Aberrant interactions of cortical networks in chronic migraine

Neurology ◽  
2019 ◽  
Vol 92 (22) ◽  
pp. e2550-e2558 ◽  
Author(s):  
Gianluca Coppola ◽  
Antonio Di Renzo ◽  
Barbara Petolicchio ◽  
Emanuele Tinelli ◽  
Cherubino Di Lorenzo ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Chronic Migraine ◽  
Large Scale ◽  
Preventive Therapy ◽  
Cortical Networks ◽  
Executive Control Network ◽  
Acute Medication ◽  
Connectivity Patterns ◽  
Mri Scans ◽  
Group Ica

ObjectiveWe investigated resting-state (RS)-fMRI using independent component analysis (ICA) to determine the functional connectivity (FC) between networks in chronic migraine (CM) patients and their correlation with clinical features.MethodsTwenty CM patients without preventive therapy or acute medication overuse underwent 3T MRI scans and were compared to a group of 20 healthy controls (HC). We used MRI to collect RS data in 3 selected networks, identified using group ICA: the default mode network (DMN), the executive control network (ECN), and the dorsal attention system (DAS).ResultsCompared to HC, CM patients had significantly reduced functional connectivity between the DMN and the ECN. Moreover, in patients, the DAS showed significantly stronger FC with the DMN and weaker FC with the ECN. The higher the severity of headache, the increased the strength of DAS connectivity, and the lower the strength of ECN connectivity.ConclusionThese results provide evidence for large-scale reorganization of functional cortical networks in chronic migraine. They suggest that the severity of headache is associated with opposite connectivity patterns in frontal executive and dorsal attentional networks.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Data on subjective recollection effects reflected in large-scale functional connectivity patterns in postpartum women

Data in Brief ◽  
2018 ◽  
Vol 19 ◽  
pp. 1142-1147
Author(s):  
Yoonjin Nah ◽  
Na-Young Shin ◽  
Sehjung Yi ◽  
Seung-Koo Lee ◽  
Sanghoon Han
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Postpartum Women ◽  
Connectivity Patterns

Verbal working memory-related functional connectivity alterations in boys with attention-deficit/hyperactivity disorder and the effects of methylphenidate

2017 ◽  
Vol 31 (8) ◽  
pp. 1061-1069 ◽  
Author(s):  
Zhao-Min Wu ◽  
Janita Bralten ◽  
Li An ◽  
Qing-Jiu Cao ◽  
Xiao-Hua Cao ◽  
...  
Keyword(s):  
Working Memory ◽  
Single Dose ◽  
Functional Connectivity ◽  
Executive Control ◽  
Verbal Working Memory ◽  
Control Network ◽  
Executive Control Network ◽  
Hyperactivity Disorder ◽  
Connectivity Patterns ◽  
Behavioural Performance

Objective: Few studies have investigated verbal working memory-related functional connectivity patterns in participants with attention-deficit/hyperactivity disorder (ADHD). Thus, we aimed to compare working memory-related functional connectivity patterns in healthy children and those with ADHD, and study effects of methylphenidate (MPH). Method: Twenty-two boys with ADHD were scanned twice, under either MPH (single dose, 10 mg) or placebo, in a randomised, cross-over, counterbalanced placebo-controlled design. Thirty healthy boys were scanned once. We used fMRI during a numerical n-back task to examine functional connectivity patterns in case-control and MPH-placebo comparisons, using independent component analysis. Results: There was no significant difference in behavioural performance between children with ADHD, treated with MPH or placebo, and healthy controls. Compared with controls, participants with ADHD under placebo showed increased functional connectivity within fronto-parietal and auditory networks, and decreased functional connectivity within the executive control network. MPH normalized the altered functional connectivity pattern and significantly enhanced functional connectivity within the executive control network, though in non-overlapping areas. Conclusion: Our study contributes to the identification of the neural substrates of working memory. Single dose of MPH normalized the altered brain functional connectivity network, but had no enhancing effect on (non-impaired) behavioural performance.

scholarly journals Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

2021 ◽  
Author(s):  
Hadas Benisty ◽  
Andrew H Moberly ◽  
Sweyta Lohani ◽  
Daniel Barson ◽  
Ronald R Coifman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Neural Activity ◽  
Large Scale ◽  
Analytical Framework ◽  
Network Activity ◽  
Wide Field ◽  
Time Varying ◽  
Cortical Networks ◽  
Novel Approach ◽  
Spontaneous Behavior

