Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging

An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer's disease

Cortical and subcortical nuclei degenerate in the dementias, but less is known about changes in the white matter tracts that connect them. To better understand white matter changes in behavioral variant frontotemporal dementia (bvFTD) and early-onset Alzheimer’s disease (EOAD), we used a novel approach to extract full 3D profiles of fiber bundles from diffusion-weighted MRI (DWI) and map white matter abnormalities onto detailed models of each pathway. The result is a spatially complex picture of tract-by-tract microstructural changes. Our atlas of tracts for each disease consists of 21 anatomically clustered and recognizable white matter tracts generated from whole-brain tractography in 20 patients with bvFTD, 23 with age-matched EOAD, and 33 healthy elderly controls. To analyze the landscape of white matter abnormalities, we used a point-wise tract correspondence method along the 3D profiles of the tracts and quantified the pathway disruptions using common diffusion metrics – fractional anisotropy, mean, radial, and axial diffusivity. We tested the hypothesis that bvFTD and EOAD are associated with preferential degeneration in specific neural networks. We mapped axonal tract damage that was best detected with mean and radial diffusivity metrics, supporting our network hypothesis, highly statistically significant and more sensitive than widely studied fractional anisotropy reductions. From white matter diffusivity, we identified abnormalities in bvFTD in all 21 tracts of interest but especially in the bilateral uncinate fasciculus, frontal callosum, anterior thalamic radiations, cingulum bundles and left superior longitudinal fasciculus. This network of white matter alterations extends beyond the most commonly studied tracts, showing greater white matter abnormalities in bvFTD versus controls and EOAD patients. In EOAD, network alterations involved more posterior white matter – the parietal sector of the corpus callosum and parahipoccampal cingulum bilaterally. Widespread but distinctive white matter alterations are a key feature of the pathophysiology of these two forms of dementia.

Hippocampal volume and cingulum bundle fractional anisotropy are independently associated with verbal memory in older adults

The objective of this study was to investigate the relationship of medial temporal lobe and posterior cingulate cortex (PCC) volumetrics as well as fractional anisotropy of the cingulum angular bundle (CAB) and the cingulum cingulate gyrus (CCG) bundle to performance on measures of verbal memory in non-demented older adults. The participants were 100 non-demented adults over the age of 70 years from the Einstein Aging Study. Volumetric data were estimated from T1-weighted images. The entire cingulum was reconstructed using diffusion tensor MRI and probabilistic tractography. Association between verbal episodic memory and MRI measures including volume of hippocampus (HIP), entorhinal cortex (ERC), PCC and fractional anisotropy of CAB and CCG bundle were modeled using linear regression. Relationships between atrophy of these structures and regional cingulum fractional anisotropy were also explored. Decreased HIP volume on the left and decreased fractional anisotropy of left CAB were associated with lower memory performance. Volume changes in ERC, PCC and CCG disruption were not associated with memory performance. In regression models, left HIP volume and left CAB-FA were each independently associated with episodic memory. The results suggest that microstructural changes in the left CAB and decreased left HIP volume independently influence episodic memory performance in older adults without dementia. The importance of these findings in age and illness-related memory decline require additional exploration.

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".

Connectome imaging for mapping human brain pathways

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research.

Myelin content changes in probable Alzheimer's disease and mild cognitive impairment: Associations with age and severity of neuropsychiatric impairment

BACKGROUND

Existing indices of white matter integrity such as fractional anisotropy and magnetization transfer ratio may not provide optimal specificity to myelin content. In contrast, myelin water fraction (MWF) derived from the multiecho T₂ relaxation time technique may serve as a more direct measure of myelin content.

PURPOSE/HYPOTHESIS

The goal of the present study was to identify markers of regional demyelination in patients with probable Alzheimer's disease (AD) and mild cognitive impairment (MCI) in relation to age and severity of neuropsychiatric impairment.

POPULATION

The sample included patients diagnosed with probable AD (n = 25) or MCI (n = 43), and cognitively intact elderly controls (n = 33).

FIELD STRENGTH/SEQUENCE ASSESSMENT

Long T₂ , short T₂ , and MWF values were measured with a 1.5T scanner in periventricular and deep normal-appearing white matter (NAWM), serving as indices of intra/extracellular water content and myelin content. A comprehensive neuropsychological and neuropsychiatric assessment was administered to all participants.

STATISTICAL TESTS, RESULTS

AD patients displayed higher age-adjusted long and short T₂ values and reduced MWF values in left temporal/parietal and bilateral periventricular NAWM than controls and MCI patients (P < 0.004; one-way analysis of covariance [ANCOVA] tests). Short T₂ /MWF values in temporal, frontal, and periventricular NAWM of controls and/or MCI patients were significantly associated with episodic and semantic memory performance and depressive symptomatology (P < 0.004; partial correlation indices). The impact of age on memory performance was significantly (P < 0.01; mediated linear regression analyses) mediated by age-related changes in short T₂ and MWF values in these regions.

DATA CONCLUSION

Age-related demyelination is associated with memory impairment (especially in prodromal dementia states) and symptoms of depression in an anatomically specific manner.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1359-1372.

