Resting-State Functional Magnetic Resonance Imaging Networks as a Quantitative Metric for Impact of Neurosurgical Interventions

Objective: Resting-state functional MRI (rs-fMRI) has been used to evaluate brain network connectivity as a result of intracranial surgery but has not been used to compare different neurosurgical procedures. Laser interstitial thermal therapy (LITT) is an alternative to conventional craniotomy for the treatment of brain lesions such as tumors and epileptogenic foci. While LITT is thought of as minimally invasive, its effect on the functional organization of the brain is still under active investigation and its impact on network changes compared to conventional craniotomy has not yet been explored. We describe a novel computational method for quantifying and comparing the impact of two neurosurgical procedures on brain functional connectivity.Methods: We used a previously described seed-based correlation analysis to generate resting-state network (RSN) correlation matrices, and compared changes in correlation patterns within and across RSNs between LITT and conventional craniotomy for treatment of 24 patients with singular intracranial tumors at our institution between 2014 and 2017. Specifically, we analyzed the differences in patient-specific changes in the within-hemisphere correlation patterns of the contralesional hemisphere.Results: In a post-operative follow-up period up to 2 years within-hemisphere connectivity of the contralesional hemisphere after surgery was more highly correlated to the pre-operative state in LITT patients when compared to craniotomy patients (P = 0.0287). Moreover, 4 out of 11 individual RSNs demonstrated significantly higher degrees of correlation between pre-operative and post-operative network connectivity in patients who underwent LITT (all P < 0.05).Conclusion: Rs-fMRI may be used as a quantitative metric to determine the impact of different neurosurgical procedures on brain functional connectivity. Global and individual network connectivity in the contralesional hemisphere may be more highly preserved after LITT when compared to craniotomy for the treatment of brain tumors.

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Resting-state brain function in schizophrenia and psychotic bipolar probands and their first-degree relatives

Psychological Medicine ◽

10.1017/s003329171400110x ◽

2014 ◽

Vol 45 (1) ◽

pp. 97-108 ◽

Cited By ~ 70

Author(s):

S. Lui ◽

L. Yao ◽

Y. Xiao ◽

S. K. Keedy ◽

J. L. Reilly ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Low Frequency ◽

Network Connectivity ◽

Orbital Frontal Cortex ◽

Cortical Networks ◽

First Degree Relatives ◽

Family Associations ◽

Brain Functional Connectivity ◽

Psychotic Bipolar Disorder

BackgroundSchizophrenia (SCZ) and psychotic bipolar disorder (PBD) share considerable overlap in clinical features, genetic risk factors and co-occurrence among relatives. The common and unique functional cerebral deficits in these disorders, and in unaffected relatives, remain to be identified.MethodA total of 59 healthy controls, 37 SCZ and 57 PBD probands and their unaffected first-degree relatives (38 and 28, respectively) were studied using resting-state functional magnetic resonance imaging (rfMRI). Regional cerebral function was evaluated by measuring the amplitude of low-frequency fluctuations (ALFF). Areas with ALFF alterations were used as seeds in whole-brain functional connectivity analysis. We then tested whether abnormalities identified in probands were present in unaffected relatives.ResultsSCZ and PBD probands both demonstrated regional hypoactivity in the orbital frontal cortex and cingulate gyrus, as well as abnormal connectivity within striatal-thalamo-cortical networks. SCZ probands showed greater and more widely distributed ALFF alterations including the thalamus and bilateral parahippocampal gyri. Increased parahippocampal ALFF was related to positive symptoms and cognitive deficit. PBD patients showed uniquely increased functional connectivity between the thalamus and bilateral insula. Only PBD relatives showed abnormal connectivity within striatal-thalamo-cortical networks seen in both proband groups.ConclusionsThe present findings reveal a common pattern of deficits in frontostriatal circuitry across SCZ and PBD, and unique regional and functional connectivity abnormalities that distinguish them. The abnormal network connectivity in PBD relatives that was present in both proband groups may reflect genetic susceptibility associated with risk for psychosis, but within-family associations of this measure were not high.

