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

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.

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.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Tract-based analysis of white matter degeneration in Alzheimer's disease

Although much prior work has focused on the known cortical pathology that defines Alzheimer's disease (AD) histologically, recent work has additionally demonstrated substantial damage to the cerebral white matter in this condition. While there is large evidence of diffuse damage to the white matter in AD, it is unclear whether specific white matter tracts exhibit a more accelerated pattern of damage and whether the damage is associated with the classical neurodegenerative changes of AD. In this study, we investigated microstructural differences in the large fascicular bundles of the cerebral white matter of individuals with AD and mild cognitive impairment (MCI), using recently developed automated diffusion tractography procedures in the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset. Eighteen major fiber bundles in a total of 36 individuals with AD, 81 MCI and 60 control participants were examined with the TRActs Constrained by UnderLying Anatomy (TRACULA) procedure available as part of the FreeSurfer image processing software package. For each fiber bundle, the mean fractional anisotropy (FA), and mean, radial and axial diffusivities were calculated. Individuals with AD had increased diffusivities in both left and right cingulum-angular bundles compared to control participants (p<0.001). Individuals with MCI also had increased axial and mean diffusivities and increased FA in both cingulum-angular bundles compared to control participants (p<0.05) and decreased radial diffusivity compared to individuals with AD (p<0.05). We additionally examined how white matter deterioration relates to hippocampal volume, a traditional imaging measure of AD pathology, and found the strongest negative correlations in AD patients between hippocampal volume and the diffusivities of the cingulum-angular and cingulum-cingulate gyrus bundles and of the corticospinal tracts (p<0.05). However, statistically controlling for hippocampal volume did not remove all group differences in white matter measures, suggesting a unique contribution of white matter damage to AD unexplained by this disease biomarker. These results suggest that (1) AD-associated deterioration of white matter fibers is greatest in tracts known to be connected to areas of pathology in AD and (2) lower white matter tract integrity is more diffusely associated with lower hippocampal volume indicating that the pathology in the white matter follows to some degree the neurodegenerative staging and progression of this condition.

Glucose indices are associated with cognitive and structural brain measures in young adults

OBJECTIVE

To evaluate the possible early consequences of impaired glucose metabolism on the brain by assessing the relationship of diabetes, fasting blood glucose (FBG) levels, and insulin resistance with cognitive performance and brain integrity in healthy young and middle-aged adults.

METHODS

The sample included dementia-free participants (mean age 40 ± 9 years; 53% women) of the Framingham Heart Study third-generation cohort with cognitive testing of memory, abstract reasoning, visual perception, attention, and executive function (n = 2,126). In addition, brain MRI examination (n = 1,597) was used to determine white matter, gray matter, and white matter hyperintensity (WMH) volumes and fractional anisotropy measures. We used linear regression models to assess relationships between diabetes, FBG, and insulin resistance with cognition, lobar gray matter, and WMH volumes as well as voxel-based microstructural white matter integrity and gray matter density, adjusting for potential confounders. Mediating effect of brain lesions on the association of diabetes with cognitive performance was also tested.

RESULTS

Diabetes was associated with worse memory, visual perception, and attention performance; increased WMH; and decreased total cerebral brain and occipital lobar gray matter volumes. The link of diabetes with attention and memory was mediated through occipital and frontal atrophy, and the latter also through hippocampal atrophy. Both diabetes and increased FBG were associated with large areas of reductions in gray matter density and fractional anisotropy on voxel-based analyses.

CONCLUSIONS

We found that hyperglycemia is associated with subtle brain injury and impaired attention and memory even in young adults, indicating that brain injury is an early manifestation of impaired glucose metabolism.