Olfactory identification deficit predicts white matter tract impairment in Alzheimer's disease

Olfactory identification deficit (OID) has been associated with both aging and Alzheimer's disease (AD). In the context of an amnestic disorders, OID predicts conversion to AD. Neuroanatomical correlates could increase specificity and sensitivity and elucidate the mechanistic differences between OID in AD and aging. Cross-sectional analysis of white matter microstructural changes was performed using diffusion tensor imaging (DTI) and tract-based-spatial-statistics in amnestic mild cognitive impairment (aMCI), AD and normal controls (NC) in 66 subjects (26 AD, 15 aMCI, 25 NC). DTI 3-Tesla MRI scans were analyzed and subject level means for fractional anisotropy (FA), mean diffusivity (MD), radial and axial diffusivity (λ1D and λ2,3D) were calculated. Linear regression models were applied using DTI markers as predictor and OID as outcome. OID was associated with increased λ1D in aMCI and increased MD, λ1D and λ2,3D in AD. Voxel-wise analyses revealed widespread differences in all markers in AD. There were significant differences in λ1D in aMCI, particularly in the olfactory tract. OID is correlated with microstructural white matter changes as early as in aMCI. This study may help elucidate the biological basis for olfactory impairment in Alzheimer's disease. Neuroanatomical correlates could help distinguish OID associated with AD and that associated with aging.

Cerebral blood flow regulation and neurovascular dysfunction in Alzheimer disease

Cerebral blood flow (CBF) regulation is essential for normal brain function. The mammalian brain has evolved a unique mechanism for CBF control known as neurovascular coupling. This mechanism ensures a rapid increase in the rate of CBF and oxygen delivery to activated brain structures. The neurovascular unit is composed of astrocytes, mural vascular smooth muscle cells and pericytes, and endothelia, and regulates neurovascular coupling. This Review article examines the cellular and molecular mechanisms within the neurovascular unit that contribute to CBF control, and neurovascular dysfunction in neurodegenerative disorders such as Alzheimer disease.

Vulnerability of white matter tracts and cognition to the SOD2 polymorphism: A preliminary study of antioxidant defense genes in brain aging

Oxidative stress is a key mechanism of the aging process that can cause damage to brain white matter and cognitive functions. Polymorphisms in the superoxide dismutase 2 (SOD2) and catalase (CAT) genes have been associated with abnormalities in antioxidant enzyme activity in the aging brain, suggesting a risk for enhanced oxidative damage to white matter and cognition among older individuals with these genetic variants. The present study compared differences in white matter microstructure and cognition among 96 older adults with and without genetic risk factors of SOD2 (rs4880) and CAT (rs1001179). Results revealed higher radial diffusivity in the anterior thalamic radiation among SOD2 CC genotypes compared to CT/TT genotypes. Further, the CC genotype moderated the relationship between the hippocampal cingulum and processing speed, though this did not survive multiple test correction. The CAT polymorphism was not associated with brain outcomes in this cohort. These results suggest that the CC genotype of SOD2 is an important genetic marker of suboptimal brain aging in healthy individuals.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.

Enhancing Diffusion MRI Measures By Integrating Grey and White Matter Morphometry With Hyperbolic Wasserstein Distance

In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the major features, variation of the structural connectivity and the cortical surface morphometry provide different views of structural changes to determine whether AD is present on presymptomatic patients. However, the large scale tensor-valued information and relatively low imaging resolution in diffusion MRI (dMRI) have created huge challenges for analysis. In this paper, we propose a novel framework that improves dMRI analysis power by fusing cortical surface morphometry features from structural MRI (sMRI). We first compute the hyperbolic harmonic maps between cortical surfaces with the landmark constraints thus to precisely evaluate surface tensor-based morphometry. Meanwhile, the graph-based analysis of structural connectivity derived from dMRI is conducted. Next, we fuse these two features via the optimal mass transportation (OMT) and eventually the Wasserstein distance (WD) based single image index is computed as a potential clinical multimodality imaging score. We apply our framework to brain images of 20 AD patients and 20 matched healthy controls, randomly chosen from the Alzheimer's Disease Neuroimaging Initiative (AD-NI2) dataset. Our preliminary experimental results of group classification outperformed those of some other single dMRI-based features, such as regional hippocampal volume, mean scores of fractional anisotropy (FA) and mean axial (MD). The novel image fusion pipeline and simple imaging score of structural changes may benefit the preclinical AD and AD prevention research.

Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits

PURPOSE

In diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FA^DTI ) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors.

METHODS

We compared the ability of standard FA^DTI and TDF-derived FA (FA^TDF ), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume.

RESULTS

Across angular resolutions and statistical tests, FA^TDF showed larger effect sizes than FA^DTI , particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies.

CONCLUSION

The TDF "corrected" form of FA may be a more sensitive and accurate alternative to the commonly used FA^DTI , even in clinical quality dMRI data. Magn Reson Med 78:2322-2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Topological Measurements of DWI Tractography for Alzheimer's Disease Detection

Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer's disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%-99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.

A Multimodal Structural and Functional Neuroimaging Study of Amnestic Mild Cognitive Impairment

Examination of brain structural and functional abnormalities in amnestic mild cognitive impairment (aMCI) has the potential to enhance our understanding of the initial pathophysiological changes in dementia. We examined gray matter volumes and white matter microstructural integrity, as well as resting state functional connectivity (rsFC) in patients with aMCI (N = 48) in comparison to elderly cognitively healthy comparison subjects (N = 48). Brain volumetric comparisons were carried out using voxel-based morphometric analysis of T1-weighted images using the FMRIB Software Library. White matter microstructural integrity was examined using whole-brain tract-based spatial statistics analysis of fractional anisotropy maps generated from diffusion tensor imaging data. Finally, rsFC differences between the samples were examined by Multivariate Exploratory Linear Optimised Decomposition into Independent Components of the resting state functional magnetic resonance imaging time series, followed by between-group comparisons of selected networks using dual regression analysis. Patients with aMCI showed significant gray matter volumetric reductions in bilateral parahippocampal gyri as well as multiple other brain regions including frontal, temporal, and parietal cortices. Additionally, reduced rsFC in the anterior subdivision of the default mode network (DMN) and increased rsFC in the executive network were noted in the absence of demonstrable impairment of white matter microstructural integrity. We conclude that the demonstrable neuroimaging findings in aMCI include significant gray matter volumetric reductions in the fronto-temporo-parietal structures as well as resting state functional connectivity disturbances in DMN and executive network. These findings differentiate aMCI from healthy aging and could constitute the earliest demonstrable neuroimaging findings of incipient dementia.