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Advanced Meditation Alters Resting-State Brain Network Connectivity Correlating With Improved Mindfulness

Frontiers in Psychology ◽

10.3389/fpsyg.2021.745344 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ramana V. Vishnubhotla ◽

Rupa Radhakrishnan ◽

Kestas Kveraga ◽

Rachael Deardorff ◽

Chithra Ram ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Network Connectivity ◽

Blood Oxygen Level Dependent ◽

Brain Network ◽

Resting State Functional Connectivity ◽

Blood Oxygen Level ◽

Default Mode ◽

Before And After ◽

Brain Network Connectivity

Purpose: The purpose of this study was to investigate the effect of an intensive 8-day Samyama meditation program on the brain functional connectivity using resting-state functional MRI (rs-fMRI).Methods: Thirteen Samyama program participants (meditators) and 4 controls underwent fMRI brain scans before and after the 8-day residential meditation program. Subjects underwent fMRI with a blood oxygen level dependent (BOLD) contrast at rest and during focused breathing. Changes in network connectivity before and after Samyama program were evaluated. In addition, validated psychological metrics were correlated with changes in functional connectivity.Results: Meditators showed significantly increased network connectivity between the salience network (SN) and default mode network (DMN) after the Samyama program (p < 0.01). Increased connectivity within the SN correlated with an improvement in self-reported mindfulness scores (p < 0.01).Conclusion: Samyama, an intensive silent meditation program, favorably increased the resting-state functional connectivity between the salience and default mode networks. During focused breath watching, meditators had lower intra-network connectivity in specific networks. Furthermore, increased intra-network connectivity correlated with improved self-reported mindfulness after Samyama.Clinical Trials Registration: [https://clinicaltrials.gov], Identifier: [NCT04366544]. Registered on 4/17/2020.

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Functional Connectivity Alterations in Children with Spastic and Dyskinetic Cerebral Palsy

Neural Plasticity ◽

10.1155/2018/7058953 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 7

Author(s):

Yun Qin ◽

Yanan Li ◽

Bo Sun ◽

Hui He ◽

Rui Peng ◽

...

Keyword(s):

Cerebral Palsy ◽

Functional Connectivity ◽

Cognitive Dysfunction ◽

Resting State ◽

Network Connectivity ◽

Brain Network ◽

Spastic Cerebral Palsy ◽

Pathophysiological Mechanism ◽

Healthy Children ◽

Sensorimotor Network

Cerebral palsy (CP) has long been investigated to be associated with a range of motor and cognitive dysfunction. As the two most common CP subtypes, spastic cerebral palsy (SCP) and dyskinetic cerebral palsy (DCP) may share common and distinct elements in their pathophysiology. However, the common and distinct dysfunctional characteristics between SCP and DCP on the brain network level are less known. This study aims to detect the alteration of brain functional connectivity in children with SCP and DCP based on resting-state functional MRI (fMRI). Resting-state networks (RSNs) were established based on the independent component analysis (ICA), and the functional network connectivity (FNC) was performed on the fMRI data from 16 DCP, 18 bilateral SCP, and 18 healthy children. Compared with healthy controls, altered functional connectivity within the cerebellum network, sensorimotor network (SMN), left frontoparietal network (LFPN), and salience network (SN) were found in DCP and SCP groups. Furthermore, the disconnections of the FNC consistently focused on the visual pathway; covariance of the default mode network (DMN) with other networks was observed both in DCP and SCP groups, while the DCP group had a distinct connectivity abnormality in motor pathway and self-referential processing-related connections. Correlations between the functional disconnection and the motor-related clinical measurement in children with CP were also found. These findings indicate functional connectivity impairment and altered integration widely exist in children with CP, suggesting that the abnormal functional connectivity is a pathophysiological mechanism of motor and cognitive dysfunction of CP.

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Altered Intrinsic Connectivity Networks in Frontal Lobe Epilepsy: A Resting-State fMRI Study

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/864979 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 16

Author(s):

Xinzhi Cao ◽

Zhiyu Qian ◽

Qiang Xu ◽

Junshu Shen ◽

Zhiqiang Zhang ◽

...

Keyword(s):

Functional Connectivity ◽

Frontal Lobe ◽

Resting State ◽

Healthy Subjects ◽

Network Connectivity ◽

Neural Mechanism ◽

Resting State Fmri ◽

Control Group ◽

Frontal Lobe Epilepsy ◽

The Impact

Examining the resting-state networks (RSNs) may help us to understand the neural mechanism of the frontal lobe epilepsy (FLE). Resting-state functional MRI (fMRI) data were acquired from 46 patients with FLE (study group) and 46 age- and gender-matched healthy subjects (control group). The independent component analysis (ICA) method was used to identify RSNs from each group. Compared with the healthy subjects, decreased functional connectivity was observed in all the networks; however, in some areas of RSNs, functional connectivity was increased in patients with FLE. The duration of epilepsy and the seizure frequency were used to analyze correlation with the regions of interest (ROIs) in the nine RSNs to determine their influence on FLE. The functional network connectivity (FNC) was used to study the impact on the disturbance and reorganization of FLE. The results of this study may offer new insight into the neuropathophysiological mechanisms of FLE.

