Regional white matter integrity differentiates between vascular dementia and Alzheimer disease

Probing Evidence of Cerebral White Matter Microstructural Disruptions in Ischemic Heart Disease Before and Following Cardiac Rehabilitation: A Diffusion Tensor MR Imaging Study

BACKGROUND

Ischemic heart disease (IHD) is linked to brain white matter (WM) breakdown but how age or disease effects WM integrity, and whether it is reversible using cardiac rehabilitation (CR), remains unclear.

PURPOSE

To assess the effects of brain aging, cardiovascular disease, and CR on WM microstructure in brains of IHD patients following a cardiac event.

STUDY TYPE

Retrospective.

POPULATION

Thirty-five IHD patients (9 females; mean age = 59 ± 8 years), 21 age-matched healthy controls (10 females; mean age = 59 ± 8 years), and 25 younger controls (14 females; mean age = 26 ± 4 years).

FIELD STRENGTH/SEQUENCE

3 T diffusion-weighted imaging with single-shot echo planar imaging acquired at 3 months and 9 months post-cardiac event.

ASSESSMENT

Tract-based spatial statistics (TBSS) and tractometry were used to compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in cerebral WM between: 1) older and younger controls to distinguish age-related from disease-related WM changes; 2) IHD patients at baseline (pre-CR) and age-matched controls to investigate if cardiovascular disease exacerbates age-related WM changes; and 3) IHD patients pre-CR and post-CR to investigate the neuroplastic effect of CR on WM microstructure.

STATISTICAL TESTS

Two-sample unpaired t-test (age: older vs. younger controls; IHD: IHD pre-CR vs. age-matched controls). One-sample paired t-test (CR: IHD pre- vs. post-CR). Statistical threshold: P < 0.05 (FWE-corrected).

RESULTS

TBSS and tractometry revealed widespread WM changes in older controls compared to younger controls while WM clusters of decreased FA in the fornix and increased MD in body of corpus callosum were observed in IHD patients pre-CR compared to age-matched controls. Robust WM improvements (increased FA, increased AD) were observed in IHD patients post-CR.

DATA CONCLUSION

In IHD, both brain aging and cardiovascular disease may contribute to WM disruptions. IHD-related WM disruptions may be favorably modified by CR.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Intercellular Signaling Pathways as Therapeutic Targets for Vascular Dementia Repair

Vascular dementia (VaD) is a white matter ischemic disease and the second-leading cause of dementia, with no direct therapy. Within the lesion site, cell-cell interactions dictate the trajectory towards disease progression or repair. To elucidate the underlying intercellular signaling pathways, a VaD mouse model was developed for transcriptomic and functional studies. The mouse VaD transcriptome was integrated with a human VaD snRNA-Seq dataset. A custom-made database encompassing 4053 human and 2032 mouse ligand-receptor (L-R) interactions identified significantly altered pathways shared between human and mouse VaD. Two intercellular L-R systems, Serpine2-Lrp1 and CD39-A3AR, were selected for mechanistic study as both the ligand and receptor were dysregulated in VaD. Decreased Seprine2 expression enhances OPC differentiation in VaD repair. A clinically relevant drug that reverses the loss of CD39-A3AR function promotes tissue and behavioral recovery in the VaD model. This study presents novel intercellular signaling targets and may open new avenues for VaD therapies.

Joint diffusional kurtosis magnetic resonance imaging analysis of white matter and the thalamus to identify subcortical ischemic vascular disease

Identifying subcortical ischemic vascular disease (SIVD) in older adults is important but challenging. Growing evidence suggests that diffusional kurtosis imaging (DKI) can detect SIVD-relevant microstructural pathology, and a systematic assessment of the discriminant power of DKI metrics in various brain tissue microstructures is urgently needed. Therefore, the current study aimed to explore the value of DKI and diffusion tensor imaging (DTI) metrics in detecting early-stage SIVD by combining multiple diffusion metrics, analysis strategies, and clinical-radiological constraints. This prospective study compared DKI with diffusivity and macroscopic imaging evaluations across the aging spectrum including SIVD, Alzheimer's disease (AD), and cognitively normal (NC) groups. Using a white matter atlas and segregated thalamus analysis with considerations of the pre-identified macroscopic pathology, the most effective diffusion metrics were selected and then examined using multiple clinical-radiological constraints in a two-group or three-group paradigm. A total of 122 participants (mean age, 74.6 ± 7.38 years, 72 women) including 42 with SIVD, 50 with AD, and 30 NC were evaluated. Fractional anisotropy, mean kurtosis, and radial kurtosis were critical metrics in detecting early-stage SIVD. The optimal selection of diffusion metrics showed 84.4-100% correct classification of the results in a three-group paradigm, with an area under the curve of .909-.987 in a two-group paradigm related to SIVD detection (all P < .001). We therefore concluded that greatly resilient to the effect of pre-identified macroscopic pathology, the combination of DKI/DTI metrics showed preferable performance in identifying early-stage SIVD among adults across the aging spectrum.

Unsupervised machine learning model to predict cognitive impairment in subcortical ischemic vascular disease

INTRODUCTION

It is challenging to predict which patients who meet criteria for subcortical ischemic vascular disease (SIVD) will ultimately progress to subcortical vascular cognitive impairment (SVCI).

METHODS

We collected clinical information, neuropsychological assessments, T1 imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging from 83 patients with SVCI and 53 age-matched patients with SIVD without cognitive impairment. We built an unsupervised machine learning model to isolate patients with SVCI. The model was validated using multimodal data from an external cohort comprising 45 patients with SVCI and 32 patients with SIVD without cognitive impairment.

RESULTS

The accuracy, sensitivity, and specificity of the unsupervised machine learning model were 86.03%, 79.52%, and 96.23% and 80.52%, 71.11%, and 93.75% for internal and external cohort, respectively.

DISCUSSION

We developed an accurate and accessible clinical tool which requires only data from routine imaging to predict patients at risk of progressing from SIVD to SVCI.

HIGHLIGHTS

Our unsupervised machine learning model provides an accurate and accessible clinical tool to predict patients at risk of progressing from subcortical ischemic vascular disease (SIVD) to subcortical vascular cognitive impairment (SVCI) and requires only data from imaging routinely used during the diagnosis of suspected SVCI. The model yields good accuracy, sensitivity, and specificity and is portable to other cohorts and to clinical practice to distinguish patients with SIVD at risk for progressing to SVCI. The model combines assessment of diffusion tensor imaging and functional magnetic resonance imaging measures in patients with SVCI to analyze whether the "disconnection hypothesis" contributes to functional and structural changes and to the clinical presentation of SVCI.

Diffusion Tensor Imaging as a Tool to Evaluate the Cognitive Function of Patients With Vascular Dementia: A Meta-Analysis

BACKGROUND

Vascular dementia (VaD) is the most common type of dementia secondary to Alzheimer's disease. The pathologic mechanism of VaD is complex, and VaD still lacks a more objective diagnosis and evaluation method. Diffusion tensor imaging (DTI) can better detect the organizational structure and functional characteristics compared with any other diagnosis methods. Therefore, DTI has broad application in evaluating the severity and prognosis of VaD. This study aimed to assess the value of DTI in evaluating the cognitive function of patients with VaD.