Independent value added by diffusion MRI for prediction of cognitive function in older adults

The purpose of this study was to determine whether white matter microstructure measured by diffusion magnetic resonance imaging (dMRI) provides independent information about baseline level or change in executive function (EF) or memory (MEM) in older adults with and without cognitive impairment. Longitudinal data was acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study from phases GO and 2 (2009–2015). ADNI participants included were diagnosed as cognitively normal (n = 46), early mild cognitive impairment (MCI) (n = 48), late MCI (n = 29), and dementia (n = 39) at baseline. We modeled the association between dMRI-based global white matter mean diffusivity (MD) and baseline level and change in EF and MEM composite scores, in models controlling for baseline bilateral hippocampal volume, regional cerebral FDG PET metabolism and global cerebral AV45 PET uptake. EF and MEM composite scores were measured at baseline, 6, 12, 24 and 36 months. In the baseline late MCI and dementia groups, greater global MD was associated with lesser baseline EF, but not EF change nor MEM baseline or change. As expected, lesser hippocampal volume and lesser FDG PET metabolism was associated with greater rates of EF and MEM decline. In ADNI-GO/2 participants, white matter integrity provided independent information about current executive function, but was not sensitive to future cognitive change. Since individuals experiencing executive function declines progress to dementia more rapidly than those with only memory impairment, better biomarkers of future executive function decline are needed.

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In the ADNI cohort, MRI and PET predictors of baseline and change in executive function were tested.

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Global mean diffusivity was associated with baseline, but not change in, executive function.

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Diffusion MRI provides independent information on current executive function in older adults.

Morphological and Microstructural Changes of the Hippocampus in Early MCI: A Study Utilizing the Alzheimer's Disease Neuroimaging Initiative Database

Background and Purpose

With the aim of facilitating the early detection of Alzheimer's disease, the Alzheimer's Disease Neuroimaging Initiative proposed two stages based on the memory performance: early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). The current study was designed to investigate structural differences in terms of surface atrophy and microstructural changes of the hippocampus in EMCI and LMCI.

Methods

Hippocampal shape modeling based on progressive template surface deformation was performed on T1-weighted MRI images obtained from 20 cognitive normal (CN) subjects, 17 EMCI patients, and 20 LMCI patients. A template surface in CN was used as a region of interest for diffusion-tensor imaging (DTI) voxel-based morphometry (VBM) analysis. Cluster-wise group comparison was performed based on DTI indices within the hippocampus. Linear regression was performed to identify correlations between DTI metrics and clinical scores.

Results

The hippocampal surface analysis showed significant atrophies in bilateral CA1 regions and the right ventral subiculum in EMCI, in contrast to widespread atrophy in LMCI. DTI VBM analysis showed increased diffusivity in the CA2–CA4 regions in EMCI and additionally in the subiculum region in LMCI. Hippocampal diffusivity was significantly correlated with scores both for the Mini Mental State Examination and on the Modified Alzheimer Disease Assessment Scale cognitive subscale. However, the hippocampal diffusivity did not vary significantly with the fractional anisotropy.

Conclusions

EMCI showed hippocampal surface changes mainly in the CA1 region and ventral subiculum. Diffusivity increased mainly in the CA2–CA4 regions in EMCI, while it decreased throughout the hippocampus in LMCI. Although axial diffusivity showed prominent changes in the right hippocampus in EMCI, future studies need to confirm the presence of this laterality difference. In addition, diffusivity is strongly correlated with the cognitive performance, indicating the possibility of using diffusivity as a biomarker for disease progression.

White matter hyperintensity microstructure in amyloid dysmetabolism

Accumulating evidence suggests associations between cerebrovascular disease (CVD) and Alzheimer's disease (AD). White matter hyperintensities of presumed vascular origin (WMHs) are increased in subjects with mild cognitive impairment (MCI) and AD, but the exact pathomechanistic link is unknown. The current study investigated effects of amyloid dysmetabolism on the microstructure of WMHs in subjects with MCI or subjective cognitive decline (N = 51), dichotomized according to pathological or normal levels of amyloid-β peptide (Aβ42) in cerebrospinal fluid (CSF). Thirty-one subjects with low CSF Aβ42 (Aβ+) and 20 subjects with normal CSF Aβ42 (Aβ-) were assessed with magnetic resonance diffusion tensor imaging (DTI), and fractional anisotropy (FA), radial diffusivity (DR), axial diffusivity (DA), and mean diffusivity (MD) were determined. There were no significant differences in WMH volume or distribution between the groups, and neither age nor WMH volume had significant impact on the DTI indices. Nevertheless, there were significantly higher DA, DR, and MD in WMHs in Aβ+ relative to Aβ-; however, no differences in FA were found. The present results suggest that amyloid accumulation is associated with impaired structural integrity (e.g. relating to more extensive demyelination and loss of axons) in WMHs putatively adding to effects of ischemia.

Novel Effective Connectivity Network Inference for MCI Identification

Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference. This model utilizes not only the discrepancy between observed signals and the model prediction, but also the discrepancy between the associated weak derivatives of the observed and the model signals for a more accurate effective connectivity inference. What's more, a group constraint is applied to minimize the inter-subject variability and the proposed modeling was validated on a mild cognitive impairment dataset with superior results achieved.

