Aphasia outcome: the interactions between initial severity, lesion size and location

Dissociation of White Matter Bundles in Different Recovery Measures in Poststroke Aphasia

BACKGROUND

Poststroke aphasia (PSA) recovery shows high variability across individuals and at different time points. Although diffusion biomarkers from the ventral and dorsal streams have demonstrated strong predictive power for language outcomes, it is still unclear how these biomarkers relate to the various stages of PSA recovery. In this study, we aim to compare diffusion metrics and language measures as predictors of language recovery in a longitudinal cohort of participants with PSA.

METHODS

Participants were recruited at a stroke unit at the emergency room, and underwent diffusion magnetic resonance imaging scanning and language assessment within 3 days (acute phase) after stroke, with behavioral follow-ups at subacute (10±3 days) and chronic phases (>6 months). We conducted regression analyses on language performance (cross-sectional), Δscores between all time points (acute-subacute, subacute-chronic, acute-chronic), and relative Δscores between all time points (Δscore/language baseline score), with acute diffusion metrics from language-related white matter tracts, lesion size, language baseline scores, and demographic data as predictors.

RESULTS

Thirty-nine participants presenting PSA were recruited, and 24 participants (mean age, 73 years; 8 women) completed the 3-time point assessment in total. The best prediction model of performance scores used axial diffusivity from the left arcuate fasciculus in both the subacute (R2=0.785) and chronic stages (R2=0.626). Moreover, the prediction of ∆scores depended on axial diffusivity from the left inferior frontal-occipital fasciculus in the subacute stage (R2=0.5) and depended additionally on axial diffusivity from the right inferior frontal-occipital fasciculus in the chronic stage (R2=0.68). The prediction of mediation analyses showed that the lesion load of the left arcuate fasciculus mediated the relationship between axial diffusivity from the left arcuate fasciculus and chronic language performance.

CONCLUSIONS

Language performance at subacute and chronic time points could be predicted by the integrity of the left arcuate fasciculus, whereas Δscores in the subacute and chronic phases depended on the left inferior frontal-occipital fasciculus, showing a dissociation of the white matter pathways about language outcomes. These results suggest a functional differentiation of the dual-stream components in PSA recovery.

Prediction models of the aphasia severity after stroke by lesion load of cortical language areas and white matter tracts: An atlas-based study

OBJECTIVE

To construct relatively objective, atlas-based multivariate models for predicting early aphasia severity after stroke, using structural magnetic resonance imaging.

METHODS

We analyzed the clinical and imaging data of 46 patients with post-stroke aphasia. The aphasia severity was identified with a Western Aphasia Battery Aphasia Quotient. The assessments of stroke lesions were indicated by the lesion load of both the cortical language areas (Areas-LL) and four white matter tracts (i.e., the superior longitudinal fasciculus, SLF-LL; the inferior frontal occipital fasciculi, IFOF-LL; the inferior longitudinal, ILF-LL; and the uncinate fasciculi, UF-LL) extracted from human brain atlas. Correlation analyses and multiple linear regression analyses were conducted to evaluate the correlations between demographic, stroke- and lesion-related variables and aphasia severity. The predictive models were then established according to the identified significant variables. Finally, the receiver operating characteristic (ROC) curve was utilized to assess the accuracy of the predictive models.

RESULTS

The variables including Areas-LL, the SLF-LL, and the IFOF-LL were significantly negatively associated with aphasia severity (p < 0.05). In multiple linear regression analyses, these variables accounted for 59.4 % of the variance (p < 0.05). The ROC curve analyses yielded the validated area under the curve (AUC) 0.84 both for Areas-LL and SLF-LL and 0.76 for IFOF-LL, indicating good predictive performance (p < 0.01). Adding the combination of SLF-LL and IFOF-LL to this model increased the explained variance to 62.6 % and the AUC to 0.92.

CONCLUSIONS

The application of atlas-based multimodal lesion assessment may help predict the aphasia severity after stroke, which needs to be further validated and generalized for the prediction of more outcome measures in populations with various brain injuries.

Neural mechanisms driving speech and language recovery following childhood stroke: a scoping review

PURPOSE

This scoping review aimed to inform future research priorities by collating evidence on neural correlates of speech and language recovery following childhood stroke.

METHODS

Neuroimaging, motor speech, or language outcomes following childhood stroke (28 days to 18 years age) in the subacute to chronic community stages (care occurring after acute medical management, including inpatient and outpatient rehabilitation, and community-based programs) were identified and extracted from Medline, Embase, PsycInfo, and Clinical databases.

RESULTS

Of the 3990 studies screened, 11 met the inclusion criteria. Of the included articles, no papers formally assessed speech outcomes, 11 articles reported language outcomes through standardized testing, 11 utilized structural imaging (CT, MRI), and four reported functional neuroimaging outcomes (fMRI).

INTERPRETATION

This review revealed a rudimentary accounting of speech and language profiles in children post-stroke; limited by the use of varied and incomplete speech and language assessment batteries, inconsistent reporting of lesion locations associated with speech and language outcomes, a dearth of functional neuroimaging studies, and lack of information about speech and language function throughout the rehabilitation period, a time when the brain is most plastic and receptive to therapy. Future research should provide complete and accurate accounts of speech and language function and their neural correlates throughout rehabilitation and recovery to inform care, education, and employment planning.

Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI

Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning and interpreting the predictive features, as well as, how to effectively combine neuroimaging and tabular data (e.g. demographic information and clinical characteristics). This paper evaluates several solutions based on two strategies. The first is to use 2D images that summarise MRI scans. The second is to select key features that improve classification accuracy. Additionally, we introduce the novel approach of training a convolutional neural network (CNN) on images that combine regions-of-interests (ROIs) extracted from MRIs, with symbolic representations of tabular data. We evaluate a series of CNN architectures (both 2D and a 3D) that are trained on different representations of MRI and tabular data, to predict whether a composite measure of post-stroke spoken picture description ability is in the aphasic or non-aphasic range. MRI and tabular data were acquired from 758 English speaking stroke survivors who participated in the PLORAS study. Each participant was assigned to one of five different groups that were matched for initial severity of symptoms, recovery time, left lesion size and the months or years post-stroke that spoken description scores were collected. Training and validation were carried out on the first four groups. The fifth (lock-box/test set) group was used to test how well model accuracy generalises to new (unseen) data. The classification accuracy for a baseline logistic regression was 0.678 based on lesion size alone, rising to 0.757 and 0.813 when initial symptom severity and recovery time were successively added. The highest classification accuracy (0.854), area under the curve (0.899) and F1 score (0.901) were observed when 8 regions of interest were extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network (ResNet). This was also the best model when data were limited to the 286 participants with moderate or severe initial aphasia (with area under curve = 0.865), a group that would be considered more difficult to classify. Our findings demonstrate how imaging and tabular data can be combined to achieve high post-stroke classification accuracy, even when the dataset is small in machine learning terms. We conclude by proposing how the current models could be improved to achieve even higher levels of accuracy using images from hospital scanners.

The effect of mobile application-based technology on post-stroke aphasia: a systematic review

Background

Enhancing speech-language therapy remains the most effective strategy for improving post-stroke aphasia, However, conventional face-to-face interventions often lack the necessary therapeutic intensity. In recent years, mobile application-based speech-language therapy has emerged progressively, offering new opportunities for independent rehabilitation among aphasic patients. This review aims to evaluate the impact of mobile application-based interventions on post-stroke aphasic.

Methods

By conducting a systematic search across five databases (PubMed, Web of Science, EMBASE, CINAHL, and Scopus), we identified and included studies that investigated the utilization of mobile application-based technologies (such as computers, iPads, etc.) for treating post-stroke aphasia.

Results

This study included 15 research investigations, including 10 randomized controlled trials (RCTs), four self-controlled studies and one cross-over experimental design study. Among these, eight studies demonstrated the efficacy of mobile application-based therapy in enhancing overall language functionality for post-stroke aphasia patients, three studies highlighted its potential for improving communication skills, three studies observed its positive impact on spontaneous speech expression. Moreover, four studies indicated its effectiveness in enhancing naming abilities, two studies underscored the positive influence of mobile application-based interventions on the quality of life for individuals with aphasia. Six studies noted that speech improvement effects were maintained during the follow-up period.

Conclusion

The results of this review demonstrate the potential of mobile application-based interventions for improving speech-language function in individuals with aphasia. However, further high-quality research is needed to establish their effects across different domains and to delve into the comparative advantages of various treatment approaches.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=405248.

Disentangling neuroplasticity mechanisms in post-stroke language recovery

A major objective in post-stroke aphasia research is to gain a deeper understanding of neuroplastic mechanisms that drive language recovery, with the ultimate goal of enhancing treatment outcomes. Subsequent to recent advances in neuroimaging techniques, we now have the ability to examine more closely how neural activity patterns change after a stroke. However, the way these neural activity changes relate to language impairments and language recovery is still debated. The aim of this review is to provide a theoretical framework to better investigate and interpret neuroplasticity mechanisms underlying language recovery in post-stroke aphasia. We detail two sets of neuroplasticity mechanisms observed at the synaptic level that may explain functional neuroimaging findings in post-stroke aphasia recovery at the network level: feedback-based homeostatic plasticity and associative Hebbian plasticity. In conjunction with these plasticity mechanisms, higher-order cognitive control processes dynamically modulate neural activity in other regions to meet communication demands, despite reduced neural resources. This work provides a network-level neurobiological framework for understanding neural changes observed in post-stroke aphasia and can be used to define guidelines for personalized treatment development.

Looking Upstream to Understand Race/Ethnicity as a Moderator for Poststroke Neuroinflammation and a Social Determinant for Poststroke Aphasia Outcomes

INTRODUCTION

Over the past decade, the stroke literature has begun to acknowledge and explore explanations for longstanding racial/ethnic differences in stroke outcomes. Poststroke cognitive impairment (PSCI) and poststroke aphasia are two such negative poststroke outcomes where racial/ethnic differences exist. Physiological differences, such as stroke type and lesion size, have been used to partially explain the variation in PSCI and aphasia. However, there is some evidence, although limited, that suggests neuroinflammatory processes as part of allostatic load may be a key contributor to the observed disparities.

METHOD

In this tutorial, we explore the influence of race differences in inflammation on poststroke cognitive outcomes. We suggest lifetime stress and other external determinants of health such as neighborhood environment and discriminatory practices through "weathering" explain differences in inflammation. While using an allostatic load framework, we explore the literature focusing specifically on the role of neuroinflammation on poststroke outcomes.

CONCLUSIONS

Examination of the immune response poststroke provides a foundation for understanding the mechanisms of PSCI and poststroke aphasia and the potential contributions of neuroinflammatory processes on poststroke cognitive outcomes. Furthermore, understanding of racial differences in those processes may contribute to a better understanding of racial disparities in general stroke outcomes as well as poststroke aphasia.

Distinct parallel activation and interaction between dorsal and ventral pathways during phonological and semantic processing: A cTBS-fMRI study

Successful visual word recognition requires the integration of phonological and semantic information, which is supported by the dorsal and ventral pathways in the brain. However, the functional specialization or interaction of these pathways during phonological and semantic processing remains unclear. Previous research has been limited by its dependence on correlational functional magnetic resonance imaging (fMRI) results or causal validation using patient populations, which are susceptible to confounds such as plasticity and lesion characteristics. To address this, the present study employed continuous theta-burst stimulation combined with fMRI in a within-subject design to assess rapid adaptation in regional activity and functional connectivity of the dorsal and ventral pathways during phonological and semantic tasks. This assessment followed the precise inhibition of the left inferior parietal lobule and anterior temporal lobe in the dorsal and ventral pathways, respectively. Our results reveal that both the dorsal and ventral pathways were activated during phonological and semantic processing, while the adaptation activation and interactive network were modulated by the task type and inhibited region. The two pathways exhibited interconnectivity in phonological processing, and disruption of either pathway led to rapid adaptation across both pathways. In contrast, only the ventral pathway exhibited connectivity in semantic processing, and disruption of this pathway alone resulted in adaptive effects primarily in the ventral pathway. These findings provide essential evidence supporting the interactive theory, phonological information processing in particular, potentially providing meaningful implications for clinical populations.

