Clinically accessible neuroimaging predictors of post-stroke neurocognitive disorder: a prospective observational study

Cortical atrophy on baseline computed tomography imaging predicts clinical outcome in patients undergoing endovascular treatment for acute ischemic stroke

OBJECTIVE

Multiple variables beyond the extent of recanalization can impact the clinical outcome after acute ischemic stroke due to large vessel occlusions. Here, we assessed the influence of small vessel disease and cortical atrophy on clinical outcome using native cranial computed tomography (NCCT) in a large single-center cohort.

METHODS

A total of 1103 consecutive patients who underwent endovascular treatment (EVT) due to occlusion of the middle cerebral artery territory were included. NCCT data were visually assessed for established markers of age-related white matter changes (ARWMC) and brain atrophy. All images were evaluated separately by two readers to assess the inter-observer variability. Regression and machine learning models were built to determine the predictive relevance of ARWMC and atrophy in the presence of important baseline clinical and imaging metrics.

RESULTS

Patients with favorable outcome presented lower values for all measured metrics of pre-existing brain deterioration (p < 0.001). Both ARWMC (p < 0.05) and cortical atrophy (p < 0.001) were independent predictors of clinical outcome at 90 days when controlled for confounders in both regression analyses and led to a minor improvement of prediction accuracy in machine learning models (p < 0.001), with atrophy among the top-5 predictors.

CONCLUSION

NCCT-based cortical atrophy and ARWMC scores on NCCT were strong and independent predictors of clinical outcome after EVT.

CLINICAL RELEVANCE STATEMENT

Visual assessment of cortical atrophy and age-related white matter changes on CT could improve the prediction of clinical outcome after thrombectomy in machine learning models which may be integrated into existing clinical routines and facilitate patient selection.

KEY POINTS

• Cortical atrophy and age-related white matter changes were quantified using CT-based visual scores. • Atrophy and age-related white matter change scores independently predicted clinical outcome after mechanical thrombectomy and improved machine learning-based prediction models. • Both scores could easily be integrated into existing clinical routines and prediction models.

Characterizing upper extremity fine motor function in the presence of white matter hyperintensities: A 7 T MRI cross-sectional study in older adults

BACKGROUND

White matter hyperintensities (WMH) are a prevalent radiographic finding in the aging brain studies. Research on WMH association with motor impairment is mostly focused on the lower-extremity function and further investigation on the upper-extremity is needed. How different degrees of WMH burden impact the network of activation recruited during upper limb motor performance could provide further insight on the complex mechanisms of WMH pathophysiology and its interaction with aging and neurological disease processes.

METHODS

40 healthy elderly subjects without a neurological/psychiatric diagnosis were included in the study (16F, mean age 69.3 years). All subjects underwent ultra-high field 7 T MRI including structural and finger tapping task-fMRI. First, we quantified the WMH lesion load and its spatial distribution. Secondly, we performed a data-driven stratification of the subjects according to their periventricular and deep WMH burdens. Thirdly, we investigated the distribution of neural recruitment and the corresponding activity assessed through BOLD signal changes among different brain regions for groups of subjects. We clustered the degree of WMH based on location, numbers, and volume into three categories; ranging from mild, moderate, and severe. Finally, we explored how the spatial distribution of WMH, and activity elicited during task-fMRI relate to motor function, measured with the 9-Hole Peg Test.

RESULTS

Within our population, we found three subgroups of subjects, partitioned according to their periventricular and deep WMH lesion load. We found decreased activity in several frontal and cingulate cortex areas in subjects with a severe WMH burden. No statistically significant associations were found when performing the brain-behavior statistical analysis for structural or functional data.

CONCLUSION

WMH burden has an effect on brain activity during fine motor control and the activity changes are associated with varying degrees of the total burden and distributions of WMH lesions. Collectively, our results shed new light on the potential impact of WMH on motor function in the context of aging and neurodegeneration.

Diagnostic accuracy of the Clock Drawing Test in screening for early post-stroke neurocognitive disorder: the Nor-COAST study

BACKGROUND

Post-stroke neurocognitive disorder, though common, is often overlooked by clinicians. Moreover, although the Montreal Cognitive Assessment (MoCA) has proven to be a valid screening test for neurocognitive disorder, even more time saving tests would be preferred. In our study, we aimed to determine the diagnostic accuracy of the Clock Drawing Test (CDT) for post-stroke neurocognitive disorder and the association between the CDT and MoCA.