Hydration water mobility is enhanced around tau amyloid fibers

The paired helical filaments (PHF) formed by the intrinsically disordered human protein tau are one of the pathological hallmarks of Alzheimer disease. PHF are fibers of amyloid nature that are composed of a rigid core and an unstructured fuzzy coat. The mechanisms of fiber formation, in particular the role that hydration water might play, remain poorly understood. We combined protein deuteration, neutron scattering, and all-atom molecular dynamics simulations to study the dynamics of hydration water at the surface of fibers formed by the full-length human protein htau40. In comparison with monomeric tau, hydration water on the surface of tau fibers is more mobile, as evidenced by an increased fraction of translationally diffusing water molecules, a higher diffusion coefficient, and increased mean-squared displacements in neutron scattering experiments. Fibers formed by the hexapeptide (306)VQIVYK(311) were taken as a model for the tau fiber core and studied by molecular dynamics simulations, revealing that hydration water dynamics around the core domain is significantly reduced after fiber formation. Thus, an increase in water dynamics around the fuzzy coat is proposed to be at the origin of the experimentally observed increase in hydration water dynamics around the entire tau fiber. The observed increase in hydration water dynamics is suggested to promote fiber formation through entropic effects. Detection of the enhanced hydration water mobility around tau fibers is conjectured to potentially contribute to the early diagnosis of Alzheimer patients by diffusion MRI.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease

Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods - four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one "ball-and-stick" approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.

AUTOMATED MULTI-ATLAS LABELING OF THE FORNIX AND ITS INTEGRITY IN ALZHEIMER'S DISEASE

Alzheimer's disease is the most common form of dementia. Diffusion imaging provides information on white matter integrity not available with standard MRI, revealing additional information on how Alzheimer's disease affects the brain. Here we implemented and tested a multi-atlas labeling algorithm to segment the fornix and a point-correspondence tract matching scheme to assess fiber integrity in the fornix in diffusion MRI from 210 participants scanned as part of the Alzheimer's Disease Neuroimaging Initiative. Various diffusion-derived measures were used to relate fornix degeneration to cognitive decline. On 3D parametric tract models, mean diffusivity (MD) was more sen-sitive to group differences than fractional anisotropy (FA). Compared to previous studies, we mapped diffusion information along the fornix, yielding 3-D maps of degenerative changes along the tract in people with different stages of Alzheimer's disease.

FEATURE SELECTION IMPROVES THE ACCURACY OF CLASSIFYING ALZHEIMER DISEASE USING DIFFUSION TENSOR IMAGES

Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.

White matter disruption at the prodromal stage of Alzheimer's disease: relationships with hippocampal atrophy and episodic memory performance

White matter tract alterations have been consistently described in Alzheimer's disease (AD). In particular, limbic fronto-temporal connections, which are critical to episodic memory function, may degenerate early in the course of the disease. However the relation between white matter tract degeneration, hippocampal atrophy and episodic memory impairment at the earliest stages of AD is still unclear. In this magnetic resonance imaging study, white matter integrity and hippocampal volumes were evaluated in patients with amnestic mild cognitive impairment due to AD (Albert et al., 2011) (n = 22) and healthy controls (n = 15). Performance in various episodic memory tasks was also evaluated in each participant. Relative to controls, patients showed a significant reduction of white matter fractional anisotropy (FA) and increase of radial diffusivity (RD) in the bilateral uncinate fasciculus, parahippocampal cingulum and fornix. Within the patient group, significant intra-hemispheric correlations were notably found between hippocampal grey matter volume and FA in the uncinate fasciculus, suggesting a relationship between atrophy and disconnection of the hippocampus. Moreover, episodic recognition scores were related with uncinate fasciculus FA across patients. These results indicate that fronto-hippocampal connectivity is reduced from the earliest pre-demential stages of AD. Disruption of fronto-hippocampal connections may occur progressively, in parallel with hippocampal atrophy, and may specifically contribute to early initial impairment in episodic memory.

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Limbic fronto-temporal connections (cingulum, uncinate fasciculus and fornix) are altered from the prodromal stage of AD.

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In prodromal AD patients, intra-hemispheric correlations were found between uncinate fasciculus FA and hippocampal atrophy.

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In prodromal AD patients, uncinate fasciculus FA was correlated with scores on episodic recognition.

Cooccurrence of vascular risk factors and late-life white-matter integrity changes

Hypertension, hyperlipidemia, and diabetes are increasingly prevalent with advancing age and have been shown to cause white-matter (WM) injury that may contribute to dementia risk. However, cumulative and over time effects of these medical illnesses have not been systematically examined. One hundred twenty-one cognitively normal old participants received comprehensive clinical evaluations and brain diffusion tensor imaging on 2 occasions. Clinical history and medical treatment of diabetes, hypertension, and hyperlipidemia were assessed at both evaluations. We examined whether exposure to a greater number of vascular risk factors (VRFs) was associated with greater rate of WM integrity change using longitudinal differences in fractional anisotropy (FA). Compared with individuals with no VRF, individuals with 1 VRF did not exhibit significantly different change in FA. However, those with ≥ 2 VRFs had greater decrease in FA within multiple WM regions including the splenium of the corpus callosum. The accumulation of VRF increasingly affected WM integrity, particularly in areas known to be injured in patients with mild cognitive impairment and dementia.