METHODS

Authors searched Pubmed, Embase, and Cochrane Library, using the search terms, such as "diffusion tensor imaging," "DTI," "Vascular Dementia," "Arteriosclerotic Dementia," "Cognition," and "Cognitive." A voxel-based meta-analysis combined with quality statistics was performed, using the anisotropic effect-size version of the signed differential mapping method.

RESULTS

A total of 8 case-control studies were included in this meta-analysis. The sample size of patients ranged from 35 to 60, including 166 patients in the VaD group and 177 healthy individuals. The DTI imaging of the brain tissue of VaD patients was significantly different from that of healthy individuals.

CONCLUSIONS

DTI imaging of the brain tissue of VaD patients was clearly different from that of healthy controls. Therefore it may be feasible to use DTI imaging as a diagnostic method for VaD.

Alzheimer's and vascular disease classification using regional texture biomarkers in FLAIR MRI

Interactions between subcortical vascular disease and dementia due to Alzheimer's disease (AD) are unclear, and clinical overlap between the diseases makes diagnosis challenging. Existing studies have shown regional microstructural changes specific to each disease, and that textures in fluid-attenuated inversion recovery (FLAIR) MRI images may characterize abnormalities in tissue microstructure. This work aims to investigate regional FLAIR biomarkers that can differentiate dementia cohorts with and without subcortical vascular disease. FLAIR and diffusion MRI (dMRI) volumes were obtained in 65 mild cognitive impairment (MCI), 21 AD, 44 subcortical vascular MCI (scVMCI), 22 Mixed etiology, and 48 healthy elderly patients. FLAIR texture and intensity biomarkers were extracted from the normal appearing brain matter (NABM), WML penumbra, blood supply territory (BST), and white matter tract regions of each patient. All FLAIR biomarkers were correlated to dMRI metrics in each region and global WML load, and biomarker means between groups were compared using ANOVA. Binary classifications were performed using Random Forest classifiers to investigate the predictive nature of the regional biomarkers, and SHAP feature analysis was performed to further investigate optimal regions of interest for differentiating disease groups. The regional FLAIR biomarkers were strongly correlated to MD, while all biomarker regions but white matter tracts were strongly correlated to WML burden. Classification between Mixed disease and healthy, AD, and scVMCI patients yielded accuracies of 97%, 81%, and 72% respectively using WM tract biomarkers. Classification between scVMCI and healthy, MCI, and AD patients yielded accuracies of 89%, 84%, and 79% respectively using penumbra biomarkers. Only the classification between AD and healthy patients had optimal results using NABM biomarkers. This work presents novel regional FLAIR biomarkers that may quantify white matter degeneration related to subcortical vascular disease, and which indicate that investigating degeneration in specific regions may be more important than assessing global WML burden in vascular disease groups.

Untangling white matter fibre changes in Alzheimer's disease and small vessel disease

This scientific commentary refers to ‘Disentangling the effects of Alzheimer’s and small vessel disease on white matter fibre tracts’ by Dewenter et al. (https://doi.org/10.1093/brain/awac265).

Fornix degeneration in risk factors of Alzheimer's disease, possible trigger of cognitive decline

Risk factors of late-onset Alzheimer's disease (AD) such as aging, type 2 diabetes, obesity, heart failure, and traumatic brain injury can facilitate the appearance of cognitive decline and dementia by triggering cerebrovascular pathology and neuroinflammation. White matter (WM) microstructure and function are especially vulnerable to these conditions. Microstructural WM changes, assessed with diffusion weighted magnetic resonance imaging, can already be detected at preclinical stages of AD, and in the presence of the aforementioned risk factors. Particularly, the limbic system and cortico-cortical association WM tracts, which myelinate late during brain development, degenerate at the earliest stages. The fornix, a C-shaped WM tract that originates from the hippocampus, is one of the limbic tracts that shows early microstructural changes. Fornix integrity is necessary for ensuring an intact executive function and memory performance. Thus, a better understanding of the mechanisms that cause fornix degeneration is critical in the development of therapeutic strategies aiming to prevent cognitive decline in populations at risk. In this literature review, i) we deepen the idea that partial loss of forniceal integrity is an early event in AD, ii) we describe the role that common risk factors of AD can play in the degeneration of the fornix, and iii) we discuss some potential cellular and physiological mechanisms of WM degeneration in the scenario of cerebrovascular disease and inflammation.

Effects of exercise types on white matter microstructure in late midlife adults: Preliminary results from a diffusion tensor imaging study

Higher aerobic fitness during late midlife is associated with higher white matter (WM) microstructure. Compared with individuals engaged in irregular exercise, those who engage in regular aerobic exercise show higher fractional anisotropy (FA), a diffusion tenor imaging (DTI) measure that provides an index of WM microstructural integrity. However, whether other types of exercise, such as Tai Chi, can also facilitate WM changes in adults during late midlife remains unknown. The present study compares two types of exercise, Tai Chi and walking, with a sedentary control group, in order to examine the effects of exercise on WM microstructure and determine the regional specificity of WM differences. Thirty-six healthy adults between the ages of 55 and 65 years participated in the study. Based on the participants' exercise habits, they were allocated into three groups: Tai Chi, walking, or sedentary control. All participants were required to complete physical fitness measurements and completed magnetic reasoning imaging (MRI) scans. Our results revealed that the Tai Chi group exhibited a higher FA value in the left cerebral peduncle, compared to the sedentary control group. We also observed that both the Tai Chi and walking groups exhibited higher FA values in the right uncinate fasciculus and the left external capsule, in comparison to the sedentary control group. Increased FA values in these regions was positively correlated with higher levels of physical fitness measurements (i.e., peak oxygen uptake [VO2peak], muscular endurance/number of push-up, agility, power). These findings collectively suggest that regular exercise is associated with improved WM microstructural integrity, regardless of the exercise type, which could guide the development and application of future prevention and intervention strategies designed to address age-related cognitive impairments during late midlife.

The mean diffusivity of forceps minor is useful to distinguish amnestic mild cognitive impairment from mild cognitive impairment caused by cerebral small vessel disease

Objectives

In recent years, the desire to make a more fine-grained identification on mild cognitive impairment (MCI) has become apparent, the etiological diagnosis of MCI in particular. Nevertheless, new methods for the etiological diagnosis of MCI are currently insufficient. The objective of this study was to establish discriminative measures for amnestic mild cognitive impairment (a-MCI) and MCI caused by cerebral small vessel disease (CSVD).

Materials and methods

In total, 20 normal controls (NCs), 33 a-MCI patients, and 25 CSVD-MCI patients performed comprehensive neuropsychological assessments concerning global cognitive function and five cognitive domains as well as magnetic resonance imaging scan with diffusion tensor imaging (DTI). Diffusion parameters including fractional anisotropy and mean diffusivity of 20 major white matter metrics were obtained by ROI-based analyses. The neuropsychological tests and diffusion measurements were compared and binary logistic regression was used to identify the best differential indicator for the two MCI subgroups. The discriminating power was calculated by receiver operating characteristic analysis.