Stroke lesion size - Still a useful biomarker for stroke severity and outcome in times of high-dimensional models

BACKGROUND

The volumetric size of a brain lesion is a frequently used stroke biomarker. It stands out among most imaging biomarkers for being a one-dimensional variable that is applicable in simple statistical models. In times of machine learning algorithms, the question arises of whether such a simple variable is still useful, or whether high-dimensional models on spatial lesion information are superior.

METHODS

We included 753 first-ever anterior circulation ischemic stroke patients (age 68.4±15.2 years; NIHSS at 24 h 4.4±5.1; modified Rankin Scale (mRS) at 3-months median[IQR] 1[0.75;3]) and traced lesions on diffusion-weighted MRI. In an out-of-sample model validation scheme, we predicted stroke severity as measured by NIHSS 24 h and functional stroke outcome as measured by mRS at 3 months either from spatial lesion features or lesion size.

RESULTS

For stroke severity, the best regression model based on lesion size performed significantly above chance (p < 0.0001) with R2 = 0.322, but models with spatial lesion features performed significantly better with R2 = 0.363 (t(752) = 2.889; p = 0.004). For stroke outcome, the best classification model based on lesion size again performed significantly above chance (p < 0.0001) with an accuracy of 62.8%, which was not different from the best model with spatial lesion features (62.6%, p = 0.80). With smaller training data sets of only 150 or 50 patients, the performance of high-dimensional models with spatial lesion features decreased up to the point of being equivalent or even inferior to models trained on lesion size. The combination of lesion size and spatial lesion features in one model did not improve predictions.

CONCLUSIONS

Lesion size is a decent biomarker for stroke outcome and severity that is slightly inferior to spatial lesion features but is particularly suited in studies with small samples. When low-dimensional models are desired, lesion size provides a viable proxy biomarker for spatial lesion features, whereas high-precision prediction models in personalised prognostic medicine should operate with high-dimensional spatial imaging features in large samples.

Intersectional sociodemographic and neurological relationships in the naming ability of persons with post-stroke aphasia

INTRODUCTION

Significant attention has been given to the role of brain function and disruption in determining performance on naming tasks among individuals with aphasia. However, scholarly pursuit of a neurological explanation has overlooked the fundamental cornerstone of individual health-the underlying social, economic, and environmental factors that shape how they live, work, and age, also known as the social determinants of health (SDOH). This study examines the correlation between naming performance and these underlying factors.

METHODS

Individual level data from the 2010 Moss Aphasia Psycholinguistic Project Database (MAPPD) was matched with the 2009-2011 Medical Expenditure Panel Survey (MEPS) using a propensity score algorithm based on functional, health, and demographic characteristics. Multilevel, generalized, nonlinear regression models were applied to the resulting data set to assess the correlation between the Boston Naming Test (BNT) percentile score and age, income, sex, race, household size, marital status, aphasia type, and region of residence. Poisson regression models with bootstrapped standard errors were used to estimate these relationships RESULTS: Discrete dependent variable estimation with non-normal prior specification included individual level (age, marital status, years of education), socioeconomic (family income), health (aphasia type), household (family size), and environmental (region of residence) characteristics. Regression results indicated that, relative to individuals with Wernicke's, individuals with Anomic (0.74, SE = 0.0008) and Conduction (0.42, SE = 0.0009) aphasia performed better on the BNT. While age at the time of testing was not significantly correlated, higher income level (0.15, SE = 0.0003) and larger family size (0.002, SE = 0.002) was associated with higher BNT score percentiles. Finally, Black persons with aphasia (PWA) (-0.0124, SE = 0.0007) had lower average score percentiles when other factors were held constant.

CONCLUSIONS

The findings reported here suggest higher income and larger family size are associated with better outcomes. As expected, aphasia type was significantly associated with naming outcomes. However, poorer performance by Black PWA and individuals with low income suggests that SDOH can play a critical role (positive and negative) in naming impairment in some populations with aphasia.

The Moroccan Arabic Bedside Western Aphasia Battery-Revised: Linguistic and Psychometric Properties

PURPOSE

The objectives of this study were (a) to linguistically and culturally adapt the English bedside version of the Western Aphasia Battery-Revised (Bedside WAB-R) into Moroccan Arabic (MA) and (b) to assess its psychometric properties.

METHOD

The Bedside MA-WAB-R was piloted on a population of 20 participants after a process of linguistic adaptation from English to MA. To verify the psychometric properties of the test, a group of healthy controls (n = 106) and a group of persons with chronic aphasia (n = 52) completed the Bedside MA-WAB-R. The test's content validity, construct validity, and the concurrent validity of the aphasia classification system were examined. In addition, the interrater reliability, intrarater reliability, test-retest reliability, and internal consistency of the instrument were evaluated.

RESULTS

The results indicated that the Bedside MA-WAB-R overall met standard criteria for excellent psychometric properties, as evinced by high content and construct validity and concurrent validity with independent measures of aphasia diagnosis (speech-language pathologists' impression and lesion location) as well as high internal consistency, inter- and intrarater reliability, and test-retest reliability. The test was also found to have very high sensitivity and specificity for the detection of aphasia, as revealed by the performance of controls. By controlling for age and educational level effects, specific cutoff values were determined to optimize the aphasia diagnosis.

CONCLUSIONS

The Bedside MA-WAB-R is the first standardized quick aphasia assessment tool with associated psychometric properties for clinical use with an MA-speaking population, particularly within the 26- to 58-year age range. It meets standard criteria for a valid and reliable measure than can have a variety of clinical and research applications.

