MRI markers for mild cognitive impairment: comparisons between white matter integrity and gray matter volume measurements

Perturbation-based dual task assessment in older adults with mild cognitive impairment

Background

Dual tasking (i.e., concurrent performance of motor and cognitive task) is significantly impaired in older adults with mild cognitive impairment (OAwMCI) compared to cognitively intact older adults (CIOA) and has been associated with increased fall risk. Dual task studies have primarily examined volitionally driven events, and the effects of mild cognitive impairment on reactive balance control (i.e., the ability to recover from unexpected balance threats) are unexplored. We examined the effect of cognitive tasks on reactive balance control in OAwMCI compared to CIOA.

Methods

Adults >55 years were included and completed the Montreal Cognitive Assessment (MoCA) to categorize them as OAwMCI (MoCA: 18-24, n = 15) or CIOA (MoCA: ≥25, n = 15). Both OAwMCI [MoCA: 22.4 (2.2), 65.4 (6.1) years, 3 females] and CIOA [MoCA: 28.4 (1.3), 68.2 (5.5) years, 10 females] responded to large magnitude stance slip-like perturbations alone (single task) and while performing perceptual cognitive tasks targeting the visuomotor domain (target and tracking game). In these tasks, participants rotated their head horizontally to control a motion mouse and catch a falling target (target game) or track a moving object (track). Margin of stability (MOS) and fall outcome (harness load cell >30% body weight) were used to quantify reactive balance control. Cognitive performance was determined using performance error (target) and sum of errors (tracking). A 3 × 2 repeated measures ANOVA examined the effect of group and task on MOS, and generalized estimating equations (GEE) model was used to determine changes in fall outcome between groups and tasks. 2 × 2 repeated measures ANOVAs examined the effect of group and task on cognitive performance.

Results

Compared to CIOA, OAwMCI exhibited significantly deteriorated MOS and greater number of falls during both single task and dual task (p < 0.05), and lower dual task tracking performance (p < 0.01). Compared to single task, both OAwMCI and CIOA exhibited significantly deteriorated perceptual cognitive performance during dual task (p < 0.05); however, no change in MOS or fall outcome between single task and dual task was observed.

Conclusion

Cognitive impairment may diminish the ability to compensate and provide attentional resources demanded by sensory systems to integrate perturbation specific information, resulting in deteriorated ability to recover balance control among OAwMCI.

Advanced brain imaging for the diagnosis of Alzheimer disease

PURPOSE OF REVIEW

The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD).

RECENT FINDINGS

Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities.

SUMMARY

Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.

Combining ADC values in DWI with rCBF values in arterial spin labeling (ASL) for the diagnosis of mild cognitive impairment (MCI)

We aimed to investigate the role of combined apparent diffusion coefficient (ADC) values and relative cerebral blood flow (rCBF) values in the diagnosis of mild cognitive impairment (MCI) patients. The present prospective research enrolled 156 MCI patients and 58 healthy elderly people who came to our hospital from January 2021 to February 2023. T1W, T2W, diffusion-weighted imaging, and arterial spin labeling sequences were performed on all subjects, and ADC values and rCBF values were measured at the workstation. Clinical and demographic data of all patients were collected while mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores were used to assess patients' cognitive abilities. The MCI group had significantly lower rCBF values in the left frontal lobe, left occipital lobe, right frontal lobe, and right occipital lobe than the HC group. The ADC values in the left frontal lobe as well as the right frontal lobe were remarkably elevated in the MCI group than in the HC group. MoCA and MMSE scores were positively correlated with rCBF values in the left frontal, right frontal, left occipital, and right occipital lobes and negatively correlated with ADC values in the left and right frontal lobes. Combined ADC values and rCBF values from the left frontal lobe for the diagnosis of MCI had a higher sensitivity and specificity with the AUC was 0.877, sensitivity 81.0%, specificity 82.7%. Additionally, pressure fasting plasma glucose, ADC of the left frontal lobe, right frontal lobe, rCBF of left frontal lobe and rCBF of left frontal lobe were the risk factors of patients with MCI. In summary, our results indicated that the ADC values and rCBF values were changed in MCI group compared to HC group and correlated with MMSE and MoCA scores.

Cocaine use is associated with cerebral white matter hyperintensities in HIV disease

BACKGROUND

White matter hyperintensities (WMH), a marker of cerebral small vessel disease and predictor of cognitive decline, are observed at higher rates in persons with HIV (PWH). The use of cocaine, a potent central nervous system stimulant, is disproportionately common in PWH and may contribute to WMH.

METHODS

The sample included of 110 PWH on antiretroviral therapy. Fluid-attenuated inversion recovery (FLAIR) and T1-weighted anatomical MRI scans were collected, along with neuropsychological testing. FLAIR images were processed using the Lesion Segmentation Toolbox. A hierarchical regression model was run to investigate predictors of WMH burden [block 1: demographics; block 2: cerebrovascular disease (CVD) risk; block 3: lesion burden].

RESULTS

The sample was 20% female and 79% African American with a mean age of 45.37. All participants had persistent HIV viral suppression, and the median CD4+ T-cell count was 750. Nearly a third (29%) currently used cocaine regularly, with an average of 23.75 (SD = 20.95) days in the past 90. In the hierarchical linear regression model, cocaine use was a significant predictor of WMH burden (β = .28). WMH burden was significantly correlated with poorer cognitive function (r = -0.27). Finally, higher WMH burden was significantly associated with increased serum concentrations of interferon-γ-inducible protein 10 (IP-10) but lower concentrations of myeloperoxidase (MPO); however, these markers did not differ by COC status.

CONCLUSIONS

WMH burden is associated with poorer cognitive performance in PWH. Cocaine use and CVD risk independently contribute to WMH, and addressing these conditions as part of HIV care may mitigate brain injury underlying neurocognitive impairment.

Disconnectome associated with progressive white matter hyperintensities in aging: a virtual lesion study

Objective

White matter hyperintensities (WMH) are commonly seen on T2-weighted magnetic resonance imaging (MRI) in older adults and are associated with an increased risk of cognitive decline and dementia. This study aims to estimate changes in the structural connectome due to age-related WMH by using a virtual lesion approach.

