Brain connectivity in patients with dystonia during motor tasks

Baltazar, Carlos Arruda; Machado, Birajara Soares; Faria, Danilo Donizete de; Paulo, Artur José Marques; Silva, Sônia Maria Cezar Azevedo; Ferraz, Henrique Ballalai; Aguiar, Patrícia de Carvalho

doi:10.1088/1741-2552/abbbd6

Cited by 13 publications

(16 citation statements)

References 46 publications

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“…Using independent component analysis, we found that motor tasks modulated the same networks as rest state brain in both healthy and dystonia subjects. Specifically, the sensorimotor, parietal, frontoparietal, visual, basal ganglia, and cerebellum were all modulated as previously reported (Battistella and Simonyan 2019;Mantel et al 2018;Huang et al 2017;Baltazar et al 2020). Our study also found that insular, orbitofrontal and default mode networks were also modulated during motor tasks.…”

Section: Discussionsupporting

confidence: 88%

“…The remaining components in this study showed a high overlap with previously identified ICN networks and anatomic regions indicating their independent validation and biological relevance. Functional networks previously shown to play a role in patients with dystonia were then selected for further analysis (Battistella and Simonyan 2019;Mantel et al 2018;Huang et al 2017;Baltazar et al 2020) and they are listed in Table 1. The components denoted as the sensorimotor cortex (SM) showed bilateral activation in the motor and sensory cortical regions, however, were denoted left and right based on which side showed more activation.…”

Section: Group Independent Component Analysis: a Group Independent Component Analysis (Gica)mentioning

confidence: 99%

“…Functional Network Connectivity: Functional networks that showed highly positive or highly negative correlations with the writing task model time course and those known to play a key role in patients with dystonia were selected for functional network connectivity analysis (Battistella and Simonyan 2019;Mantel et al 2018;Huang et al 2017;Baltazar et al 2020). FNC measures was determined by computing pairwise correlations between selected functional networks in each subject (Jafri et al 2008).…”

Section: Analysis Of Component Correlation To Task Time Coursementioning

confidence: 99%

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Functional Brain Networks in Focal Dystonia and their Associations with Dystonic Behavior

Bukhari-Parlakturk

Voyvodic

et al. 2021

Preprint

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Background: Dystonia is a network level brain disorder with limited understanding of interactions between functional networks. Prior studies have demonstrated abnormalities both between and within functional networks. However, these findings were based on a hypothesis driven region of interest (ROI) approach or used resting state fMRI to extrapolate network differences in task-specific dystonia called writers cramp (WC). As a result, it remains unclear if these same networks would be active during the actual performance of a dystonic task. It's also unclear from prior studies which networks play a role in the primary disease versus a compensatory region. Lastly, neuroimaging studies have used sequencing tapping task with equal motor performance to study networks changes due to concern that differences in motor performance during the dystonic writing task may confound the findings. Objective: The aim of this research study was: 1) to use a data driven approach on a task-specific focal dystonia to understand network level interactions during task-fMRI, 2) to determine if differences in observed network level could distinguish between brain regions of primary vs compensatory change in task-specific dystonia, and 3) if sequence tapping task captured similar level network changes as the dystonic writing task. Methods: We used the data-driven independent component analysis to compare functional network correlation with task time course and network connectivity during 3 motor tasks of writing, sequence tapping and finger flexion-extension. Results: Our analysis shows that in WC there is significantly decreased correlation of the basal ganglia and increased correlation of the orbitofrontal network to the writing task time course. Analysis of network connectivity showed significantly decreased connectivity of the basal ganglia to the left sensorimotor network specifically during the writing task in WC patients. We also showed significantly increased correlation of the cerebellum to the orbitofrontal and parietal networks. Conclusions: Collectively, our analysis supports a global network disorders in which the basal ganglia plays a primary role in the disease mechanism while the cerebellum plays a role as a compensatory network, partially through increased connectivity to the orbitofrontal network, a region important in motor planning. Our analysis also shows that the sequence tapping task does not capture the same network level dysfunction as the primary dystonic task. Our study is the first to apply a purely data-driven approach to understand network interactions in WC dystonia patients during task performance.