Results

Amnestic mild cognitive impairment group showed significant impairment in memory and language function, while CSVD-MCI group revealed more deficits in multi-cognitive domains of memory, language, attention and executive function than controls. Compared to the a-MCI, CSVD-MCI was significantly dysfunctional in the executive function. The CSVD-MCI group had decreased fractional anisotropy and increased mean diffusivity values throughout widespread white matter areas. CSVD-MCI presented more severe damage in the anterior thalamic radiation, forceps major, forceps minor and right inferior longitudinal fasciculus compared with a-MCI group. No significant neuropsychological tests were found in the binary logistic regression model, yet the DTI markers showed a higher discriminative power than the neuropsychological tests. The Stroop test errors had moderate potential (AUC = 0.747; sensitivity = 76.0%; specificity = 63.6%; P = 0.001; 95% CI: 0.617–0.877), and the mean diffusivity value of forceps minor demonstrated the highest predictive power to discriminate each MCI subtype (AUC = 0.815; sensitivity = 88.0%; specificity = 72.7%; P &lt; 0.001; 95% CI: 0.698–0.932).

Conclusion

The mean diffusivity of forceps minor may serve as an optimal indicator to differentiate between a-MCI and CSVD-MCI.

Altered Microstructural Changes Detected by Diffusion Kurtosis Imaging in Patients With Cognitive Impairment After Acute Cerebral Infarction

Objective

To detect the microstructural changes in patients with cognitive impairment after acute cerebral infarction using diffusion kurtosis imaging (DKI).

Materials and Methods

A total of 70 patients with acute cerebral infarction were divided into two groups: 35 patients with cognitive impairment (VCI group), and 35 patients without cognitive impairment (N-VCI group), according to mini-mental state examination (MMSE) score. Healthy individuals (n = 36) were selected as the normal control (NORM) group. DKI parameters from 28 different brain regions of interest (ROIs) were selected, measured, and compared.

Results

VCI group patients had significantly higher mean diffusion (MD) and significantly lower mean kurtosis (MK) values in most ROIs than those in the N-VCI and NORM groups. DKI parameters in some ROIs correlated significantly with MMSE score. The splenium of corpus callosum MD was most correlated with MMSE score, the correlation coefficient was −0.652, and this parameter had good ability to distinguish patients with VCI from healthy controls; at the optimal cut-off MD value (0.9915), sensitivity was 91.4%, specificity 100%, and the area under the curve value 0.964.

Conclusions

Pathological changes in some brain regions may underlie cognitive impairment after acute cerebral infarction, especially the splenium of corpus callosum. These preliminary results suggest that, in patients with VCI, DKI may be useful for assessing microstructural tissue damage.

Conductance-Based Structural Brain Connectivity in Aging and Dementia

Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.

Advances in computerized MRI‐based biomarkers in Alzheimer’s disease

The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.

Neuroimaging in Vascular Cognitive Impairment and Dementia: A Systematic Review

Cerebrovascular diseases are well established causes of cognitive impairment. Different etiologic entities, such as vascular dementia (VaD), vascular cognitive impairment, subcortical (ischemic) VaD, and vascular cognitive disorder, are included in the umbrella definition of vascular cognitive impairment and dementia (VCID). Because of the variability of VCID clinical presentation, there is no agreement on criteria defining the neuropathological threshold of this disorder. In fact, VCID is characterized by cerebral hemodynamic alteration which ranges from decreased cerebral blood flow to small vessels disease and involves a multifactorial process that leads to demyelination and gliosis, including blood-brain barrier disruption, hypoxia, and hypoperfusion, oxidative stress, neuroinflammation and alteration on neurovascular unit coupling, cerebral microbleeds, or superficial siderosis. Numerous criteria for the definition of VaD have been described: the National Institute of Neurological Disorders and Stroke Association Internationale pour Recherche'-et-l'Enseignement en Neurosciences criteria, the State of California Alzheimer's Disease Diagnostic and Treatment Centers criteria, DSM-V criteria, the Diagnostic Criteria for Vascular Cognitive Disorders (a VASCOG Statement), and Vascular Impairment of Cognition Classification Consensus Study. Neuroimaging is fundamental for definition and diagnosis of VCID and should be used to assess the extent, location, and type of vascular lesions. MRI is the most sensible technique, especially if used according to standardized protocols, even if CT plays an important role in several conditions. Functional neuroimaging, in particular functional MRI and PET, may facilitate differential diagnosis among different forms of dementia. This systematic review aims to explore the state of the art and future perspective of non-invasive diagnostics of VCID.

Default mode network integrity changes contribute to cognitive deficits in subcortical vascular cognitive impairment, no dementia

Vascular cognitive impairment, no dementia (VCIND) refers to cognitive deficits associated with underlying vascular causes that are insufficient to confirm a diagnosis of dementia. The default mode network (DMN) is a large-scale brain network of interacting brain regions involved in attention, working memory and executive function. The role of DMN white matter integrity in cognitive deficits of VCIND patients is unclear. Using diffusion tensor imaging (DTI), this study was carried out to investigate white matter microstructural changes in the DMN in VCIND patients and their contributions to cognitive deficits. Thirty-one patients with subcortical VCIND and twenty-two healthy elderly subjects were recruited. All patients underwent neuropsychological assessments and DTI examination. Voxel-based analyses were performed to extract fractional anisotropy (FA) and mean diffusivity (MD) measures in the DMN. Compared with the healthy elderly subjects, patients diagnosed with subcortical VCIND presented with abnormal white matter integrity in several key hubs of the DMN. The severity of damage in the white matter microstructure in the DMN significantly correlated with cognitive dysfunction. Mediation analyses demonstrated that DTI values could account for attention, executive and language impairments, and partly mediated global cognitive dysfunction in the subcortical VCIND patients. DMN integrity is significantly impaired in subcortical VCIND patients. The disrupted DMN connectivity could explain the attention, language and executive dysfunction, which indicates that the white matter integrity of the DMN may be a neuroimaging marker for VCIND.

Beyond Alzheimer's disease: Can bilingualism be a more generalized protective factor in neurodegeneration?

Bilingualism has been argued to have an impact on cognition and brain structure. Effects have been reported across the lifespan: from healthy children to ageing adults, including clinical (ageing) populations. It has been argued that active bilingualism may significantly contribute to the delaying of the expression of Alzheimer's disease symptoms. If bilingualism plays an ameliorative role against the expression of neurodegeneration in dementia, it is possible that it could have similar effects for other neurodegenerative disorders, including Multiple Sclerosis, Parkinson's and Huntington's Diseases. To date, however, direct relevant evidence remains limited, not least because the necessary scientific motivations for investigating this with greater depth have not yet been fully articulated. Herein, we provide a roadmap that reviews the relevant literatures, highlighting potential links across neurodegenerative disorders and bilingualism more generally.

Micro-structural white matter abnormalities and cognitive impairment in asymptomatic carotid plaque patients: A DTI study using TBSS analysis

BACKGROUND

It has been shown that symptomatic or severe carotid atherosclerosis is closely related to cognitive impairment and brain white matter damage. However, there is still a lack of effective and non-invasive imaging biomarkers to identify early high-risk cerebrovascular diseases. Therefore, the purpose of this study is to explore the integrity of brain white matter and cognitive impairment in patients with asymptomatic carotid plaques by using imaging technology.