Methods

High-quality diffusion-weighted imaging data of 30 healthy subjects were obtained from the Human Connectome Project (HCP) database. Diffusion tractography using q-space diffeomorphic reconstruction (QSDR) and whole brain fiber tracking with 107 seed points was conducted using diffusion spectrum imaging studio and the brainnetome atlas was used to parcellate a total of 246 cortical and subcortical nodes. Previously published WMH frequency maps across age ranges (50's, 60's, 70's, and 80's) were used to generate virtual lesion masks for each decade at three lesion frequency thresholds, and these virtual lesion masks were applied as regions of avoidance (ROA) in fiber tracking to estimate connectivity changes. Connections showing significant differences in fiber density with and without ROA were identified using paired tests with False Discovery Rate (FDR) correction.

Results

Disconnections appeared first from the striatum to middle frontal gyrus (MFG) in the 50's, then from the thalamus to MFG in the 60's and extending to the superior frontal gyrus in the 70's, and ultimately including much more widespread cortical and hippocampal nodes in the 80's.

Conclusion

Changes in the structural disconnectome due to age-related WMH can be estimated using the virtual lesion approach. The observed disconnections may contribute to the cognitive and sensorimotor deficits seen in aging.

Localized White Matter Tract Integrity Measured by Diffusion Tensor Imaging Is Altered in People with Mild Cognitive Impairment and Associated with Dual-Task and Single-Task Gait Speed

BACKGROUND

Altered white matter (WM) tract integrity may contribute to mild cognitive impairment (MCI) and gait abnormalities.

OBJECTIVE

The purpose of this study was to determine whether diffusion tensor imaging (DTI) metrics were altered in specific portions of WM tracts in people with MCI and to determine whether gait speed variations were associated with the specific DTI metric changes.

METHODS

DTI was acquired in 44 people with MCI and 40 cognitively normal elderly controls (CNCs). Fractional anisotropy (FA) and radial diffusivity (RD) were measured along 18 major brain WM tracts using probabilistic tractography. The average FA and RD along the tracts were compared between the groups using MANCOVA and post-hoc tests. The tracts with FA or RD differences between the groups were examined using an along-tract exploratory analysis to identify locations that differed between the groups. Associations between FA and RD in whole tracts and in the segments of the tracts that differed between the groups and usual/dual-task gait velocities and gross cognition were examined.

RESULTS

Lower FA and higher RD was observed in right cingulum-cingulate gyrus endings (rh.ccg) of the MCI group compared to the CNC group. These changes were localized to the posterior portions of the rh.ccg and correlated with gait velocities.

CONCLUSION

Lower FA and higher RD in the posterior portion of the rh.ccg adjacent to the posterior cingulate suggests decreased microstructural integrity in the MCI group. The correlation of these metrics with gait velocities suggests an important role for this tract in maintaining normal cognitive-motor function.

Decreased Cerebrovascular Reactivity in Mild Cognitive Impairment Phenotypes

BACKGROUND

Cerebrovascular health plays an important role in cognitive health in older adults. Cerebrovascular reactivity (CVR), a measure of cerebrovascular health, changes in both normal and pathological aging, and is increasingly being conceptualized as contributory to cognitive decline. Interrogation of this process will yield new insights into cerebrovascular correlates of cognition and neurodegeneration.

OBJECTIVE

The current study examines CVR using advanced MRI in prodromal dementia states (amnestic and non-amnestic mild cognitive impairment phenotypes; aMCI and naMCI, respectively) and older adult controls.

METHODS

CVR was assessed in 41 subjects (20 controls, 11 aMCI, 10 naMCI) using multiband multi-echo breath-holding task functional magnetic resonance imaging. Imaging data were preprocessed and analyzed using AFNI. All participants also completed a battery of neuropsychological tests. T-tests and ANOVA/ANCOVA analyses were conducted to compare controls to MCI groups on CVR and cognitive metrics. Partial correlation analyses between CVR derived from regions-of-interest (ROIs) and different cognitive functions were conducted.

RESULTS

CVR was found to be significantly lower in aMCI and naMCI patients compared to controls. naMCI showed intermediate patterns between aMCI and controls (though aMCI and naMCI groups did not significantly differ). CVR of ROIs were positively correlated with neuropsychological measures of processing speed, executive functioning, and memory.

CONCLUSION

The findings highlight regional CVR differences in MCI phenotypes compared to controls, where aMCI may have lower CVR than naMCI. Our results suggest possible cerebrovascular abnormalities associated with MCI phenotypes.

Speech- and Language-Based Classification of Alzheimer's Disease: A Systematic Review

Background: Alzheimer’s disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer’s Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Background:

The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease.

Objective:

To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques.

Methods:

We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques.

Results:

Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients.

Conclusion:

Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer's Disease Patients

Mild Cognitive Impairment (MCI) is the stage between the declining of normal brain function and the more serious decline of dementia. Alzheimer's disease (AD) is one of the leading forms of dementia. Although MCI does not always lead to AD, an early diagnosis of MCI may be helpful in finding those with early signs of AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has utilized magnetic resonance imaging (MRI) for the diagnosis of MCI and AD. MCI can be separated into two types: Early MCI (EMCI) and Late MCI (LMCI). Furthermore, MRI results can be separated into three views of axial, coronal and sagittal planes. In this work, we perform binary classifications between healthy people and the two types of MCI based on limited MRI images using deep learning approaches. Specifically, we implement and compare two various convolutional neural network (CNN) architectures. The MRIs of 516 patients were used in this study: 172 control normal (CN), 172 EMCI patients and 172 LMCI patients. For this data set, 50% of the images were used for training, 20% for validation, and the remaining 30% for testing. The results showed that the best classification for one model was between CN and LMCI for the coronal view with an accuracy of 79.67%. In addition, we achieved 67.85% accuracy for the second proposed model for the same classification group.

Perturbation-based balance assessment: Examining reactive balance control in older adults with mild cognitive impairments

Background

Older adults with mild cognitive impairment (OAwMCI) present subtle balance and gait deficits along with subjective memory decline. Although these presentations might not affect activities of daily living (ADLs), they attribute to a two-folded increase in falls. While changes occurring in volitional balance control during ADLs have been extensively examined among OAwMCI, reactive balance control, required to recover from external perturbations, has received little attention. Therefore, this study examined reactive balance control in OAwMCI compared to their healthy counterparts.