Recovery from aphasia in the first year after stroke

Most individuals who experience aphasia after a stroke recover to some extent, with the majority of gains taking place in the first year. The nature and time course of this recovery process is only partially understood, especially its dependence on lesion location and extent, which are the most important determinants of outcome. The aim of this study was to provide a comprehensive description of patterns of recovery from aphasia in the first year after stroke. We recruited 334 patients with acute left hemisphere supratentorial ischaemic or haemorrhagic stroke and evaluated their speech and language function within 5 days using the Quick Aphasia Battery (QAB). At this initial time point, 218 patients presented with aphasia. Individuals with aphasia were followed longitudinally, with follow-up evaluations of speech and language at 1 month, 3 months, and 1 year post-stroke, wherever possible. Lesions were manually delineated based on acute clinical MRI or CT imaging. Patients with and without aphasia were divided into 13 groups of individuals with similar, commonly occurring patterns of brain damage. Trajectories of recovery were then investigated as a function of group (i.e. lesion location and extent) and speech/language domain (overall language function, word comprehension, sentence comprehension, word finding, grammatical construction, phonological encoding, speech motor programming, speech motor execution, and reading). We found that aphasia is dynamic, multidimensional, and gradated, with little explanatory role for aphasia subtypes or binary concepts such as fluency. Patients with circumscribed frontal lesions recovered well, consistent with some previous observations. More surprisingly, most patients with larger frontal lesions extending into the parietal or temporal lobes also recovered well, as did patients with relatively circumscribed temporal, temporoparietal, or parietal lesions. Persistent moderate or severe deficits were common only in patients with extensive damage throughout the middle cerebral artery distribution or extensive temporoparietal damage. There were striking differences between speech/language domains in their rates of recovery and relationships to overall language function, suggesting that specific domains differ in the extent to which they are redundantly represented throughout the language network, as opposed to depending on specialized cortical substrates. Our findings have an immediate clinical application in that they will enable clinicians to estimate the likely course of recovery for individual patients, as well as the uncertainty of these predictions, based on acutely observable neurological factors.

Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging

Advances in deep learning can be applied to acute stroke imaging to build powerful and explainable prediction models that could supersede traditionally used biomarkers. We aimed to evaluate the performance and interpretability of a deep learning model based on convolutional neural networks (CNN) in predicting long-term functional outcome with diffusion-weighted imaging (DWI) acquired at day 1 post-stroke. Ischemic stroke patients (n = 322) were included from the ASTER and INSULINFARCT trials as well as the Pitié-Salpêtrière registry. We trained a CNN to predict long-term functional outcome assessed at 3 months with the modified Rankin Scale (dichotomized as good [mRS ≤ 2] vs. poor [mRS ≥ 3]) and compared its performance to two logistic regression models using lesion volume and ASPECTS. The CNN contained an attention mechanism, which allowed to visualize the areas of the brain that drove prediction. The deep learning model yielded a significantly higher area under the curve (0.83 95%CI [0.78-0.87]) than lesion volume (0.78 [0.73-0.83]) and ASPECTS (0.77 [0.71-0.83]) (p < 0.05). Setting all classifiers to the specificity as the deep learning model (i.e., 0.87 [0.82-0.92]), the CNN yielded a significantly higher sensitivity (0.67 [0.59-0.73]) than lesion volume (0.48 [0.40-0.56]) and ASPECTS (0.50 [0.41-0.58]) (p = 0.002). The attention mechanism revealed that the network learned to naturally attend to the lesion to predict outcome.

Which is responsible for aphasia by subcortical lesions? Subcortical lesions or the cortical hypoperfusion?

Cerebral lesions causing aphasia involve morphological and functional changes. In this study, it was aimed to explain the connection between aphasia and subcortical lesions with SPECT. The study included 30 patients diagnosed in the first three days of stroke with a single hemorrhagic or ischemic lesion in the dominant hemisphere subcortical area. Gulhane Aphasia Test and SPECT were performed. Aphasia was detected in 19 cases (63.3%). The relationship between aphasia and perfusion dysfunction in cortical and subcortical regions of the brain was evaluated, aphasia was found to be present in 15 (71.4%) of the 21 patients with cortical hypoperfusion in the dominant hemisphere and 4 (44.4%) of the 9 patients without cortical hypoperfusion; the difference was not statistically significant (p = 0.16). In the ischemia group, aphasia was present in 11 (78.5%) of the 14 cases with cortical hypoperfusion in the dominant hemisphere. Aphasia wasn't detected in any of the 5 cases that did not have cortical hypoperfusion, the difference was statistically significant (p = 0.005). When cerebral regions were evaluated separately, significant difference was reported in the aphasia seen with frontal, anterior parietal, and occipital hypoperfusion compared to cases with normal perfusion in these areas, with p = 0.003, p = 0.021, and p = 0.004, respectively. This study showed that aphasia to be more common in cases with cortical hypoperfusion in the dominant hemisphere than in cases without hypoperfusion. Our results provide evidence that direct effect of the lesion in the basal ganglia on the development of aphasia is doubtful.

Aphasia severity is modulated by race and lesion size in chronic survivors: A retrospective study

INTRODUCTION

In stroke survivors with aphasia (SWA), differences in behavioral language performance have been observed between Black and White Americans. These racial differences in aphasia outcomes may reflect biological stroke severity, disparities in access to care, potential assessment bias, or interactions between these factors and race. Understanding the origin of disparities in aphasia outcomes is critical to any efforts to promote health equity among SWA. In this study, we explore aphasia outcomes by examining the relationship between race, socioeconomic status, and neurological factors in SWA.

METHOD

Eighty-five chronic left-hemisphere SWA (31 Black, 54 White) participated in the study. The primary aphasia outcome measure was the Western Aphasia Battery-Revised (WAB-R). Lesion size was measured based on manual lesion segmentations. FLAIR and T2 images were scored for severity of white matter disease. Independent sample t-tests were used to determine differences by race in education, age, income, aphasia severity, white matter disease, and lesion size. A linear regression model was used to explore factors that predicted aphasia severity on the WAB-R.

RESULT

Level of education and estimated income differed by race in our sample. For predictors of aphasia severity, the regression model revealed a significant effect of lesion size on WAB Aphasia Quotient and an interaction of race x lesion size, such that Black and White participants with small lesions had similar WAB scores, but in individuals with larger lesions, Black participants had lower WAB scores than White participants.

CONCLUSION

We suggest two explanations for the difference between Black and White SWA in the relationship between lesion size and aphasia severity. First, the impact of disparities in access to rehabilitation after stroke may be more evident when a stroke is larger and causes significant aphasia. Additionally, an assessment bias in aphasia outcome measures may be more evident with increasing severity of aphasia. Future studies should further discern the drivers of observed disparities in aphasia outcomes in order to identify opportunities to improve equity in aphasia care.