White Matter Abnormalities Track Disease Progression in PSEN1 Autosomal Dominant Alzheimer's Disease

PSEN1 mutations are the most frequent cause of autosomal dominant Alzheimer's disease (ADAD), and show nearly full penetrance. There is presently increasing interest in the study of biomarkers that track disease progression in order to test therapeutic interventions in ADAD. We used white mater (WM) volumetric characteristics and diffusion tensor imaging (DTI) metrics to investigate correlations with the normalized time to expected symptoms onset (relative age ratio) and group differences in a cohort of 36 subjects from PSEN1 ADAD families: 22 mutation carriers, 10 symptomatic (SMC) and 12 asymptomatic (AMC), and 14 non-carriers (NC). Subjects underwent a 3T MRI. WM morphometric data and DTI metrics were analyzed. We found that PSEN1 MC showed significant negative correlation between fractional anisotropy (FA) and the relative age ratio in the genus and body of corpus callosum and corona radiate (p <  0.05 Family-wise error correction (FWE) at cluster level) and positive correlation with mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD) in the splenium of corpus callosum. SMC presented WM volume loss, reduced FA and increased MD, AxD, and RD in the anterior and posterior corona radiate, corpus callosum (p <  0.05 FWE) compared with NC. No significant differences were observed between AMC and NC in WM volume or DTI measures. These findings suggest that the integrity of the WM deteriorates linearly in PSEN1 ADAD from the early phases of the disease; thus DTI metrics might be useful to monitor the disease progression. However, the lack of significant alterations at the preclinical stages suggests that these indexes might not be good candidates for early markers of the disease.

3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.

Diffusion Tensor Imaging Predictors of Episodic Memory Decline in Healthy Elders at Genetic Risk for Alzheimer's Disease

OBJECTIVES

White matter (WM) integrity within the mesial temporal lobe (MTL) is important for episodic memory (EM) functioning. The current study investigated the ability of diffusion tensor imaging (DTI) in MTL WM tracts to predict 3-year changes in EM performance in healthy elders at disproportionately higher genetic risk for Alzheimer's disease (AD).

METHODS

Fifty-one cognitively intact elders (52% with family history (FH) of dementia and 33% possessing an Apolipoprotein E ε4 allelle) were administered the Rey Auditory Verbal Learning Test (RAVLT) at study entry and at 3-year follow-up. DTI scanning, conducted at study entry, examined fractional anisotropy and mean, radial and axial diffusion within three MTL WM tracts: uncinate fasciculus (UNC), cingulate-hippocampal (CHG), and fornix-stria terminalis (FxS). Correlations were performed between residualized change scores computed from RAVLT trials 1-5, immediate recall, and delayed recall scores and baseline DTI measures; MTL gray matter (GM) and WM volumes; demographics; and AD genetic and metabolic risk factors.

RESULTS

Higher MTL mean and axial diffusivity at baseline significantly predicted 3-year changes in EM, whereas baseline MTL GM and WM volumes, FH, and metabolic risk factors did not. Both ε4 status and DTI correlated with change in immediate recall.

CONCLUSIONS

Longitudinal EM changes in cognitively intact, healthy elders can be predicted by disruption of the MTL WM microstructure. These results are derived from a sample with a disproportionately higher genetic risk for AD, suggesting that the observed WM disruption in MTL pathways may be related to early neuropathological changes associated with the preclinical stage of AD. (JINS, 2016, 22, 1005-1015).

Hippocampal phospho-tau/MAPT neuropathology in the fornix in Alzheimer disease: an immunohistochemical autopsy study

Whereas early Alzheimer disease (AD) neuropathology and mild cognitive impairment are relatively common in aging, accurate prediction of patients that will progress to dementia requires new biomarkers. Recently, substantial work has focused on phospho-tau/MAPT (p-MAPT) neuropathology since its regional propagation correlates with the degree of cognitive impairment in AD. Recent diffusion tensor imaging studies in AD suggest that increased diffusion in the fornix secondary to p-MAPT-related axonal injury could serve as a predictive biomarker of the risk of disease progression. However, our knowledge of p-MAPT neuropathology in the fornix is limited. To address this gap in knowledge, we examined p-MAPT neuropathology in the fornix and basal forebrain nuclei via AT8 immunohistochemistry in 39 brain autopsies spanning the spectrum of AD neuropathologic changes. We found that the fornix and its precommissural efferent target nuclei (septum and nucleus accumbens) demonstrated neuronal and thread-like p-MAPT neuropathology only in National Institute on Aging/Alzheimer Association (NIA/AA) stages B2 and B3 of neurofibrillary degeneration, consistent with involvement after (and propagation from) the hippocampal formation. Interestingly, although tau astrogliopathy was frequently observed in the mammillary bodies in stage B2, neuronal tauopathy was not observed in the postcommissural targets (mammillary bodies and anterior thalamic nucleus) until stage B3. Tauopathy in the nucleus basalis of Meynert was strongly correlated with p-MAPT-positive axons in the fornix, suggesting that projections to the hippocampus also likely contribute to fornix tauopathy. Our cross-sectional autopsy findings indicate that the fornix is involved by p-MAPT neuropathology secondary to hippocampal involvement by AD neuropathology. Furthermore, our findings are compatible with the goal of in vivo detection of p-MAPT-related axonal pathology in the fornix in AD as a possible biomarker of p-MAPT progression from the hippocampal formation and underscore a need for additional clinical-radiologic-pathologic correlation studies.