Methods

Fifteen older adults with mild cognitive impairment (OAwMCI), fifteen cognitively intact older adults (CIOA) (>55 years), and fifteen young adults (18-30 years) were exposed to stance perturbations at three different intensities. Behavioral outcomes postural COM state stability, step length, step initiation, and step execution were computed.

Results

Postural COM state stability was the lowest in OAwMCI compared to CIOA and young adults, and it deteriorated at higher perturbation intensities (P < 0.001). Step length was the lowest among OAwMCI and was significantly different from young adults (P < 0.001) but not from CIOA. Unlike OAwMCI, CIOA and young adults increased their step length at higher perturbation intensities (P < 0.001). OAwMCI showed longer recovery step initiation times and shorter execution times compared to CIOA and young adults at higher perturbation intensities (P < 0.001).

Conclusion

OAwMCI exhibit exacerbated reactive instability and are unable to modulate their responses as the threat to balance control altered. Thus, they are at a significantly higher risk of falls than their healthy counterparts.

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.

Combination of Plasma Neurofilament Light Chain and Mini-Mental State Examination Score Predicts Progression from Mild Cognitive Impairment to Alzheimer's Disease within 5 Years

BACKGROUND

Individuals with mild cognitive impairment (MCI) are at high risk of progression to Alzheimer's disease (AD) dementia, but some remain stable. There is a need to identify those at higher risk of progression to improve patient management and outcomes.

OBJECTIVE

To evaluate the trajectory of plasma neurofilament light chain (pNFL) prior to progression from MCI to AD dementia, the performance of pNFL, in combination with the Mini-Mental State Examination (MMSE), as a predictor of progression from MCI to AD dementia and to inform clinicians on the use of pNFL as a predictive biomarker.

METHODS

Participants (n = 440) with MCI and longitudinal follow-up (mean = 4.2 years) from the AD Neuroimaging Initiative dataset were included. pNFL as a marker for neurodegeneration and the MMSE as a cognitive measure were investigated as simple/practical predictors of progression. The risk of progressing from MCI to AD dementia associated with pNFL and MMSE scores was assessed using Cox and logistic regression models.

RESULTS

The current risk of progression to AD dementia was 37%higher in individuals with high pNFL (> 56 ng/L) compared to those with average pNFL (≤40 ng/L). A combination of baseline pNFL and MMSE could differentiate those who progressed within 5 years (AUC = 0.75) from stable individuals. Including change in MMSE over 6-12 months further improved the model (AUC = 0.84).

CONCLUSION

Our findings reveal that combining pNFL with a simple dementia screener (MMSE) can reliably predict whether a person with MCI is likely to progress to AD dementia within 5 years.

Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers

Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.

Radial diffusivity is the best global biomarker able to discriminate healthy elders, mild cognitive impairment, and Alzheimer's disease: A diagnostic study of DTI-derived data

Introduction

For the past two decades, diffusion tensor imaging (DTI)-derived metrics allowed the characterization of Alzheimer's disease (AzD). Previous studies reported only a few parameters (most commonly fractional anisotropy, mean diffusivity, and axial and radial diffusivities measured at selected regions). We aimed to assess the diagnostic performance of 11 DTI-derived tensor metrics by using a global approach.

Materials and Methods

A prospective study performed in 34 subjects: 12 healthy elders, 11 mild cognitive impairment (MCI) patients, and 11 patients with AzD. Postprocessing of DTI magnetic resonance imaging allowed the calculation of 11 tensor metrics. Anisotropies included fractional (FA), and relative (RA). Diffusivities considered simple isotropic diffusion (p), simple anisotropic diffusion (q), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Tensors included the diffusion tensor total magnitude (L); and the linear (Cl), planar (Cp), and spherical tensors (Cs). We performed a multivariate discriminant analysis and diagnostic tests assessment.

Results

RD was the only variable selected to assemble a predictive model: Wilks' λ = 0.581, χ² (2) = 14.673, P = 0.001. The model's overall accuracy was 64.5%, with areas under the curve of 0.81, 0.73 and 0.66 to diagnose AzD, MCI, and healthy brains, respectively.

Conclusions

Global DTI-derived RD alone can discriminate between healthy elders, MCI, and AzD patients. Although this study proves evidence of a potential biomarker, it does not provide clinical guidance yet. Additional studies comparing DTI metrics might determine their usefulness to monitor disease progression, measure outcome in drug trials, and even perform the screening of pre-AzD subjects.

Associations of Dual Task Exergaming with Cognitive-Motor Interference in Older Adults with Mild Cognitive Impairment: A Single-Arm Pilot Study

Purpose

To examine the feasibility and effectiveness of dual task (DT) exergaming to improve volitional balance control in older adults with mild cognitive impairment (MCI).

Methods

Ten older adults with MCI were examined at baseline (week-0) and post-training (week-5) on volitional balance control (maximum excursion of center of gravity, MXE [%]) while performing cognitive task (auditory clock test or letter number sequencing task) and on the NIH-motor and cognitive toolboxes. DT exergaming training lasted for 12 sessions which consisted of performing explicit cognitive tasks while playing the Wii-Fit balance games.

Results

From pre- to post-training, MXE improved (p<0.05); however, cognitive accuracy (cognitive task) remained the same (p>0.05). Improvement in NIH motor and cognitive toolbox tests was observed post-training (p<0.05).

Conclusion

DT exergaming was associated to improvements in balance control under attention-demanding conditions in MCI. Future studies may focus on examining the efficacy of such training.

Venous dysfunction plays a critical role in "normal" white matter disease of aging

A ubiquitous finding on MRI in older individuals, age-related cerebral white matter hyperintensities (WMHs) are associated with cognitive decline, dementia, disability, and death. Currently, these findings are thought to represent small infarcts secondary to lipohyalinotic arteriosclerosis. Commonly though, the anatomic distribution of WMHs is often non-arterial, and parallel the deep venous system. Furthermore, there is discrepant evidence for the role of conventional vascular risk factors such as hypertension, carotid atherosclerosis and diabetes for the development and progression of these. Interventions targeting conventional vascular risk factors lack consistency in preventing the progression of WMHs. There is evidence for age-related hemodynamic cervical venous dysfunction resulting in reduced internal jugular vein venous compliance, venous dilatation, and venous reflux. Similarly, venous collagenosis increases with age. Increased blood-brain barrier (BBB) permeability is also noted with aging. Both hemodynamic venous dysfunction, venous sclerosis, and increased BBB permeability are associated with WMHs. We propose that age-related WMHs are a sequalae of venous dysfunction. Venous dysfunction results initially in increased transmission of venous pressures to the brain. Subsequent BBB disruption leads to increased permeability with progression to end-stage findings of age-related WMHs.