Experimental work across a variety of species has demonstrated that spontaneously generated behaviors are robustly coupled to variation in neural activity within the cerebral cortex. Indeed, functional magnetic resonance imaging (fMRI) data suggest that functional connectivity in cortical networks varies across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these studies generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior typically observed in awake animals. Here, we took advantage of recent developments in wide-field, mesoscopic calcium imaging to monitor neural activity across the neocortex of awake mice. Applying a novel approach to quantifying time-varying functional connectivity, we show that spontaneous behaviors are more accurately represented by fast changes in the correlational structure versus the magnitude of large-scale network activity. Moreover, dynamic functional connectivity reveals subnetworks that are not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide insight into how behavioral information is represented across the mammalian neocortex and demonstrate a new analytical framework for investigating time-varying functional connectivity in neural networks.

scholarly journals Large-scale intrinsic connectivity is consistent across varying task demands

2018 ◽  
Author(s):  
Paulina Kieliba ◽  
Sasidhar Madugula ◽  
Nicola Filippini ◽  
Eugene P. Duff ◽  
Tamar R. Makin
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Activation ◽  
Whole Brain ◽  
Activation Patterns ◽  
Intrinsic Connectivity ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

scholarly journals Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas

2021 ◽  
Author(s):  
Ayan S Mandal ◽  
Rafael Romero-Garcia ◽  
Jakob Seidlitz ◽  
Michael G Hart ◽  
Aaron Alexander-Bloch ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Subventricular Zone ◽  
Large Scale ◽  
Brain Connectivity ◽  
Tumor Development ◽  
Strong Relationship ◽  
Low Grade ◽  
Connectivity Patterns ◽  
Transcriptomic Signature ◽  
Diffuse Gliomas

Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone. Here, we evaluated this hypothesis by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers, and large-scale connectivity networks. Using lesion data from a total of 410 patients with glioma, we identified -- and replicated in an independent sample -- three lesion covariance networks (LCNs), which reflect clusters of frequent glioma co-localization. Each LCN overlapped with a distinct horn of the lateral ventricles. The first LCN, which overlapped with the anterior horn, was associated with low-grade, IDH-mutated/1p19q-codeleted tumors, as well as a neural transcriptomic signature and improved overall survival. Each LCN significantly corresponded with multiple brain networks, with LCN1 bearing an especially strong relationship with structural and functional connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each LCN. Cumulatively, our findings support a model wherein periventricular brain connectivity guides tumor development.

scholarly journals Increased amygdala volume and functional connectivity with cognitive control networks in chronic migraine

2020 ◽  
Author(s):  
Danielle D DeSouza ◽  
Samuel R Krimmel ◽  
Bharati M Sanjanwala ◽  
Addie Peretz ◽  
Vinod Menon ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Chronic Migraine ◽  
Resting State ◽  
Large Scale ◽  
Pain Catastrophizing ◽  
Headache Frequency ◽  
Control Networks ◽  
Affective Symptoms ◽  
Amygdala Volume

Objective: To characterize the role of the amygdala in episodic (EM) and chronic (CM) migraine, we evaluated amygdala volumes, functional connectivity (FC), and associations with clinical and affective measures. Methods: Eighty-eight patients (44 with EM and 44 age- and sex-matched patients with CM) completed anatomical and resting-state functional MRI scans. Amygdala volumes and resting-state FC to three core large-scale cognitive control networks (default mode (DMN), salience (SN), central executive (CEN)) were compared between groups. Associations between amygdala volume and FC, measures of headache severity (frequency and intensity), and cognitive-affective measures (depression, anxiety, pain catastrophizing) were evaluated. Results: Compared to EM, patients with CM had larger amygdala volume bilaterally. Headache frequency and intensity were associated with increased left and right amygdala volume, and depression was associated with increased right amygdala volume. Patients with CM also demonstrated increased left amygdala FC with the DMN, which across patients was related to headache frequency. Left amygdala FC to the SN was correlated with headache intensity while right amygdala FC to the CEN was correlated with pain catastrophizing. Conclusion: Our findings reveal increased amygdala volume and FC with large-scale neurocognitive networks in patients with CM compared to EM. Aberrant amygdala volume and FC measures were associated with increased migraine severity, depression, and pain catastrophizing, pointing to a link between emotion and pain in migraine. Our findings provide novel insights into amygdala involvement in chronic migraine and may inform future interventions aimed at preventing the progression of both headache and its negative cognitive-affective symptoms.

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