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An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

Frontiers in Integrative Neuroscience ◽

10.3389/fnint.2020.491403 ◽

2021 ◽

Vol 14 ◽

Author(s):

Sarah J. A. Carr ◽

Arthur Gershon ◽

Nassim Shafiabadi ◽

Samden D. Lhatoo ◽

Curtis Tatsuoka ◽

...

Keyword(s):

Functional Connectivity ◽

Hierarchical Clustering ◽

Structural Connectivity ◽

Network Connectivity ◽

Brain Network ◽

Reference Points ◽

Functional Network ◽

Patient Specific ◽

Integrative Approach ◽

Imaging Data

A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.

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Reconfiguration of Directed Functional Connectivity Among Neurocognitive Networks with Aging: Considering the Role of Thalamo-Cortical Interactions

Cerebral Cortex ◽

10.1093/cercor/bhaa334 ◽

2020 ◽

Author(s):

Moumita Das ◽

Vanshika Singh ◽

Lucina Q Uddin ◽

Arpan Banerjee ◽

Dipanjan Roy

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Fmri Data ◽

Independent Dataset ◽

Left And Right

Abstract A complete picture of how subcortical nodes, such as the thalamus, exert directional influence on large-scale brain network interactions across age remains elusive. Using directed functional connectivity and weighted net causal outflow on resting-state fMRI data, we provide evidence of a comprehensive reorganization within and between neurocognitive networks (default mode: DMN, salience: SN, and central executive: CEN) associated with age and thalamocortical interactions. We hypothesize that thalamus subserves both modality-specific and integrative hub role in organizing causal weighted outflow among large-scale neurocognitive networks. To this end, we observe that within-network directed functional connectivity is driven by thalamus and progressively weakens with age. Secondly, we find that age-associated increase in between CEN- and DMN-directed functional connectivity is driven by both the SN and the thalamus. Furthermore, left and right thalami act as a causal integrative hub exhibiting substantial interactions with neurocognitive networks with aging and play a crucial role in reconfiguring network outflow. Notably, these results were largely replicated on an independent dataset of matched young and old individuals. Our findings strengthen the hypothesis that the thalamus is a key causal hub balancing both within- and between-network connectivity associated with age and maintenance of cognitive functioning with aging.

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Exploring attention in the bilingualism continuum: A resting-state functional connectivity study

10.31234/osf.io/jrp8k ◽

2021 ◽

Author(s):

Tanya Dash ◽

Yves Joanette ◽

Ana Ines Ansaldo

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Network Connectivity ◽

Language Performance ◽

Resting State Functional Connectivity ◽

Executive Control Network ◽

Attentional Processes ◽

Attention Networks ◽

The Impact

This study explores the effects of bilingualism on the subcomponents of attention using resting-state functional connectivity analysis (rsFC). Resting-state functional connectivity studies have established stronger within and between network connectivity for categorically defined bilinguals – based on L2 variables. In this study, L2 AoA, L2 exposure, and L2 proficiency were examined along a continuum, instead of dichotomizing them. 20 seed regions centering on the three subcomponents of attention were pre-selected. First, a stronger rsFC for the seeds in alerting and orienting network is positively associated with the behavioral performance; this was not true for the seeds in the executive control network. Second, different levels of bilingualism have distinct patterns of rsFC for the subcomponents of attention after controlling for age and cognitive reserve variables. The impact of objective measures of bilingualism, i.e., L2 task proficiency, modulates all three attention networks. While the subjective measures such as L2 AOA modulates specific attention network. Thus, language performance in contrast to self-reported information, as a measure of bilingualism, has a greater potential to tap into the role of bilingualism in attentional processes.

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The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

Scientific Reports ◽

10.1038/s41598-021-94825-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rossana Mastrandrea ◽

Fabrizio Piras ◽

Andrea Gabrielli ◽

Nerisa Banaj ◽

Guido Caldarelli ◽

...

Keyword(s):

Resting State ◽

Functional Organization ◽

Functional Integration ◽

Brain Network ◽

Modular Organization ◽

Node Degree ◽

Imaging Data ◽

Brain Disorder ◽

Functional Connections ◽

The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

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A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

Brain Informatics ◽

10.1186/s40708-021-00129-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Federico Calesella ◽

Alberto Testolin ◽

Michele De Filippo De Grazia ◽

Marco Zorzi

Keyword(s):

Feature Extraction ◽

Functional Connectivity ◽

Dimensionality Reduction ◽

Verbal Memory ◽

Resting State ◽

Component Analysis ◽

Model Complexity ◽

Stroke Patients ◽

Prediction Ability ◽

The Impact

AbstractMultivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

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