Using intraindividual variability as an indicator of cognitive improvement in a physical exercise intervention of older women with mild cognitive impairment

OBJECTIVES

Intervention programs designed to improve cognitive ability in older adults with mild cognitive impairment (MCI) have often focused on physical exercise as a means to improve traditional measures of cognition, with mixed success. Individuals with MCI show high levels of intraindividual variability (IIV) in response speed, and IIV may be sensitive to intervention-related changes. The current study evaluated if participants who participated in a physical activity intervention (aerobic or resistance training) showed a reduction in IIV, compared to a balance and tone control group.

METHOD

This study was a secondary analysis of the EXercise for Cognition and Everyday Living (EXCEL) Study. Women Aged 70-80 years with probable MCI (n = 86) participated in a 6-month randomized controlled trial designed to investigate the effects of different physical exercises on cognitive ability. Participants completed 1-back, task switching, and spatial working memory tasks at baseline, 13 weeks (midpoint) and upon completion of the program.

RESULTS

Analyses were conducted following both the intent-to-treat principle and complier average casual effect (CACE) modeling. Participants in the intervention group who complied with the program showed reduced IIV on task switching in the CACE models. The intent-to-treat analyses were all nonsignificant.

CONCLUSIONS

Physical exercise resulted in improved IIV in older adults with probable MCI, showing that IIV is modifiable by lifestyle engagement. IIV may be a useful complementary index of cognitive plasticity particularly among those with cognitive impairment. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.

Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging

INTRODUCTION

Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.

METHODS

We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier.

RESULTS

Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%).

DISCUSSION

This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.

Longitudinal brain atrophy patterns and neuropsychological performance in older adults with HIV-associated neurocognitive disorder compared with early Alzheimer's disease

Older HIV-infected patients are at risk for both HIV-associated neurocognitive disorder (HAND) and Alzheimer's disease. We investigated neuroimaging and neuropsychological performance of 61 virally suppressed older adults with HAND (mean (SD) age 64.3 (3.9) years), 53 demographically matched individuals with mild cognitive impairment of the Alzheimer's type (MCI-AD; 65.0 [4.8]), and 89 healthy controls (65.0 [4.3]) cross-sectionally and over 20 months. At the baseline, both disease groups exhibited lower volumes in multiple cortical and subcortical regions compared with controls. Hippocampal volume differentiated MCI-AD from HAND. Cognitively, MCI-AD performed worse on memory and language compared with HAND. Adjusted longitudinal models revealed greater diffuse brain atrophy in MCI-AD compared with controls, whereas HAND showed greater atrophy in frontal gray matter and cerebellum compared with controls. Comparing HAND with MCI-AD showed similar atrophy rates in all brain regions explored, with no significant findings. MCI-AD exhibited more pronounced language decline compared with HAND. These findings reveal the need for further work on unique cognitive phenotypes and neuroimaging signatures of HAND compared with early AD, providing preliminary clinical insight for differential diagnosis of age-related brain dysfunction in geriatric neuroHIV.

A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images

Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer's disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease's progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively.

Brain White Matter: A Substrate for Resilience and a Substance for Subcortical Small Vessel Disease

Age-related brain white matter disease is a form of small vessel disease (SVD) that may be associated with lacunar and other small subcortical infarcts, cerebral microbleeds, and perivascular spaces. This common form of cerebrovascular disease may manifest clinically as cognitive impairment of varying degrees and difficulty with mobility. Whereas some persons show cognitive decline and mobility failure when there are brain white matter hyperintensities (WMH) and acute stroke, others recover, and not everyone with brain white matter disease is disabled. Thus, repair or compensation of brain white matter may be possible, and furthermore, certain vascular risks, such as raised blood pressure, are targets for prevention of white matter disease or are administered to reduce the burden of such disease. Vascular risk modification may be useful, but alone may not be sufficient to prevent white matter disease progression. In this chapter, we specifically focus on WMH of vascular origin and explore white matter development, plasticity, and enduring processes of myelination across the health span in the context of experimental and human data, and compare and contrast resilient brain white matter propensity to a diseased white matter state. We conclude with thoughts on novel ways one might study white matter resilience, and predict future healthy cognitive and functional outcomes.

Examining the relationship between nutrition and cerebral structural integrity in older adults without dementia

The proportion of adults aged 60 years and over is expected to increase over the coming decades. This ageing of the population represents an important health issue, given that marked reductions to cerebral macro- and microstructural integrity are apparent with increasing age. Reduced cerebral structural integrity in older adults appears to predict poorer cognitive performance, even in the absence of clinical disorders such as dementia. As such, it is becoming increasingly important to identify those factors predicting cerebral structural integrity, especially factors that are modifiable. One such factor is nutritional intake. While the literature is limited, data from available cross-sectional studies indicate that increased intake of nutrients such as B vitamins (for example, B6, B12 and folate), choline, n-3 fatty acids and vitamin D, or increased adherence to prudent whole diets (for example, the Mediterranean diet) predicts greater cerebral structural integrity in older adults. There is even greater scarcity of randomised clinical trials investigating the effects of nutritional supplementation on cerebral structure, though it appears that supplementation with B vitamins (B6, B12 and folic acid) or n-3 fatty acids (DHA or EPA) may be beneficial. The current review presents an overview of available research examining the relationship between key nutrients or adherence to select diets and cerebral structural integrity in dementia-free older adults.