The role of the hippocampus in statistical learning and language recovery in persons with post stroke aphasia

Although several studies have aimed for accurate predictions of language recovery in post stroke aphasia, individual language outcomes remain hard to predict. Large-scale prediction models are built using data from patients mainly in the chronic phase after stroke, although it is clinically more relevant to consider data from the acute phase. Previous research has mainly focused on deficits, i.e., behavioral deficits or specific brain damage, rather than compensatory mechanisms, i.e., intact cognitive skills or undamaged brain regions. One such unexplored brain region that might support language (re)learning in aphasia is the hippocampus, a region that has commonly been associated with an individual's learning potential, including statistical learning. This refers to a set of mechanisms upon which we rely heavily in daily life to learn a range of regularities across cognitive domains. Against this background, thirty-three patients with aphasia (22 males and 11 females, M = 69.76 years, SD = 10.57 years) were followed for 1 year in the acute (1-2 weeks), subacute (3-6 months) and chronic phase (9-12 months) post stroke. We evaluated the unique predictive value of early structural hippocampal measures for short-term and long-term language outcomes (measured by the ANELT). In addition, we investigated whether statistical learning abilities were intact in patients with aphasia using three different tasks: an auditory-linguistic and visual task based on the computation of transitional probabilities and a visuomotor serial reaction time task. Finally, we examined the association of individuals' statistical learning potential with acute measures of hippocampal gray and white matter. Using Bayesian statistics, we found moderate evidence for the contribution of left hippocampal gray matter in the acute phase to the prediction of long-term language outcomes, over and above information on the lesion and the initial language deficit (measured by the ScreeLing). Non-linguistic statistical learning in patients with aphasia, measured in the subacute phase, was intact at the group level compared to 23 healthy older controls (8 males and 15 females, M = 74.09 years, SD = 6.76 years). Visuomotor statistical learning correlated with acute hippocampal gray and white matter. These findings reveal that particularly left hippocampal gray matter in the acute phase is a potential marker of language recovery after stroke, possibly through its statistical learning ability.

Brain age predicts long-term recovery in post-stroke aphasia

The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine learning algorithm trained on a large sample of healthy adults. A subsample of 30 participants returned for follow-up language assessments at least 2 years after stroke onset. To account for variability in age at stroke, we calculated proportional brain age difference, i.e. the proportional difference between brain age and chronological age. Multiple regression models were constructed to test the effects of proportional brain age difference on language outcomes. Lesion volume and chronological age were included as covariates in all models. Accelerated brain age compared with age was associated with worse overall aphasia severity (F(1, 48) = 5.65, P = 0.022), naming (F(1, 48) = 5.13, P = 0.028), and speech repetition (F(1, 48) = 8.49, P = 0.006) at stroke onset. Follow-up assessments were carried out ≥2 years after onset; decelerated brain age relative to age was significantly associated with reduced overall aphasia severity (F(1, 26) = 5.45, P = 0.028) and marginally failed to reach statistical significance for auditory comprehension (F(1, 26) = 2.87, P = 0.103). Proportional brain age difference was not found to be associated with changes in naming (F(1, 26) = 0.23, P = 0.880) and speech repetition (F(1, 26) = 0.00, P = 0.978). Chronological age was only associated with naming performance at stroke onset (F(1, 48) = 4.18, P = 0.047). These results indicate that brain age as estimated based on routine clinical brain scans may be a strong biomarker for language function and recovery after stroke.

Time course of right-hemisphere recruitment during word production following left-hemisphere damage: A single case of young stroke

Our understanding of post-stroke language function is largely based on older age groups, who show increasing age-related brain pathology and neural reorganisation. To illustrate language outcomes in the young-adult brain, we present the case of J., a 23-year-old woman with chronic aphasia from a left-hemisphere stroke affecting the temporal lobe. Diffusion MRI-based tractography indicated that J.'s language-relevant white-matter structures were severely damaged. Employing magnetoencephalography (MEG), we explored J.'s conceptual preparation and word planning abilities using context-driven and bare picture-naming tasks. These revealed naming deficits, manifesting as word-finding difficulties and semantic paraphasias about half of the time. Naming was however facilitated by semantically constraining lead-in sentences. Altogether, this pattern indicates disrupted lexical-semantic and phonological retrieval abilities. MEG revealed that J.'s conceptual and naming-related neural responses were supported by the right hemisphere, compared to the typical left-lateralised brain response of a matched control. Differential recruitment of right-hemisphere structures (330-440 ms post-picture onset) was found concurrently during successful naming (right mid-to-posterior temporal lobe) and word-finding attempts (right inferior frontal gyrus). Disconnection of the temporal lobes via corpus callosum was not critical for recruitment of the right hemisphere in visually guided naming, possibly due to neural activity right lateralising from the outset. Although J.'s right hemisphere responded in a timely manner during word planning, its lexical and phonological retrieval abilities remained modest.

Better long-term speech outcomes in stroke survivors who received early clinical speech and language therapy: What's driving recovery?

Establishing whether speech and language therapy after stroke has beneficial effects on speaking ability is challenging because of the need to control for multiple non-therapy factors known to influence recovery. We investigated how speaking ability at three time points post-stroke differed in patients who received varying amounts of clinical therapy in the first month post-stroke. In contrast to prior studies, we factored out variance from: initial severity of speaking impairment, amount of later therapy, and left and right hemisphere lesion size and site. We found that speaking ability at one month post-stroke was significantly better in patients who received early therapy (n = 79), versus those who did not (n = 64), and the number of hours of early therapy was positively related to recovery at one year post-stroke. We offer two non-mutually exclusive interpretations of these data: (1) patients may benefit from the early provision of self-management strategies; (2) therapy is more likely to be provided to patients who have a better chance of recovery (e.g., poor physical and/or mental health may impact suitability for therapy and chance of recovery). Both interpretations have implications for future studies aiming to predict individual patients' speech outcomes after stroke, and their response to therapy.