METHODS

This study is part of the Norwegian Cognitive Impairment After Stroke study, a multicentre prospective cohort study following patients admitted with acute stroke. At the three-month follow-up, patients were classified with normal cognition, mild neurocognitive disorder, or major neurocognitive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Any neurocognitive disorder compromised both mild- and major neurocognitive disorder. The CDT at the three-month assessment was given scores ranging from 0 to 5. Patients able to complete the CDT and whose cognitive status could be classified were included in analyses. The CDT diagnostic accuracy for post-stroke neurocognitive disorder was identified using receiver operating characteristic curves, sensitivity, specificity, positive predictive value, and negative predictive value. The association between the MoCA and CDT was analysed with Spearman's rho.

RESULTS

Of 554 participants, 238 (43.0%) were women. Mean (SD) age was 71.5 (11.8) years, while mean (SD) National Institutes of Health Stroke Scale score was 2.6 (3.7). The area under the receiver operating characteristic curve of the CDT for major neurocognitive disorder and any neurocognitive disorder was 0.73 (95% CI, 0.68-0.79) and 0.68 (95% CI, 0.63-0.72), respectively. A CDT cutoff of < 5 yielded 68% sensitivity and 60% specificity for any neurocognitive disorder and 78% sensitivity and 53% specificity for major neurocognitive disorder. Spearman's correlation coefficient between scores on the MoCA and CDT was 0.50 (95% CI, 0.44-0.57, p < .001).

CONCLUSIONS

The CDT is not accurate enough to diagnose post-stroke neurocognitive disorder but shows acceptable accuracy in identifying major neurocognitive disorder. Performance on the CDT was associated with performance on MoCA; however, the CDT is inferior to MoCA in identifying post-stroke neurocognitive disorder.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT02650531). Retrospectively registered January 8, 2016.

White Matter Hyperintensity Volume and Poststroke Cognition: An Individual Patient Data Pooled Analysis of 9 Ischemic Stroke Cohort Studies

BACKGROUND

White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet, uncertainty remains about affected domains, the role of other preexisting brain injury, and infarct types in the relation between WMH burden and poststroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts.

METHODS

We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available magnetic resonance imaging and multidomain cognitive assessment <15 months poststroke. In this individual patient data meta-analysis, linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning (Z scores; attention and executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct type. Preexisting brain injury was accounted for in the multivariable models and all analyses were corrected for the study site as a random effect.

RESULTS

In the total sample (67 years [SD, 11.5], 40% female), we found a dose-dependent inverse relationship between WMH volume and poststroke cognitive functioning across all 4 cognitive domains (coefficients ranging from -0.09 [SE, 0.04, P=0.01] for verbal memory to -0.19 [SE, 0.03, P<0.001] for attention and executive functioning). This relation was independent of acute infarct volume and the presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain-specific functioning was also largely independent of infarct type.

CONCLUSIONS

In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct type.

Factors Contributing to Life-Change Adaptation in Family Caregivers of Community-Dwelling Individuals with Acquired Brain Injury

Acquired brain injury (ABI) is a public health issue that affects family caregivers, because individuals with ABI often require semi-permanent care and community support in daily living. Identifying the characteristics of family caregivers and individuals with ABI and examining life-change adaptation may provide valuable insights. The current study sought to explore the factors contributing to life-change adaptation in family caregivers of community-dwelling individuals with ABI. As a secondary analysis, a cross-sectional study was conducted using data obtained in a previous study of 1622 family caregivers in Japan. We hypothesized that life-change adaptation in family caregivers of individuals with ABI would also be related to family caregivers' characteristics and the characteristics of individuals with ABI. In total, 312 valid responses were analyzed using Poisson regression analysis. The results revealed that life-change adaptation in family caregivers of individuals with ABI was related to sex (prevalence ratio [PR]: 0.65, confidence interval [CI]: -0.819;-0.041) and mental health (PR: 2.04, CI: 0.354; 1.070) as family caregivers' characteristics, and topographical disorientation (PR: 1.51, CI: 0.017; 0.805) and loss of control over behavior (PR: 1.61, CI: 0.116; 0.830) as the characteristics of individuals with ABI, after adjusting for the effects of the caregiver's age, sex, and the duration of the caregiver's role. The current study expands existing knowledge and provides a deeper understanding to enhance the development of specific policies for improving caregiving services and supporting families.

Prevalence and factors influencing cognitive impairment among the older adult stroke survivors: a cross-sectional study

Background

Cognitive impairment as a complication in post-stroke patients has high prevalence throughout the world. However, few studies have focused on the older adult stroke survivors and explored their prevalence and factors of post-stroke cognitive impairment (PSCI). The study aims to evaluate the cognitive status of stroke patients in Hunan Province, China and to determine the potential risk factors associated with PSCI in order to identify the older adult population in advance and promote healthy aging.

Methods

This cross-sectional study was carried out from August to December, 2021. A total of 520 stroke survivors from 6 tertiary hospitals were randomly selected. The information was collected using the general questionnaire, the Barthel Index Rating Scale and the Mini-mental State Examination (MMSE). Analysis was based on descriptive statistics, chi-square test and the significant variables were included in multivariate logistic regression. The reporting of this cross-sectional study followed the STROBE checklist.

Results

A total of 195 older adults (40.37%) were screened for cognitive impairment based on the results of the MMSE score. Patients in the PSCI group had a higher proportion of individuals aged 70 or older (35.90% vs. 24.65%, p<0.001). The potential risk factors for post-stroke cognitive impairment in older adults were being aged between 70 and 79 years old (OR = 3.973, 95% CI, 2.346-6.729, p<0.001), being aged 80 years or older (OR = 3.590, 95% CI, 1.373-9.387, p = 0.009), having a low level of education (OR = 9.183, 95% CI, 5.341-15.789, p<0.001), having hypertension (OR = 1.756, 95% CI, 1.121-2.753, p = 0.014), and having a dominant hemisphere lesion (OR = 1.880, 95% CI, 1.193-2.962, p<0.001).

Conclusion

The prevalence of PSCI was high among Chinese older adults, particularly those aged 80 years or older. The factors identified in our study could assist in the early identification of older adults at risk, develop personalized management plans, and promote healthy aging.

Neural correlates of verbal fluency revealed by longitudinal T1, T2 and FLAIR imaging in stroke

Diffusion-weighted imaging has been widely used in the research on post-stroke verbal fluency but acquiring diffusion data is not always clinically feasible. Achieving comparable reliability for detecting brain variables associated with verbal fluency impairments, based on more readily available anatomical, non-diffusion images (T1, T2 and FLAIR), enables clinical practitioners to have complementary neurophysiological information at hand to facilitate diagnosis and treatment of language impairment. Meanwhile, although the predominant focus in the stroke recovery literature has been on cortical contributions to verbal fluency, it remains unclear how subcortical regions and white matter disconnection are related to verbal fluency. Our study thus utilized anatomical scans of ischaemic stroke survivors (n = 121) to identify longitudinal relationships between subcortical volume, white matter tract disconnection, and verbal fluency performance at 3- and 12-months post-stroke. Subcortical grey matter volume was derived from FreeSurfer. We used an indirect probabilistic approach to quantify white matter disconnection in terms of disconnection severity, the proportion of lesioned voxel volume to the total volume of a tract, and disconnection probability, the probability of the overlap between the stroke lesion and a tract. These disconnection variables of each subject were identified based on the disconnectome map of the BCBToolkit. Using a linear mixed multiple regression method with 5-fold cross-validations, we correlated the semantic and phonemic fluency scores with longitudinal measurements of subcortical grey matter volume and 22 bilateral white matter tracts, while controlling for demographic variables (age, sex, handedness and education), total brain volume, lesion volume, and cortical thickness. The results showed that the right subcortical grey matter volume was positively correlated with phonemic fluency averaged over 3 months and 12 months. The finding generalized well on the test data. The disconnection probability of left superior longitudinal fasciculus II and left posterior arcuate fasciculus was negatively associated with semantic fluency only on the training data, but the result aligned with our previous study using diffusion scans in the same clinical population. In sum, our results presented evidence that routinely acquired anatomical scans can serve as a reliable source for deriving neural variables of post-stroke verbal fluency performance. The use of this method might provide an ecologically valid and more readily implementable analysis tool.

Longitudinal brain age prediction and cognitive function after stroke

Advanced age is associated with post-stroke cognitive decline. Machine learning based on brain scans can be used to estimate brain age of patients, and the corresponding difference from chronological age, the brain age gap (BAG), has been investigated in a range of clinical conditions, yet not thoroughly in post-stroke neurocognitive disorder (NCD). We aimed to investigate the association between BAG and post-stroke NCD over time. Lower BAG (younger appearing brain compared to chronological age) was found associated with lower risk of post-stroke NCD up to 36 months after stroke, even among those showing no evidence of impairments 3 months after hospital admission. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post-stroke NCD at 18 and 36 months. In conclusion, a younger appearing brain is associated with a lower risk of post-stroke NCD.