Quantitative tract-based white matter heritability in 1- and 2-year-old twins

White matter (WM) microstructure, as determined by diffusion tensor imaging (DTI), is increasingly recognized as an important determinant of cognitive function and is also altered in neuropsychiatric disorders. Little is known about genetic and environmental influences on WM microstructure, especially in early childhood, an important period for cognitive development and risk for psychiatric disorders. We studied the heritability of DTI parameters, fractional anisotropy (FA), radial diffusivity (RD) and axial diffusivity (AD) along 34 tracts, including 10 bilateral fiber pathways and the respective subdivision, using quantitative tractography in a longitudinal sample of healthy children at 1 year (N = 215) and 2 years (N = 165) of age. We found that heritabilities for whole brain AD, RD, and FA were 0.48, 0.69, and 0.72 at age 1, and 0.59, 0.77, and 0.76 at age 2 and that mean heritabilities of tract-averaged AD, RD, and FA for individual bundles were moderate (over 0.4). However, the heritability of DTI change between 1 and 2 years of age was not significant for most tracts. We also demonstrated that point-wise heritability tended to be significant in the central portions of the tracts and was generally spatially consistent at ages 1 and 2 years. These results, especially when compared to heritability patterns in neonates, indicate that the heritability of WM microstructure is dynamic in early childhood and likely reflect heterogeneous maturation of WM tracts and differential genetic and environmental influences on maturation patterns.

Evaluating the relationship between white matter integrity, cognition, and varieties of video game learning

BACKGROUND

Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games.

OBJECTIVE

To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research.

METHODS

Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas.

RESULTS

Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age.

CONCLUSION

Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related disorders or declines in processing speed, particularly older adults.

Glycerophospholipid Supplementation as a Potential Intervention for Supporting Cerebral Structure in Older Adults

Modifying nutritional intake through supplementation may be efficacious for altering the trajectory of cerebral structural decline evident with increasing age. To date, there have been a number of clinical trials in older adults whereby chronic supplementation with B vitamins, omega-3 fatty acids, or resveratrol, has been observed to either slow the rate of decline or repair cerebral tissue. There is also some evidence from animal studies indicating that supplementation with glycerophospholipids (GPL) may benefit cerebral structure, though these effects have not yet been investigated in adult humans. Despite this paucity of research, there are a number of factors predicting poorer cerebral structure in older humans, which GPL supplementation appears to beneficially modify or protect against. These include elevated concentrations of homocysteine, unbalanced activity of reactive oxygen species both increasing the risk of oxidative stress, increased concentrations of pro-inflammatory messengers, as well as poorer cardio- and cerebrovascular function. As such, it is hypothesized that GPL supplementation will support cerebral structure in older adults. These cerebral effects may influence cognitive function. The current review aims to provide a theoretical basis for future clinical trials investigating the effects of GPL supplementation on cerebral structural integrity in older adults.

Putative mechanisms of cognitive decline with implications for clinical research and practice

Multiple intrinsic and extrinsic mechanisms contribute to vulnerability of cognitive decline and nurses play a significant role in assisting individuals and families to use strategies for healthy cognitive aging. The objective of this narrative review is to provide a synthesis of the intrinsic and extrinsic mechanisms of cognitive decline and conditions that are associated with cognitive decline. Well-established intrinsic mechanisms of cognitive decline include aging, apolipoprotein E (APOE) ε4 carrier status, SORL1 mutations, neuroinflammation, mitochondrial dysfunction, amyloid deposition, and demyelination. Extrinsic risk factors include obesity, diabetes, hypertension, elevated lipid panel, metabolic syndrome, depression, traumatic brain injury, substance use, heart failure, and stroke. The various definitions of cognitive decline as well as the intrinsic and extrinsic factors that impact cognition as humans age should be incorporated in future clinical research studies. Nurses may use this information to help patients make lifestyle choices regarding cognitive health.

The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.

White matter microstructure within the superior longitudinal fasciculus modulates the degree of response conflict indexed by N2 in healthy adults

Response conflict can be induced by priming multiple responses competing for control of action in trials. The N2 is one functionally-related cognitive control index for response conflict. And yet the underlying whiter matter neural substrates of inter-individual difference in conflict N2 remain unclear. So the aim of present study was to address the white matter microstructure of the N2 responsible for conflict by directly relating the amplitude cost of the event-related potential (ERP) N2 component to diffusion tensor imaging (DTI) indices in healthy subjects. Thirty healthy subjects underwent DTI scanning and electrophysiology recording during a modified Flanker task. N2 was a stimulus-locked negative ERP component. Fractional anisotropy (FA) was calculated based on DTI measures and was assumed to reflect the integrity of myelinate fiber bundles. Therefore, we tested the relationship between N2 amplitude and FA in brain white matter. Results showed that FA, an index for white matter characteristics, in the right superior longitudinal fasciculus (SLF) was significantly positively associated with N2 amplitude cost. The N2 amplitude cost also predicted response time (RT) cost in the Flanker task. Higher FA was associated with larger N2 amplitude cost, suggesting that changes in white matter integrity in the SLF may account for changes in efficient transmission of fronto-parietal modulatory conflict signals.

Disrupted Thalamus White Matter Anatomy and Posterior Default Mode Network Effective Connectivity in Amnestic Mild Cognitive Impairment

Alzheimer’s disease (AD) and its prodromal state amnestic mild cognitive impairment (aMCI) are characterized by widespread abnormalities in inter-areal white matter fiber pathways and parallel disruption of default mode network (DMN) resting state functional and effective connectivity. In healthy subjects, DMN and task positive network interaction are modulated by the thalamus suggesting that abnormal task-based DMN deactivation in aMCI may be a consequence of impaired thalamo-cortical white matter circuitry. Thus, this article uses a multimodal approach to assess white matter integrity between thalamus and DMN components and associated effective connectivity in healthy controls (HCs) relative to aMCI patients. Twenty-six HC and 20 older adults with aMCI underwent structural, functional and diffusion MRI scanning using the high angular resolution diffusion-weighted acquisition protocol. The DMN of each subject was identified using independent component analysis (ICA) and resting state effective connectivity was calculated between thalamus and DMN nodes. White matter integrity changes between thalamus and DMN were investigated with constrained spherical deconvolution (CSD) tractography. Significant structural deficits in thalamic white matter projection fibers to posterior DMN components posterior cingulate cortex (PCC) and lateral inferior parietal lobe (IPL) were identified together with significantly reduced effective connectivity from left thalamus to left IPL. Crucially, impaired thalamo-cortical white matter circuitry correlated with memory performance. Disrupted thalamo-cortical structure was accompanied by significant reductions in IPL and PCC cortico-cortical effective connectivity. No structural deficits were found between DMN nodes. Abnormal posterior DMN activity may be driven by changes in thalamic white matter connectivity; a view supported by the close anatomical and functional association of thalamic nuclei effected by AD pathology and the posterior DMN nodes. We conclude that dysfunctional posterior DMN activity in aMCI is consistent with disrupted cortico-thalamo-cortical processing and thalamic-based dissemination of hippocampal disease agents to cortical hubs.