METHODS

All subjects were from a project of Stroke Risk Screening and Prevention and were defined as stroke high-risk patients (with three or more stroke risk factors). Tract-based spatial statistics (TBSS) based on diffusion tensor imaging (DTI) was used to analyze the whole brain white matter abnormalities in 61 patients with carotid artery plaque and in 40 healthy controls. At the same time, the general clinical data between the two groups were compared, such as age, gender, smoking, hypertension and cognitive function scores etc. Furthermore, the plaque group was divided into the have-hyperintensities group and the no-hyperintensities group to compare their microstructure of white matter injuries.

RESULTS

The cognitive scores of plaque group were significantly lower than that of control group. We found that when plaque group and control group were compared, no white matter fiber tracts with difference was found in FA, MD, AD and RD. However, the decrease of FA and the increase of RD were found in some white matter regions (P < 0.05) when comparing the have-hyperintensities group and the no-hyperintensities group. These white matter regions included anterior thalamic radiation, corticospinal tract, cingulum (cingulate gyrus), forceps minor, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus. What's more, there were significant differences in blood pressure between the two groups.

CONCLUSION

The cognitive function of patients with early high-risk cerebrovascular diseases (asymptomatic carotid plaques) has a downward trend. TBSS based on DTI can help to find out the actual damage of brain white matter in patients with early carotid plaque, and reflect the early pathological changes from the micro level.

Prospective associations between physical activity levels and white matter integrity in older adults: results from the MAPT study

BACKGROUND

Higher levels of physical activity (PA) are known to be associated with better white matter integrity measured by diffusion tensor imaging (DTI) in older adults in cross-sectional studies. However, no studies have investigated the association between PA levels and the evolution of DTI parameters (fractional anisotropy and mean diffusivity).

OBJECTIVES

To examine the cross-sectional associations between PA levels and DTI parameters, then to investigate the association between baseline PA levels and the evolution of DTI parameters in older adults.

METHODS

Data on magnetic resonance imaging with DTI method from the Multidomain Alzheimer's Preventive Trial (MAPT) study were used; 228 participants had data on DTI measured at three time-points over five years. Fractional anisotropy and mean diffusivity were acquired for six different brain regions.

RESULTS

No significant associations were found in the cross-sectional analyses. Only one association was found: compared with active individuals, a faster worsening in the mean diffusivity of the uncinate fasciculus region was found in inactive individuals (-5.0 × 10^-6 (-9.5 × 10^-5, 4.9 × 10^-6)).

CONCLUSIONS

In this study, we found that the condition of the uncinate fasciculus region may be susceptible to changes in PA levels in older adults. Longitudinal studies that assess fitness and PA using objective measurements (e.g. cardiorespiratory fitness and accelerometry) could shed some new light on this topic.

Innovative MRI Techniques in Neuroimaging Approaches for Cerebrovascular Diseases and Vascular Cognitive Impairment

Cognitive impairment and dementia are recognized as major threats to public health. Many studies have shown the important role played by challenges to the cerebral vasculature and the neurovascular unit. To investigate the structural and functional characteristics of the brain, MRI has proven an invaluable tool for visualizing the internal organs of patients and analyzing the parameters related to neuronal activation and blood flow in vivo. Different strategies of imaging can be combined to obtain various parameters: (i) measures of cortical and subcortical structures (cortical thickness, subcortical structures volume); (ii) evaluation of microstructural characteristics of the white matter (fractional anisotropy, mean diffusivity); (iii) neuronal activation and synchronicity to identify functional networks across different regions (functional connectivity between specific regions, graph measures of specific nodes); and (iv) structure of the cerebral vasculature and its efficacy in irrorating the brain (main vessel diameter, cerebral perfusion). The high amount of data obtainable from multi-modal sources calls for methods of advanced analysis, like machine-learning algorithms that allow the discrimination of the most informative features, to comprehensively characterize the cerebrovascular network into specific and sensitive biomarkers. By using the same techniques of human imaging in pre-clinical research, we can also investigate the mechanisms underlying the pathophysiological alterations identified in patients by imaging, with the chance of looking for molecular mechanisms to recover the pathology or hamper its progression.

Characterization of white matter changes along fibers by automated fiber quantification in the early stages of Alzheimer's disease

Brain white matter fiber bundles in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) have abnormalities not usually seen in unaffected subjects. Ideal algorithm of the localization-specific properties in white matter integrity might reveal the changes of tissue properties varying along each tract, while previous studies only detected the mean DTI parameters of each fiber. The aim of this study was to investigate whether these abnormalities of nerve fiber tracts are localized to specific regions of the tracts or spread throughout and to analyze which of the examined fiber tracts are involved in the early stages of Alzheimer's disease. In this study, we utilized VBA, TBSS as well as AFQ together to comprehensively investigate the white matter fiber impairment on 25 CE patients, 29 MCI patients and 34 normal control (NC) subjects. Two tract profiles, fractional anisotropy (FA) and mean diffusivity (MD), were extracted to evaluate the white matter integrity at 100 locations along each of 20 fiber tracts and then we validated the results with 27 CE patients, 21 MCI patients and 22 NC from the ADNI cohort. Also, we compare the AFQ with VBA and TBSS in our cohort. In comparison with NC, AD patients showed widespread FA reduction in 25% (5 /20) and MD increase in 65%(13/20) of the examined fiber tracts. The MCI patients showed a regional FA reduction in 5% (1/20) of the examined fiber tracts (right cingulum cingulate) and MD increase in 5%(1/20) of the examined fiber tracts (left arcuate fasciculus). Among these changed tracts, only the right cingulum cingulate showed widespread disruption of myelin or/and fiber axons in MCI and aggravated deterioration in AD, findings supported by FA/MD changes both by the mean and FA changes by point wise methods and TBSS. And the AFQ findings from ADNI cohort showed some similarity with our cohort, especially in the pointwise comparison of MD profiles between AD vs NC. Furthermore, the pattern of white matter abnormalities was different across neuronal fiber tracts; for example, the MCI and AD patients showed similar FA reduction in the middle part of the right cingulum cingulate, and the anterior part were not damaged. However, the left arcuate fasciculus showed MD elevation located at the temporal part of the fibers in the MCI patients and expanding to the temporal and middle part of the fibers in AD patients. So, the AFQ may be an alternative complementary method of VBA and TBSS, and may provide new insights into white matter degeneration in MCI and its association with AD.

Specific Patterns of White Matter Alterations Help Distinguishing Alzheimer's and Vascular Dementia

Alzheimer disease (AD) and vascular dementia (VaD) together represent the majority of dementia cases. Since their neuropsychological profiles often overlap and white matter lesions are observed in elderly subjects including AD, differentiating between VaD and AD can be difficult. Characterization of these different forms of dementia would benefit by identification of quantitative imaging biomarkers specifically sensitive to AD or VaD. Parameters of microstructural abnormalities derived from diffusion tensor imaging (DTI) have been reported to be helpful in differentiating between dementias, but only few studies have used them to compare AD and VaD with a voxelwise approach. Therefore, in this study a whole brain statistical analysis was performed on DTI data of 93 subjects (31 AD, 27 VaD, and 35 healthy controls—HC) to identify specific white matter patterns of alteration in patients affected by VaD and AD with respect to HC. Parahippocampal tracts were found to be mainly affected in AD, while VaD showed more spread white matter damages associated with thalamic radiations involvement. The genu of the corpus callosum was predominantly affected in VaD, while the splenium was predominantly affected in AD revealing the existence of specific patterns of alteration useful in distinguishing between VaD and AD. Therefore, DTI parameters of these regions could be informative to understand the pathogenesis and support the etiological diagnosis of dementia. Further studies on larger cohorts of subjects, characterized for brain amyloidosis, will allow to confirm and to integrate the present findings and, furthermore, to elucidate the mechanisms of mixed dementia. These steps will be essential to translate these advances to clinical practice.

