Predicting the Emergence of Major Neurocognitive Disorder Within Three Months After a Stroke

Automated brain segmentation and volumetry in dementia diagnostics: a narrative review with emphasis on FreeSurfer

Background

Dementia can be caused by numerous different diseases that present variable clinical courses and reveal multiple patterns of brain atrophy, making its accurate early diagnosis by conventional examinative means challenging. Although highly accurate and powerful, magnetic resonance imaging (MRI) currently plays only a supportive role in dementia diagnosis, largely due to the enormous volume and diversity of data it generates. AI-based software solutions/algorithms that can perform automated segmentation and volumetry analyses of MRI data are being increasingly used to address this issue. Numerous commercial and non-commercial software solutions for automated brain segmentation and volumetry exist, with FreeSurfer being the most frequently used.

Objectives

This Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis.

Methods

We performed a PubMed search for “FreeSurfer AND Dementia” and obtained 493 results. Based on these search results, we conducted an in-depth source analysis to identify additional publications, software tools, and methods. Studies were analyzed for design, patient collective, and for statistical evaluation (mathematical methods, correlations).

Results

In the studies identified, the main diseases and cohorts represented were Alzheimer’s disease (n = 276), mild cognitive impairment (n = 157), frontotemporal dementia (n = 34), Parkinson’s disease (n = 29), dementia with Lewy bodies (n = 20), and healthy controls (n = 356). The findings and methods of a selection of the studies identified were summarized and discussed.

Conclusion

Our evaluation showed that, while a large number of studies and software solutions are available, many diseases are underrepresented in terms of their incidence. There is therefore plenty of scope for targeted research.

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.

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

Objective

Cognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.

Methods

Relevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.

Results

A total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively.

Conclusion

ML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

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.

Aberrant Energy Metabolism in Alzheimer's Disease

To maintain energy supply to the brain, a direct energy source called adenosine triphosphate (ATP) is produced by oxidative phosphorylation and aerobic glycolysis of glucose in the mitochondria and cytoplasm. Brain glucose metabolism is reduced in many neurodegenerative diseases, including Alzheimer's disease (AD), where it appears presymptomatically in a progressive and region-specific manner. Following dysregulation of energy metabolism in AD, many cellular repair/regenerative processes are activated to conserve the energy required for cell viability. Glucose metabolism plays an important role in the pathology of AD and is closely associated with the tricarboxylic acid cycle, type 2 diabetes mellitus, and insulin resistance. The glucose intake in neurons is from endothelial cells, astrocytes, and microglia. Damage to neurocentric glucose also damages the energy transport systems in AD. Gut microbiota is necessary to modulate bidirectional communication between the gastrointestinal tract and brain. Gut microbiota may influence the process of AD by regulating the immune system and maintaining the integrity of the intestinal barrier. Furthermore, some therapeutic strategies have shown promising therapeutic effects in the treatment of AD at different stages, including the use of antidiabetic drugs, rescuing mitochondrial dysfunction, and epigenetic and dietary intervention. This review discusses the underlying mechanisms of alterations in energy metabolism in AD and provides potential therapeutic strategies in the treatment of AD.

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.