White matter microstructural abnormalities in amnestic mild cognitive impairment: A meta-analysis of whole-brain and ROI-based studies

Studies that examined white matter (WM) alterations in amnestic mild cognitive impairment (aMCI) abound. This timely meta-analysis aims to synthesize the results of these studies. Seventy-seven studies (totalN_aMCI=1844) were included. Fourteen region-of-interest-based (ROI-based) (k≥8;N_aMCI≥284 per ROI) and two activation likelihood estimation (ALE) meta-analyses (fractional anisotropy [FA]: k=15;N_aMCI=463; mean diffusivity [MD]: k=8;N_aMCI=193) were carried out. Among the many significant ROI-related findings, reliable FA and MD alterations in the fornix, uncinate fasciculus, and parahippocampal cingulum were observed in aMCI. Larger effects were observed in MD relative to FA. The ALE meta-analysis revealed a significant FA decrease among aMCI subjects in the posterior corona radiata. These results provide robust evidence of the presence of WM abnormalities in aMCI. Our findings also highlight the importance of carrying out both ROI-based and whole-brain-based research to obtain a complete picture of WM microstructural alterations associated with the condition..

Genetic and environmental influences on mean diffusivity and volume in subcortical brain regions

Increased mean diffusivity (MD) is hypothesized to reflect tissue degeneration and may provide subtle indicators of neuropathology as well as age-related brain changes in the absence of volumetric differences. Our aim was to determine the degree to which genetic and environmental variation in subcortical MD is distinct from variation in subcortical volume. Data were derived from a sample of 387 male twins (83 MZ twin pairs, 55 DZ twin pairs, and 111 incomplete twin pairs) who were MRI scanned as part of the Vietnam Era Twin Study of Aging. Quantitative estimates of MD and volume for 7 subcortical regions were obtained: thalamus, caudate nucleus, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens. After adjusting for covariates, bivariate twin models were fitted to estimate the size and significance of phenotypic, genotypic, and environmental correlations between MD and volume at each subcortical region. With the exception of the amygdala, familial aggregation in MD was entirely explained by additive genetic factors across all subcortical regions with estimates ranging from 46 to 84%. Based on bivariate twin modeling, variation in subcortical MD appears to be both genetically and environmentally unrelated to individual differences in subcortical volume. Therefore, subcortical MD may be an alternative biomarker of brain morphology for complex traits worthy of future investigation. Hum Brain Mapp 38:2589-2598, 2017. © 2017 Wiley Periodicals, Inc.

Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI

Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer's disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer's Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures. In this study we propose to exploit the anatomical development within the entire brain, as found by a non-rigid registration approach. Specifically, this anatomical development is represented by the Stationary Velocity Field (SVF) from registration between the baseline and follow-up images. To make the SVFs comparable among subjects, we use the parallel transport method to align them in a common space. The normalized SVF together with derived features are then used to distinguish between MCIc and MCIs subjects. This novel feature space is reduced using a Kernel Principal Component Analysis method, and a linear support vector machine is used as a classifier. Extensive comparative experiments are performed to inspect the influence of several aspects of our method on classification performance, specifically the feature choice, the smoothing parameter in the registration and the use of dimensionality reduction. The optimal result from a 10-fold cross-validation using 36 month follow-up data shows competitive results: accuracy 92%, sensitivity 95%, specificity 90%, and AUC 94%. Based on the same dataset, the proposed approach outperforms two alternative ones that either depends on the baseline image only, or uses longitudinal information from larger brain areas. Good results were also obtained when scans at 6, 12, or 24 months were used for training the classifier. Besides the classification power, the proposed method can quantitatively compare brain regions that have a significant difference in development between the MCIc and MCIs groups.

Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers

For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer's disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).

A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

Regional Gray Matter Atrophy Coexistent with Occipital Periventricular White Matter Hyper Intensities

White matter hyperintensities (WMHs) and brain atrophy often coexist in the elderly. Additionally, WMH is often observed as occipital periventricular hyperintensities (OPVHs) with low-grade periventricular (PV) white matter (WM) lesions and is usually confined within an anatomical structure. However, the effects of OPVHs on gray matter (GM) atrophy remain largely unknown. In this study, we investigated GM atrophy in OPVHs patients and explored the relationship between such atrophy and clinical risk factors. T1-weighted and T2-weighted Magnetic resonance imaging (MRI) were acquired, and voxel-based morphometry (VBM) analysis was applied. The clinical (demographic and cardiovascular) risk factors of the OPVHs patients and healthy controls were then compared. Lastly, scatter plots and correlation analysis were applied to explore the relationship between the MRI results and clinical risk factors in the OPVHs patients. OPVHs patients had significantly reduced GM in the right supramarginal gyrus, right angular gyrus, right middle temporal gyrus, right anterior cingulum and left insula compared to healthy controls. Additionally, OPVHs patients had GM atrophy in the left precentral gyrus and left insula cortex, and such atrophy is associated with a reduction in low-density lipoprotein cholesterol (LDL-C) and apolipoprotein-B (Apo-B).

Thalamic Shape and Cognitive Performance in Amnestic Mild Cognitive Impairment

OBJECTIVE

This study aimed to investigate thalamic shape alterations and their relationships with various episodic memory impairments in subjects with amnestic mild cognitive impairment (aMCI).

METHODS

We compared volumes and morphological alterations of the thalamus between aMCI subjects and healthy controls. In addition, we investigated the correlation between thalamic deformations and various memory impairments in aMCI subjects using a comprehensive neuropsychological battery.

RESULTS

The normalized left thalamic volumes of the aMCI group were significantly smaller than those of the healthy control group (p<0.0001). aMCI subjects exhibited significant thalamic deformations in the left thalamic dorso-medial and antero-medial areas compared with healthy individuals. CERAD-K Word List Memory scores were significantly correlated with the left dorso-medial areas in aMCI subjects. There were no significant correlations between verbal fluency, Boston naming test, constructional praxis, Word List Recognition, and Visuospatial Recall scores and thalamic shape in aMCI subjects. Verbal delayed recall scores were also significantly correlated with the left dorso-medial areas in the aMCI group.