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Section: Discussionsupporting

confidence: 88%

Section: Group Independent Component Analysis: a Group Independent Component Analysis (Gica)mentioning

confidence: 99%

Section: Analysis Of Component Correlation To Task Time Coursementioning

confidence: 99%

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Functional Brain Networks in Focal Dystonia and their Associations with Dystonic Behavior

Bukhari-Parlakturk

Voyvodic

et al. 2021

Preprint

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“…A similar change of hub status was reported for the primary motor cortex in hand dystonia (including WC and musicians focal hand dystonia) ( 19 ). In an EEG study, WC patients exhibited disruption of intracortical inhibition during a writing-from memory task ( 39 ). While EEG does not have the spatial resolution to pinpoint this finding to the primary motor cortex, it supports the notion that inhibitory connections are impaired in WC.…”

Section: Discussionmentioning

confidence: 99%

Functional Neural Networks in Writer's Cramp as Determined by Graph-Theoretical Analysis

et al. 2021

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Dystonia, a debilitating neurological movement disorder, is characterized by involuntary muscle contractions and develops from a complex pathophysiology. Graph theoretical analysis approaches have been employed to investigate functional network changes in patients with different forms of dystonia. In this study, we aimed to characterize the abnormal brain connectivity underlying writer's cramp, a focal hand dystonia. To this end, we examined functional magnetic resonance scans of 20 writer's cramp patients (11 females/nine males) and 26 healthy controls (10 females/16 males) performing a sequential finger tapping task with their non-dominant (and for patients non-dystonic) hand. Functional connectivity matrices were used to determine group averaged brain networks. Our data suggest that in their neuronal network writer's cramp patients recruited fewer regions that were functionally more segregated. However, this did not impair the network's efficiency for information transfer. A hub analysis revealed alterations in communication patterns of the primary motor cortex, the thalamus and the cerebellum. As we did not observe any differences in motor outcome between groups, we assume that these network changes constitute compensatory rerouting within the patient network. In a secondary analysis, we compared patients with simple writer's cramp (only affecting the hand while writing) and those with complex writer's cramp (affecting the hand also during other fine motor tasks). We found abnormal cerebellar connectivity in the simple writer's cramp group, which was less prominent in complex writer's cramp. Our preliminary findings suggest that longitudinal research concerning cerebellar connectivity during WC progression could provide insight on early compensatory mechanisms in WC.

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“…Correlation between EEG channels, also known as coherence, was evaluated in time-domain to analyze the connectivity patterns in dystonia patients [1]. Coherences have the advantage of yielding the possibility to recognize motor-imagery related activation even without typical activation observed, often giving small standard deviations [13].…”

Section: Related Workmentioning

confidence: 99%

Resting-state EEG sex classification using selected brain connectivity representation

Li¹,

Deng²,

Adhia³

et al. 2020

Preprint

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IntroductionElectroencephalography (EEG) is a widely used non-invasive technique to measure multi-channel potentials that reflect the electrical activity of the brain. Over the last a few decades, EEG analysis has been an intensively explored research topic due to its potentials in being applied to the diagnosis of neurological diseases, such as epilepsy, brain tumors, head injury, sleep disorders, dementia, etc. [21]. Despite many advances made in recent years, EEG signal analysis remains a challenging task. In addition to being non-stationary, EEG signals often have high noise-to-information ratios, and they can be significantly affected by various artifacts, demonstrating characteristics that differ from signals generated by activities in the brain [22]. Common artifacts include eye movements, jaw tension, and muscle contractions. To make effective signal analysis even more challenging, EEG signals are highly individual-specific, and cross-subject pattern identification can be elusive.In a more proactive approach, EEG can also be applied to biofeedback training as an operant conditioning technique to reinforce or inhibit specific forms of EEG activities. It has been used in anxiety and addiction treatment, also employed for attentional, cognitive, and psychosocial functioning improvement [18]. It is noted that popular EEG biofeedback treatment is largely based on sex-neutral protocols [19]. Our proposition is that if there are innate sex differences found in EEG signals, then

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Brain connectivity in patients with dystonia during motor tasks

Cited by 13 publications

References 46 publications

Functional Brain Networks in Focal Dystonia and their Associations with Dystonic Behavior

Functional Brain Networks in Focal Dystonia and their Associations with Dystonic Behavior

Functional Neural Networks in Writer's Cramp as Determined by Graph-Theoretical Analysis

Resting-state EEG sex classification using selected brain connectivity representation

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