Seemingly unrelated regression empowers detection of network failure in dementia

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model-combining genotype, educational level, and clinical diagnosis.

Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease

Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.

White matter microstructure in chronic moderate-to-severe traumatic brain injury: Impact of acute-phase injury-related variables and associations with outcome measures

This study examines how injury mechanisms and early neuroimaging and clinical measures impact white matter (WM) fractional anisotropy (FA), mean diffusivity (MD), and tract volumes in the chronic phase of traumatic brain injury (TBI) and how WM integrity in the chronic phase is associated with different outcome measures obtained at the same time. Diffusion tensor imaging (DTI) at 3 T was acquired more than 1 year after TBI in 49 moderate-to-severe-TBI survivors and 50 matched controls. DTI data were analyzed with tract-based spatial statistics and automated tractography. Moderate-to-severe TBI led to widespread FA decreases, MD increases, and tract volume reductions. In severe TBI and in acceleration/deceleration injuries, a specific FA loss was detected. A particular loss of FA was also present in the thalamus and the brainstem in all grades of diffuse axonal injury. Acute-phase Glasgow Coma Scale scores, number of microhemorrhages on T2*, lesion volume on fluid-attenuated inversion recovery, and duration of posttraumatic amnesia were associated with more widespread FA loss and MD increases in chronic TBI. Episodes of cerebral perfusion pressure <70 mmHg were specifically associated with reduced MD. Neither episodes of intracranial pressure >20 mmHg nor acute-phase Rotterdam CT scores were associated with WM changes. Glasgow Outcome Scale Extended scores and performance-based cognitive control functioning were associated with FA and MD changes, but self-reported cognitive control functioning was not. In conclusion, FA loss specifically reflects the primary injury severity and mechanism, whereas FA and MD changes are associated with objective measures of general and cognitive control functioning.

Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis

Identifying predictors of mild cognitive impairment (MCI) and Alzheimer's disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer's Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau(181p), and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau(181p). For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau(181p). ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.

White matter in the older brain is more plastic than in the younger brain

Visual perceptual learning (VPL) with younger subjects is associated with changes in functional activation of the early visual cortex. Although overall brain properties decline with age, it is unclear whether these declines are associated with visual perceptual learning. Here we use diffusion tensor imaging to test whether changes in white matter are involved in VPL for older adults. After training on a texture discrimination task for three daily sessions, both older and younger subjects show performance improvements. While the older subjects show significant changes in fractional anisotropy (FA) in the white matter beneath the early visual cortex after training, no significant change in FA is observed for younger subjects. These results suggest that the mechanism for VPL in older individuals is considerably different from that in younger individuals and that VPL of older individuals involves reorganization of white matter.

Altered white matter microstructure is associated with social cognition and psychotic symptoms in 22q11.2 microdeletion syndrome

22q11.2 Microdeletion Syndrome (22q11DS) is a highly penetrant genetic mutation associated with a significantly increased risk for psychosis. Aberrant neurodevelopment may lead to inappropriate neural circuit formation and cerebral dysconnectivity in 22q11DS, which may contribute to symptom development. Here we examined: (1) differences between 22q11DS participants and typically developing controls in diffusion tensor imaging (DTI) measures within white matter tracts; (2) whether there is an altered age-related trajectory of white matter pathways in 22q11DS; and (3) relationships between DTI measures, social cognition task performance, and positive symptoms of psychosis in 22q11DS and typically developing controls. Sixty-four direction diffusion weighted imaging data were acquired on 65 participants (36 22q11DS, 29 controls). We examined differences between 22q11DS vs. controls in measures of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD), using both a voxel-based and region of interest approach. Social cognition domains assessed were: Theory of Mind and emotion recognition. Positive symptoms were assessed using the Structured Interview for Prodromal Syndromes. Compared to typically developing controls, 22q11DS participants showed significantly lower AD and RD in multiple white matter tracts, with effects of greatest magnitude for AD in the superior longitudinal fasciculus. Additionally, 22q11DS participants failed to show typical age-associated changes in FA and RD in the left inferior longitudinal fasciculus. Higher AD in the left inferior fronto-occipital fasciculus (IFO) and left uncinate fasciculus was associated with better social cognition in 22q11DS and controls. In contrast, greater severity of positive symptoms was associated with lower AD in bilateral regions of the IFO in 22q11DS. White matter microstructure in tracts relevant to social cognition is disrupted in 22q11DS, and may contribute to psychosis risk.