CONCLUSION

Structural alterations in the thalamic deformations in the left dorso-medial and antero-medial areas might be core underlying neurobiological mechanisms of thalamic dysfunction related to Word List Memory and delayed verbal recall in individuals with aMCI.

Identification of Amnestic Mild Cognitive Impairment Using Multi-Modal Brain Features: A Combined Structural MRI and Diffusion Tensor Imaging Study

Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheimer's disease. The present study aimed to explore the feasibility of accurately identifying aMCI with a magnetic resonance imaging (MRI) biomarker. We integrated measures of both gray matter (GM) abnormalities derived from structural MRI and white matter (WM) alterations acquired from diffusion tensor imaging at the voxel level across the entire brain. In particular, multi-modal brain features, including GM volume, WM fractional anisotropy, and mean diffusivity, were extracted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an area under the curve of 0.862. The most discriminative regions of GM were mainly located in the medial temporal lobe, temporal lobe, precuneus, cingulate gyrus, parietal lobe, and frontal lobe, whereas the most discriminative regions of WM were mainly located in the corpus callosum, cingulum, corona radiata, frontal lobe, and parietal lobe. Our findings suggest that aMCI is characterized by a distributed pattern of GM abnormalities and WM alterations that represent discriminative power and reflect relevant pathological changes in the brain, and these changes further highlight the advantage of multi-modal feature integration for identifying aMCI.

Neural substrates of vulnerability to postsurgical delirium as revealed by presurgical diffusion MRI

Despite the significant impact of postoperative delirium on surgical outcomes and the long-term prognosis of older patients, its neural basis has not yet been clarified. In this study we investigated the impact of premorbid brain microstructural integrity, as measured by diffusion tensor imaging before surgery, on postoperative delirium incidence and severity, as well as the relationship among presurgical cognitive performance, diffusion tensor imaging abnormalities and postoperative delirium. Presurgical diffusion tensor imaging scans of 136 older (≥70 years), dementia-free subjects from the prospective Successful Aging after Elective Surgery study were analysed blind to the clinical data and delirium status. Primary outcomes were postoperative delirium incidence and severity during the hospital stay, as assessed by the Confusion Assessment Method. We measured cognition before surgery using general cognitive performance, a composite score based on a battery of neuropsychological tests. We investigated the association between presurgical diffusion tensor imaging parameters of brain microstructural integrity (i.e. fractional anisotropy, axial, mean and radial diffusivity) with postoperative delirium incidence and severity. Analyses were adjusted for the following potential confounders: age, gender, vascular comorbidity status, and general cognitive performance. Postoperative delirium occurred in 29 of 136 subjects (21%) during hospitalization. Presurgical diffusion tensor imaging abnormalities of the cerebellum, cingulum, corpus callosum, internal capsule, thalamus, basal forebrain, occipital, parietal and temporal lobes, including the hippocampus, were associated with delirium incidence and severity, after controlling for age, gender and vascular comorbidities. After further controlling for general cognitive performance, diffusion tensor imaging abnormalities of the cerebellum, hippocampus, thalamus and basal forebrain still remained associated with delirium incidence and severity. This study raises the intriguing possibility that structural dysconnectivity involving interhemispheric and fronto-thalamo-cerebellar networks, as well as microstructural changes of structures involved in limbic and memory functions predispose to delirium under the stress of surgery. While the diffusion tensor imaging abnormalities observed in the corpus callosum, cingulum, and temporal lobe likely constitute the neural substrate for the association between premorbid cognition, as measured by general cognitive performance, and postoperative delirium, the microstructural changes observed in the cerebellum, hippocampus, thalamus and basal forebrain seem to constitute a separate phenomenon that predisposes to postsurgical delirium independent of presurgical cognitive status.

Combining multiple anatomical MRI measures improves Alzheimer's disease classification

Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920-1929, 2016. © 2016 Wiley Periodicals, Inc.

Speaking in Alzheimer's Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer's Disease

It is known that Alzheimer’s disease (AD) influences the temporal characteristics of spontaneous speech. These phonetical changes are present even in mild AD. Based on this, the question arises whether an examination based on language analysis could help the early diagnosis of AD and if so, which language and speech characteristics can identify AD in its early stage. The purpose of this article is to summarize the relation between prodromal and manifest AD and language functions and language domains. Based on our research, we are inclined to claim that AD can be more sensitively detected with the help of a linguistic analysis than with other cognitive examinations. The temporal characteristics of spontaneous speech, such as speech tempo, number of pauses in speech, and their length are sensitive detectors of the early stage of the disease, which enables an early simple linguistic screening for AD. However, knowledge about the unique features of the language problems associated with different dementia variants still has to be improved and refined.

Effects of low-level sarin and cyclosarin exposure on white matter integrity in Gulf War Veterans

BACKGROUND

We previously found evidence of reduced gray and white matter volume in Gulf War (GW) veterans with predicted low-level exposure to sarin (GB) and cyclosarin (GF). Because loss of white matter tissue integrity has been linked to both gray and white matter atrophy, the current study sought to test the hypothesis that GW veterans with predicted GB/GF exposure have evidence of disrupted white matter microstructural integrity.

METHODS

Measures of fractional anisotropy and directional (i.e., axial and radial) diffusivity were assessed from the 4T diffusion tensor images (DTI) of 59 GW veterans with predicted GB/GF exposure and 59 "matched" unexposed GW veterans (mean age: 48 ± 7 years). The DTI data were analyzed using regions of interest (ROI) analyses that accounted for age, sex, total brain gray and white matter volume, trauma exposure, posttraumatic stress disorder, current major depression, and chronic multisymptom illness status.

RESULTS

There were no significant group differences in fractional anisotropy or radial diffusivity. However, there was increased axial diffusivity in GW veterans with predicted GB/GF exposure compared to matched, unexposed veterans throughout the brain, including the temporal stem, corona radiata, superior and inferior (hippocampal) cingulum, inferior and superior fronto-occipital fasciculus, internal and external capsule, and superficial cortical white matter blades. Post hoc analysis revealed significant correlations between higher fractional anisotropy and lower radial diffusivity with better neurobehavioral performance in unexposed GW veterans. In contrast, only increased axial diffusivity in posterior limb of the internal capsule was associated with better psychomotor function in GW veterans with predicted GB/GF exposure.