French Phonological Component Analysis and aphasia recovery: A bilingual perspective on behavioral and structural data

Studies show bilingualism entails an advantage in cognitive control tasks. There is evidence of a bilingual advantage in the context of aphasia, resulting in better cognitive outcomes and recovery in bilingual persons with aphasia compared to monolingual peers. This bilingual advantage also results in structural changes in the right hemisphere gray matter. Very few studies have examined the so-called bilingual advantage by reference to specific anomia therapy efficacy. This study aims to compare the effect of French-Phonological Component Analysis (Fr-PCA) in monolinguals and bilingual persons with aphasia, both at the linguistic and cognitive control level, and to examine the structural impact of left hemisphere lesion location and right hemisphere structural data. Eight participants with chronic aphasia received Fr-PCA for a total of 15 h over 5 weeks. The results showed improved accuracy for treated words and generalization to untreated items and discourse in both groups, and improved Flanker task performance for some participants. Bilingual participants improved more than monolinguals for picture-naming tasks and narrative discourse. Damage to the left postcentral gyrus and the middle frontal gyrus was associated with less therapy-induced improvement. Additionally, left hemisphere damage to the inferior parietal gyrus and postcentral gyrus was associated with reduced cognitive control pre-therapy. Undamaged right hemisphere cortical thicknesses were significantly different between groups; the inferior frontal gyrus and the middle frontal gyrus were greater for the bilingual participants and correlated with cognitive control skills. These results suggest a bilingual advantage in anomia recovery following Fr-PCA, potentially resulting from enhanced cognitive control abilities that could be supported by right hemisphere neural reserve.

Predictors of Therapy Response in Chronic Aphasia: Building a Foundation for Personalized Aphasia Therapy

Chronic aphasia, a devastating impairment of language, affects up to a third of stroke survivors. Speech and language therapy has consistently been shown to improve language function in prior clinical trials, but few clinicially applicable predictors of individual therapy response have been identified to date. Consequently, clinicians struggle substantially with prognostication in the clinical management of aphasia. A rising prevalence of aphasia, in particular in younger populations, has emphasized the increasing demand for a personalized approach to aphasia therapy, that is, therapy aimed at maximizing language recovery of each individual with reference to evidence-based clinical recommendations. In this narrative review, we discuss the current state of the literature with respect to commonly studied predictors of therapy response in aphasia. In particular, we focus our discussion on biographical, neuropsychological, and neurobiological predictors, and emphasize limitations of the literature, summarize consistent findings, and consider how the research field can better support the development of personalized aphasia therapy. In conclusion, a review of the literature indicates that future research efforts should aim to recruit larger samples of people with aphasia, including by establishing multisite aphasia research centers.

Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia

BACKGROUND

Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke.

METHODS

Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data.

RESULTS

The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68-0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87).

CONCLUSIONS

While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.

Diagnosis and differential diagnosis flow diagram of Chinese post-stroke aphasia types and treatment of post-stroke aphasia

This review aimed to explore the concept, etiology, classification, classical cortical mapping, assessment, diagnosis and differential diagnosis, treatment, rehabilitation, mechanism, recovery, prognosis, and influencing factors for Chinese post-stroke aphasia (PSA). The review emphasized the necessity and significance of neuroimaging assessment of brain and blood vessels and neuropsychological assessment in diagnosis and differential diagnosis of Chinese PSA. In addition, it suggested and recommended to use "dichotomies of internal and external, and anterior and posterior" as a starting point, based on the anatomic location of brain and blood vessels and their relationship with language area and language disorder. As a result, the formulated Chinese PSA classification was more suitable to guide the clinical treatment of cerebral stroke. Diagnosis, classification, and differential diagnosis of Chinese PSA types were performed according to the "dichotomy" and "four elements." The formulated "flow diagram" enabled to determine the classification of Chinese PSA types. It was beneficial for patients to establish targeted and individualized rehabilitation training plans. This review introduced the use of memantine, piracetam, donepezil, etc. in PSA treatment, evaluated clinical studies conducted in China and abroad, investigated the mechanism of action related to the use of drugs in PSA treatment, and explored the therapeutic mechanism of rehabilitation training. It suggested the use drugs of memantine, piracetam, donepezil, etc. combine with non-pharmacotherapy and rehabilitation training in clinical studies on PSA treatment and also in practical settings.

Neural Resources Supporting Language Production vs. Comprehension in Chronic Post-stroke Aphasia: A Meta-Analysis Using Activation Likelihood Estimates

In post-stroke aphasia, language tasks recruit a combination of residual regions within the canonical language network, as well as regions outside of it in the left and right hemispheres. However, there is a lack of consensus as to how the neural resources engaged by language production and comprehension following a left hemisphere stroke differ from one another and from controls. The present meta-analysis used activation likelihood estimates to aggregate across 44 published fMRI and PET studies to characterize the functional reorganization patterns for expressive and receptive language processes in persons with chronic post-stroke aphasia (PWA). Our results in part replicate previous meta-analyses: we find that PWA activate residual regions within the left lateralized language network, regardless of task. Our results extend this work to show differential recruitment of the left and right hemispheres during language production and comprehension in PWA. First, we find that PWA engage left perilesional regions during language comprehension, and that the extent of this activation is likely driven by stimulus type and domain-general cognitive resources needed for task completion. In contrast to comprehension, language production was associated with activation of the right frontal and temporal cortices. Further analyses linked right hemisphere regions involved in motor speech planning for language production with successful naming in PWA, while unsuccessful naming was associated with the engagement of the right inferior frontal gyrus, a region often implicated in domain-general cognitive processes. While the within-group findings indicate that the engagement of the right hemisphere during language tasks in post-stroke aphasia differs for expressive vs. receptive tasks, the overall lack of major between-group differences between PWA and controls implies that PWA rely on similar cognitive-linguistic resources for language as controls. However, more studies are needed that report coordinates for PWA and controls completing the same tasks in order for future meta-analyses to characterize how aphasia affects the neural resources engaged during language, particularly for specific tasks and as a function of behavioral performance.