Characterization of ten white matter tracts in a representative sample of Cuban population

Background

The diffusion tensor imaging technique (DTI) combined with tractography methods, has achieved the tridimensional reconstruction of white matter tracts in the brain. It allows their characterization in vivo in a non-invasive way. However, one of the largest sources of variability originates from the location of regions of interest, is therefore necessary schemes which make it possible to establish a protocol to be insensitive to variations in drawing thereof. The purpose of this paper is to stablish a reliable protocol to reconstruct ten prominent tracts of white matter and characterize them according to volume, fractional anisotropy and mean diffusivity. Also we explored the relationship among these factors with gender and hemispheric symmetry.

Methods

This study aims to characterize ten prominent tracts of white matter in a representative sample of Cuban population using this technique, including 84 healthy subjects. Diffusion tensors and subsequently fractional anisotropy and mean diffusivity maps were calculated from each subject’s DTI scans. The trajectory of ten brain tracts was estimated by using deterministic tractography methods of fiber tracking. In such tracts, the volume, the FA and MD were calculated, creating a reference for their study in the Cuban population. The interactions between these variables with age, cerebral hemispheres and gender factors were explored using Repeated Measure Analysis of Variance.

Results

The volume values showed that a most part of tracts have bigger volume in left hemisphere. Also, the data showed bigger values of MD for males than females in all the tracts, an inverse behavior than FA values.

Conclusions

This work showed that is possible reconstruct white matter tracts using a unique region of interest scheme defined from standard to native space. Also, this study indicates differing developmental trajectories in white matter for males and females and the importance of taking gender into account in developmental DTI studies and in underlie gender-related cognitive differences.

Electronic supplementary material

The online version of this article (doi:10.1186/s12880-016-0163-7) contains supplementary material, which is available to authorized users.

Brain atrophy in Alzheimer's Disease and aging

Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.

Diffusion Kurtosis Imaging: A Possible MRI Biomarker for AD Diagnosis?

The purpose of this explorative study was to investigate whether diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameter changes are reliable measures of white matter integrity changes in Alzheimer's disease (AD) patients using a whole brain voxel-based analysis (VBA). Therefore, age- and gender-matched patients with mild cognitive impairment (MCI) due to AD (n = 18), dementia due to AD (n = 19), and age-matched cognitively healthy controls (n = 14) were prospectively included. The magnetic resonance imaging protocol included routine structural brain imaging and DKI. Datasets were transformed to a population-specific atlas space. Groups were compared using VBA. Differences in diffusion and mean kurtosis measures between MCI and AD patients and controls were shown, and were mainly found in the splenium of the corpus callosum and the corona radiata. Hence, DTI and DKI parameter changes are suggestive of white matter changes in AD.

Quantitative T2 mapping of white matter: applications for ageing and cognitive decline

In MRI, the coherence lifetime T2 is sensitive to the magnetic environment imposed by tissue microstructure and biochemistry in vivo. Here we explore the possibility that the use of T2 relaxometry may provide information complementary to that provided by diffusion tensor imaging (DTI) in ageing of healthy controls (HC), Alzheimer’s disease (AD) and mild cognitive impairment (MCI). T2 and diffusion MRI metrics were quantified in HC and patients with MCI and mild AD using multi-echo MRI and DTI. We used tract-based spatial statistics (TBSS) to evaluate quantitative MRI parameters in white matter (WM). A prolonged T2 in WM was associated with AD, and able to distinguish AD from MCI, and AD from HC. Shorter WM T2 was associated with better cognition and younger age in general. In no case was a reduction in T2 associated with poorer cognition. We also applied principal component analysis, showing that WM volume changes independently of  T2, MRI diffusion indices and cognitive performance indices. Our data add to the evidence that age-related and AD-related decline in cognition is in part attributable to WM tissue state, and much less to WM quantity. These observations suggest that WM is involved in AD pathology, and that T2 relaxometry is a potential imaging modality for detecting and characterising WM in cognitive decline and dementia.

Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease

Recent pharmaceutical trials have demonstrated that slowing or reversing pathology in Alzheimer's disease is likely to be possible only in the earliest stages of disease, perhaps even before significant symptoms develop. Pathology in Alzheimer's disease accumulates for well over a decade before symptoms are detected giving a large potential window of opportunity for intervention. It is therefore important that imaging techniques detect subtle changes in brain tissue before significant macroscopic brain atrophy. Current diagnostic techniques often do not permit early diagnosis or are too expensive for routine clinical use. Magnetic Resonance Imaging (MRI) is the most versatile, affordable, and powerful imaging modality currently available, being able to deliver detailed analyses of anatomy, tissue volumes, and tissue state. In this mini-review, we consider how MRI might detect patients at risk of future dementia in the early stages of pathological change when symptoms are mild. We consider the contributions made by the various modalities of MRI (structural, diffusion, perfusion, relaxometry) in identifying not just atrophy (a late-stage AD symptom) but more subtle changes reflective of early dementia pathology. The sensitivity of MRI not just to gross anatomy but to the underlying "health" at the cellular (and even molecular) scales, makes it very well suited to this task.

A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis

OBJECTIVE

Damage to the cerebral tissue structural connectivity associated with amyotrophic lateral sclerosis (ALS), which extends beyond the motor pathways, can be visualised by diffusion tensor imaging (DTI). The effective translation of DTI metrics as biomarker requires its application across multiple MRI scanners and patient cohorts. A multicentre study was undertaken to assess structural connectivity in ALS within a large sample size.

METHODS

442 DTI data sets from patients with ALS (N=253) and controls (N=189) were collected for this retrospective study, from eight international ALS-specialist clinic sites. Equipment and DTI protocols varied across the centres. Fractional anisotropy (FA) maps of the control participants were used to establish correction matrices to pool data, and correction algorithms were applied to the FA maps of the control and ALS patient groups.

RESULTS

Analysis of data pooled from all centres, using whole-brain-based statistical analysis of FA maps, confirmed the most significant alterations in the corticospinal tracts, and captured additional significant white matter tract changes in the frontal lobe, brainstem and hippocampal regions of the ALS group that coincided with postmortem neuropathological stages. Stratification of the ALS group for disease severity (ALS functional rating scale) confirmed these findings.

INTERPRETATION

This large-scale study overcomes the challenges associated with processing and analysis of multiplatform, multicentre DTI data, and effectively demonstrates the anatomical fingerprint patterns of changes in a DTI metric that reflect distinct ALS disease stages. This success paves the way for the use of DTI-based metrics as read-out in natural history, prognostic stratification and multisite disease-modifying studies in ALS.

Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease

We analyzed, in an integrative fashion, the morphometry and structural integrity of the bilateral hippocampi and amygdalas in Alzheimer's disease (AD) using T1-weighted images and diffusion tensor images (DTIs). We detected significant hippocampal and amygdalar volumetric atrophies in AD relative to healthy controls (HCs). Shape analysis revealed significant region-specific atrophies with the hippocampal atrophy mainly being concentrated on the CA1 and CA2 while the amygdalar atrophy was concentrated on the basolateral and basomedial. In all structures, the structural integrity displayed a significantly decreased mean fractional anisotropy (FA) value and an increased mean trace value in AD. In addition to the inter-group comparisons, we systematically evaluated the discriminative power of our three types of features (volume, shape, and DTI), both individually and in their possible combinations, when differentiating between AD and HCs. We found the volume features to be redundant when the more sophisticated shape features were available. A combination of the shape and DTI features of the right hippocampus, with classification automatically performed by support vector machine, yielded the strongest classification result (overall accuracy, 94.6%; sensitivity, 95.5%; specificity, 93.3%).

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

INTRODUCTION

Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders.

METHODS

We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3.

RESULTS

Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials.

DISCUSSION

Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials.

METHODS

We searched for ADNI publications using established methods.

RESULTS

ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis.

DISCUSSION

ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.

Prefrontal white matter pathology in air pollution exposed Mexico City young urbanites and their potential impact on neurovascular unit dysfunction and the development of Alzheimer's disease

Millions of urban children are chronically exposed to high concentrations of air pollutants, i.e., fine particulate matter (PM2.5) and ozone, associated with increased risk for Alzheimer's disease. Compared with children living with clear air those in Mexico City (MC) exhibit systemic, brain and intrathecal inflammation, low CSF Aβ42, breakdown of the BBB, attention and short-term memory deficits, prefrontal white matter hyperintensities, damage to epithelial and endothelial barriers, tight junction and neural autoantibodies, and Alzheimer and Parkinson's hallmarks. The prefrontal white matter is a target of air pollution. We examined by light and electron microscopy the prefrontal white matter of MC dogs (n: 15, age 3.17±0.74 years), children and teens (n: 34, age: 12.64±4.2 years) versus controls. Major findings in MC residents included leaking capillaries and small arterioles with extravascular lipids and erythrocytes, lipofuscin in pericytes, smooth muscle and endothelial cells (EC), thickening of cerebrovascular basement membranes with small deposits of amyloid, patchy absence of the perivascular glial sheet, enlarged Virchow-Robin spaces and nanosize particles (20-48nm) in EC, basement membranes, axons and dendrites. Tight junctions, a key component of the neurovascular unit (NVU) were abnormal in MC versus control dogs (χ(2)<0.0001), and white matter perivascular damage was significantly worse in MC dogs (p=0.002). The integrity of the NVU, an interactive network of vascular, glial and neuronal cells is compromised in MC young residents. Characterizing the early NVU damage and identifying biomarkers of neurovascular dysfunction may provide a fresh insight into Alzheimer pathogenesis and open opportunities for pediatric neuroprotection.

REDUCING CSF PARTIAL VOLUME EFFECTS TO ENHANCE DIFFUSION TENSOR IMAGING METRICS OF BRAIN MICROSTRUCTURE

Technological advances over recent decades now allow for in vivo observation of human brain tissue through the use of neuroimaging methods. While this field originated with techniques capable of capturing macrostructural details of brain anatomy, modern methods such as diffusion tensor imaging (DTI) that are now regularly implemented in research protocols have the ability to characterize brain microstructure. DTI has been used to reveal subtle micro-anatomical abnormalities in the prodromal phase ofº various diseases and also to delineate "normal" age-related changes in brain tissue across the lifespan. Nevertheless, imaging artifact in DTI remains a significant limitation for identifying true neural signatures of disease and brain-behavior relationships. Cerebrospinal fluid (CSF) contamination of brain voxels is a main source of error on DTI scans that causes partial volume effects and reduces the accuracy of tissue characterization. Several methods have been proposed to correct for CSF artifact though many of these methods introduce new limitations that may preclude certain applications. The purpose of this review is to discuss the complexity of signal acquisition as it relates to CSF artifact on DTI scans and review methods of CSF suppression in DTI. We will then discuss a technique that has been recently shown to effectively suppress the CSF signal in DTI data, resulting in fewer errors and improved measurement of brain tissue. This approach and related techniques have the potential to significantly improve our understanding of "normal" brain aging and neuropsychiatric and neurodegenerative diseases. Considerations for next-level applications are discussed.

Effects of Arterial Stiffness on Brain Integrity in Young Adults From the Framingham Heart Study

BACKGROUND AND PURPOSE

Previous work from the Framingham Heart Study suggests that brain changes because of arterial aging may begin in young adulthood and that such changes precede cognitive deficits. The objective of this study was to determine the association of arterial stiffness with measures of white matter and gray matter (GM) integrity in young adults.

