Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia

Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. Methods: We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS). Results: Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. Conclusion: To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia

Regions defined for the seed-based functional connectivity analysis in areas are known to be relevant for addictive behaviour, including the left insula, the right insula, the left DLPFC (BA 9, 46) and the right DLPFC (BA 9, 46).

<p>Regions defined for the seed-based functional connectivity analysis in areas are known to be relevant for addictive behaviour, including the left insula, the right insula, the left DLPFC (BA 9, 46) and the right DLPFC (BA 9, 46).</p

Results of a single patient with true neurofeedback: A) Neuronal response in the ROI during the fourth neurofeedback session compared to the first session [alcohol-related pictures > neutral pictures].

<p>B) Time course of activity within the specific ROI during the fourth neurofeedback session [green: presentation of alcohol-related pictures; grey: presentation of neutral pictures; p(Bonf)<0.002; x = 3; y = 57; z = 15].</p

Experimental paradigm: presentation of neutral and alcohol-related pictures in blocks of 40 seconds with 10 pictures of the respective category; participants were instructed to reduce brain activity during the presentation of alcohol-associated information; during the presentation of neutral information, participants were instructed to simply gaze at the pictures [green: presentation of alcohol-related pictures; grey: presentation of neutral pictures].

<p>Experimental paradigm: presentation of neutral and alcohol-related pictures in blocks of 40 seconds with 10 pictures of the respective category; participants were instructed to reduce brain activity during the presentation of alcohol-associated information; during the presentation of neutral information, participants were instructed to simply gaze at the pictures [green: presentation of alcohol-related pictures; grey: presentation of neutral pictures].</p

Seed based fMRI analysis: Comparison of resting state functional connectivity before and after neurofeedback training (p(FEW) < 0.05).

<p>After neurofeedback, increased functional connectivity was detected between the left Brodman Area 46 and different cortical and subcortical brain regions, including the dorsolateral prefrontal cortex (DLPFC), the insula (Ins) and the putamen (P) of the ipsilateral hemisphere. Connectivity patterns are superimposed on the TT N27-template. Images are displayed in the radiological convention (coordinates in Talairach space are given in parenthesis).</p

Comparison of neuronal responses during the fourth neurofeedback-session compared to the first neurofeedback-session (alcohol-related pictures > neutral pictures) in patients as well as healthy controls with <i>true</i> rtfMRI: Patients: reduced activity especially in the insula, the ACC, the medial frontal gyrus, the inferior temporal gyrus and the cuneus (p(Bonf)<0.0001; x = 0; y = -16; z = 22).

<p>Healthy controls: slightly increased activity especially in the medial frontal gyrus (p(Bonf)<0.0001; x = -7; y = -1; z = 60).</p

Results of the default mode network in patients and controls: increased medial frontal and dorsolateral prefrontal responses after rtfMRI in patients; in healthy controls, the activity was comparable before and after rtfMRI (p < 0.05, FWE corrected).

<p>Results of the default mode network in patients and controls: increased medial frontal and dorsolateral prefrontal responses after rtfMRI in patients; in healthy controls, the activity was comparable before and after rtfMRI (p < 0.05, FWE corrected).</p

Additional file 1 of Associations between sex, body mass index and the individual microglial response in Alzheimer’s disease

Additional file 1: Table S1. Detailed regional z-scores of TSPO-PET and Aβ-PET for female and male AD patients in six Braak-stage regions of interest and four amyloidosis regions of interest. CI =95% confidence interval. P –values show false discovery rate (FDR) corrected significance levels for the comparison of medium and high affinity binders (ANOVA). Figure S1. Validation of late-phase [18F]PI-2620 tau-PET quantification via carotid artery image derived input function (IDIF). Images show IDIF derived volume of distribution (VT) of [18F]PI-2620 tau-PET for female and male AD patients and mixed sex cognitively normal individuals, presented as axial overlays on a standard magnetic resonance imaging template. Plots show correlation of tau-PET z-scores for Braak-stage regions I–VI with tau-PET VT. AD female n = 13, AD male n = 9, cognitively normal mixed sex n = 3

Data_Sheet_1_Midlife occupational cognitive requirements protect cognitive function in old age by increasing cognitive reserve.pdf

IntroductionSeveral lifestyle factors promote protection against Alzheimer's disease (AD) throughout a person's lifespan. Although such protective effects have been described for occupational cognitive requirements (OCR) in midlife, it is currently unknown whether they are conveyed by brain maintenance (BM), brain reserve (BR), or cognitive reserve (CR) or a combination of them.MethodsWe systematically derived hypotheses for these resilience concepts and tested them in the population-based AgeCoDe cohort and memory clinic-based AD high-risk DELCODE study. The OCR score (OCRS) was measured using job activities based on the O*NET occupational classification system. Four sets of analyses were conducted: (1) the interaction of OCR and APOE-ε4 with regard to cognitive decline (N = 2,369, AgeCoDe), (2) association with differentially shaped retrospective trajectories before the onset of dementia of the Alzheimer's type (DAT; N = 474, AgeCoDe), (3) cross-sectional interaction of the OCR and cerebrospinal fluid (CSF) AD biomarkers and brain structural measures regarding memory function (N = 873, DELCODE), and (4) cross-sectional and longitudinal association of OCR with CSF AD biomarkers and brain structural measures (N = 873, DELCODE).ResultsRegarding (1), higher OCRS was associated with a reduced association of APOE-ε4 with cognitive decline (mean follow-up = 6.03 years), consistent with CR and BR. Regarding (2), high OCRS was associated with a later onset but subsequently stronger cognitive decline in individuals converting to DAT, consistent with CR. Regarding (3), higher OCRS was associated with a weaker association of the CSF Aβ42/40 ratio and hippocampal volume with memory function, consistent with CR. Regarding (4), OCR was not associated with the levels or changes in CSF AD biomarkers (mean follow-up = 2.61 years). We found a cross-sectional, age-independent association of OCRS with some MRI markers, but no association with 1-year-change. OCR was not associated with the intracranial volume. These results are not completely consistent with those of BR or BM.DiscussionOur results support the link between OCR and CR. Promoting and seeking complex and stimulating work conditions in midlife could therefore contribute to increased resistance to pathologies in old age and might complement prevention measures aimed at reducing pathology.</p