Strategic infarct locations for post-stroke depressive symptoms: a lesion- and disconnection-symptom mapping study

BACKGROUND: Depression is the most common neuropsychiatric complication after stroke. Infarct location is associated with poststroke depressive symptoms (PSDS), but it remains debated which brain structures are critically involved. We performed a large-scale lesion-symptom mapping study to identify infarct locations and white matter disconnections associated with PSDS. METHODS: We included 553 patients (mean [SD] age = 69 [11] years, 42% female) with acute ischemic stroke. PSDS were measured using the 30-item Geriatric Depression Scale. Multivariable support vector regression (SVR)-based analyses were performed both at the level of individual voxels (voxel-based lesion-symptom mapping) and at predefined regions of interest to relate infarct location to PSDS. We externally validated our findings in an independent stroke cohort (N = 459). Finally, disconnectome-based analyses were performed using SVR voxel-based lesion-symptom mapping, in which white matter fibers disconnected by the infarct were analyzed instead of the infarct itself. RESULTS: Infarcts in the right amygdala, right hippocampus, and right pallidum were consistently associated with PSDS (permutation-based p < .05) in SVR voxel-based lesion-symptom mapping and SVR region-of-interest analyses. External validation confirmed the association between infarcts in the right amygdala and pallidum, but not the right hippocampus, and PSDS. Disconnectome-based analyses revealed that disconnections in the right parahippocampal white matter, right thalamus and pallidum, and right anterior thalamic radiation were significantly associated (permutation-based p < .05) with PSDS. CONCLUSIONS: Infarcts in the right amygdala and pallidum and disconnections of right limbic and frontal cortico-basal ganglia-thalamic circuits are associated with PSDS. Our findings provide a comprehensive and integrative picture of strategic infarct locations for PSDS and shed new light on pathophysiological mechanisms of depression after stroke

Post-stroke cognitive impairment on the Mini-Mental State Examination primarily relates to left middle cerebral artery infarcts

Background: Post-stroke cognitive impairment can occur after damage to various brain regions, and cognitive deficits depend on infarct location. The Mini-Mental State Examination (MMSE) is still widely used to assess post-stroke cognition, but it has been criticized for capturing only certain cognitive deficits. Along these lines, it might be hypothesized that cognitive deficits as measured with the MMSE primarily involve certain infarct locations. Aims: This comprehensive lesion-symptom mapping study aimed to determine which acute infarct locations are associated with post-stroke cognitive impairment on the MMSE. Methods: We examined associations between impairment on the MMSE (15) in the thalamus and superior temporal gyrus. In comparison, domain-specific impairments were related to various infarct patterns across both hemispheres including the left medial temporal lobe (verbal memory) and right parietal lobe (visuospatial functioning). Conclusions: Our findings indicate that post-stroke cognitive impairment on the MMSE primarily relates to infarct locations in the left middle cerebral artery territory. The MMSE is apparently less sensitive to cognitive deficits that specifically relate to other locations

Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion-symptom mapping

Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion-symptom mapping (LSM) techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof-of-concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single-outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well-defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single-outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single-outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single-outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI 24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) 24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/

A VALIDITY STUDY ON THE KOREAN-CALIFORNIA VERBAL LEARNING TEST

X11sciescopu

A KOREAN-CALIFORNIA VERBAL LEARNING TEST(K-CVLT): A STANDARDIZATION

X11sciescopu

Subjective cognitive decline in patients with migraine and its relationship with depression, anxiety, and sleep quality

Abstract Background Cognitive decline is a major concern in patients with migraine. Depression, anxiety, and/or poor sleep quality are well-known comorbidities of migraine, but available evidence on the subjective cognitive decline (SCD) is limited. This study aimed to investigate the presence and frequency of SCD and its relationship with anxiety, depression and sleep quality in patients with migraine. Methods We enrolled patients with migraine who scored within the normal range of the Korean-Mini Mental State Examination and the Korean-Montreal Cognitive Assessment. Using the Subjective Cognitive Decline Questionnaire (SCD-Q), participants with ≥7 were assigned to the SCD group. The Headache Impact Test-6, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Pittsburgh Sleep Quality Index were used and analyzed between the two groups. Results A total of 188 patients with migraine, aged 38.1 ± 9.9 years, were enrolled. The mean SCD-Q score was 6.5 ± 5.5, and 44.7% of participants were identified as SCD. Migraineurs with SCD reported higher headache pain intensity and headache impact, as well as greater prevalence of anxiety, depression, reduced quality of sleep, and shorter sleep duration during weekdays compared to migraineurs without SCD. There were no significant differences in terms of age, sex, migraine type (chronic/episodic), medication, or sleep duration during weekends between the two groups. Upon multivariate logistic analysis adjusted for age, sex, headache characteristics, and psychological variables, depression was associated with increased risk of SCD (Odds ratio 1.31, 95% confidence interval 1.16–1.49) and sleep duration during weekdays was associated with decreased risk of SCD (Odds ratio 0.66, 95% confidence interval 0.44–0.97). Conclusions A non-negligible number of patients with migraine complained of SCD. Depression and short sleep duration during weekdays were related to SCD among adult migraineurs

FEP Encapsulated Crack-Based Sensor for Measurement in Moisture-Laden Environment

Among many flexible mechanosensors, a crack-based sensor inspired by a spider&#8217;s slit organ has received considerable attention due to its great sensitivity compared to previous strain sensors. The sensor&#8217;s limitation, however, lies on its vulnerability to stress concentration and the metal layers&#8217; delamination. To address this issue of vulnerability, we used fluorinated ethylene propylene (FEP) as an encapsulation layer on both sides of the sensor. The excellent waterproof and chemical resistance capability of FEP may effectively protect the sensor from damage in water and chemicals while improving the durability against friction