Diffuse white matter tract abnormalities in clinically normal ageing retired athletes with a history of sports-related concussions

Sports-related concussions have been shown to lead to persistent subclinical anomalies of the motor and cognitive systems in young asymptomatic athletes. In advancing age, these latent alterations correlate with detectable motor and cognitive function decline. Until now, the interacting effects of concussions and the normal ageing process on white matter tract integrity remain unknown. Here we used a tract-based spatial statistical method to uncover potential white matter tissue damage in 15 retired athletes with a history of concussions, free of comorbid medical conditions. We also investigated potential associations between white matter integrity and declines in cognitive and motor functions. Compared to an age- and education-matched control group of 15 retired athletes without concussions, former athletes with concussions exhibited widespread white matter anomalies along many major association, interhemispheric, and projection tracts. Group contrasts revealed decreases in fractional anisotropy, as well as increases in mean and radial diffusivity measures in the concussed group. These differences were primarily apparent in fronto-parietal networks as well as in the frontal aspect of the corpus callosum. The white matter anomalies uncovered in concussed athletes were significantly associated with a decline in episodic memory and lateral ventricle expansion. Finally, the expected association between frontal white matter integrity and motor learning found in former non-concussed athletes was absent in concussed participants. Together, these results show that advancing age in retired athletes presenting with a history of sports-related concussions is linked to diffuse white matter abnormalities that are consistent with the effects of traumatic axonal injury and exacerbated demyelination. These changes in white matter integrity might explain the cognitive and motor function declines documented in this population.

Multimodal magnetic resonance imaging study of treatment-naïve adults with attention-deficit/hyperactivity disorder

Background

Attention-Deficit/Hiperactivity Disorder (ADHD) is a prevalent disorder, but its neuroanatomical circuitry is still relatively understudied, especially in the adult population. The few morphometric magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) studies available to date have found heterogeneous results. This may be at least partly attributable to some well-known technical limitations of the conventional voxel-based methods usually employed to analyze such neuroimaging data. Moreover, there is a great paucity of imaging studies of adult ADHD to date that have excluded patients with history of use of stimulant medication.

Methods

A newly validated method named optimally-discriminative voxel-based analysis (ODVBA) was applied to multimodal (structural and DTI) MRI data acquired from 22 treatment-naïve ADHD adults and 19 age- and gender-matched healthy controls (HC).

Results

Regarding DTI data, we found higher fractional anisotropy in ADHD relative to HC encompassing the white matter (WM) of the bilateral superior frontal gyrus, right middle frontal left gyrus, left postcentral gyrus, bilateral cingulate gyrus, bilateral middle temporal gyrus and right superior temporal gyrus; reductions in trace (a measure of diffusivity) in ADHD relative to HC were also found in fronto-striatal-parieto-occipital circuits, including the right superior frontal gyrus and bilateral middle frontal gyrus, right precentral gyrus, left middle occipital gyrus and bilateral cingulate gyrus, as well as the left body and right splenium of the corpus callosum, right superior corona radiata, and right superior longitudinal and fronto-occipital fasciculi. Volumetric abnormalities in ADHD subjects were found only at a trend level of significance, including reduced gray matter (GM) in the right angular gyrus, and increased GM in the right supplementary motor area and superior frontal gyrus.

Conclusions

Our results suggest that adult ADHD is associated with neuroanatomical abnormalities mainly affecting the WM microstructure in fronto-parieto-temporal circuits that have been implicated in cognitive, emotional and visuomotor processes.

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.

Destruction of white matter integrity in patients with mild cognitive impairment and Alzheimer disease

BACKGROUND

Accumulating evidence shows that gradual loss of white matter integrity plays an important role in the development of Alzheimer disease (AD).

OBJECTIVE

The aim of this research was to study the microstructural integrity of white matter in AD in vivo.

