Changes in Functional Connectivity of Default Mode Network with Auditory and Right Frontoparietal Networks in Poststroke Aphasia

Балаев, В.В.; Petrushevsky, A. G.; Martynova, Olga

doi:10.1089/brain.2016.0419

Cited by 28 publications

(14 citation statements)

References 59 publications

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“…However, compensatory processes may result in “adaptive” or “maladaptive” mechanisms: as a consequence, discrete evidence of an abnormal (i.e., increased) structure and function of the right hemisphere, in DS, may also partially contribute to the maintenance of the disturbance and its pathophysiological mechanisms, speculatively due to excessive inhibitory mechanisms likely related to a conscious motor control (e.g., Neef et al, 2016 , 2018 ). This may resemble the similar evidence highlighted in stroke-induced aphasia: the damaged speech/motor regions of the left hemisphere may be compensated by the intervention of the homologous regions of the right one (e.g., Hamilton et al, 2011 ; Balaev et al, 2016 ; Skipper-Kallal et al, 2017 ). Anyway, this hemispheric “disequilibrium” may also result in stronger inhibitory projections that arise from the “healthy” side of the brain toward the regions of the affected one (see Hamilton et al, 2011 ).…”

Section: Neuromodulatory Nibs In Ds: Insights From Available Evidence and Neural Modeling Of Stutteringsupporting

confidence: 68%

Speech Fluency Improvement in Developmental Stuttering Using Non-invasive Brain Stimulation: Insights From Available Evidence

Busan

Moret

Masina

et al. 2021

Front. Hum. Neurosci.

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Developmental stuttering (DS) is a disturbance of the normal rhythm of speech that may be interpreted as very debilitating in the most affected cases. Interventions for DS are historically based on the behavioral modifications of speech patterns (e.g., through speech therapy), which are useful to regain a better speech fluency. However, a great variability in intervention outcomes is normally observed, and no definitive evidence is currently available to resolve stuttering, especially in the case of its persistence in adulthood. In the last few decades, DS has been increasingly considered as a functional disturbance, affecting the correct programming of complex motor sequences such as speech. Compatibly, understanding of the neurophysiological bases of DS has dramatically improved, thanks to neuroimaging, and techniques able to interact with neural tissue functioning [e.g., non-invasive brain stimulation (NIBS)]. In this context, the dysfunctional activity of the cortico-basal-thalamo-cortical networks, as well as the defective patterns of connectivity, seems to play a key role, especially in sensorimotor networks. As a consequence, a direct action on the functionality of “defective” or “impaired” brain circuits may help people who stutter to manage dysfluencies in a better way. This may also “potentiate” available interventions, thus favoring more stable outcomes of speech fluency. Attempts aiming at modulating (and improving) brain functioning of people who stutter, realized by using NIBS, are quickly increasing. Here, we will review these recent advancements being applied to the treatment of DS. Insights will be useful not only to assess whether the speech fluency of people who stutter may be ameliorated by acting directly on brain functioning but also will provide further suggestions about the complex and dynamic pathophysiology of DS, where causal effects and “adaptive''/‘‘maladaptive” compensation mechanisms may be strongly overlapped. In conclusion, this review focuses future research toward more specific, targeted, and effective interventions for DS, based on neuromodulation of brain functioning.

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Section: Neuromodulatory Nibs In Ds: Insights From Available Evidence and Neural Modeling Of Stutteringsupporting

confidence: 68%

Speech Fluency Improvement in Developmental Stuttering Using Non-invasive Brain Stimulation: Insights From Available Evidence

Busan

Moret

Masina

et al. 2021

Front. Hum. Neurosci.

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“…Functional connectivity (FC)-based rs-fMRI analysis has been used to evaluate the interaction between language network and the whole brain in patients with PSA, with impressive findings ( Zhu et al, 2014 ). A study by Balaev et al (2016) reported that FC in multiple subnetworks was demonstrated to be disrupted in patients with PSA, by using independent component analysis (ICA).…”

Section: Introductionmentioning

confidence: 99%

Disrupted Functional Connectivity Within and Between Resting-State Networks in the Subacute Stage of Post-stroke Aphasia

et al. 2021

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Background: Post-stroke aphasia (PSA) results from brain network disorders caused by focal stroke lesions. However, it still remains largely unclear whether the impairment is present in intra- and internetwork functional connectivity (FC) within each resting-state network (RSN) and between RSNs in the subacute stage of PSA.Objectives: This study aimed to investigate the resting-state FC within and between RSNs in patients with PSA and observe the relationships between FC alterations and Western Aphasia Battery (WAB) measures.Methods: A total of 20 individuals with subacute PSA and 20 healthy controls (HCs) were recruited for functional MRI (fMRI) scanning, and only patients with PSA underwent WAB assessment. Independent component analysis was carried out to identify RSNs. Two-sample t-tests were used to calculate intra- and internetwork FC differences between patients with PSA and HCs. The results were corrected for multiple comparisons using the false discovery rate (FDR correction, p < 0.05). Partial correlation analysis was performed to observe the relationship between FC and WAB scores with age, gender, mean framewise displacement, and lesion volume as covariates (p < 0.05).Results: Compared to HCs, patients with PSA showed a significant increase in intranetwork FC in the salience network (SN). For internetwork FC analysis, patients showed a significantly increased coupling between left frontoparietal network (lFPN) and SN and decreased coupling between lFPN and right frontoparietal network (rFPN) as well as between lFPN and posterior default mode network (pDMN) (FDR correction, p < 0.05). Finally, a significant positive correlation was found between the intergroup difference of FC (lFPN-rFPN) and auditory-verbal comprehension (p < 0.05).Conclusion: Altered FC was revealed within and between multiple RSNs in patients with PSA at the subacute stage. Reduced FC between lFPN and rFPN was the key element participating in language destruction. These findings proved that PSA is a brain network disorder caused by focal lesions; besides, it may improve our understanding of the pathophysiological mechanisms of patients with PSA at the subacute stage.

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“…Interpreting neuropsychological measures alongside data-driven and/or multi-modal neuroimaging features has yielded effective baseline and longitudinal models of stroke aphasia severity (23)(24)(25)(26). Resting-state functional MRI (rsfMRI) has speci cally demonstrated potential as an assessment tool, as patients with aphasia have observable differences in their resting functional connectivity and resting networks as compared to healthy controls (27)(28)(29). Furthermore, the initial aphasia severity pro le can be inferred from resting network activity, and changes in global network activity tracks the extent of language recovery (30)(31)(32)(33).…”

Section: Introductionmentioning

confidence: 99%

Predicting Language Recovery in Post-Stroke Aphasia using Behavior and Functional MRI

Iorga¹,

Higgins²,

Caplan

et al. 2020

Preprint

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Background: Language outcomes after speech and language therapy in post-stroke aphasia are challenging to predict. This study examines behavioral language measures and resting state fMRI (rsfMRI) as prognostics for response to language therapy. Methods: Seventy patients with chronic aphasia were recruited and treated for one of three deficits: anomia, agrammatism, or dysgraphia. Treatment effect was measured by performance on a treatment-specific language measure, assessed before and after three months of language therapy. Each patient also underwent an additional 27 language assessments and an fMRI scan at baseline. Patient scans were decomposed into 20 components by group independent component analysis, and each component time series was summarized by its fractional amplitude of low-frequency fluctuations (fALFF). Results: Treatment effects were modelled with elastic net regression, using clinical language measures and fALFF imaging predictors independently. Correlation analyses showed high performance for language measures in anomia (r = 0.958, n = 30) and for fALFF predictors in agrammatism (r = 0.940, n = 11) and dysgraphia (r = 0.925, n = 18). These models are state-of-the-art for aphasia recovery prediction. Conclusion: Predicting aphasia recovery with rsfMRI features may outperform predictions from clinical language measures in some patient populations. This suggests rsfMRI may have prognostic value for chronic aphasia patients undergoing language therapy. Differentiating patients who respond to therapy from those who do not is a first step towards personalized treatment in post-stroke aphasia.

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Changes in Functional Connectivity of Default Mode Network with Auditory and Right Frontoparietal Networks in Poststroke Aphasia

Cited by 28 publications

References 59 publications

Speech Fluency Improvement in Developmental Stuttering Using Non-invasive Brain Stimulation: Insights From Available Evidence

Speech Fluency Improvement in Developmental Stuttering Using Non-invasive Brain Stimulation: Insights From Available Evidence

Disrupted Functional Connectivity Within and Between Resting-State Networks in the Subacute Stage of Post-stroke Aphasia

Predicting Language Recovery in Post-Stroke Aphasia using Behavior and Functional MRI

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