How does white matter microstructure differ between the vascular and amnestic mild cognitive impairment?

Changes in white matter (WM) microstructure may relate to the pathophysiology of cognitive impairment. Whether WM microstructure differs in two common pre-dementia subtypes, vascular mild cognitive impairment (VaMCI) and amnestic mild cognitive impairment (aMCI), is largely unknown. This study included 28 VaMCI (12 men, age: 46 ~ 77 years) and 34 aMCI patients (14 men, age: 51 ~ 79 years). All patients underwent a battery of neuropsychological tests and structural and diffusion magnetic resonance imaging (MRI) scanning. WM microstructure was quantified using diffusion MRI parameters: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD) and radial diffusivity (RD). These parameters were compared between the two patient groups using tract-based spatial statistics (TBSS) after controlling for age, gender, and education. No significant differences in FA/MD/AxD/RD were observed between the VaMCI and aMCI groups, which suggests a similar pattern of WM microstructure in the early stage of cognitive impairment for different dementia types. However, the two groups exhibited significant differences in the relationship between FA and the Auditory Verbal Learning Test (AVLT), which were primarily located around the corona radiate and corpus callosum. Specifically, there were significant positive correlations (R = 0.64, P < 0.001) between the FA and AVLT in the VaMCI group, but the opposite trend was observed in the aMCI group (R = -0.34, P = 0.047). The differential relationship between WM and memory between VaMCI and aMCI indicates an independent neuropathology for specific memory deficits in different types of dementia.

Contribution of diffusion, perfusion and functional MRI to the disconnection hypothesis in subcortical vascular cognitive impairment

Vascular cognitive impairment (VCI) describes all forms of cognitive impairment caused by any type of cerebrovascular disease. Early identification of VCI is quite difficult due to the lack of both sensitive and specific biomarkers. Extensive damage to the white matter tracts, which connect the cortical and subcortical regions, has been shown in subcortical VCI (SVCI), the most common subtype of VCI that is caused by small vessel disease. Two specific MRI sequences, including diffusion tensor imaging (DTI) and functional MRI (fMRI), have emerged as useful tools for identifying subtle white matter changes and the intrinsic connectivity between distinct cortical regions. This review describes the advantages of these two modalities in SVCI research and the current DTI and fMRI findings on SVCI. Using DTI technique, a variety of studies found that white matter microstructural damages in the anterior and superior areas are more specific to SVCI. Similarly, functional brain abnormalities detected by fMRI have also been mainly shown in anterior brain areas in SVCI. The characteristic distribution of brain abnormalities in SVCI interrupts the prefrontal-subcortical loop that results in cognitive impairments in particular domains, which further confirms the ‘disconnection syndrome’ hypothesis. In addition, another MRI technique, arterial spin labelling (ASL), has been used to describe the disconnection patterns in a variety of conditions by measuring cerebral blood flow. The role of the ASL technique in SVCI research is also assessed. Finally, the review proposes the application of multimodality fusion in the investigation of SVCI pathogenesis.

Frontal Assessment Battery as a Useful Tool to Differentiate Mild Cognitive Impairment due to Subcortical Ischemic Vascular Disease from Alzheimer Disease

BACKGROUND

Prominent executive dysfunction can differentiate vascular dementia from Alzheimer disease (AD). However, it is unclear whether the Frontal Assessment Battery (FAB) screening tool can differentiate subcortical ischemic vascular disease (SIVD) from AD at the pre-dementia stage. In addition, the neural correlates of FAB performance have yet to be clarified.

METHODS

Patients with mild cognitive impairment (MCI) due to SIVD (MCI-V), MCI due to AD (MCI-A), and demographically matched controls completed the Mini-Mental State Examination, Taiwanese FAB (TFAB), Category Fluency, and Chinese Version of the Verbal Learning Test, and underwent magnetic resonance imaging. White matter hyperintensities were rated according to the Scheltens scale.

RESULTS

TFAB total scale and its Orthographical Fluency subtest were the only measures that could differentiate MCI-V from MCI-A. Discriminative analysis showed that Orthographical Fluency scores successfully identified 73.2% of the cases with MCI-V, with 85.0% sensitivity. Orthographical Fluency scores were specifically associated with lesion load within frontal periventricular, frontal deep white matter, and basal ganglia regions.

CONCLUSION

The TFAB, and especially its 1-min Orthographical Fluency subtest, is a useful screening procedure to differentiate MCI due to SIVD from MCI due to AD. The discriminative ability is probably due to frontosubcortical white matter pathologies disproportionately involved in the two disease entities.

Dementia risk and prevention by targeting modifiable vascular risk factors

The incidence of dementia is expected to double in the next 20 years and will contribute to heavy social and economic burden. Dementia is caused by neuronal loss that leads to brain atrophy years before symptoms manifest. Currently, no cure exists and extensive efforts are being made to mitigate cognitive impairment in late life in order to reduce the burden on patients, caregivers, and society. The most common type of dementia, Alzheimer's disease (AD), and vascular dementia (VaD) often co-exists in the brain and shares common, modifiable risk factors, which are targeted in numerous secondary prevention trials. There is a growing need for non-pharmacological interventions and infrastructural support from governments to encourage psychosocial and behavioral interventions. Secondary prevention trials need to be redesigned based on the risk profile of individual subjects, which require the use of validated and standardized clinical, biological, and neuroimaging biomarkers. Multi-domain approaches have been proposed in high-risk populations that target optimal treatment; clinical trials need to recruit individuals at the highest risk of dementia before symptoms develop, thereby identifying an enriched disease group to test preventative and disease modifying strategies. The underlying aim should be to reduce microscopic brain tissue loss by modifying vascular and lifestyle risk factors over a relatively short period of time, thus optimizing the opportunity for preventing dementia in the future. Collaboration between international research groups is of key importance to the optimal use and allocation of existing resources, and the development of new techniques in preventing dementia. This article is part of the Special Issue "Vascular Dementia".

Changes to white matter microstructure in transient ischemic attack: A longitudinal diffusion tensor imaging study

Transient ischemic attack (TIA) is associated with localized ischemic changes, identifiable by diffusion-weighted imaging. Past research has not considered whether TIA is also associated with diffuse changes to white matter microstructure; further past work has not tracked changes longitudinally. Here we examine whole-brain changes in fractional anisotropy (FA) in individuals with TIA presenting with sensorimotor symptoms. Twenty individuals with a recent (within 30 days) TIA and 12 healthy older adults were recruited. Participants underwent 3.0 T diffusion MRI at baseline; scans were repeated for the TIA group 90 days post-TIA. Track-based spatial statistics (TBSS) was used to conduct a voxel-wise analysis of FA between groups. FA was significantly lower in the TIA group relative to healthy controls, primarily in anterior white matter tracts including: forceps minor, anterior thalamic radiations, cingulum, inferior fronto-occipital fasciculus, and corticospinal tract. TBSS results informed an ROI-based longitudinal examination of FA in the TIA group. There were no changes to TBSS-identified clusters, forceps minor, or the corticospinal tract over time. There was lower FA in the anterior thalamic radiations in the TIA-affected hemisphere at baseline, but no difference between hemispheres at 90 days. In summary, individuals with TIA presenting with sensorimotor symptoms have decreased FA in tracts that are also implicated in sensorimotor function, which outlast the clinical symptoms associated with TIA. This suggests a more profound type of brain damage associated with TIA than has been typically described in past work. Diffusion tensor imaging may have utility as a marker of TIA-associated changes to white matter pathways. Hum Brain Mapp 38:5795-5803, 2017. © 2017 Wiley Periodicals, Inc.