METHODS

Global fractional anisotropy, global axial diffusivity (AxD), and global radial diffusivity (RD) were analyzed in subjects with normal controls (NC), mild cognitive impairment (MCI), and AD using Alzheimer's Disease Neuroimaging Initiative data (total N = 210). We further compared specific white matter tracts among the 3 groups.

RESULTS

Compared with the NC group, the MCI group had significantly increased global AxD and global RD. Compared with the NC and MCI groups, the AD group had significantly decreased global fractional anisotropy, increased global AxD, and increased global RD. With regard to specific white matter tracts, in the MCI group, we found increased AxD and increased RD in the external capsule, part of the lateral cholinergic pathway, in addition to the tracts connecting the limbic regions, predominantly in the left hemisphere. In the AD group, white matter abnormalities were widespread, including in the external capsule (cholinergic pathway) and limbic region tracts as well as tracts connecting anterior to posterior regions bilaterally.

CONCLUSIONS

The radiographic manifestation of damaged white matter microstructural integrity in the cholinergic pathway in MCI patients may provide a rational basis for the use of cholinesterase inhibitor drugs in the MCI stage of AD.

Skull-stripping with machine learning deformable organisms

BACKGROUND

Segmentation methods for medical images may not generalize well to new data sets or new tasks, hampering their utility. We attempt to remedy these issues using deformable organisms to create an easily customizable segmentation plan. We validate our framework by creating a plan to locate the brain in 3D magnetic resonance images of the head (skull-stripping).

NEW METHOD

Our method borrows ideas from artificial life to govern a set of deformable models. We use control processes such as sensing, proactive planning, reactive behavior, and knowledge representation to segment an image. The image may have landmarks and features specific to that dataset; these may be easily incorporated into the plan. In addition, we use a machine learning method to make our segmentation more accurate.

RESULTS

Our method had the least Hausdorff distance error, but included slightly less brain voxels (false negatives). It also had the lowest false positive error and performed on par to skull-stripping specific method on other metrics.

COMPARISON WITH EXISTING METHOD(S)

We tested our method on 838 T1-weighted images, evaluating results using distance and overlap error metrics based on expert gold standard segmentations. We evaluated the results before and after the learning step to quantify its benefit; we also compare our results to three other widely used methods: BSE, BET, and the Hybrid Watershed algorithm.

CONCLUSIONS

Our framework captures diverse categories of information needed for brain segmentation and will provide a foundation for tackling a wealth of segmentation problems.

Obesity gene NEGR1 associated with white matter integrity in healthy young adults

Obesity is a crucial public health issue in developed countries, with implications for cardiovascular and brain health as we age. A number of commonly-carried genetic variants are associated with obesity. Here we aim to see whether variants in obesity-associated genes--NEGR1, FTO, MTCH2, MC4R, LRRN6C, MAP2K5, FAIM2, SEC16B, ETV5, BDNF-AS, ATXN2L, ATP2A1, KCTD15, and TNN13K--are associated with white matter microstructural properties, assessed by high angular resolution diffusion imaging (HARDI) in young healthy adults between 20 and 30 years of age from the Queensland Twin Imaging study (QTIM). We began with a multi-locus approach testing how a number of common genetic risk factors for obesity at the single nucleotide polymorphism (SNP) level may jointly influence white matter integrity throughout the brain and found a wide spread genetic effect. Risk allele rs2815752 in NEGR1 was most associated with lower white matter integrity across a substantial portion of the brain. Across the area of significance in the bilateral posterior corona radiata, each additional copy of the risk allele was associated with a 2.2% lower average FA. This is the first study to find an association between an obesity risk gene and differences in white matter integrity. As our subjects were young and healthy, our results suggest that NEGR1 has effects on brain structure independent of its effect on obesity.