Resting-State Functional Connectivity following Phonological Component Analysis: The Combined Action of Phonology and Visual Orthographic Cues

Anomia is the most frequent and pervasive symptom for people with aphasia (PWA). Phonological component analysis (PCA) is a therapy incorporating phonological cues to treat anomia. Investigations of neural correlates supporting improvements following PCA remain scarce. Resting-state functional connectivity (rsFC) as a marker of therapy-induced neuroplasticity has been reported by our team. The present study explores the efficacy of PCA in French and associated therapy-induced neuroplasticity using whole-brain rsFC analysis. Ten PWA participated in a pre-/post-PCA fMRI study with cognitive linguistic assessments. PCA was delivered in French following the standard procedure. PCA led to significant improvement with trained and untrained items. PCA also led to changes in rsFC between distributed ROIs in the semantic network, visual network, and sub-cortical areas. Changes in rsFC can be interpreted within the frame of the visual and phonological nature of PCA. Behavioral and rsFC data changes associated with PCA in French highlight its efficacy and point to the importance of phonological and orthographic cues to consolidate the word-retrieval strategy, contributing to generalization to untrained words.

The inter-rater reliability of the Turkish version of Aphasia Rapid Test for stroke

Background: The Aphasia Rapid Test (ART) is a short bedside aphasia screening test developed originally in French for use with post-stroke patients in the acute phase of recovery.Objectives: The purpose of this study was to assess the inter-rater reliability of the Turkish version of the ART in stroke patients (ICTRP ID: NCT04386837).Methods: The French ART was translated into Turkish. Inter-rater reliability was assessed in 30 post-stroke patients with aphasia by two independent speech-language pathologists blind to each other's ratings.Results: 30 patients (18 men, 12 women; mean age 64.43 ± 16.60 years) were included within 10 days of stroke onset, as assessed by clinical examination and confirmed by CT and/or MRI. The mean (± SD) ART value was 16.26 (± 8.05) for rater 1 and 16.43 (±8.14) for rater 2. The inter-rater concordance coefficient was 0.997 (95% CI 0.994-0.999; p < .001).Conclusions: Findings indicate that the Turkish version of the original ART was successfully administered in Turkish-speaking patients.

Predicting language recovery in post-stroke aphasia using behavior and functional MRI

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 predictors of treatment outcome. Fifty-seven patients with chronic aphasia were recruited and treated for one of three aphasia impairments: 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 a rsfMRI scan at baseline. Patient scans were decomposed into 20 components by group independent component analysis, and the fractional amplitude of low-frequency fluctuations (fALFF) was calculated for each component time series. Post-treatment performance was modelled with elastic net regression, using pre-treatment performance and either behavioral language measures or fALFF imaging predictors. Analysis showed strong performance for behavioral measures in anomia (R² = 0.948, n = 28) and for fALFF predictors in agrammatism (R² = 0.876, n = 11) and dysgraphia (R² = 0.822, n = 18). Models of language outcomes after treatment trained using rsfMRI features may outperform models trained using behavioral language measures in some patient populations. This suggests that rsfMRI may have prognostic value for aphasia therapy outcomes.

German Language Adaptation of the NAVS (NAVS-G) and of the NAT (NAT-G): Testing Grammar in Aphasia

Grammar provides the framework for understanding and producing language. In aphasia, an acquired language disorder, grammatical deficits are diversified and widespread. However, the few assessments for testing grammar in the German language do not consider current linguistic, psycholinguistic, and functional imaging data, which have been shown to be crucial for effective treatment. This study developed German language versions of the Northwestern Assessment of Verbs and Sentences (NAVS-G) and the Northwestern Anagram Test (NAT-G) to examine comprehension and production of verbs, controlling for the number and optionality of verb arguments, and sentences with increasing syntactic complexity. The NAVS-G and NAT-G were tested in 27 healthy participants, 15 right hemispheric stroke patients without aphasia, and 15 stroke patients with mild to residual aphasia. Participants without aphasia showed near-perfect performance, with the exception of (object) relative sentences, where accuracy was associated with educational level. In each patient with aphasia, deficits in more than one subtest were observed. The within and between population-groups logistic mixed regression analyses identified significant impairments in processing syntactic complexity at the verb and sentence levels. These findings indicate that the NAVS-G and NAT-G have potential for testing grammatical competence in (German) stroke patients.

Word-finding in confrontation naming and picture descriptions produced by individuals with early post-stroke aphasia

OBJECTIVE

The present study aims to assess the relationship between quantitative measures of connected speech production and performance in confrontation naming in early post-stroke aphasia (8-14 days post-stroke). Method: We collected connected speech samples elicited by a picture description task and administered a confrontation naming task to 20 individuals with early post-stroke aphasia and 20 healthy controls. Transcriptions were made in compliance with the CHAT format guidelines. Several micro- (i.e. duration, total number of words, words per minute, mean length of utterances, ratio of open- to closed-class words and noun-to-verb ratio, VOC-D, repetitions, self-corrections, and phonological and semantic errors) and macrolinguistic (i.e. informativeness and efficiency) measures were extracted. Results: We provide evidence for the presence of impairments in an array of micro- and macrolinguistic measures of speech in individuals with early post-stroke aphasia. We show that in the patient group, confrontation naming abilities most strongly relate to informativeness in a picture description task. Conclusion: Our findings contribute to a better understanding of the relationship between performance in confrontation naming and in connected speech production in the first days after stroke onset and also suggest that discourse analysis may provide unique, possibly more complex information.

Cortical and Subcortical Control of Swallowing-Can We Use Information From Lesion Locations to Improve Diagnosis and Treatment for Patients With Stroke?

Purpose Swallowing is a complex process, mediated by a broad bilateral neural network that spans from the brainstem to subcortical and cortical brain structures. Although the cortex's role in swallowing was historically neglected, we now understand, especially through clinical observations and research of patients with stroke, that it substantially contributes to swallowing control. Neuroimaging techniques (e.g., magnetic resonance imaging) have helped significantly to elucidate the role of cortical and subcortical brain areas, in general, and the importance of specific areas in swallowing control in healthy individuals and patients with stroke. We will review recent discoveries in cortical and subcortical neuroimaging research studies and their generalizability across patients to discuss their potential implications and translation to dysphagia diagnosis and treatment in clinical practice. Conclusions Stroke lesion locations have been identified that are commonly associated across patients with the occurrence and recovery of dysphagia, suggesting that clinical brain scans provide useful information for improving the diagnosis and treatment of patients with stroke. However, individual differences in brain structure and function limit the generalizability of these relationships and emphasize that the extent of the motor and sensory pathology in swallowing, and how the patient recovers, also depends on a patient's individual brain constitution. The involvement of the damaged brain tissue in swallowing control before the stroke and the health of the residual, undamaged brain tissue are crucial factors that can differ between individuals.

Predicting Early Post-stroke Aphasia Outcome From Initial Aphasia Severity

Background: The greatest degree of language recovery in post-stroke aphasia takes place within the first weeks. Aphasia severity and lesion measures have been shown to be good predictors of long-term outcomes. However, little is known about their implications in early spontaneous recovery. The present study sought to determine which factors better predict early language outcomes in individuals with post-stroke aphasia. Methods: Twenty individuals with post-stroke aphasia were assessed <72 h (acute) and 10-14 days (subacute) after stroke onset. We developed a composite score (CS) consisting of several linguistic sub-tests: repetition, oral comprehension and naming. Lesion volume, lesion load and diffusion measures [fractional anisotropy (FA) and axial diffusivity (AD)] from both arcuate fasciculi (AF) were also extracted using MRI scans performed at the same time points. A series of regression analyses were performed to predict the CS at the second assessment. Results: Among the diffusion measures, only FA from right AF was found to be a significant predictor of early subacute aphasia outcome. However, when combined in two hierarchical models with FA, age and either lesion load or lesion size, the initial aphasia severity was found to account for most of the variance (R 2 = 0.678), similarly to the complete models (R 2 = 0.703 and R 2 = 0.73, respectively). Conclusions: Initial aphasia severity was the best predictor of early post-stroke aphasia outcome, whereas lesion measures, though highly correlated, show less influence on the prediction model. We suggest that factors predicting early recovery may differ from those involved in long-term recovery.

Neural architectures of music - Insights from acquired amusia

The ability to perceive and produce music is a quintessential element of human life, present in all known cultures. Modern functional neuroimaging has revealed that music listening activates a large-scale bilateral network of cortical and subcortical regions in the healthy brain. Even the most accurate structural studies do not reveal which brain areas are critical and causally linked to music processing. Such questions may be answered by analysing the effects of focal brain lesions in patients´ ability to perceive music. In this sense, acquired amusia after stroke provides a unique opportunity to investigate the neural architectures crucial for normal music processing. Based on the first large-scale longitudinal studies on stroke-induced amusia using modern multi-modal magnetic resonance imaging (MRI) techniques, such as advanced lesion-symptom mapping, grey and white matter morphometry, tractography and functional connectivity, we discuss neural structures critical for music processing, consider music processing in light of the dual-stream model in the right hemisphere, and propose a neural model for acquired amusia.

Thrombolysis' benefits on early post-stroke language recovery in aphasia patients

INTRODUCTION

Thrombolysis may affect ischemic stroke-related size, pattern and nature of infarcts, and has the potential to change aphasia presentation and recovery. Data on evolution of post-stroke aphasia following thrombolysis are still scarce. The aim of this study was to determine the course of language recovery through a well-validated language assessment battery after acute ischemic stroke and investigate whether traditional categorical classifications of aphasia can describe the clinical picture in post-thrombolysis phase.

MATERIALS AND METHODS

Demographic, clinical, and language assessment data of 116 patients presenting sub-acute ischemic stroke aphasia (41 treated with r-tPA; 75 non-treated) were retrospectively analyzed. The participants were assessed by a clinical neuropsychologist with a variety of subtests taken from a well-validated Italian language battery (Neuro-Psychological Aphasia Evaluation).

RESULTS

The percentage of resolved aphasia was significantly higher in treated patients compared to non-treated patients (p = 0.005) and global aphasia was more common in the non-treated group (non-treated 30.7% vs treated 17.1%). Aphasia subtypes and stroke etiologies showed no significant association, except for small vessel etiology and resolved aphasia (p = 0.041). Reperfusion treatment, baseline NIHSS, and lacunar stroke were the predictors of aphasia recovery.

CONCLUSION

The percentage of resolved aphasia was significantly higher in the treated patients compared to the non-treated, with the latter showing a higher percentage of global aphasia. Identifying classic aphasia subtypes after thrombolysis is still possible since reperfused areas do not necessary change the classification or lead to completely different aphasic syndromes. Reperfusion treatment, baseline NIHSS, and lacunar stroke were the main predictors of aphasia recovery.

Trait Mindfulness and Functional Connectivity in Cognitive and Attentional Resting State Networks

Mindfulness has been described as an orienting of attention to the present moment, with openness and compassion. Individuals displaying high trait mindfulness exhibit this tendency as a more permanent personality attribute. Given the numerous physical and mental health benefits associated with mindfulness, there is a great interest in understanding the neural substrates of this trait. The purpose of the current research was to examine how individual differences in trait mindfulness associated with functional connectivity in five resting-state networks related to cognition and attention: the default mode network (DMN), the salience network (SN), the central executive network (CEN), and the dorsal and ventral attention networks (DAN and VAN). Twenty-eight undergraduate participants completed the Five-Facet Mindfulness Questionnaire (FFMQ), a self-report measure of trait mindfulness which also provides scores on five of its sub-categories (Observing, Describing, Acting with Awareness, Non-judging of Inner Experience, and Non-reactivity to Inner Experience). Participants then underwent a structural MRI scan and a 7-min resting state functional MRI scan. Resting-state data were analyzed using independent-component analyses. An analysis of covariance (ANCOVA) was performed to determine the relationship between each resting state network and each FFMQ score. These analyses indicated that: (1) trait mindfulness and its facets showed increased functional connectivity with neural regions related to attentional control, interoception, and executive function; and (2) trait mindfulness and its facets showed decreased functional connectivity with neural regions related to self-referential processing and mind wandering. These patterns of functional connectivity are consistent with some of the benefits of mindfulness-enhanced attention, self-regulation, and focus on present experience. This study provides support for the notion that non-judgmental attention to the present moment facilitates the integration of regions in neural networks that are related to cognition, attention, and sensation.