showed significant changes of functional connectivity (FC) between the pair of cortical motor-related regions. After treatment, both patient groups showed a recovery of brain connectivity to the nearly normal level compared with the controls in these pairs. Moreover, a significant correlation between the percent changes of NDS and the pretreatment FC values of bilateral primary motor cortex (M1) in all patients was found. In conclusion, our results showed that AG therapy can be an effective means for ischemic stroke patients to recover their motor function ability. The FC strengths between bilateral M1 of stroke patients can predict stroke patients’ treatment outcome after rehabilitation therapy.

scholarly journals Variability in Infants' Functional Brain Network Connectivity Is Associated With Differences in Affect and Behavior

2021 ◽  
Vol 12 ◽  
Author(s):  
Caroline M. Kelsey ◽  
Katrina Farris ◽  
Tobias Grossmann
Keyword(s):  
Individual Differences ◽  
Long Range ◽  
Network Connectivity ◽  
Brain Network ◽  
Functional Brain ◽  
Default Mode ◽  
Behavioral Traits ◽  
Functional Brain Network ◽  
Brain Network Connectivity ◽  
And Behavior

Variability in functional brain network connectivity has been linked to individual differences in cognitive, affective, and behavioral traits in adults. However, little is known about the developmental origins of such brain-behavior correlations. The current study examined functional brain network connectivity and its link to behavioral temperament in typically developing newborn and 1-month-old infants (M [age] = 25 days; N = 75) using functional near-infrared spectroscopy (fNIRS). Specifically, we measured long-range connectivity between cortical regions approximating fronto-parietal, default mode, and homologous-interhemispheric networks. Our results show that connectivity in these functional brain networks varies across infants and maps onto individual differences in behavioral temperament. Specifically, connectivity in the fronto-parietal network was positively associated with regulation and orienting behaviors, whereas connectivity in the default mode network showed the opposite effect on these behaviors. Our analysis also revealed a significant positive association between the homologous-interhemispheric network and infants' negative affect. The current results suggest that variability in long-range intra-hemispheric and cross-hemispheric functional connectivity between frontal, parietal, and temporal cortex is associated with individual differences in affect and behavior. These findings shed new light on the brain origins of individual differences in early-emerging behavioral traits and thus represent a viable novel approach for investigating developmental trajectories in typical and atypical neurodevelopment.

scholarly journals Electrocorticography and stereo EEG provide distinct measures of brain connectivity: Implications for network models

2020 ◽  
Author(s):  
John M. Bernabei ◽  
T. Campbell Arnold ◽  
Preya Shah ◽  
Andrew Revell ◽  
Ian Z. Ong ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Network Models ◽  
Brain Network ◽  
Seizure Freedom ◽  
Epileptogenic Zone ◽  
Depth Electrodes ◽  
Brain Network Connectivity ◽  
Patients With Epilepsy

AbstractBrain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by center, region and country, from cortical grid & strip electrodes, to purely stereotactic depth electrodes, to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocortiography (ECoG) and stereoelectroencephalography (SEEG) in a matched cohort of patients who underwent epilepsy surgery. We show that ECoG and SEEG map broad network structure differently, and demonstrate substantial disparity in the ability of node strength to localize the epileptogenic zone in SEEG compared to ECoG. We demonstrate that eliminating white matter contacts and reducing network nodes to anatomical regions of interest rather than individual contacts improves the ability of these models to distinguish between epileptogenic and non-epileptogenic regions in SEEG. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analyzing both ECoG and SEEG recordings in the same cohort. Finally, we share all code and data in this study, aiming for our findings to accelerate research in brain network connectivity in epilepsy and beyond.

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