Central white matter degeneration in bulbar- and limb-onset amyotrophic lateral sclerosis

Previous studies using diffusion tensor imaging (DTI) have examined for differences between bulbar- and limb-onset amyotrophic lateral sclerosis (ALS). Findings between studies have been markedly inconsistent, though possibly as a consequence of poor matching for confounding variables. To address this problem, this study contrasted the DTI profiles of limb-onset (ALS-L) and bulbar-onset (ALS-B) in groups that were tightly matched for the potential confounding effects of power, age, cognitive impairment and motor dysfunction. 14 ALS-L and 14 ALS-B patients were selected from a large prospective study so as to be matched on clinical and demographic features. All subjects, including 29 controls, underwent neuropsychological and neurological assessment. Tract-based spatial statistics and region of interest techniques were used to analyse fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (λ₁). Extensive bilateral FA and RD changes along the corticospinal tract were found in ALS-B compared to controls, p (corrected) <0.05; a similar distribution was seen for ALS-L at a less stringent statistical threshold. ROI analyses also showed more significant changes in ALS-B than ALS-L when each was compared to controls; for FA, MD and RD the changes reached statistical significance in the direct contrast between the two patient groups. With careful matching for confounding factors, the results suggest that ALS-B is associated with greater central white matter degeneration than ALS-L, possibly contributing to the known worse prognosis of ALS-B. The study, however, found no evidence that the spatial distribution of white matter degeneration differs between these groups.

Improved DTI registration allows voxel-based analysis that outperforms tract-based spatial statistics

Tract-Based Spatial Statistics (TBSS) is a popular software pipeline to coregister sets of diffusion tensor Fractional Anisotropy (FA) images for performing voxel-wise comparisons. It is primarily defined by its skeleton projection step intended to reduce effects of local misregistration. A white matter "skeleton" is computed by morphological thinning of the inter-subject mean FA, and then all voxels are projected to the nearest location on this skeleton. Here we investigate several enhancements to the TBSS pipeline based on recent advances in registration for other modalities, principally based on groupwise registration with the ANTS-SyN algorithm. We validate these enhancements using simulation experiments with synthetically-modified images. When used with these enhancements, we discover that TBSS's skeleton projection step actually reduces algorithm accuracy, as the improved registration leaves fewer errors to warrant correction, and the effects of this projection's compromises become stronger than those of its benefits. In our experiments, our proposed pipeline without skeleton projection is more sensitive for detecting true changes and has greater specificity in resisting false positives from misregistration. We also present comparative results of the proposed and traditional methods, both with and without the skeleton projection step, on three real-life datasets: two comparing differing populations of Alzheimer's disease patients to matched controls, and one comparing progressive supranuclear palsy patients to matched controls. The proposed pipeline produces more plausible results according to each disease's pathophysiology.

Discriminative analysis of multivariate features from structural MRI and diffusion tensor images

Imaging markers derived from magnetic resonance images, together with machine learning techniques allow for the recognition of unique anatomical patterns and further differentiating Alzheimer's disease (AD) from normal states. T1-based imaging markers, especially volumetric patterns have demonstrated their discriminative potential, however, rely on the tissue abnormalities of gray matter alone. White matter abnormalities and their contribution to AD discrimination have been studied by measuring voxel-based intensities in diffusion tensor images (DTI); however, no systematic study has been done on the discriminative power of either region-of-interest (ROI)-based features from DTI or the combined features extracted from both T1 images and DTI. ROI-based analysis could potentially reduce the feature dimensionality of DTI indices, usually from more than 10e+5, to 10-150 which is almost equal to the order of magnitude with respect to volumetric features from T1. Therefore it allows for straight forward combination of intensity based landmarks of DTI indices and volumetric features of T1. In the present study, the feasibility of tract-based features related to Alzheimer's disease was first evaluated by measuring its discriminative capability using support vector machine on fractional anisotropy (FA) maps collected from 21 subjects with Alzheimer's disease and 15 normal controls. Then the performance of the tract-based FA+gray matter volumes-combined feature was evaluated by cross-validation. The combined feature yielded good classification result with 94.3% accuracy, 95.0% sensitivity, 93.3% specificity, and 0.96 area under the receiver operating characteristic curve. The tract-based FA and the tract-based FA+gray matter volumes-combined features are certified their feasibilities for the recognition of anatomical features and may serve to complement classification methods based on other imaging markers.