An endothelial link between the benefits of physical exercise in dementia

The current absence of a disease-modifying treatment for Alzheimer's disease (AD) and vascular cognitive impairment and dementia (VCID) highlights the necessity for investigating the benefits of non-pharmacological approaches such as physical exercise (PE). Although evidence exists to support an association between regular PE and higher scores on cognitive function tests, and a slower rate of cognitive decline, there is no clear consensus on the underlying molecular mechanisms of the advantages of PE. This review seeks to summarize the positive effects of PE in human and animal studies while highlighting the vascular link between these benefits. Lifestyle factors such as cardiovascular diseases, metabolic syndrome, and sleep apnea will be addressed in relation to the risk they pose in developing AD and VCID, as will molecular factors known to have an impact on either the initiation or the progression of AD and/or VCID. This will include amyloid-beta clearance, oxidative stress, inflammatory responses, neurogenesis, angiogenesis, glucose metabolism, and white matter integrity. Particularly, this review will address how engaging in PE can counter factors that contribute to disease pathogenesis, and how these alterations are linked to endothelial cell function.

MRI-based comparative study of different mild cognitive impairment subtypes: protocol for an observational case-control study

INTRODUCTION

Amnestic mild cognitive impairment (aMCI) and vascular mild cognitive impairment (VaMCI) comprise the 2 main types of mild cognitive impairment (MCI). The first condition generally progresses to Alzheimer's disease, whereas the second is likely to develop into vascular dementia (VD). The brain structure and function of patients with MCI differ from those of normal elderly individuals. However, whether brain structures or functions differ between these 2 MCI subtypes has not been studied. This study is designed to analyse neuroimages of brain in patients with VaMCI and aMCI using multimodality MRI (structural MRI (sMRI), functional MRI and diffusion tensor imaging (DTI)).

METHODS AND ANALYSIS

In this study, 80 participants diagnosed with aMCI, 80 participants diagnosed with VaMCI, and 80 age-matched, gender-matched and education-matched normal controls (NCs) will be recruited to the Hongqi Hospital of Mudanjiang Medical University, Heilongjiang, China. All participants will undergo neuroimaging and neuropsychological evaluations. The primary outcome measures will be (1) microstructural alterations revealed by multimodal MRIs, including sMRI, resting-state functional MRI and DTI; and (2) a neuropsychological evaluation, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Memory and Executive Screening (MES), trail making test, Stroop colour naming condition and Clinical Dementia Rating (CDR) scale, to evaluate global cognition, memory function, attention, visuospatial skills, processing speed, executive function and emotion, respectively.

TRIAL REGISTRATION NUMBER

NCT02706210; Pre-results.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

The Disconnection Hypothesis in Alzheimer's Disease Studied Through Multimodal Magnetic Resonance Imaging: Structural, Perfusion, and Diffusion Tensor Imaging

According to the so-called disconnection hypothesis, the loss of synaptic inputs from the medial temporal lobes (MTL) in Alzheimer's disease (AD) may lead to reduced activity of target neurons in cortical areas and, consequently, to decreased cerebral blood flow (CBF) in those areas. The aim of this study was to assess whether hypoperfusion in parietotemporal and frontal cortices of patients with mild cognitive impairment who converted to AD (MCI-c) and patients with mild AD is associated with atrophy in the MTL and/or microstructural changes in the white matter (WM) tracts connecting these areas. We assessed these relationships by investigating correlations between CBF in hypoperfused areas, mean cortical thickness in atrophied regions of the MTL, and fractional anisotropy (FA) in WM tracts. In the MCI-c group, a strong correlation was observed between CBF of the superior parietal gyri and FA in the parahippocampal tracts (left: r = 0.90, p <  0.0001; right: r = 0.597, p = 0.024), and between FA in the right parahippocampal tract and the right precuneus (r = 0.551, p = 0.041). No significant correlations between CBF in hypoperfused regions and FA in the WM tract were observed in the AD group. These results suggest an association between perfusion deficits and altered WM tracts in prodromal AD, while microvasculature impairments may have a greater influence in more advanced stages. We did not find correlations between cortical thinning in the medial temporal lobes and decreased FA in the WM tracts of the limbic system in either group.

NODDI reproducibility and variability with magnetic field strength: A comparison between 1.5 T and 3 T

Diffusion models are advantageous for examining brain microstructure non-invasively and their validation is important for transference into the clinical domain. Neurite Orientation Dispersion and Density Imaging (NODDI) is a promising model for estimating multiple diffusion compartments from MRI data acquired in a clinically feasible time. As a relatively new model, it is necessary to examine NODDI under certain experimental conditions, such as change in magnetic field-strength, and assess it in relation to diffusion tensor imaging (DTI), an established model that is largely understood by the neuroimaging community. NODDI measures (intracellular volume fraction, ν_ic , and orientation distribution, OD) were compared with DTI at 1.5 and 3 T data in healthy adults in whole-brain tissue masks and regions of white- and deep grey-matter. Within-session reproducibility and between-subject differences of NODDI with field-strength were also investigated. Field-strength had a significant effect on NODDI measures, suggesting careful interpretation of results from data acquired at 1.5 and 3 T. It was demonstrated that NODDI is feasible at 1.5 T, but with lower ν_ic in white-matter regions compared with 3 T. Furthermore, the advantages of NODDI over DTI in a region of complex microstructure were shown. Specifically, in the centrum-semiovale where FA is typically as low as in grey-matter, ν_ic was comparable to other white-matter regions yet accompanied by an OD similar to deep grey-matter. In terms of reproducibility, NODDI measures varied more than DTI. It may be that NODDI is more susceptible to noisier parameter estimates when compared with DTI, conversely it may have greater sensitivity to true within- and between-subject heterogeneity. Hum Brain Mapp 37:4550-4565, 2016. © 2016 Wiley Periodicals, Inc.

Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer's disease: a systematic review

Background

Neuroimaging-biomarkers of Mild Cognitive Impairment (MCI) allow an early diagnosis in preclinical stages of Alzheimer’s disease (AD). The goal in this paper was to review of biomarkers for Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), with emphasis on neuroimaging biomarkers.

Methods

A systematic review was conducted from existing literature that draws on markers and evidence for new measurement techniques of neuroimaging in AD, MCI and non-demented subjects. Selection criteria included: 1) age ≥ 60 years; 2) diagnosis of AD according to NIAAA criteria, 3) diagnosis of MCI according to NIAAA criteria with a confirmed progression to AD assessed by clinical follow-up, and 4) acceptable clinical measures of cognitive impairment, disability, quality of life, and global clinical assessments.

