Prediction of Impaired Performance in Trail Making Test in MCI Patients With Small Vessel Disease Using DTI Data

Mild cognitive impairment (MCI) is a common condition in patients with diffuse hyperintensities of cerebral white matter (WM) in T2-weighted magnetic resonance images and cerebral small vessel disease (SVD). In MCI due to SVD, the most prominent feature of cognitive impairment lies in degradation of executive functions, i.e., of processes that supervise the organization and execution of complex behavior. The trail making test is a widely employed test sensitive to cognitive processing speed and executive functioning. MCI due to SVD has been hypothesized to be the effect of WM damage, and diffusion tensor imaging (DTI) is a well-established technique for in vivo characterization of WM. We propose a machine learning scheme tailored to 1) predicting the impairment in executive functions in patients with MCI and SVD, and 2) examining the brain substrates of this impairment. We employed data from 40 MCI patients with SVD and created feature vectors by averaging mean diffusivity (MD) and fractional anisotropy maps within 50 WM regions of interest. We trained support vector machines (SVMs) with polynomial as well as radial basis function kernels using different DTI-derived features while simultaneously optimizing parameters in leave-one-out nested cross validation. The best performance was obtained using MD features only and linear kernel SVMs, which were able to distinguish an impaired performance with high sensitivity (72.7%-89.5%), specificity (71.4%-83.3%), and accuracy (77.5%-80.0%). While brain substrates of executive functions are still debated, feature ranking confirm that MD in several WM regions, not limited to the frontal lobes, are truly predictive of executive functions

Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis

BackgroundThe relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis.MethodsSixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model.ResultsAfter 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia.DiscussionOur findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features

Application of the DSM-5 Criteria for Major Neurocognitive Disorder to Vascular MCI Patients

Aims: The DSM-5 introduced the term "major neurocognitive disorders" (NCDs) to replace the previous term "dementia." However, psychometric and functional definitions of NCDs are missing. We aimed to apply the DSM-5 criteria for diagnosing the transition to NCD to patients with mild cognitive impairment (MCI) and small vessel disease (SVD), and to define clinically significant thresholds for this transition. Methods: The functional and cognitive features of the NCD criteria were evaluated as change from baseline and operationalized according to hierarchically ordered psychometric rules. Results: According to the applied criteria, out of 138 patients, 44 were diagnosed with major NCD (21 with significant cognitive worsening in ≥1 additional cognitive domain), 84 remained stable, and 10 reverted to normal. Single-domain MCI patients were the most likely to revert to normal, and none progressed to major NCD. The amnestic multiple-domain MCI patients had the highest rate of progression to NCD. Conclusion: We provide rules for the DSM-5 criteria for major NCD based on cognitive and functional changes over time, and define psychometric thresholds for clinically significant worsening to be used in longitudinal studies. According to these operationalized criteria, one-third of the MCI patients with SVD progressed to major NCD after 2 years, but only within the multiple-domain subtypes

Risk and Determinants of Dementia in Patients with Mild Cognitive Impairment and Brain Subcortical Vascular Changes: A Study of Clinical, Neuroimaging, and Biological Markers—The VMCI-Tuscany Study: Rationale, Design, and Methodology

Dementia is one of the most disabling conditions. Alzheimer's disease and vascular dementia (VaD) are the most frequent causes. Subcortical VaD is consequent to deep-brain small vessel disease (SVD) and is the most frequent form of VaD. Its pathological hallmarks are ischemic white matter changes and lacunar infarcts. Degenerative and vascular changes often coexist, but mechanisms of interaction are incompletely understood. The term mild cognitive impairment defines a transitional state between normal ageing and dementia. Pre-dementia stages of VaD are also acknowledged (vascular mild cognitive impairment, VMCI). Progression relates mostly to the subcortical VaD type, but determinants of such transition are unknown. Variability of phenotypic expression is not fully explained by severity grade of lesions, as depicted by conventional MRI that is not sensitive to microstructural and metabolic alterations. Advanced neuroimaging techniques seem able to achieve this. Beside hypoperfusion, blood-brain-barrier dysfunction has been also demonstrated in subcortical VaD. The aim of the Vascular Mild Cognitive Impairment Tuscany Study is to expand knowledge about determinants of transition from mild cognitive impairment to dementia in patients with cerebral SVD. This paper summarizes the main aims and methodological aspects of this multicenter, ongoing, observational study enrolling patients affected by VMCI with SVD

Intelligence, cognition, and major neurocognitive disorders: From constructs to measures

The study of intelligence's role in development of major neurocognitive disorders (MND) is influenced by the approaches used to conceptualize and measure these constructs. In the field of cognitive impairment, the use of single ‘intelligence’ tests is a common approach to estimate intelligence. Despite being a practical compromise between feasibility and constructs, variance of these tests is only partially explained by general intelligence, and some tools (e.g., lexical tasks for premorbid intelligence) presented inherent limitations. Alternatively, factorial models allow an actual measure of intelligence as a latent factor superintending all mental abilities. Royall and colleagues used structural equation modeling to decompose the Spearman's general intelligence factor g in δ (shared variance across cognitive and functional measures) and g’ (shared variance across cognitive measures only). Authors defined δ as the ‘cognitive correlates of functional status’, and thus a ‘phenotype for all cause dementia’. Compared to g’, δ explained a little rate of cognitive measures’ variance, but it demonstrated a higher accuracy in dementia case-finding. From the methodological perspective, given g ‘indifference’ to its indicators, further studies are needed to identify the minimal set of tools necessary to extract g, and to test also non-cognitive variables as measures of δ. From the clinical perspective, general intelligence seems to influence MND presence and severity more than domain specific cognitive abilities. Giving δ ‘blindness’ to etiology, its association with biomarkers and contribution to differential diagnosis might be limited. Classical neuropsychological approaches based on patterns of performances at cognitive tests remained fundamental for differential diagnosis

Statistical significance and its meaning

The most frequently method of data analysis in psychology is null hypothesis significance testing (NHST); this work identifies the misunderstandings and consequent biases due to the unwitting use of this hybrid approach, derived by the fusion of the p-value approach (PVA) of Fisher (1925) and the fixed alpha approach (FAA) of Neyman and Pearson (1933). The aim of this work is to contribute to the critical debate about the use of this test, how researchers interpret test results, and the logic of the NHST approach. In the same vein of recent recommendations of the APA Task Force, we examine different methods of data analysis, that are used with NHST including confidence intervals, effect size measures, power analysis, and resampling