Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

White and Gray Matter Changes are Associated With Neurocognitive Decline in HIV Infection

OBJECTIVE

To investigate the relationship between neurocognitive deficits and structural changes on brain magnetic resonance imaging in people living with HIV (PLWH) with good virological control on combination antiretroviral therapy, compared with socioeconomically matched control participants recruited from the same communities.

METHODS

Brain magnetic resonance imaging scans, and clinical and neuropsychological data were obtained from virologically controlled PLWH (viral load of <50 c/mL and at least 1 year of combination antiretroviral therapy) and socioeconomically matched control participants. Magnetic resonance imaging was carried out on 3 T scanner with 8-channel head coils and segmented using Classification using Derivative-based Features. Multiple regression analysis was performed to examine the association between brain volume and various clinical and neuropsychiatric parameters adjusting for age, race, and sex. To evaluate longitudinal changes in brain volumes, a random coefficient model was used to evaluate the changes over time (age) adjusting for sex and race.

RESULTS

The cross-sectional study included 164 PLWH and 51 controls, and the longitudinal study included 68 PLWH and 20 controls with 2 or more visits (mean 2.2 years, range 0.8-5.1 years). Gray matter (GM) atrophy rate was significantly higher in PLWH compared with control participants, and importantly, the GM and global atrophy was associated with the various neuropsychological domain scores. Higher volume of white matter hyperintensities were associated with increased atherosclerotic cardiovascular disease risk score, and decreased executive functioning and memory domain scores in PLWH.

INTERPRETATION

These findings suggest ongoing neurological damage even in virologically controlled participants, with significant implications for clinical management of PLWH. ANN NEUROL 2024;95:941-950.

A multi-site study on sex differences in cortical thickness in non-demented Parkinson's disease

Clinical, cognitive, and atrophy characteristics depending on sex have been previously reported in Parkinson's disease (PD). However, though sex differences in cortical gray matter measures in early drug naïve patients have been described, little is known about differences in cortical thickness (CTh) as the disease advances. Our multi-site sample comprised 211 non-demented PD patients (64.45% males; mean age 65.58 ± 8.44 years old; mean disease duration 6.42 ± 5.11 years) and 86 healthy controls (50% males; mean age 65.49 ± 9.33 years old) with available T1-weighted 3 T MRI data from four international research centers. Sex differences in regional mean CTh estimations were analyzed using generalized linear models. The relation of CTh in regions showing sex differences with age, disease duration, and age of onset was examined through multiple linear regression. PD males showed thinner cortex than PD females in six frontal (bilateral caudal middle frontal, bilateral superior frontal, left precentral and right pars orbitalis), three parietal (bilateral inferior parietal and left supramarginal), and one limbic region (right posterior cingulate). In PD males, lower CTh values in nine out of ten regions were associated with longer disease duration and older age, whereas in PD females, lower CTh was associated with older age but with longer disease duration only in one region. Overall, male patients show a more widespread pattern of reduced CTh compared with female patients. Disease duration seems more relevant to explain reduced CTh in male patients, suggesting worse prognostic over time. Further studies should explore sex-specific cortical atrophy trajectories using large longitudinal multi-site data.

Association between cognitive function and body composition in older adults: data from NHANES (1999-2002)

Aim

To investigate the association between cognitive function and body composition in older adults.

Methods

We collected data on 2080 older adults (>60 years of age) from the National Health and Nutrition Examination Survey (NHANES) for the years 1999-2000 and 2001-2002. Candidate variables included: demographic data (sex, age, race, education level, marital status, poverty-to-income ratio), alcohol consumption, cardiovascular disease, diabetes, osteoporosis, total bone mineral density, and total fat mass. A logistic regression model was established to analyze the association between cognitive function and body composition in older adults. In addition, stratified logics regression analysis was performed by sex and age.

Results

Bone mineral density significantly affects cognitive function in older adults (p<0.01). When examining the data according to sex, this correlation is present for women (p < 0.01). For men, though, it is not significant (p = 0.081). Stratified by age, total bone mineral density was significantly correlated with cognitive function in 60-70 and 70-80 years old people, but not in older adults older than 80 years(for 60-70 years old, p = 0.019; for 70-80 years old, p = 0.022). There was no significant correlation between total bone mineral density and cognitive function (p = 0.575).

Conclusion

The decrease of total bone mineral density was significantly correlated with cognitive decline in the older adults, especially among women and older people in the 60 to 80 age group. There was no connection between total fat mass, total percent fat, total lean mass, appendicular lean mass, appendicular lean mass /BMI and cognitive function in the older adults.