Results

Seventy-two articles were included in the review. With the development of new radioligands of neuroimaging, today it is possible to measure different aspects of AD neuropathology, early diagnosis of MCI and AD become probable from preclinical stage of AD to AD dementia and non-AD dementia.

Conclusions

The panel of noninvasive neuroimaging-biomarkers reviewed provides a set methods to measure brain structural and functional pathophysiological changes in vivo, which are closely associated with preclinical AD, MCI and non-AD dementia. The dynamic measures of these imaging biomarkers are used to predict the disease progression in the early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials.

Novel imaging techniques in cerebral small vessel diseases and vascular cognitive impairment

Dementia is a global growing concern, affecting over 35 million people with a global economic impact of over $604 billion US. With an ageing population the number of people affected is expected double over the next two decades. Vascular cognitive impairment can be caused by various types of cerebrovascular disease, including cortical and subcortical infarcts, and the more diffuse white matter injury due to cerebral small vessel disease. Although this type of cognitive impairment is usually considered the second most common form of dementia after Alzheimer's disease, there is increasing recognition of the vascular contribution to neurodegeneration, with both pathologies frequently coexisting. The aim of this review is to highlight the recent advances in the understanding of vascular cognitive impairment, with a focus on small vessel diseases of the brain. We discuss recently identified small vessel imaging markers that have been associated with cognitive impairment, namely cerebral microbleeds, enlarged perivascular spaces, cortical superficial siderosis, and microinfarcts. We will also consider quantitative techniques including diffusion tensor imaging, magnetic resonance perfusion imaging with arterial spin labelling, functional magnetic resonance imaging and positron emission tomography. As well as potentially shedding light on the mechanism by which cerebral small vessel diseases cause dementia, these novel imaging biomarkers are also of increasing relevance given their ability to guide diagnosis and reflect disease progression, which may in the future be useful for therapeutic interventions. This article is part of a Special Issue entitled: Vascular Contributions to Cognitive Impairment and Dementia edited by M. Paul Murphy, Roderick A. Corriveau and Donna M. Wilcock.

Multimodality Imaging Approach in Alzheimer disease. Part I: Structural MRI, Functional MRI, Diffusion Tensor Imaging and Magnetization Transfer Imaging

The authors make a complete review of the potential clinical applications of traditional and novel magnetic resonance imaging (MRI) techniques in the evaluation of patients with Alzheimer's disease, including structural MRI, functional MRI, diffusion tension imaging and magnetization transfer imaging.

Disrupted topological organization of resting-state functional brain network in subcortical vascular mild cognitive impairment

AIMS

Neuroimaging studies have demonstrated both structural and functional abnormalities in widespread brain regions in patients with subcortical vascular mild cognitive impairment (svMCI). However, whether and how these changes alter functional brain network organization remains largely unknown.

METHODS

We recruited 21 patients with svMCI and 26 healthy control (HC) subjects who underwent resting-state functional magnetic resonance imaging scans. Graph theory-based network analyses were used to investigate alterations in the topological organization of functional brain networks.

RESULTS

Compared with the HC individuals, the patients with svMCI showed disrupted global network topology with significantly increased path length and modularity. Modular structure was also impaired in the svMCI patients with a notable rearrangement of the executive control module, where the parietal regions were split out and grouped as a separate module. The svMCI patients also revealed deficits in the intra- and/or intermodule connectivity of several brain regions. Specifically, the within-module degree was decreased in the middle cingulate gyrus while it was increased in the left anterior insula, medial prefrontal cortex and cuneus. Additionally, increased intermodule connectivity was observed in the inferior and superior parietal gyrus, which was associated with worse cognitive performance in the svMCI patients.

CONCLUSION

Together, our results indicate that svMCI patients exhibit dysregulation of the topological organization of functional brain networks, which has important implications for understanding the pathophysiological mechanism of svMCI.

Diffusion-MRI in neurodegenerative disorders

The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.

Using structural and diffusion magnetic resonance imaging to differentiate the dementias

Dementia is one of the major causes of personal, societal and financial dependence in older people and in today's ageing society there is a pressing need for early and accurate markers of cognitive decline. There are several subtypes of dementia but the four most common are Alzheimer's disease, Lewy body dementia, vascular dementia and frontotemporal dementia. These disorders can only be diagnosed at autopsy, and ante-mortem assessments of "probable dementia (e.g. of Alzheimer type)" are traditionally driven by clinical symptoms of cognitive or behavioural deficits. However, owing to the overlapping nature of symptoms and age of onset, a significant proportion of dementia cases remain incorrectly diagnosed. Misdiagnosis can have an extensive impact, both at the level of the individual, who may not be offered the appropriate treatment, and on a wider scale, by influencing the entry of patients into relevant clinical trials. Magnetic resonance imaging (MRI) may help to improve diagnosis by providing non-invasive and detailed disease-specific markers of cognitive decline. MRI-derived measurements of grey and white matter structural integrity are potential surrogate markers of disease progression, and may also provide valuable diagnostic information. This review summarises the latest evidence on the use of structural and diffusion MRI in differentiating between the four major dementia subtypes.

Effects of age on white matter integrity and negative symptoms in schizophrenia

The current study examined the relationship between white matter integrity as indexed by diffusion tensor imaging and negative symptom severity in schizophrenia. The current study included statistical controls for age effects on the relationship of interest, a major weakness of the existing literature on the subject. Participants included 59 chronic schizophrenia patients, and 31 first-episode schizophrenia patients. Diffusion-weighted neuroimaging was used to calculate fractional anisotropy (FA) in each major brain region (frontal, temporal, parietal, and occipital lobes). Negative symptoms were measured using the Scale for the Assessment of Negative Symptoms (SANS) in all schizophrenia patients. Significant bivariate correlations were observed between global SANS scores and global FA, as well as in most brain regions. These relationships appeared to be driven by SANS items measuring facial expressiveness, poor eye contact, affective flattening, inappropriate affect, poverty of speech, poverty of speech content, alogia, and avolition. However, upon addition of age as a covariate, the observed relationships became non-significant. Further analysis revealed very strong age effects on both FA and SANS scores in the current sample. The findings of this study refute previous reports of significant relationships between DTI variables and negative symptoms in schizophrenia, and they suggest an important confounding variable to be considered in future studies in this population.

Age-related differences in medial temporal lobe involvement during conceptual fluency

Not all memory processes are equally affected by aging. A widely accepted hypothesis is that older adults rely more on familiarity-based processing, typically linked with the perirhinal cortex (PRC), in the context of impaired recollection, linked with the hippocampus (HC). However, according to the dedifferentiation hypothesis, healthy aging reduces the specialization of MTL memory subregions so that they may mediate different memory processes than in young adults. Using fMRI, we tested this possibility using a conceptual fluency manipulation known to induce familiarity-related PRC activity. The study yielded two main findings. First, although fluency equivalently affected PRC in both young (18-28; N=14) and older (62-80; N=15) adults, it also uniquely affected HC activity in older adults. Second, the fluency manipulation reduced functional connectivity between HC and PRC in young adults, but it increased it in older adults. Taken together, the results suggest that aging may result in reduced specialization of the HC for recollection, such that the HC may be recruited when fluency increases familiarity-based responding. This article is part of a Special Issue entitled SI: Memory & Aging.