Neuroimaging markers of dual impairment in cognition and physical performance following stroke: The Nor-COAST study

Background

Cognitive decline and decline in physical performance are common after stroke. Concurrent impairments in the two domains are reported to give increased risk of dementia and functional decline. The concept of dual impairment of physical performance and cognition after stroke is poorly investigated. Clinically accessible imaging markers of stroke and pre-existing brain pathology might help identify patients at risk.

Objective

The primary aim of this study was to investigate to which extent pre-stroke cerebral pathology was associated with dual impairment in cognition and physical performance at time of stroke. Secondary aims were to examine whether white matter hyperintensities, medial temporal lobe atrophy, and stroke lesion volume and location were associated with dual impairment.

Methods

Participants from the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study with available MRI data at baseline were included in this cross-sectional study. Logistic regression analyses were conducted, with impairment status (no impairment, impaired cognition, impaired physical performance, and dual impairment) as the dependent variable and MRI markers as covariates. Pre-existing brain pathologies were classified into neurodegenerative, cerebrovascular, or mixed pathology. In addition, white matter hyperintensities and medial temporal lobe atrophy were included as independent covariates. Stroke volume and location were also ascertained from study-specific MRI scans.

Results

Participants' (n = 348) mean (SD) age was 72.3 (11.3) years; 148 (42.5%) were women. Participants with dual impairment (n = 99) were significantly older, had experienced a more severe stroke, and had a higher comorbidity burden and poorer pre-stroke function. Stroke lesion volume (odds ratio 1.03, 95%, confidence interval 1.00 to 1.05, p = 0.035), but not stroke location or pre-existing brain pathology, was associated with dual impairment, after adjusting for age and sex.

Conclusion

In this large cohort of stroke survivors having suffered mainly mild to moderate stroke, stroke lesion volume-but not pre-existing brain pathology-was associated with dual impairment early after stroke, confirming the role of stroke severity in functional decline.

Predictors of post-stroke cognitive impairment using acute structural MRI neuroimaging: A systematic review and meta-analysis

BACKGROUND

Stroke survivors are at an increased risk of developing post-stroke cognitive impairment and post-stroke dementia; those at risk could be identified by brain imaging routinely performed at stroke onset.

AIM

This systematic review aimed to identify features which are associated with post-stroke cognitive impairment (including dementia) on magnetic resonance imaging (MRI) performed at stroke diagnosis.

SUMMARY OF REVIEW

We searched the literature from inception to January 2022 and identified 10,284 records. We included studies that performed MRI at the time of stroke (0-30 days after a stroke) and assessed cognitive outcome at least 3 months after stroke. We synthesized findings from 26 papers, comprising 27 stroke-populations (N = 13,114, average age range = 40-80 years, 19-62% female). When data were available, we pooled unadjusted (OR_u) and adjusted (OR_a) odds ratios.We found associations between cognitive outcomes and presence of cerebral atrophy (three studies, N = 453, OR_u = 2.48, 95% CI = 1.15-4.62), presence of microbleeds (two studies, N = 9151, OR_a = 1.36, 95% CI = 1.08-1.70), and increasing severity of white matter hyperintensities (three studies, N = 704, OR_a = 1.26, 95% CI = 1.06-1.49). Increasing cerebral small vessel disease score was associated with cognitive outcome following unadjusted analysis only (two studies, N = 499, OR_u = 1.34, 95%CI = 1.12-1.61; three studies, N = 950, OR_a = 1.23, 95% CI = 0.96-1.57). Associations remained after controlling for pre-stroke cognitive impairment. We did not find associations between other stroke features and cognitive outcome, or there were insufficient data.

CONCLUSION

Acute stroke MRI features may enable healthcare professionals to identify patients at risk of post-stroke cognitive problems. However, there is still substantial uncertainty about the prognostic utility of acute MRI for this.

Longitudinal Brain Changes After Stroke and the Association With Cognitive Decline

Background

Cognitive impairment is common after stroke. So is cortical- and subcortical atrophy, with studies reporting more atrophy in the ipsilesional hemisphere than the contralesional hemisphere. The current study aimed to investigate the longitudinal associations between (I) lateralization of brain atrophy and stroke hemisphere, and (II) cognitive impairment and brain atrophy after stroke. We expected to find that (I) cortical thickness and hippocampal-, thalamic-, and caudate nucleus volumes declined more in the ipsilesional than the contralesional hemisphere up to 36 months after stroke. Furthermore, we predicted that (II) cognitive decline was associated with greater stroke volumes, and with greater cortical thickness and subcortical structural volume atrophy across the 36 months.

Methods

Stroke survivors from five Norwegian hospitals were included from the multisite-prospective “Norwegian Cognitive Impairment After Stroke” (Nor-COAST) study. Analyses were run with clinical, neuropsychological and structural magnetic resonance imaging (MRI) data from baseline, 18- and 36 months. Cortical thicknesses and subcortical volumes were obtained via FreeSurfer segmentations and stroke lesion volumes were semi-automatically derived using ITK-SNAP. Cognition was measured using MoCA.

Results

Findings from 244 stroke survivors [age = 72.2 (11.3) years, women = 55.7%, stroke severity NIHSS = 4.9 (5.0)] were included at baseline. Of these, 145 (59.4%) had an MRI scan at 18 months and 72 (49.7% of 18 months) at 36 months. Most cortices and subcortices showed a higher ipsi- compared to contralesional atrophy rate, with the effect being more prominent in the right hemisphere. Next, greater degrees of atrophy particularly in the medial temporal lobe after left-sided strokes and larger stroke lesion volumes after right-sided strokes were associated with cognitive decline over time.

Conclusion

Atrophy in the ipsilesional hemisphere was greater than in the contralesional hemisphere over time. This effect was found to be more prominent in the right hemisphere, pointing to a possible higher resilience to stroke of the left hemisphere. Lastly, greater atrophy of the cortex and subcortex, as well as larger stroke volume, were associated with worse cognition over time and should be included in risk assessments of cognitive decline after stroke.

Leveraging Factors of Self-Efficacy and Motivation to Optimize Stroke Recovery

The International Classification of Functioning, Disability and Health framework recognizes that an individual's functioning post-stroke reflects an interaction between their health condition and contextual factors encompassing personal and environmental factors. Personal factors significantly impact rehabilitation outcomes as they determine how an individual evaluates their situation and copes with their condition in daily life. A key personal factor is self-efficacy-an individual's belief in their capacity to achieve certain outcomes. Self-efficacy influences an individual's motivational state to execute behaviors necessary for achieving desired rehabilitation outcomes. Stroke rehabilitation practice and research now acknowledge self-efficacy and motivation as critical elements in post-stroke recovery, and increasing evidence highlights their contributions to motor (re)learning. Given the informative value of neuroimaging-based biomarkers in stroke, elucidating the neurological underpinnings of self-efficacy and motivation may optimize post-stroke recovery. In this review, we examine the role of self-efficacy and motivation in stroke rehabilitation and recovery, identify potential neural substrates underlying these factors from current neuroimaging literature, and discuss how leveraging these factors and their associated neural substrates has the potential to advance the field of stroke rehabilitation.

Diagnosis and Treatment Effect of Convolutional Neural Network-Based Magnetic Resonance Image Features on Severe Stroke and Mental State

The purpose of this paper is to explore the impact of magnetic resonance imaging (MRI) image features based on convolutional neural network (CNN) algorithm and conditional random field on the diagnosis and mental state of patients with severe stroke. 208 patients with severe stroke who all received MRI examination were recruited as the research objects. According to cerebral small vascular disease (CSVD) score, the patients were divided into CSVD 0∼4 groups. The patients who completed the three-month follow-up were classified into cognitive impairment group (124 cases) and the noncognitive impairment group (84 cases) according to the cut-off point of the Montreal cognitive assessment (MOCA) scale score of 26. A novel image segmentation algorithm was proposed based on U-shaped fully CNN (U-Net) and conditional random field, which was compared with the fully CNN (FCN) algorithm and U-Net algorithm, and was applied to the MRI segmentation training of patients with severe stroke. It was found that the average symmetric surface distance (ASSD) (3.13 ± 1.35), Hoffman distance (HD) (28.71 ± 9.05), Dice coefficient (0.78 ± 1.35), accuracy (0.74 ± 0.11), and sensitivity (0.85 ± 0.13) of the proposed algorithm were superior to those of FCN algorithm and U-Net algorithm. There were significant differences in the MOCA scores among the five groups of patients from CSVD 0 to CSVD 4 in the three time periods (0, 1, and 3 months) (P < 0.05). Differences in cerebral microhemorrhage (CMB), perivascular space (PVS), and number of cavities, Fazekas, and total CSVD scores between the two groups were significant (P < 0.05). Multivariate regression found that the number of PVS, white matter hyperintensity (WMH) Fazekas, and total CSVD score were independent factors of cognitive impairment. In short, MRI images based on deep learning image segmentation algorithm had good application value for clinical diagnosis and treatment of stroke and can effectively improve the detection effect of brain domain characteristics and psychological state of patients after stroke.