Gray matter loss relates to dual task gait in Lewy body disorders and aging

BACKGROUND

Within the spectrum of Lewy body disorders (LBD), both Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are characterized by gait and balance disturbances, which become more prominent under dual-task (DT) conditions. The brain substrates underlying DT gait variations, however, remain poorly understood in LBD.

OBJECTIVE

To investigate the relationship between gray matter volume loss and DT gait variations in LBD.

METHODS

Seventy-nine participants including cognitively unimpaired PD, PD with mild cognitive impairment, PD with dementia (PDD), or DLB and 20 cognitively unimpaired controls were examined across a multi-site study. PDD and DLB were grouped together for analyses. Differences in gait speed between single and DT conditions were quantified by dual task cost (DTC). Cortical, subcortical, ventricle, and cerebellum brain volumes were obtained using FreeSurfer. Linear regression models were used to examine the relationship between gray matter volumes and DTC.

RESULTS

Smaller amygdala and total cortical volumes, and larger ventricle volumes were associated with a higher DTC across LBD and cognitively unimpaired controls. No statistically significant interaction between group and brain volumes were found. Adding cognitive and motor covariates or white matter hyperintensity volumes separately to the models did not affect brain volume and DTC associations.

CONCLUSION

Gray matter volume loss is associated with worse DT gait performance compared to single task gait, across cognitively unimpaired controls through and the LBD spectrum. Impairment in DT gait performance may be driven by age-related cortical neurodegeneration.

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.

Identifying and reverting the adverse effects of white matter hyperintensities on cortical surface analyses

The Human Connectome Project (HCP)-style surface-based brain MRI analysis is a powerful technique that allows precise mapping of the cerebral cortex. However, the strength of its surface-based analysis has not yet been tested in the older population that often presents with white matter hyperintensities (WMHs) on T2-weighted (T2w) MRI (hypointensities on T1w MRI). We investigated T1-weighted (T1w) and T2w structural MRI in 43 healthy middle-aged to old participants. Juxtacortical WMHs were often misclassified by the default HCP pipeline as parts of the gray matter in T1w MRI, leading to incorrect estimation of the cortical surfaces and cortical metrics. To revert the adverse effects of juxtacortical WMHs, we incorporated the Brain Intensity AbNormality Classification Algorithm into the HCP pipeline (proposed pipeline). Blinded radiologists performed stereological quality control (QC) and found a decrease in the estimation errors in the proposed pipeline. The superior performance of the proposed pipeline was confirmed using an originally-developed automated surface QC based on a large database. Here we showed the detrimental effects of juxtacortical WMHs for estimating cortical surfaces and related metrics and proposed a possible solution for this problem. The present knowledge and methodology should help researchers identify adequate cortical surface biomarkers for aging and age-related neuropsychiatric disorders.

Sjogren-Larsson syndrome brain volumetric reductions demonstrated with an automated software

BACKGROUND

Sjogren-Larsson syndrome (SLS) is a neurocutaneous disease with an autosomal recessive inheritance, caused by mutations in the gene that encodes fatty aldehyde dehydrogenase (ALDH3A2), clinically characterized by ichthyosis, spastic diplegia, and cognitive impairment. Brain imaging plays an essential role in the diagnosis, demonstrating a nonspecific leukoencephalopathy. Data regarding brain atrophy and grey matter involvement is scarce and discordant.

OBJECTIVE

We performed a volumetric analysis of the brain of two siblings with SLS with the aim of detecting deep grey matter nuclei, cerebellar grey matter, and brainstem volume reduction in these patients.

METHODS

Volume data obtained from the brain magnetic resonance imaging (MRI) of the two patients using an automated segmentation software (Freesurfer) was compared with the volumes of a healthy control group.

RESULTS

Statistically significant volume reduction was found in the cerebellum cortex, the brainstem, the thalamus, and the pallidum nuclei.

CONCLUSION

Volume reduction in grey matter leads to the hypothesis that SLS is not a pure leukoencephalopathy. Grey matter structures affected in the present study suggest a dysfunction more prominent in the thalamic motor pathways.