Diffusion tensor imaging in Alzheimer's disease: insights into the limbic-diencephalic network and methodological considerations

Glucose hypometabolism and gray matter atrophy are well known consequences of Alzheimer's disease (AD). Studies using these measures have shown that the earliest clinical stages, in which memory impairment is a relatively isolated feature, are associated with degeneration in an apparently remote group of areas—mesial temporal lobe (MTL), diencephalic structures such as anterior thalamus and mammillary bodies, and posterior cingulate. These sites are thought to be strongly anatomically inter-connected via a limbic-diencephalic network. Diffusion tensor imaging or DTI—an imaging technique capable of probing white matter tissue microstructure—has recently confirmed degeneration of the white matter connections of the limbic-diencephalic network in AD by way of an unbiased analysis strategy known as tract-based spatial statistics (TBSS). The present review contextualizes the relevance of these findings, in which the fornix is likely to play a fundamental role in linking MTL and diencephalon. An interesting by-product of this work has been in showing that alterations in diffusion behavior are complex in AD—while early studies tended to focus on fractional anisotropy, recent work has highlighted that this measure is not the most sensitive to early changes. Finally, this review will discuss in detail several technical aspects of DTI both in terms of image acquisition and TBSS analysis as both of these factors have important implications to ensure reliable observations are made that inform understanding of neurodegenerative diseases.

Alzheimer's disease and the fornix

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia. Researchers have long been focused on the cortical pathology of AD, since the most important pathologic features are the senile plaques found in the cortex, and the neurofibrillary tangles and neuronal loss that begin in the entorhinal cortex and the hippocampus. In addition to these gray matter (GM) structures, histopathological studies indicate that the white matter (WM) is also a good target for both the early diagnosis of AD and for monitoring disease progression. The fornix is a WM bundle that constitutes a core element of the limbic circuits, and is one of the most important anatomical structures related to memory. Functional and anatomical features of the fornix have naturally captured researchers’ attention as possible diagnostic and prognostic markers of AD. Indeed, neurodegeneration of the fornix has been histologically observed in AD, and growing evidence indicates that the alterations seen in the fornix are potentially a good marker to predict future conversion from mild cognitive impairment (MCI) to AD, and even from cognitively normal individuals to AD. The degree of alteration is correlated with the degree of memory impairment, indicating the potential for the use of the fornix as a functional marker. Moreover, there have been attempts to stimulate the fornix using deep brain stimulation (DBS) to augment cognitive function in AD, and ongoing research has suggested positive effects of DBS on brain glucose metabolism in AD patients. On the other hand, disease specificity for fornix degeneration, methodologies to evaluate fornix degeneration, and the clinical significance of the fornix DBS, especially for the long-term impact on the quality of life, are mostly unknown and need to be elucidated.

Abnormalities of cortical thickness, subcortical shapes, and white matter integrity in subcortical vascular cognitive impairment

Subcortical vascular cognitive impairment (sVCI) is caused by lacunar infarcts or extensive and/or diffuse lesions in the white matter that may disrupt the white matter circuitry connecting cortical and subcortical regions and result in the degeneration of neurons in these regions. This study used structural magnetic resonance imaging (MRI) and high angular resolution diffusion imaging (HARDI) techniques to examine cortical thickness, subcortical shapes, and white matter integrity in mild vascular cognitive impairment no dementia (VCIND Mild) and moderate-to-severe VCI (MSVCI). Our study found that compared to controls (n = 25), VCIND Mild (n = 25), and MSVCI (n = 30) showed thinner cortex predominantly in the frontal cortex. The cortex in MSVCI was thinner in the parietal and lateral temporal cortices than that in VCIND Mild. Moreover, compared to controls, VCIND Mild and MSVCI showed smaller shapes (i.e., volume reduction) in the thalamus, putamen, and globus pallidus and ventricular enlargement. Finally, compared to controls, VCIND Mild, and MSVCI showed an increased mean diffusivity in the white matter, while decreased generalized fractional anisotropy was only found in the MSVCI subjects. The major axonal bundles involved in the white matter abnormalities were mainly toward the frontal regions, including the internal capsule/corona radiata, uncinate fasciculus, and anterior section of the inferior fronto-occipital fasciculus, and were anatomically connected to the affected cortical and subcortical structures. Our findings suggest that abnormalities in cortical, subcortical, and white matter morphology in sVCI occur in anatomically connected structures, and that abnormalities progress along a similar trajectory from the mild to moderate and severe conditions.

Diffusion tensor parameters and principal eigenvector coherence: relation to b-value intervals and field strength

Diffusion-weighted MRI images acquired at b-value greater than 1000 s mm(-2) measure the diffusion of a restricted pool of water molecules. High b-value images are accompanied by a reduction in signal-to-noise ratio (SNR) due to the application of large diffusion gradients. By fitting the diffusion tensor model to data acquired at incremental b-value intervals, we determined the effect of SNR on tensor parameters in normal human brains, in vivo. In addition, we also investigated the impact of field strength on the diffusion tensor model. Data were acquired at 1.5 and 3T, at b-values 0, 1000, 2000 and 3000 s mm(-2) in twenty diffusion-sensitised directions. Fractional anisotropy (FA), mean diffusivity (MD) and principal eigenvector coherence (κ) were calculated from diffusion tensors fitted between datasets with b-values 0-1000, 0-2000, 0-3000, 1000-2000 and 2000-3000 s mm(-2). Field strength and b-value effects on diffusion parameters were analysed in white and grey matter regions of interest. Decreases in FA, κ and MD were found with increasing b-value in white matter. Univariate analysis showed a significant increase in FA with increasing field strength in highly organised white matter. These results suggest there are significant differences in diffusion parameters at 1.5 and 3T and that the optimal results, in terms of the highest values of FA in white matter, are obtained at 3T with a maximum b=1000 s mm(-2).

Different patterns of white matter degeneration using multiple diffusion indices and volumetric data in mild cognitive impairment and Alzheimer patients

Alzheimeŕs disease (AD) represents the most prevalent neurodegenerative disorder that causes cognitive decline in old age. In its early stages, AD is associated with microstructural abnormalities in white matter (WM). In the current study, multiple indices of diffusion tensor imaging (DTI) and brain volumetric measurements were employed to comprehensively investigate the landscape of AD pathology. The sample comprised 58 individuals including cognitively normal subjects (controls), amnestic mild cognitive impairment (MCI) and AD patients. Relative to controls, both MCI and AD subjects showed widespread changes of anisotropic fraction (FA) in the corpus callosum, cingulate and uncinate fasciculus. Mean diffusivity and radial changes were also observed in AD patients in comparison with controls. After controlling for the gray matter atrophy the number of regions of significantly lower FA in AD patients relative to controls was decreased; nonetheless, unique areas of microstructural damage remained, e.g., the corpus callosum and uncinate fasciculus. Despite sample size limitations, the current results suggest that a combination of secondary and primary degeneration occurrs in MCI and AD, although the secondary degeneration appears to have a more critical role during the stages of disease involving dementia.

The importance of group-wise registration in tract based spatial statistics study of neurodegeneration: a simulation study in Alzheimer's disease

Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimer's disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a "ground truth" for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations.