Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach.
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ABSTRACT: Introduction
Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei.Methods
This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The corpus callosum is computationally less demanding to segment automatically compared to more complicated structures and a subject can be processed in a few minutes. We aimed to demonstrate that callosal shape and thickness measures provide a simple, effective, and accurate prognostication tool in ADNI dataset. Using longitudinal datasets, we classified MCI subjects based on conversion to AD assessed via cognitive testing. We evaluated the classification accuracy of callosal shape features in comparison with the existing "gold standard" cortical thickness and subcortical gray matter volume measures.Results
The callosal thickness measures were less accurate in classifying conversion status by cognitive scores compared to gray matter measures for AD.Conclusions
While this paper presented a negative result, this method may be more suitable for a disease of the white matter.
SUBMITTER: Adamson C
PROVIDER: S-EPMC6305917 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
Publications
Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach.
Adamson Chris C Beare Richard R Ball Gareth G Walterfang Mark M Seal Marc M
Brain and behavior 20181122 12
<h4>Introduction</h4>Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei.<h4>Methods</h4>This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The co ...[more]
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Project description:Alzheimer's Disease (AD) is a neurodegenerative disorder that mainly affects grey matter (GM). Nevertheless, a number of investigations have documented white matter (WM) pathology associated with AD. The corpus callosum (CC) is the largest WM fiber bundle in the human brain. It has been shown to be susceptible to atrophy in AD mainly as a correlate of Wallerian degeneration of commissural nerve fibers of the neocortex. The aim of this study was to investigate which callosal regions are affected and whether callosal degeneration is associated with the stage of the disease. For this purpose, we analyzed high-resolution MRI data of patients with amnesic mild cognitive impairment (MCI) (n=20), mild AD (n=20), severe AD (n=10), and of healthy controls (n=20). Callosal morphology was investigated applying two different structural techniques: mesh-based geometrical modeling methods and whole-brain voxel-based analyses. Our findings indicate significant reductions in severe AD patients compared to healthy controls in anterior (genu and anterior body) and posterior (splenium) sections. In contrast, differences between healthy controls and mild AD patients or amnesic MCI patients were less pronounced and did not survive corrections for multiple comparisons. When correlating anterior and posterior WM density of the CC with GM density of the cortex in the severe AD group, we detected significant positive relationships between posterior sections of the CC and the cortex. We conclude that callosal atrophy is present predominantly in the latest stage of AD, where two mechanisms might contribute to WM alterations in severe AD: the Wallerian degeneration in posterior subregions and the myelin breakdown process in anterior subregions.
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Project description:People diagnosed with Parkinson's disease (PD) can experience significant neuropsychiatric symptoms, including cognitive impairment and dementia, the neuroanatomical substrates of which are not fully characterised. Symptoms associated with cognitive impairment and dementia in PD may relate to direct structural changes to the corpus callosum via primary white matter pathology or as a secondary outcome due to the degeneration of cortical regions. Using magnetic resonance imaging, the corpus callosum can be investigated at the midsagittal plane, where it converges to a contiguous mass and is not intertwined with other tracts. The objective of this project was thus twofold: First, we investigated possible changes in the thickness of the midsagittal callosum and cortex in patients with PD with varying levels of cognitive impairment; and secondly, we investigated the relationship between the thickness of the midsagittal corpus callosum and the thickness of the cortex. Study participants included cognitively unimpaired PD participants (n = 35), PD participants with mild cognitive impairment (n = 22), PD participants with dementia (n = 17) and healthy controls (n = 27). We found thinning of the callosum in PD-related dementia compared with PD-related mild cognitive impairment and cognitively unimpaired PD participants. Regression analyses found thickness of the left medial orbitofrontal cortex to be positively correlated with thickness of the anterior callosum in PD-related mild cognitive impairment. This study suggests that a midsagittal thickness model can uncover changes to the corpus callosum in PD-related dementia, which occur in line with changes to the cortex in this advanced disease stage.
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Project description:BackgroundAlzheimer's disease (AD) is a major healthcare challenge, with existing diagnostics being costly/infeasible. This study explores retinal biomarkers from optical coherence tomography (OCT) and OCT angiography (OCTA) as a cost-effective and non-invasive solution to differentiate AD, mild cognitive impairment (MCI), and healthy controls (HCs).MethodsParticipants from the CALLIOPE Research Program were classified as "Dem" (AD and early AD), "MCI," and "HCs" using neuropsychological tests and clinical diagnosis by a neurologist. OCT/OCTA examinations were conducted using the RTVue XR 100 Avanti SD-OCT system (VISIONIX), with retinal parameters extracted. Statistical analysis included normality and homogeneity of variance (HOV) tests to select ANOVA methods. Post-hoc analyses utilized Mann-Whitney U, Dunnett, or Tukey-HSD tests based on parameters' normality and HOV. Correlations with age were assessed via Pearson or Spearman tests. A generalized linear model (GLM) using Tweedie regression modeled the relationship between OCT/OCTA parameters and MMSE scores, correcting for age. Another ordinal logistic GLM (OL-GLM) modeled OCT/OCTA parameters against classes, adjusting for multiple confounders.ResultsWe analyzed 357 participants: 44 Dem, 139 MCI, and 174 HCs. Significant microvascular density (VD) reductions around the fovea were linked with MCI and Dem compared to HCs. Age-related analysis associated thickness parameters with HCs' old age. Our OL-GLM demonstrated significant thickness/volume reductions in Inner_Retina and Full_Retina layers. Foveal avascular zone (FAZ) area and perimeter were initially not correlated with cognitive decline; however, OL-GLM significantly associated FAZ perimeter enlargement with Dem and MCI groups. Significant average and inferior peripapillary RNFL thinning were linked to Dem and MCI groups.ConclusionThis is the first study to examine VD changes in G grid sections among Dem, MCI, and HCs. We found a significant association between various VD parameters and cognitive decline. Most macular thickness/volume changes did not correlate with cognitive decline initially; however, our OL-GLM succeeded, highlighting the importance of the confounders' corrections. Our analysis excluded individual retinal layer parameters due to limitations; however, the literature suggests their value. Our study confirmed existing biomarkers' efficacy and uncovered novel retinal parameters for cognitive decline, requiring further validation.
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Project description:IntroductionManual motor problems have been reported in mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the specific aspects that are affected, their neuropathology, and potential value for classification modeling is unknown. The current study examined if multiple measures of motor strength, dexterity, and speed are affected in MCI and AD, related to AD biomarkers, and are able to classify MCI or AD.MethodsFifty-three cognitively normal (CN), 33 amnestic MCI, and 28 AD subjects completed five manual motor measures: grip force, Trail Making Test A, spiral tracing, finger tapping, and a simulated feeding task. Analyses included (1) group differences in manual performance; (2) associations between manual function and AD biomarkers (PET amyloid β, hippocampal volume, and APOE ε4 alleles); and (3) group classification accuracy of manual motor function using machine learning.ResultsAmnestic MCI and AD subjects exhibited slower psychomotor speed and AD subjects had weaker dominant hand grip strength than CN subjects. Performance on these measures was related to amyloid β deposition (both) and hippocampal volume (psychomotor speed only). Support vector classification well-discriminated control and AD subjects (area under the curve of 0.73 and 0.77, respectively) but poorly discriminated MCI from controls or AD.ConclusionGrip strength and spiral tracing appear preserved, while psychomotor speed is affected in amnestic MCI and AD. The association of motor performance with amyloid β deposition and atrophy could indicate that this is due to amyloid deposition in and atrophy of motor brain regions, which generally occurs later in the disease process. The promising discriminatory abilities of manual motor measures for AD emphasize their value alongside other cognitive and motor assessment outcomes in classification and prediction models, as well as potential enrichment of outcome variables in AD clinical trials.
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