Brain perivascular space imaging across the human lifespan

Enlarged perivascular spaces (PVS) are considered a biomarker for vascular pathology and are observed in normal aging and neurological conditions; however, research on the role of PVS in health and disease are hindered by the lack of knowledge regarding the normative time course of PVS alterations with age. To this end, we characterized the influence of age, sex and cognitive performance on PVS anatomical characteristics in a large cross-sectional cohort (∼1400) of healthy subjects between 8 and 90 years of age using multimodal structural MRI data. Our results show age is associated with wider and more numerous MRI-visible PVS over the course of the lifetime with spatially-varying patterns of PVS enlargement trajectories. In particular, regions with low PVS volume fraction in childhood are associated with rapid age-related PVS enlargement (e.g., temporal regions), while regions with high PVS volume fraction in childhood are associated with minimal age-related PVS alterations (e.g., limbic regions). PVS burden was significantly elevated in males compared to females with differing morphological time courses with age. Together, these findings contribute to our understanding of perivascular physiology across the healthy lifespan and provide a normative reference for the spatial distribution of PVS enlargement patterns to which pathological alterations can be compared.

Associations between sleep-related symptoms, obesity, cardiometabolic conditions, brain structural alterations and cognition in the UK biobank

OBJECTIVES

Sleep disturbances are increasingly recognized as adversely affecting brain health in aging. Our aim was to investigate interrelations between subjective sleep-related symptoms, obesity, cardiometabolic disorders, brain structure and cognitive decline in a population-based aging sample.

METHODS

Data were extracted from the UK Biobank for anthropometric and demographic information, self-reported sleep behaviours, cardiometabolic measures, structural brain magnetic resonance imaging and cognitive test scores. "Sleep-related symptoms" (SRS) were measured using four questionnaire items: loud snoring, daytime sleepiness, likelihood to nap and difficulty getting up in the morning. Associations were tested using a structural equation model (SEM), adjusted for confounders. Further, multiple regression analysis was used to test for direct relationships between SRS and specific cognitive domains.

RESULTS

Among 36,468 participants with an average age of 63.6 (SD 7.5) years and 46.7% male, we found that SRS were associated with obesity and several pre-existing cardiometabolic disturbances. In turn, cardiometabolic disorders were associated with increased white matter hyperintensities and cortical thinning, which were related to cognitive dysfunction. SRS were also directly related to several structural brain changes and to cognitive dysfunction. Regression analyses showed that SRS were directly associated with slower reaction times, and lower scores in fluid intelligence, working memory and executive function.

CONCLUSIONS

Self-reported sleep-related symptoms were associated with cognitive dysfunction directly and through pre-existing cardiometabolic disorders and brain structural alterations. These findings provide evidence that symptoms of sleep disturbances, here defined primarily by hypersomnolence and snoring, are important risk factors or markers for cognitive dysfunction in an aging population.

Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.

Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic

The Oxford Brain Health Clinic (BHC) is a joint clinical-research service that provides memory clinic patients and clinicians access to high-quality assessments not routinely available, including brain MRI aligned with the UK Biobank imaging study (UKB). In this work we present how we 1) adapted the UKB MRI acquisition protocol to be suitable for memory clinic patients, 2) modified the imaging analysis pipeline to extract measures that are in line with radiology reports and 3) explored the alignment of measures from BHC patients to the largest brain MRI study in the world (ultimately 100,000 participants). Adaptations of the UKB acquisition protocol for BHC patients include dividing the scan into core and optional sequences (i.e., additional imaging modalities) to improve patients' tolerance for the MRI assessment. We adapted the UKB structural MRI analysis pipeline to take into account the characteristics of a memory clinic population (e.g., high amount of white matter hyperintensities and hippocampal atrophy). We then compared the imaging derived phenotypes (IDPs) extracted from the structural scans to visual ratings from radiology reports, non-imaging factors (age, cognition) and to reference distributions derived from UKB data. Of the first 108 BHC attendees (August 2020-November 2021), 92.5 % completed the clinical scans, 88.0 % consented to use of data for research, and 43.5 % completed the additional research sequences, demonstrating that the protocol is well tolerated. The high rates of consent to research makes this a valuable real-world quality research dataset routinely captured in a clinical service. Modified tissue-type segmentation with lesion masking greatly improved grey matter volume estimation. CSF-masking marginally improved hippocampal segmentation. The IDPs were in line with radiology reports and showed significant associations with age and cognitive performance, in line with the literature. Due to the age difference between memory clinic patients of the BHC (age range 65-101 years, average 78.3 years) and UKB participants (44-82 years, average 64 years), additional scans on elderly healthy controls are needed to improve reference distributions. Current and future work aims to integrate automated quantitative measures in the radiology reports and evaluate their clinical utility.

Early white matter connectivity and plasticity in post stroke aphasia recovery

A disruption of white matter connectivity is negatively associated with language (recovery) in patients with aphasia after stroke, and behavioral gains have been shown to coincide with white matter neuroplasticity. However, most brain-behavior studies have been carried out in the chronic phase after stroke, with limited generalizability to earlier phases. Furthermore, few studies have investigated neuroplasticity patterns during spontaneous recovery (i.e., not related to a specific treatment) in the first months after stroke, hindering the investigation of potential early compensatory mechanisms. Finally, the majority of previous research has focused on damaged left hemisphere pathways, while neglecting the potential protective value of their right hemisphere counterparts for language recovery. To address these outstanding issues, we present a longitudinal study of thirty-two patients with aphasia (21 males and 11 females, M = 69.47 years, SD = 10.60 years) who were followed up for a period of 1 year with test moments in the acute (1-2 weeks), subacute (3-6 months) and chronic phase (9-12 months) after stroke. Constrained Spherical Deconvolution-based tractography was performed in the acute and subacute phase to measure Fiber Bundle Capacity (FBC), a quantitative connectivity measure that is valid in crossing fiber regions, in the bilateral dorsal arcuate fasciculus (AF) and the bilateral ventral inferior fronto-occipital fasciculus (IFOF). First, concurrent analyses revealed positive associations between the left AF and phonology, and between the bilateral IFOF and semantics in the acute - but not subacute - phase, supporting the dual-stream language model. Second, neuroplasticity analyses revealed a decrease in connection density of the bilateral AF - but not the IFOF - from the acute to the subacute phase, possibly reflecting post stroke white matter degeneration in areas adjacent to the lesion. Third, predictive analyses revealed no contribution of acute FBC measures to the prediction of later language outcomes over and above the initial language scores, suggesting no added value ofthe diffusion measures for languageprediction. Our study provides new insights on (changes in) connectivity of damaged and undamaged language pathways in patients with aphasia in the first months after stroke, as well as if/how such measures are related to language outcomes at different stages of recovery. Individual results are discussed in the light of current frameworks of language processing and aphasia recovery.

Assessment of perivascular space filtering methods using a three-dimensional computational model

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all "vesselness" filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

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.

vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis

Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels (N &gt; 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide.

Brain aging and cardiovascular factors in HIV: a longitudinal volume and shape MRI study

OBJECTIVE

We aimed to examine the relative contributions of HIV infection, age, and cardiovascular risk factors to subcortical brain atrophy in people with HIV (PWH).

DESIGN

Longitudinal observational study.

METHODS

Virally suppressed PWH with low neuropsychological confounds (n  = 75) and demographically matched HIV-negative controls (n = 31) completed baseline and 18-month follow-up MRI scans, neuropsychological evaluation, cardiovascular assessments, and HIV laboratory tests. PWH were evaluated for HIV-associated neurocognitive disorder (HAND). Subcortical volumes were extracted with Freesurfer after removal of white matter hyperintensities. Volumetric and shape analyses were conducted using linear mixed-effect models incorporating interactions between age, time, and each of HIV status, HAND status, HIV disease factors, and cardiovascular markers.

RESULTS

Across baseline and follow-up PWH had smaller volumes of most subcortical structures compared with HIV-negative participants. In addition, over time older PWH had a more rapid decline in caudate volumes (P  = 0.041), predominantly in the more severe HAND subgroups (P = 0.042). Higher CD4+ cell counts had a protective effect over time on subcortical structures for older participants with HIV. Increased cardiovascular risk factors were associated with smaller volumes across baseline and follow-up for most structures, although a more rapid decline over time was observed for striatal volumes. There were no significant shape analyses findings.

CONCLUSION

The study demonstrates a three-hit model of general (as opposed to localized) subcortical injury in PWH: HIV infection associated with smaller volumes of most subcortical structures, HIV infection and aging synergy in the striatum, and cardiovascular-related injury linked to early and more rapid striatal atrophy.

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.

White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia

White matter hyperintensities (WMHs) are commonly assumed to represent non-specific cerebrovascular disease comorbid to neurodegenerative processes, rather than playing a synergistic role. We compared the impact of WMHs on grey matter (GM) atrophy and cognition in normal aging (n = 571), mild cognitive impairment (MCI, n = 551), Alzheimer's dementia (AD, n = 212), fronto-temporal dementia (FTD, n = 125), and Parkinson's disease (PD, n = 271). Longitudinal data were obtained from ADNI, FTLDNI, and PPMI datasets. Mixed-effects models were used to compare WMHs and GM atrophy between patients and controls and assess the impact of WMHs on GM atrophy and cognition. MCI, AD, and FTD patients had significantly higher WMH loads than controls. WMHs were related to GM atrophy in insular and parieto-occipital regions in MCI/AD, and frontal regions and basal ganglia in FTD. In addition, WMHs contributed to more severe cognitive deficits in AD and FTD compared to controls, whereas their impact in MCI and PD was not significantly different from controls. These results suggest potential synergistic effects between WMHs and proteinopathies in the neurodegenerative process in MCI, AD and FTD.

Association Between Midlife Obesity and Its Metabolic Consequences, Cerebrovascular Disease, and Cognitive Decline

CONTEXT

Chronic obesity is associated with several complications, including cognitive impairment and dementia. However, we have only piecemeal knowledge of the mechanisms linking obesity to central nervous system damage. Among candidate mechanisms are other elements of obesity-associated metabolic syndrome, such as hypertension, dyslipidemia, and diabetes, but also systemic inflammation. While there have been several neuroimaging studies linking adiposity to changes in brain morphometry, a comprehensive investigation of the relationship has so far not been done.

OBJECTIVE

To identify links between adiposity and cognitive dysfunction.

METHODS

This observational cohort study (UK Biobank), with an 8-year follow-up, included more than 20 000 participants from the general community, with a mean age of 63 years. Only participants with data available on both baseline and follow-up timepoints were included. The main outcome measures were cognitive performance and mediator variables: hypertension, diabetes, systemic inflammation, dyslipidemia, gray matter measures, and cerebrovascular disease (volume of white matter hyperintensities on magnetic resonance imaging).

RESULTS

Using structural equation modeling, we found that body mass index, waist-to-hip ratio, and body fat percentage were positively related to higher plasma C-reactive protein, dyslipidemia, hypertension, and diabetes. In turn, hypertension and diabetes were related to cerebrovascular disease. Finally, cerebrovascular disease was associated with lower cortical thickness and volume and higher subcortical volumes, but also cognitive deficits (largest significant pcorrected = 0.02).

CONCLUSIONS

We show that adiposity is related to poor cognition, with metabolic consequences of obesity and cerebrovascular disease as potential mediators. The outcomes have clinical implications, supporting a role for the management of adiposity in the prevention of late-life dementia and cognitive decline.

Data analysis with Shapley values for automatic subject selection in Alzheimer's disease data sets using interpretable machine learning

BACKGROUND

For the recruitment and monitoring of subjects for therapy studies, it is important to predict whether mild cognitive impaired (MCI) subjects will prospectively develop Alzheimer's disease (AD). Machine learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to high variability in disease patterns. Further variability originates from multicentric study designs, varying acquisition protocols, and errors in the preprocessing of magnetic resonance imaging (MRI) scans. The high variability makes the differentiation between signal and noise difficult and may lead to overfitting. This article examines whether an automatic and fair data valuation method based on Shapley values can identify the most informative subjects to improve ML classification.

METHODS

An ML workflow was developed and trained for a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The validation was executed for an independent ADNI test set and for the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) cohort. The workflow included volumetric MRI feature extraction, feature selection, sample selection using Data Shapley, random forest (RF), and eXtreme Gradient Boosting (XGBoost) for model training as well as Kernel SHapley Additive exPlanations (SHAP) values for model interpretation.

RESULTS

The RF models, which excluded 134 of the 467 training subjects based on their RF Data Shapley values, outperformed the base models that reached a mean accuracy of 62.64% by 5.76% (3.61 percentage points) for the independent ADNI test set. The XGBoost base models reached a mean accuracy of 60.00% for the AIBL data set. The exclusion of those 133 subjects with the smallest RF Data Shapley values could improve the classification accuracy by 2.98% (1.79 percentage points). The cutoff values were calculated using an independent validation set.

CONCLUSION

The Data Shapley method was able to improve the mean accuracies for the test sets. The most informative subjects were associated with the number of ApolipoproteinE ε4 (ApoE ε4) alleles, cognitive test results, and volumetric MRI measurements.

Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

Volumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer gray matter (GM) structure volumes and its possible bias on functional relationships. T1-weighted images from 1,077 participants (4,321 timepoints) from the Alzheimer's Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery images. Mixed-effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overall WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. Participants with higher WMH volumes had higher overlaps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (p < .0001). When not corrected for WMHs, caudate volumes increased with age (p < .0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimer's disease patients. After correcting for WMHs, caudate volumes decreased with age (p < .0001), and Alzheimer's disease patients had lower caudate volumes than cognitively healthy individuals (p < .01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (p < .0001). Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.