Serum cholesterol and variant in cholesterol-related gene CETP predict white matter microstructure

Several common genetic variants influence cholesterol levels, which play a key role in overall health. Myelin synthesis and maintenance are highly sensitive to cholesterol concentrations, and abnormal cholesterol levels increase the risk for various brain diseases, including Alzheimer's disease. We report significant associations between higher serum cholesterol (CHOL) and high-density lipoprotein levels and higher fractional anisotropy in 403 young adults (23.8 ± 2.4 years) scanned with diffusion imaging and anatomic magnetic resonance imaging at 4 Tesla. By fitting a multi-locus genetic model within white matter areas associated with CHOL, we found that a set of 18 cholesterol-related, single-nucleotide polymorphisms implicated in Alzheimer's disease risk predicted fractional anisotropy. We focused on the single-nucleotide polymorphism with the largest individual effects, CETP (rs5882), and found that increased G-allele dosage was associated with higher fractional anisotropy and lower radial and mean diffusivities in voxel-wise analyses of the whole brain. A follow-up analysis detected white matter associations with rs5882 in the opposite direction in 78 older individuals (74.3 ± 7.3 years). Cholesterol levels may influence white matter integrity, and cholesterol-related genes may exert age-dependent effects on the brain.

Differential white matter connectivity in early mild cognitive impairment according to CSF biomarkers

Mild cognitive impairment (MCI) is a heterogeneous group and certain MCI subsets eventually convert to dementia. Cerebrospinal fluid (CSF) biomarkers are known to predict this conversion. We sought evidence for the differences in white matter connectivity between early amnestic MCI (EMCI) subgroups according to a CSF phosphorylated tau181p/amyloid beta1-42 ratio of 0.10. From the Alzheimer's Disease Neuroimaging Initiative database, 16 high-ratio, 25 low-ratio EMCI patients, and 20 normal controls with diffusion tensor images and CSF profiles were included. Compared to the high-ratio group, radial diffusivity significantly increased in both sides of the corpus callosum and the superior and inferior longitudinal fasciculus in the low-ratio group. In widespread white matter skeleton regions, the low-ratio group showed significantly increased mean, axial, and radial diffusivity compared to normal controls. However, the high-ratio group showed no differences when compared to the normal group. In conclusion, our study revealed that there were significant differences in white matter connectivity between EMCI subgroups according to CSF phosphorylated tau181p/amyloid beta1-42 ratios.

Functional brain connectivity using fMRI in aging and Alzheimer's disease

Normal aging and Alzheimer's disease (AD) cause profound changes in the brain's structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer's disease. We cover studies that employ the three primary methods to analyze functional connectivity--seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.

Associations between white matter microstructure and amyloid burden in preclinical Alzheimer's disease: A multimodal imaging investigation

Some cognitively healthy individuals develop brain amyloid accumulation, suggestive of incipient Alzheimer's disease (AD), but the effect of amyloid on other potentially informative imaging modalities, such as Diffusion Tensor Imaging (DTI), in characterizing brain changes in preclinical AD requires further exploration. In this study, a sample (N = 139, mean age 60.6, range 46 to 71) from the Wisconsin Registry for Alzheimer's Prevention (WRAP), a cohort enriched for AD risk factors, was recruited for a multimodal imaging investigation that included DTI and [C-11]Pittsburgh Compound B (PiB) positron emission tomography (PET). Participants were grouped as amyloid positive (Aβ+), amyloid indeterminate (Aβi), or amyloid negative (Aβ-) based on the amount and pattern of amyloid deposition. Regional voxel-wise analyses of four DTI metrics, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), and radial diffusivity (Dr), were performed based on amyloid grouping. Three regions of interest (ROIs), the cingulum adjacent to the corpus callosum, hippocampal cingulum, and lateral fornix, were selected based on their involvement in the early stages of AD. Voxel-wise analysis revealed higher FA among Aβ+ compared to Aβ- in all three ROIs and in Aβi compared to Aβ- in the cingulum adjacent to the corpus callosum. Follow-up exploratory whole-brain analyses were consistent with the ROI findings, revealing multiple regions where higher FA was associated with greater amyloid. Lower fronto-lateral gray matter MD was associated with higher amyloid burden. Further investigation showed a negative correlation between MD and PiB signal, suggesting that Aβ accumulation impairs diffusion. Interestingly, these findings in a largely presymptomatic sample are in contradistinction to relationships reported in the literature in symptomatic disease stages of Mild Cognitive Impairment and AD, which usually show higher MD and lower FA. Together with analyses showing that cognitive function in these participants is not associated with any of the four DTI metrics, the present results suggest an early relationship between PiB and DTI, which may be a meaningful indicator of the initiating or compensatory mechanisms of AD prior to cognitive decline.