METHODS

One thousand nine hundred three participants from the Framingham Heart Study Third Generation (mean age, 46±8.7 years) had complete tonometry measurements and brain magnetic resonance imaging (T1-weighted and diffusion tensor imaging). Tonometry measures included carotid-femoral pulse wave velocity, augmentation index, carotid-brachial pressure amplification, and central pulse pressure. Fractional anisotropy and GM density images were computed from diffusion tensor imaging and T1 images. Registration to a common anatomic template enabled voxel-based linear regressions relating measures of fractional anisotropy and GM to tonometry measures, adjusting for relevant covariables.

RESULTS

Higher carotid-femoral pulse wave velocity was associated with lower regional fractional anisotropy, including the corpus callosum and the corona radiata (8.7 and 8.6 cc, respectively, P<0.001), as well as lower GM density in the thalamus region (0.9 cc, P<0.001). Analyses did not reveal significant associations between other tonometry measures and fractional anisotropy or GM.

CONCLUSIONS

Among young healthy adults, higher aortic stiffness was associated with measures of reduced white matter and GM integrity in areas implicated in cognitive decline and Alzheimer's disease. Greater aortic stiffness may result in subclinical vascular brain injury at ages much younger than previously described.

Characterizing White Matter Tract Degeneration in Syndromic Variants of Alzheimer's Disease: A Diffusion Tensor Imaging Study

BACKGROUND

Different clinical syndromes can arise from Alzheimer's disease (AD) neuropathology, including dementia of the Alzheimer's type (DAT), logopenic primary progressive aphasia (lvPPA), and posterior cortical atrophy (PCA).

OBJECTIVE

To assess similarities and differences in patterns of white matter tract degeneration across these syndromic variants of AD.

METHODS

Sixty-four subjects (22 DAT, 24 lvPPA, and 18 PCA) that had diffusion tensor imaging and showed amyloid-β deposition on PET were assessed in this case-control study. A whole-brain voxel-based analysis was performed to assess differences in fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity across groups.

RESULTS

All three groups showed overlapping diffusion abnormalities in a network of tracts, including fornix, corpus callosum, posterior thalamic radiations, superior longitudinal fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus. Subtle regional differences were also observed across groups, with DAT particularly associated with degeneration of fornix and cingulum, lvPPA with left inferior fronto-occipital fasciculus and uncinate fasciculus, and PCA with posterior thalamic radiations, superior longitudinal fasciculus, posterior cingulate, and splenium of the corpus callosum.

CONCLUSION

These findings show that while each AD phenotype is associated with degeneration of a specific structural network of white matter tracts, striking spatial overlap exists among the three network patterns that may be related to AD pathology.

Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats

Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. Diffusion tensor imaging (DTI) and high-angular resolution imaging (HARDI) are frequently used in radiology and neuroscience research but can be limited in describing the signal behavior in composite nerve fiber structures. Here, we developed and assessed the benefit of a comprehensive diffusion encoding scheme, known as hybrid diffusion imaging (HYDI), composed of 300 DWI volumes acquired at 7-Tesla with diffusion weightings at b = 1000, 3000, 4000, 8000 and 12000 s/mm2 and applied it in transgenic Alzheimer rats (line TgF344-AD) that model the full clinico-pathological spectrum of the human disease. We studied and visualized the effects of the multiple concentric "shells" when computing three distinct anisotropy maps-fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA). We tested the added value of the multi-shell q-space sampling scheme, when reconstructing neural pathways using mathematical frameworks from DTI and q-ball imaging (QBI). We show a range of properties of HYDI, including lower apparent anisotropy when using high b-value shells in DTI-based reconstructions, and increases in apparent anisotropy in QBI-based reconstructions. Regardless of the reconstruction scheme, HYDI improves FA-, GFA- and NQA-aided tractography. HYDI may be valuable in human connectome projects and clinical research, as well as magnetic resonance research in experimental animals.

Recent Developments in Diffusion Tensor Imaging of Brain

Magnetic resonance imaging (MRI) has come to be known as a unique radiological imaging modality because of its ability to perform tomographic imaging of body without the use of any harmful ionizing radiation. The radiologists use MRI to gain insight into the anatomy of organs, including the brain, while biomedical researchers explore the modality to gain better understanding of the brain structure and function. However, due to limited resolution and contrast, the conventional MRI fails to show the brain microstructure. Diffusion tensor imaging (DTI) harnesses the power of conventional MRI to deduce the diffusion dynamics of water molecules within the tissue and indirectly create a three-dimensional sketch of the brain anatomy. DTI enables visualization of brain tissue microstructure, which is extremely helpful in understanding various neuropathologies and neurodegenerative disorders. In this review, we briefly discuss the background and operating principles of DTI, followed by current trends in DTI applications for biomedical and clinical investigation of various brain diseases and disorders.

White Matter Changes Associated with Resting Sympathetic Tone in Frontotemporal Dementia vs. Alzheimer's Disease

BACKGROUND

Resting sympathetic tone, a measure of physiological arousal, is decreased in patients with apathy and inertia, such as those with behavioral variant frontotemporal dementia (bvFTD) and other frontally-predominant disorders.

OBJECTIVE

To identify the neuroanatomical correlates of skin conductance levels (SCLs), an index of resting sympathetic tone and apathy, among patients with bvFTD, where SCLs is decreased, compared to those with Alzheimer's disease (AD), where it is not.

METHODS

This study analyzed bvFTD (n = 14) patients and a comparison group with early-onset AD (n = 19). We compared their resting SCLs with gray matter and white matter regions of interest and white matter measures of fiber integrity on magnetic resonance imaging and diffusion tensor imaging.

RESULTS

As expected, bvFTD patients, compared to AD patients, had lower SCLs, which correlated with an apathy measure, and more gray matter loss and abnormalities of fiber integrity (fractional anisotropy and mean diffusivity) in frontal-anterior temporal regions. After controlling for group membership, the SCLs were significantly correlated with white matter volumes in the cingulum and inferior parietal region in the right hemisphere.

CONCLUSION

Among dementia patients, SCLs, and resting sympathetic tone, may correlate with quantity of white matter, rather than with gray matter or with white matter fiber integrity. Loss of white matter volumes, especially involving a right frontoparietal network, may reflect chronic loss of cortical axons that mediate frontal control of resting sympathetic tone, changes that could contribute to the apathy and inertia of bvFTD and related disorders.

Revolutionizing Alzheimer's disease and clinical trials through biomarkers

The Alzheimer's Association's Research Roundtable met in May 2014 to explore recent progress in developing biomarkers to improve understanding of disease pathogenesis and expedite drug development. Although existing biomarkers have proved extremely useful for enrichment of subjects in clinical trials, there is a clear need to develop novel biomarkers that are minimally invasive and that more broadly characterize underlying pathogenic mechanisms, including neurodegeneration, neuroinflammation, and synaptic dysfunction. These may include blood-based assays and new neuropsychological testing protocols, as well as novel ligands for positron emission tomography imaging, and advanced magnetic resonance imaging methodologies. In addition, there is a need for biomarkers that can serve as theragnostic markers of response to treatment. Standardization remains a challenge, although international consortia have made substantial progress in this area and provide lessons for future standardization efforts.

Disruption of thalamic connectivity in Alzheimer's disease: a diffusion tensor imaging study

The aim of this study was to evaluate the structural integrity of the thalamic connectivity of specific fiber tracts in different stages of Alzheimer's disease (AD) using diffusion tensor imaging (DTI). Thirty-five patients with AD and 22 normal control (NC) subjects were recruited. Based on Mini Mental State Examination score, the AD patients were divided into three subgroups for comparison with the NC group: mild (mi-AD, n = 14), moderate (mo-AD, n = 12), and severe (se-AD, n = 9) AD. The fornix (FX), anterior thalamic radiation (ATR), and posterior thalamic radiation (PTR) were selected to represent the thalamic connectivity with other brain regions. The fornix was divided into the column and body of the fornix (FX-1) and the bilateral fornix (crus)/stria terminalis (FX-2/ST) based on the atlas. Through the atlas-based analysis and fiber tracking method, we measured fractional anisotropy (FA), mean diffusivity (MD), and tract volume to reflect the microstructural and macrostructural changes of these fibers during AD progression. There were significant differences in the FA and MD of all fibers, except the right PTR, between the AD and NC subjects. Further subgroup analyses revealed that the mi-AD subgroup had decreased FA only in the FX-1 and increased MD in the FX-1 and bilateral ATR, the mo-AD subgroup showed declined FA and increased MD in the FX-1, bilateral FX-2/ST and ATR; the se-AD subgroup exhibited lower FA and higher MD values in all fibers except the right PTR. We also found reduced tract volume values in the FX and left ATR in the AD patients. Further subgroup analyses revealed that these differences only existed in the se-AD patients. Our DTI analyses indicate that the integrity of thalamic connectivity is progressively disrupted following cognitive decline in AD and that DTI parameters in the column and body of the fornix show promise as potential markers for the early diagnosis of AD and for monitoring disease progression.

Multimodal neuroimaging computing: the workflows, methods, and platforms

The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.

Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders

Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.

Diffusion MRI and its Role in Neuropsychology

Diffusion Magnetic Resonance Imaging (dMRI) is a popular method used by neuroscientists to uncover unique information about the structural connections within the brain. dMRI is a non-invasive imaging methodology in which image contrast is based on the diffusion of water molecules in tissue. While applicable to many tissues in the body, this review focuses exclusively on the use of dMRI to examine white matter in the brain. In this review, we begin with a definition of diffusion and how diffusion is measured with MRI. Next we introduce the diffusion tensor model, the predominant model used in dMRI. We then describe acquisition issues related to acquisition parameters and scanner hardware and software. Sources of artifacts are then discussed, followed by a brief review of analysis approaches. We provide an overview of the limitations of the traditional diffusion tensor model, and highlight several more sophisticated non-tensor models that better describe the complex architecture of the brain's white matter. We then touch on reliability and validity issues of diffusion measurements. Finally, we describe examples of ways in which dMRI has been applied to studies of brain disorders and how identified alterations relate to symptomatology and cognition.

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative-50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the "rich club" - a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.

Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.

Cerebrospinal fluid biomarkers of neurovascular dysfunction in mild dementia and Alzheimer's disease

Alzheimer's disease (AD) is the most common form of age-related dementias. In addition to genetics, environment, and lifestyle, growing evidence supports vascular contributions to dementias including dementia because of AD. Alzheimer's disease affects multiple cell types within the neurovascular unit (NVU), including brain vascular cells (endothelial cells, pericytes, and vascular smooth muscle cells), glial cells (astrocytes and microglia), and neurons. Thus, identifying and integrating biomarkers of the NVU cell-specific responses and injury with established AD biomarkers, amyloid-β (Aβ) and tau, has a potential to contribute to better understanding of the disease process in dementias including AD. Here, we discuss the existing literature on cerebrospinal fluid biomarkers of the NVU cell-specific responses during early stages of dementia and AD. We suggest that the clinical usefulness of established AD biomarkers, Aβ and tau, could be further improved by developing an algorithm that will incorporate biomarkers of the NVU cell-specific responses and injury. Such biomarker algorithm could aid in early detection and intervention as well as identify novel treatment targets to delay disease onset, slow progression, and/or prevent AD.