Resting-state functional connectivity: An emerging method for the study of language networks in post-stroke aphasia

Klingbeil, Julian; Wawrzyniak, Max; Stockert, Anika; Saur, Dorothee

doi:10.1016/j.bandc.2017.08.005

Cited by 58 publications

(42 citation statements)

References 114 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the present results reflect the DCM model space used to test our specific hypotheses but do not necessarily reflect the “true” semantic network. Thus, to reach more definitive conclusions regarding lesion, connectivity and language ability patterns in PWA, replication of this study's primary aims with other types of language tasks, alternative connectivity methods, or even resting state fMRI—which shows particular promise in generating meaningful phenotypes of stroke deficit profiles (Carter et al, 2012; Hartwigsen and Saur, 2017; Klingbeil et al, 2017)—is warranted.…”

Section: Discussionmentioning

confidence: 97%

A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls

Meier

Johnson

Pan

et al. 2019

NeuroImage: Clinical

View full text Add to dashboard Cite

Traditional models of left hemisphere stroke recovery propose that reactivation of remaining ipsilesional tissue is optimal for language processing whereas reliance on contralesional right hemisphere homologues is less beneficial or possibly maladaptive in the chronic recovery stage. However, neuroimaging evidence for this proposal is mixed. This study aimed to elucidate patterns of effective connectivity in patients with chronic aphasia in light of healthy control connectivity patterns and in relation to damaged tissue within left hemisphere regions of interest and according to performance on a semantic decision task. Using fMRI and dynamic causal modeling, biologically-plausible models within four model families were created to correspond to potential neural recovery patterns, including Family A: Left-lateralized connectivity (i.e., no/minimal damage), Family B: Bilateral anterior-weighted connectivity (i.e., posterior damage), Family C: Bilateral posterior-weighted connectivity (i.e., anterior damage) and Family D: Right-lateralized connectivity (i.e., extensive damage). Controls exhibited a strong preference for left-lateralized network models (Family A) whereas patients demonstrated a split preference for Families A and C. At the level of connections, controls exhibited stronger left intrahemispheric task-modulated connections than did patients. Within the patient group, damage to left superior frontal structures resulted in greater right intrahemispheric connectivity whereas damage to left ventral structures resulted in heightened modulation of left frontal regions. Lesion metrics best predicted accuracy on the fMRI task and aphasia severity whereas left intrahemispheric connectivity predicted fMRI task reaction times. These results are discussed within the context of the hierarchical recovery model of chronic aphasia.

show abstract

Section: Discussionmentioning

confidence: 97%

A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls

Meier

Johnson

Pan

et al. 2019

NeuroImage: Clinical

View full text Add to dashboard Cite

show abstract

“…Previous evidence suggests that rsfMRI is able to map language in epilepsy and tumor patients (Branco et al, 2016;Sair et al, 2016), but it is likely that damage in large tracts such as the aslant and arcuate fasciculi, or damage in connectivity hubs, may impact functional connectivity (e.g., Gratton, Nomura, Pérez, & D'Esposito, 2012), potentially leading to poor mapping results. Further, although rsfMRI has great potential to capture plastic changes triggered by pathology (Hartwigsen & Saur, 2019;Klingbeil, Wawrzyniak, Stockert, & Saur, 2019), there are many open questions that need to be answered: can rsfMRI map these changes over time? Can rsfMRI be used to predict outcomes, or used to guide treatment?…”

Section: Discussionmentioning

confidence: 99%

Mapping language with resting‐state functional magnetic resonance imaging: A study on the functional profile of the language network

Branco

Seixas

Castro

2019

Human Brain Mapping

View full text Add to dashboard Cite

Resting-state functional magnetic resonance imaging (rsfMRI) is a promising technique for language mapping that does not require task-execution. This can be an advantage when language mapping is limited by poor task performance, as is common in clinical settings. Previous studies have shown that language maps extracted with rsfMRI spatially match their task-based homologs, but no study has yet demonstrated the direct participation of the rsfMRI language network in language processes. This demonstration is critically important because spatial similarity can be influenced by the overlap of domain-general regions that are recruited during taskexecution. Furthermore, it is unclear which processes are captured by the language network: does it map rather low-level or high-level (e.g., syntactic and lexico-semantic) language processes? We first identified the rsfMRI language network and then investigated task-based responses within its regions when processing stimuli of increasing linguistic content: symbols, pseudowords, words, pseudosentences and sentences. The language network responded only to language stimuli (not to symbols), and higher linguistic content elicited larger brain responses. The left frontoparietal, the default mode, and the dorsal attention networks were examined and yet none showed language involvement. These findings demonstrate for the first time that the language network extracted through rsfMRI is able to map language in the brain, including regions subtending higher-level syntactic and semantic processes.K E Y W O R D S brain mapping, functional connectivity, language network, presurgical planning, restingstate fMRI

show abstract

“…There have been several informative studies of the functional dynamics of brain function in aphasia with fMRI using language tasks (e.g., Kiran & Thompson, 2019) or by modeling the causal dynamics of task-based fMRI (e.g., Meier et al, 2016). However, as noted by Klingbeil et al (2017), performing language tasks in an MRI scanner is awkward at best, and performance under such conditions is dubious given the known intra-and interindividual variability that is characteristic of individuals with aphasia. There continues to be a need to identify relatively coherent brain states that are impervious to differing task demands and that may be investigated (a) in recovery from aphasia and (b) as targets to assess neuroplastic change associated with a treatment.…”

Section: Functional Connectivity Studiesmentioning

confidence: 99%

Resting-State Functional Magnetic Resonance Imaging Connectivity Between Semantic and Phonological Regions of Interest May Inform Language Targets in Aphasia

Ramage

Aytur

Ballard

2020

J Speech Lang Hear Res

View full text Add to dashboard Cite

Purpose Brain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, the structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the “functional” dynamics of the brain for language processing. Here, functional connectivity (FC) among semantic–phonological regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia. Method Clinical assessment of language, using the Western Aphasia Battery–Revised, and resting-state functional magnetic resonance imaging data were obtained for 30 individuals with chronic aphasia secondary to left-hemisphere stroke and 18 age-matched healthy controls. FC between bilateral ROIs was contrasted by group and used to predict Western Aphasia Battery–Revised scores. Results Network coherence was observed in healthy controls and participants with stroke. The left–right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (a) connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical–semantic processing for auditory comprehension and (b) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic–phonological representations and processing for verbal production. Conclusions Network connectivity of brain regions associated with semantic–phonological processing is predictive of language performance in poststroke aphasia. The most predictive connections involved right-hemisphere ROIs—particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia. Supplemental Material https://doi.org/10.23641/asha.12735785

show abstract

Resting-state functional connectivity: An emerging method for the study of language networks in post-stroke aphasia

Cited by 58 publications

References 114 publications

A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls

A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls

Mapping language with resting‐state functional magnetic resonance imaging: A study on the functional profile of the language network

Resting-State Functional Magnetic Resonance Imaging Connectivity Between Semantic and Phonological Regions of Interest May Inform Language Targets in Aphasia

Contact Info

Product

Resources

About