Factors predicting post-stroke aphasia recovery

Watila, Musa Mamman; Balarabe, Salisu A.

doi:10.1016/j.jns.2015.03.020

Cited by 191 publications

(67 citation statements)

References 86 publications

(59 reference statements)

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“…This study specifically evaluated the influence of language and cognitive abilities on 24 anomia therapy outcomes; however, stroke-related (i.e., lesion site and size) and 25 demographic variables may also influence recovery and treatment response (Marshall & 26 Phillips, 1983;Meinzer et al, 2010;Plowman, Hentz, & Ellis, 2012;Watila & Balarabe, 2015). 27…”

Section: Limitations and Future Directions 28mentioning

confidence: 99%

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Influence of Cognitive Ability on Therapy Outcomes for Anomia in Adults With Chronic Poststroke Aphasia

Dignam

Copland

O’Brien

et al. 2017

J Speech Lang Hear Res

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Purpose:The relationship between cognitive abilities and aphasia rehabilitation outcomes is 2 complex and remains poorly understood. This study investigated the influence of language 3 and cognitive abilities on anomia therapy outcomes in adults with aphasia. 4Methods: 34 adults with chronic aphasia participated in Aphasia Language Impairment and 5Functioning Therapy. A language and cognitive assessment battery, including 3 baseline 6 naming probes, was administered prior to therapy. Naming accuracy for 30 treated and 30 7 untreated items was collected at post-therapy and 1 month follow-up. Multiple regression 8 models were computed to evaluate the relationship between language and cognitive abilities 9 at baseline and anomia therapy outcomes.

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Section: Limitations and Future Directions 28mentioning

confidence: 99%

“…it is also important that we understand which factors predict therapy success in order to 14 identify who may respond to therapy and to facilitate optimal recruitment and distribution of 15 therapy services (Watila & Balarabe, 2015). 16…”

Section: Introductionmentioning

confidence: 99%

Influence of Cognitive Ability on Therapy Outcomes for Anomia in Adults With Chronic Poststroke Aphasia

Dignam

Copland

O’Brien

et al. 2017

J Speech Lang Hear Res

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“…Studies have provided general support for the efficacy of aphasia rehabilitation (Brady et al, 2012;Robey, 1998;Wisenburn & Mahoney, 2009); however, not all individuals' respond to or benefit equally from intervention (Best & Nickels, 2000;Nickels & Best, 1996b;Plowman et al, 2012). A number of factors have been identified that may influence recovery from aphasia in the acute phase, such as stroke-related (e.g., site and size of lesion) patient-related (e.g., age, gender) and treatment-related factors (Laska et al, 2001;Pedersen et al, 1995;Seniow et al, 2009b;Watila & Balarabe, 2015). However, it is difficult to ascertain predictors of treatment-induced recovery in the acute and subacute phase of stroke, because of the potential confounds of spontaneous recovery.…”

Section: Discussionmentioning

confidence: 99%

“…There has previously been debate in the literature regarding the influence of age on rehabilitation outcomes for aphasia (Basso, 1992;Plowman et al, 2012;Watila & Balarabe, 2015). Whilst the neuroscience literature suggests that training induced plasticity occurs more readily in younger brains (i.e., Age Matters) (Kleim & Jones, 2008), a review conducted by Basso (1992) found that anagraphical factors, including age, had minimal effect on the therapeutic outcomes for aphasia rehabilitation.…”

Section: Participant Characteristicsmentioning

confidence: 99%

“…This study specifically evaluated the influence of language and cognitive ability on anomia therapy outcomes; however, stroke-related (i.e., lesion site and size) and demographic variables may also influence recovery and treatment response (Marshall & Phillips, 1983;Meinzer et al, 2010;Plowman et al, 2012;Watila & Balarabe, 2015). Furthermore, metacognition plays a critical role in the learning process and as such, may influence therapy outcomes (Toppino, Cohen, Davis, & Moors, 2009).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

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Factors influencing treatment-induced language recovery in chronic, post-stroke aphasia

Dignam¹

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Bayesian longitudinal tensor response regression for modeling neuroplasticity

Kundu,

Reinhardt,

Song

et al. 2023

Human Brain Mapping

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A major interest in longitudinal neuroimaging studies involves investigating voxel‐level neuroplasticity due to treatment and other factors across visits. However, traditional voxel‐wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low‐rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual‐level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel‐wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long‐term increases in brain activity, the intention treatment produced predominantly short‐term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel‐wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power.

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Factors predicting post-stroke aphasia recovery

Cited by 191 publications

References 86 publications

Influence of Cognitive Ability on Therapy Outcomes for Anomia in Adults With Chronic Poststroke Aphasia

Influence of Cognitive Ability on Therapy Outcomes for Anomia in Adults With Chronic Poststroke Aphasia

Factors influencing treatment-induced language recovery in chronic, post-stroke aphasia

Bayesian longitudinal tensor response regression for modeling neuroplasticity

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