CONCLUSIONS

The finding that increased axial diffusivity in a region of the brain that contains descending corticospinal fibers was associated with better psychomotor function and the lack of significant neurobehavioral deficits in veterans with predicted GB/GF exposure hint at the possibility that the widespread increases in axial diffusivity that we observed in GW veterans with predicted GB/GF exposure relative to unexposed controls may reflect white matter reorganization after brain injury (i.e., exposure to GB/GF).

Detection of early changes in the parahippocampal and posterior cingulum bundles during mild cognitive impairment by using high-resolution multi-parametric diffusion tensor imaging

We aimed to determine alterations occurring in the parahippocampal cingulum bundle (PhC) and posterior cingulum bundle (PoC) in patients with mild cognitive impairment (MCI) through analysis of high-resolution multi-parametric diffusion tensor imaging (DTI). Participants comprised 41 patients with MCI (21 AD converters [MCI-C] and 20 non-converters [MCI-NC]), 20 patients with Alzheimer׳s disease (AD), and 26 healthy elderly subjects who underwent prospective examination with high-resolution DTI. An atlas-based regions-of-interest (ROIs) method calculated fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA), and radial diffusivity (DR) in the PhC and PoC. For the PhC, FA values showed significant decreases, and MD and DR values showed significantly increases, in the MCI-C and AD groups compared with the healthy controls, although the MCI-C and MCI-NC groups did not differ significantly in these metrics. Conversely, none of the diffusion metrics for the PoC showed a significant difference among the MCI groups and the control groups, although there were significant differences between the AD group and control groups. High-resolution multi-parametric DTI analysis was able to detect substantial changes in diffusion anisotropy and diffusivity in the PhC of patients with MCI who were destined to convert to AD.

White matter integrity determined with diffusion tensor imaging in older adults without dementia: influence of amyloid load and neurodegeneration

IMPORTANCE

Pathophysiologic mechanisms leading to loss of white matter integrity and the temporal positioning of biomarkers of white matter integrity relative to the biomarkers of gray matter neurodegeneration and amyloid load in the course of Alzheimer disease (AD) are poorly understood.

OBJECTIVE

To investigate the effects of AD-related gray matter neurodegeneration and high β-amyloid on white matter microstructure in older adults without dementia.

DESIGN, SETTING, AND PARTICIPANTS

A population-based, longitudinal cohort study was conducted. Participants included in the Mayo Clinic Study of Aging (N = 701) who underwent magnetic resonance imaging, diffusion tensor imaging (DTI), and positron emission tomography studies with diagnoses of cognitively normal ([CN] n = 570) or mild cognitive impairment ([MCI] n = 131) were included. Both groups were divided into biomarker-negative, amyloid-positive-only, neurodegeneration-positive-only, and amyloid plus neurodegeneration-positive groups based on their amyloid load shown on carbon 11-labeled Pittsburgh Compound B positron emission tomography, AD hypometabolic pattern shown on fludeoxyglucose F 18 positron emission tomography, and/or hippocampal atrophy shown on magnetic resonance imaging.

MAIN OUTCOMES AND MEASURES

Fractional anisotropy (FA) determined using DTI.

RESULTS

No FA alterations were observed in biomarker-negative MCI and amyloid-positive-only CN and MCI groups compared with biomarker-negative CN participants on voxel-based analysis (P < .05; familywise error corrected). Conversely, the neurodegeneration-positive-only and amyloid plus neurodegeneration-positive CN and MCI groups consistently had decreased FA in the fornix, which correlated with cognitive performance (ρ = 0.38; P < .001). Patients with MCI had more extensive white matter involvement than did those with CN, and the greatest FA decreases were observed in the amyloid plus neurodegeneration-positive MCI group (P < .05; familywise error corrected).

CONCLUSIONS AND RELEVANCE

A high amyloid load does not influence diffusion tensor imaging-based measures of white matter integrity in the absence of coexistent gray matter neurodegeneration in older adults without dementia.

Fornix White Matter is Correlated with Resting-State Functional Connectivity of the Thalamus and Hippocampus in Healthy Aging but Not in Mild Cognitive Impairment - A Preliminary Study

In this study, we wished to examine the relationship between the structural connectivity of the fornix, a white matter (WM) tract in the limbic system, which is affected in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease, and the resting-state functional connectivity (FC) of two key related subcortical structures, the thalamus, and hippocampus. Twenty-two older healthy controls (HC) and 18 older adults with aMCI underwent multi-modal MRI scanning. The fornix was reconstructed using constrained-spherical deconvolution-based tractography. The FC between the thalamus and hippocampus was calculated using a region-of-interest approach from which the mean time series were exacted and correlated. Diffusion tensor imaging measures of the WM microstructure of the fornix were correlated against the Fisher Z correlation values from the FC analysis. There was no difference between the groups in the fornix WM measures, nor in the resting-state FC of the thalamus and hippocampus. We did however find that the relationship between functional and structural connectivity differed significantly between the groups. In the HCs, there was a significant positive association between linear diffusion (CL) in the fornix and the FC of the thalamus and hippocampus, however, there was no relationship between these measures in the aMCI group. These preliminary findings suggest that in aMCI, the relationship between the functional and structural connectivity of regions of the limbic system may be significantly altered compared to healthy ageing. The combined use of diffusion weighted imaging and functional MRI may advance our understanding of neural network changes in aMCI, and elucidate subtle changes in the relationship between structural and functional brain networks.

Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data

BACKGROUND

Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI).

METHODS

We applied a Support Vector Machine (SVM) classifier to DTI and volumetric magnetic resonance imaging data from 35 amyloid-β42 negative MCI subjects (MCI-Aβ42-), 35 positive MCI subjects (MCI-Aβ42+), and 25 healthy controls (HC) retrieved from the European DTI Study on Dementia. The SVM was applied to DTI-derived fractional anisotropy, mean diffusivity (MD), and mode of anisotropy (MO) maps. For comparison, we studied classification based on gray matter (GM) and WM volume.

RESULTS

We obtained accuracies of up to 68% for MO and 63% for GM volume when it came to distinguishing between MCI-Aβ42- and MCI-Aβ42+. When it came to separating MCI-Aβ42+ from HC we achieved an accuracy of up to 77% for MD and a significantly lower accuracy of 68% for GM volume. The accuracy of multimodal classification was not higher than the accuracy of the best single modality.

CONCLUSIONS

Our results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD.