scholarly journals Joint contributions of cortical morphometry and white matter microstructure in healthy brain aging: A partial least squares correlation analysis

2019 ◽  
Author(s):  
David A. Hoagey ◽  
Jenny R. Rieck ◽  
Karen M. Rodrigue ◽  
Kristen M. Kennedy
Keyword(s):  
White Matter ◽  
Least Squares ◽  
Surface Area ◽  
Partial Least Squares ◽  
Cortical Thickness ◽  
Brain Aging ◽  
Univariate Analysis ◽  
Tissue Type ◽  
Cortical Atrophy ◽  
Multivariate Techniques

AbstractCortical atrophy and degraded axonal health have been shown to coincide during normal aging; however, few studies have examined these measures together. To lend insight into both the regional specificity and the relative timecourse of structural degradation of these tissue compartments across the lifespan, we analyzed grey matter (GM) morphometry (cortical thickness, surface area, volume) and estimates of white matter (WM) microstructure (fractional anisotropy, mean diffusivity) using traditional univariate and more robust multivariate techniques to examine age associations in 186 healthy adults aged 20-94 years old. Univariate analysis of each tissue type revealed that negative age associations were largest in frontal grey and white matter tissue and weaker in temporal, cingulate, and occipital regions, representative of not only an anterior-to-posterior gradient, but also a medial-to-lateral gradient. Multivariate partial least squares correlation (PLSC) found the greatest covariance between GM and WM was driven by the relationship between WM metrics in the anterior corpus callosum and projections of the genu, anterior cingulum, and fornix; and with GM thickness in parietal and frontal regions. Surface area was far less susceptible to age effects and displayed less covariance with WM metrics, while regional volume covariance patterns largely mirrored those of cortical thickness. Results support a retrogenesis-like model of aging, revealing a coupled relationship between frontal and parietal GM and the underlying WM, which evidence the most protracted development and the most vulnerability during healthy aging.

scholarly journals Cortical Volume, Thickness, and Surface Area in the Motoric Cognitive Risk Syndrome

2021 ◽  
pp. 1-14
Author(s):  
Helena M. Blumen ◽  
Emily Schwartz ◽  
Gilles Allali ◽  
Olivier Beauchet ◽  
Michele Callisaya ◽  
...  
Keyword(s):  
Surface Area ◽  
Sex Education ◽  
Cortical Thickness ◽  
Clinical Stage ◽  
White Matter Lesions ◽  
Cortical Atrophy ◽  
Intracranial Volume ◽  
Cortical Volume ◽  
Cognitive Risk ◽  
Cognitive Complaint

Background: The motoric cognitive risk (MCR) syndrome is a pre-clinical stage of dementia characterized by slow gait and cognitive complaint. Yet, the brain substrates of MCR are not well established. Objective: To examine cortical thickness, volume, and surface area associated with MCR in the MCR-Neuroimaging Consortium, which harmonizes image processing/analysis of multiple cohorts. Methods: Two-hundred MRIs (M age 72.62 years; 47.74%female; 33.17%MCR) from four different cohorts (50 each) were first processed with FreeSurfer 6.0, and then analyzed using multivariate and univariate general linear models with 1,000 bootstrapped samples (n-1; with resampling). All models adjusted for age, sex, education, white matter lesions, total intracranial volume, and study site. Results: Overall, cortical thickness was lower in individuals with MCR than in those without MCR. There was a trend in the same direction for cortical volume (p = 0.051). Regional cortical thickness was also lower among individuals with MCR than individuals without MCR in prefrontal, insular, temporal, and parietal regions. Conclusion: Cortical atrophy in MCR is pervasive, and include regions previously associated with human locomotion, but also social, cognitive, affective, and motor functions. Cortical atrophy in MCR is easier to detect in cortical thickness than volume and surface area because thickness is more affected by healthy and pathological aging.

scholarly journals PERBANDINGAN ANTARA UNWEIGHTED LEAST SQUARES (ULS) DAN PARTIAL LEAST SQUARES (PLS) DALAM PEMODELAN PERSAMAAN STRUKTURAL (STUDI KASUS MODEL ANALISIS PRESTASI BELAJAR MAHASISWA TAHUN PERTAMA PROGRAM STUDI S1 MATEMATIKA FMIPA-INSTUTUT PERTANIAN BOGOR)

2017 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Muhammad Amin Paris
Keyword(s):  
Least Squares ◽  
Student Learning ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Generalized Least Squares ◽  
Primary Data ◽  
Equation Modeling ◽  
Multivariate Techniques ◽  
Mathematics Students

Structural Equation Modeling (SEM) is one of multivariate techniques  that can estimates a series of interrelated dependence relationships from a number of endogenous and exogenous variables, as well as latent (unobserved) variables simultaneously. Estimation of Parameter methods that is often applied in SEM are Maximum Likelihood (ML), Weighted Least Squares (WLS), Unweighted Least Squares (ULS), Generalized Least Squares (GLS) and Partial Least Squares (PLS). This research aims to compare ULS method and PLS method in estimating parameter model of achievement of student learning in first year undergraduate Mathematics students, FMIPA, Bogor  Agricultural University ( IPB). This research use secondary and primary data which amounts to 112. The result of this research indicates that ULS method is more accurate than PLS methods. The analysis done with ULS method shows that motivation, capability and environmental had an effect to achievement of student learning.

scholarly journals Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume

2017 ◽  
Author(s):  
Stuart J. Ritchie ◽  
David Alexander Dickie ◽  
Simon R. Cox ◽  
Maria del C. Valdés Hernández ◽  
Alison Pattie ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Ability ◽  
Surface Area ◽  
Cortical Thickness ◽  
White Matter Hyperintensity ◽  
Socioeconomic Background ◽  
Intracranial Volume ◽  
Cortical Surface ◽  
Age Cohorts ◽  
Cortical Surface Area

AbstractFully characterizing age differences in the brain is a key task for combatting ageing-related cognitive decline. Using propensity score matching on two independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age 92 years (n = 42) to very similar participants at mean age 73 (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large differences in grey and white matter volumes, cortical surface area, cortical thickness, and white matter hyperintensity volume and spatial extent. In a mediation analysis, the total volume of white matter hyperintensities and total cortical surface area jointly mediated 24.9% of the relation between age and general cognitive ability (tissue volumes and cortical thickness were not significant mediators in this analysis). These findings provide an unusual and valuable perspective on neurostructural ageing, in which brains from the eighth and tenth decades of life differ widely despite the same cognitive, socio-economic, and brain-volumetric starting points.

scholarly journals PCR, PLS, or OPLS Evaluation of different regression techniques for hypothesis generation

2021 ◽  
Author(s):  
Avani Ahuja
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Principal Components ◽  
Selection Process ◽  
Principal Component ◽  
Hypothesis Generation ◽  
Multivariate Techniques ◽  
Response Variable ◽  
Number Of Principal Components ◽  
Latent Structures

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

scholarly journals Investigating the Contribution of White Matter Hyperintensities and Cortical Thickness to Empathy in Neurodegenerative and Cerebrovascular Diseases

2021 ◽  
Author(s):  
Miracle Ozzoude ◽  
Brenda Varriano ◽  
Derek Beaton ◽  
Joel Ramirez ◽  
Melissa F Holmes ◽  
...  
Keyword(s):  
White Matter ◽  
Cortical Thickness ◽  
Brain Atrophy ◽  
White Matter Hyperintensities ◽  
Automatic Segmentation ◽  
Cerebrovascular Diseases ◽  
Cortical Atrophy ◽  
Reactivity Index ◽  
Empathy Deficits ◽  
Longitudinal Effects

Introduction: Change in empathy is an increasingly recognised symptom of neurodegenerative diseases and contributes to caregiver burden and patient distress. Empathy impairment has been associated with brain atrophy but its relationship to white matter hyperintensities (WMH) is unknown. We aimed to investigate the relationships amongst WMH, brain atrophy, and empathy deficits in neurodegenerative and cerebrovascular diseases. Methods: 513 participants with Alzheimers Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia (FTD), Parkinsons Disease, or Cerebrovascular Disease (CVD) were included. Empathy was assessed using the Interpersonal Reactivity Index. WMH were measured using a semi-automatic segmentation and FreeSurfer was used to measure cortical thickness. Results: A heterogeneous pattern of cortical thinning was found between groups, with FTD showing thinning in frontotemporal regions and CVD in left superior parietal, left insula, and left postcentral. Results from both univariate and multivariate analyses revealed that several variables were associated with empathy, particularly cortical thickness in the fronto-insulo-temporal and cingulate regions, sex(female), global cognition, and right parietal and occipital WMH. Conclusions: Our results suggest that cortical atrophy and WMH may be associated with empathy deficits in neurodegenerative and cerebrovascular diseases. Future work should consider investigating the longitudinal effects of WMH and atrophy on empathy deficits in neurodegenerative and cerebrovascular diseases.

scholarly journals Concurrent and Predictive Associations between the Life's Simple 7 & Brain Structure in Middle-Age and Early-Old Age

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 1005-1006
Author(s):  
Teresa Warren ◽  
McKenna Williams ◽  
Christine Fennema-Notestine ◽  
Jeremy Elman ◽  
Jennifer de Anda ◽  
...  
Keyword(s):  
White Matter ◽  
Old Age ◽  
Cortical Thickness ◽  
Cardiovascular Health ◽  
Brain Aging ◽  
Apoe Genotype ◽  
Mean Diffusivity ◽  
Life’S Simple 7 ◽  
Life's Simple 7 ◽  
Early Old Age

Abstract American Heart Association’s (AHA) Life’s Simple 7 (LS7), an index of cardiovascular health risks, has been associated with worse brain outcomes but few examined this relationship in midlife. We examined whether LS7 scores at midlife were associated with brain morphometry in early old age. Participants were 471 men who participated in the Vietnam Era Twin Study of Aging. The LS7 index was assessed at mean age 62 (range 55-66) and 68 (range 61-71) and included smoking, physical activity, diet, body mass index, cholesterol, glucose, and blood pressure. Each factor was coded, per AHA criteria, on a 3-point scale (0/poor-2/ideal) and summed to create a composite score (0-14). At mean age 68, participants underwent structural magnetic resonance imaging, which was used to create the previously validated brain measures. Scores included: the ratio of abnormal white matter to white matter, and two Alzheimer’s disease brain signatures (cortical thickness/volume signature and a mean diffusivity (MD) signature). Analyses controlled for age, education, income, ethnicity, and APOE genotype. Concurrently at mean age 68, the LS7 was associated with cortical thickness/volume (F=4.85, p = .028), MD (F=10.89, p = .001) signatures and abnormal white matter ratio (F=14.04, p < .001). Prospectively, the LS7 at mean 62 was significantly associated with age 68 cortical thickness/volume (F=5.08, p = .025) and MD (F=5.54, p = .019) signatures but not with abnormal white matter ratio. These results suggest that prevention strategies that promote heart healthy behaviors could have implications for healthy brain aging.

scholarly journals P14.102 Cerebellar atrophy patterns in paraneoplastic cerebellar degeneraiton and spinocerebellar ataxia 1 (SCA1)

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii92-iii92
Author(s):  
C Vialatte de Pémille ◽  
D Psimaras ◽  
I Adanyegu ◽  
F Graus ◽  
A Dürr ◽  
...  
Keyword(s):  
Spinocerebellar Ataxia ◽  
Cortical Thickness ◽  
Univariate Analysis ◽  
Radiological Finding ◽  
Cerebellar Atrophy ◽  
Specific Pattern ◽  
Cortical Atrophy ◽  
Spinocerebellar Ataxia Type ◽  
Multicentric Study ◽  
Brain Morphometry

Abstract BACKGROUND Brain and more specifically cerebellar atrophy is a major radiological finding in both Paraneoplastic Cerebellar Degeneration (PCD) with anti-Yo antibodies and Spinocerebellar Ataxia type 1 (SCA1).We sought to analyze the different brain volumetric patterns of cerebellar atrophy in these diseases. MATERIAL AND METHODS We performed a retrospective multicentric study (Paris, Lyon, Barcelona reference centres) with either anti-Yo PCD (n=16) or SCA1 (n=17) and 30 healthy subjects paired by age. We used VolBrain and CERES algorithms to obtain the brain and cerebellum segmentation, respectively as well as the cortical thickness. We used a Sparse Canonical Correlation Analysis (SCCAN) and Voxel Brain Morphometry (VBM) with family wise error correction to analyze volumetric differences between the different conditions. RESULTS SCA patients were younger than PCD patients (p<0.05, ANOVA). In univariate analysis, most of the atrophic regions (p<0.05) were common between PCD and SCA1 compared to controls. Isolated cortical thickness and grey matter analysis showed predominant atrophy in PCD patients. Multivariate analysis using SCCAN and VBM confirmed these results. We identified a particular atrophy pattern in PCD patients involving lobules III to VII. We observed a more diffuse atrophy distribution in SCA1 patients and a lower cortical atrophy in PCD patients. CONCLUSION We described the specific pattern of topographic cerebellar atrophy in PCD and SCA1 patients. The cerebellar atrophy in PCD is mainly localized in the neocerebellum.

scholarly journals Linking cortical atrophy to white matter hyperintensities of presumed vascular origin

2020 ◽  
pp. 0271678X2097417
Author(s):  
Carola Mayer ◽  
Benedikt M Frey ◽  
Eckhard Schlemm ◽  
Marvin Petersen ◽  
Kristin Engelke ◽  
...  
Keyword(s):  
White Matter ◽  
Cortical Thickness ◽  
White Matter Hyperintensities ◽  
Small Vessel Disease ◽  
Population Based ◽  
Vascular Lesions ◽  
Cortical Atrophy ◽  
Increased Risk ◽  
Cortical Regions ◽  
The Relationship

We examined the relationship between white matter hyperintensities (WMH) and cortical neurodegeneration in cerebral small vessel disease (CSVD) by investigating whether cortical thickness is a remote effect of WMH through structural fiber tract connectivity in a population at increased risk of CSVD. We measured cortical thickness on T1-weighted images and segmented WMH on FLAIR images in 930 participants of a population-based cohort study at baseline. DWI-derived whole-brain probabilistic tractography was used to define WMH connectivity to cortical regions. Linear mixed-effects models were applied to analyze the relationship between cortical thickness and connectivity to WMH. Factors associated with cortical thickness (age, sex, hemisphere, region, individual differences in cortical thickness) were added as covariates. Median age was 64 [IQR 46–76] years. Visual inspection of surface maps revealed distinct connectivity patterns of cortical regions to WMH. WMH connectivity to the cortex was associated with reduced cortical thickness ( p = 0.009) after controlling for covariates. This association was found for periventricular WMH ( p = 0.001) only. Our results indicate an association between WMH and cortical thickness via connecting fiber tracts. The results imply a mechanism of secondary neurodegeneration in cortical regions distant, yet connected to subcortical vascular lesions, which appears to be driven by periventricular WMH.

Sign in / Sign up

Export Citation Format

Share Document